Computers are no doubt the most radical invention of mankind so far as it has revolutionized the way we live. It has touched almost every aspect of our lives from performing simple calculations to unlimited access to the boundary less world through internet. The most radical areas of computer sciences include Artificial intelligence the study of system’s spontaneous reactions and communication sciences. The computer Sciences has touched our lives so radically that it has changed the way we shop, learn and interact.Computer based learning is where a computer serves as a tool to gain education. The computer education programs offer tremendous flexibility to its students as it has no time restrictions and can fit into your time schedules according to your convenience. It enables the Individuals to improve their qualification along with continuing their jobs due to no specific timings. http://education.ixs.net It also provides the advantage of saving traveling effort and cost as you have the access to all coaching materials right at your place. It also allows you to learn new concepts according to your own pace and understanding. In case of any disability or health issues you can enjoy the access to the learning without facing any difficulty. In computer education program now you don’t have to miss anything while you are on an official tour to another location.‘Computer Sciences’ is an immensely diverse field based basically on the study of the theoretical foundations of information and computation and their implementation and application in computer systems. Computer education has dynamic fields like computer graphics dealing with developing various 2D and 3D images and further moving images while the other field deals with, computational problems still others focus on the challenges in implementing computations. Programming language theory studies approaches to describing computations, while computer programming applies specific programming languages to solve specific computational problems. A further subfield, human-computer interaction, focuses on the challenges in making computers and computations useful, usable and universally accessible to people.Computers have a great impact on the other field of studies and provide a great aid like in areas like physics, linguistics and most importantly artificial intelligence. So when you are considering making it into computers your choices are numerous like Mathematical foundations, Artificial intelligence, Algorithms and data structures, Software engineering, networking, graphic designing, system architecture and design, cryptography and many more. The Career domain includes embedded systems, Multimedia, Telecommunications, Computer networks, Computer network security,business applications for commerce, retail, customer relationship management and ERP also Knowledge contents management.Still not sure is computers right option for you? Computers are no doubt one of the most dynamic areas where growth has been immense and is still not showing any signs of slowing down. The importance of this industry is evident from its involvement in every field of life either its lawyer’s firm, bank, corporate company, Hospital or any other sort of business IT is without a question an integral part in performing the relevant tasks with customized software and networking solutions.The computing is although one of the fastest emergent segments of industry, it is also one of the most continuously changing areas in terms of technology. Computing professionals’ education is not limited to the college degrees, but continues with seminars, conferences, and advanced courses and training as the new researches emerge thus people wanting to make big in the market should always be their toes and should have a strong ability to predict the future needs. In computer theory and applications, new ideas are developed every day. Success requires an ongoing commitment to learning to maintain knowledge, skills, and career opportunities.Keeping abreast with the evolving researches and technologies have also been made quite convenient due to availability of numerous programs according to the diverse needs of our customers whether you want to pursue traditional learning methods or get benefits of latest communication techniques choice is completely yours.For more information about Computer Education: http://education.ixs.net/content/Computer-Education.php

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In this new three-part series, leading theoretical physicist and futurist Dr Michio Kaku explores the cutting edge science of today, tomorrow, and beyond. He argues that humankind is at a turning point in history. In this century, we are going to make the historic transition from the ‘Age of Discovery’ to the ‘Age of Mastery’, a period in which we will move from being passive observers of nature to its active choreographers. This will give us not only many possibilities but also great responsibilities.

The first revolution discussed is the intelligence revolution.  In the opening series, Kaku explains how artificial intelligence will revolutionise homes, workplaces and lifestyles, and how virtual worlds will become so realistic that they will rival the physical world. Robots with human-level intelligence may finally become a reality, and in the ultimate stage of mastery, we’ll even be able to merge our minds with machine intelligence.For the first time on television, see how a severely depressed patient can be turned into a happy person at the push of a button – all thanks to the cross-pollination of neuroscience and artificial intelligence

The second revolution discussed is the biotech revolution.Genetics and biotechnology promise a future of unprecedented health and longevity: DNA screening could prevent many diseases, gene therapy could cure them and, thanks to lab-grown organs, the human body could be repaired as easily as a car, with spare parts readily available. Ultimately, the ageing process itself could be slowed down or even halted.But what impact will this have on who we are and how we will live? And, with our mastery of the genome, will the human race end up in a world divided by genetic apartheid?

The last revolution discussed is the quantum revolution.The quantum revolution could turn many ideas of science fiction into science fact – from metamaterials with mind-boggling properties like invisibility through limitless quantum energy and room temperature superconductors to Arthur C Clarke’s space elevator. Some scientists even forecast that in the latter half of the century everybody will have a personal fabricator that re-arranges molecules to produce everything from almost anything.Yet how will we ultimately use our mastery of matter? Like Samson, will we use our strength to bring down the temple? Or, like Solomon, will we have the wisdom to match our technology? These are the questions that i ponder on my mind.For more,tune into visions of the future on the science channel via dish network.

By: Frank Bilotta

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1. INTRODUCTION

         Historically, the notion of finding useful patterns in data has been given a variety of names, including data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing.

          The rapid emergence of electronic data management methods has lead some to call recent times as the “Information Age.” Powerful database systems for collecting and managing are in use in virtually all large and mid-range companies — there is hardly a transaction that does not generate a computer record somewhere. Each year more operations are being computerized, all accumulate data on operations, activities and performance. All these data hold valuable information, e.g., trends and patterns, which could be used to improve business decisions and optimize success.

          However, today’s databases contain so much data that it becomes almost impossible to manually analyze them for valuable decision-making information. In many cases, hundreds of independent attributes need to be simultaneously considered in order to accurately model system behavior. The term data mining has mostly been used by statisticians, data analysts, and the management information systems (MIS) communities. It has also gained popularity in the database field. The phrase knowledge discovery in databases was coined at the first KDD workshop in 1989 [1] (Piatetsky-Shapiro 1991)  to emphasize that knowledge is the end product of a data-driven discovery. It has been popularized in the AI and machine-learning fields. In our view, KDD refers to the overall process of discovering useful knowledge from data, and data mining refers to a particular step in this process. Data mining is the application of specific algorithms for extracting patterns from data. The distinction between the KDD process and the data-mining step (within the process) is a central point of this article. The additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data. Blind application of data-mining methods (rightly criticized as data dredging in the statistical literature) can be a dangerous activity, easily leading to the discovery of meaningless and invalid patterns.

 2. THE INTERDISCIPLINARY NATURE    OF KDD

         KDD has evolved, and continues to evolve, from the intersection of research fields such as machine learning, pattern recognition, databases, statistics, AI, knowledge acquisition for expert systems, data visualization, and high-performance computing. The unifying goal is extracting high-level knowledge from low-level data in the context of large data sets. The data-mining component of KDD currently relies heavily on known techniques from machine learning, pattern recognition, and statistics to find patterns from data in the data-mining step of the KDD process.

         A natural question is how is KDD different from pattern recognition or machine learning (and related fields)? The answer is that these fields provide some of the data-mining methods that are used in the data-mining step of the KDD process. KDD focuses on the overall process of knowledge discovery from data, including how the data are stored and accessed, how algorithms can be scaled to massive data sets still run efficiently, how results can be interpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported.

         The KDD process can be viewed as a multidisciplinary activity that encompasses techniques beyond the scope of any one particular discipline such as machine learning. In this context, there are clear opportunities for other fields of AI (besides machine learning) to contribute to KDD. KDD places a special emphasis on finding understandable patterns that can be interpreted as useful or interesting knowledge.

         Thus, for example, neural networks, although a powerful modeling tool, are relatively difficult to understand compared to decision trees. KDD also emphasizes scaling and robustness properties of modeling algorithms for large noisy data sets. Related AI research fields include machine discovery, which targets the discovery of empirical laws from observation and experimentation [10] (Shrager and Langley 1990) and causal modeling for the inference of causal models from data [11] (Spirtes, Glymour, and Scheines 1993). Statistics in particular has much in common with KDD. Knowledge discovery from data is fundamentally a statistical endeavor. Statistics provides a language and framework for quantifying the uncertainty that results when one tries to infer general patterns from a particular sample of an overall population. As mentioned earlier, the term data mining has had negative connotations in statistics since the 1960s when computer-based data analysis techniques were first introduced.

        The concern arose because if one searches long enough in any data set (even randomly generated data), one can find patterns that appear to be statistically significant but, in fact, are not. Clearly, this issue is of fundamental importance to KDD. Substantial progress has been made in recent years in understanding such issues in statistics. Much of this work is of direct relevance to KDD. Thus, data mining is a legitimate activity as long as one understands how to do it correctly; data mining carried out poorly (without regard to the statistical aspects of the problem) is to be avoided. KDD can also be viewed as encompassing a broader view of modeling than statistics. KDD aims to provide tools to automate (to the degree possible) the entire process of data analysis and the statistician’s “art” of hypothesis selection.

         A driving force behind KDD is the database field (the second D in KDD). Indeed, the problem of effective data manipulation when data cannot fit in the main memory is of fundamental importance to KDD. Database techniques for gaining efficient data access, grouping and ordering operations when accessing data, and optimizing queries constitute the basics for scaling algorithms to larger data sets. Most data-mining algorithms from statistics, pattern recognition, and machine learning assume data are in the main memory and pay no attention to how the algorithm breaks down if only limited views of the data are possible. A related field evolving from databases is data warehousing, which refers to the popular business trend of collecting and cleaning transactional data to make them available for online analysis and decision support. Data warehousing helps set the stage for KDD in two important ways:

(1) Data Cleaning

(2) Data Access.

 Data cleaning

         As organizations are forced to think about a unified logical view of the wide variety of data and databases they possess, they have to address the issues of mapping data to a single naming convention, uniformly representing and handling missing data, and handling noise and errors when possible.

 Data access

        Uniform and well-defined methods must be created for accessing the data and providing access paths to data that were historically difficult to get to (for example, stored offline). Once organizations and individuals have solved the problem of how to store and access their data, the natural next step is the question, what else do we do with all the data? This is where opportunities for KDD naturally arise.

        A popular approach for analysis of data warehouses is called online analytical processing (OLAP), named for a set of principles proposed by [12] Codd (1993). OLAP tools focus on providing multidimensional data analysis, which is superior to SQL in computing summaries and breakdowns along many dimensions. OLAP tools are targeted toward simplifying and supporting interactive data analysis, but the goal of KDD tools is to automate as much of the process as possible. Thus, KDD is a step beyond what is currently supported by most standard database systems.

 3. DATA MINING AND KNOWLEDGE DISCOVERY IN THE REAL WORLD

          A large degree of the current interest in KDD is the result of the media interest surrounding successful KDD applications, for example, the focus articles within the last two years in Business Week, Newsweek, Byte, PC Week, and other large-circulation periodicals. Unfortunately, it is not always easy to separate fact from media hype. Nonetheless, several well documented examples of successful systems can rightly be referred to as KDD applications and have been deployed in operational use on large-scale real-world problems in science and in business.

          In science, one of the primary application areas is astronomy. Here, a notable success was achieved by SKICAT, a system used by astronomers to perform image analysis, classification, and cataloging of sky objects from sky-survey images [2] (Fayyad, Djorgovski, and Weir 1996). In its first application, the system was used to process the 3 terabytes (1012 bytes) of image data resulting from the Second Palomar Observatory Sky Survey, where it is estimated that on the order of 109 sky objects are detectable. SKICAT can outperform humans and traditional computational techniques in classifying faint sky objects. See [3] Fayyad, Haussler, and Stolorz (1996) for a survey of scientific applications.

          In business, main KDD application areas includes marketing, finance (especially investment), fraud detection, manufacturing, telecommunications, and Internet agents.

Marketing

          In marketing, the primary application is database marketing systems, which analyze customer databases to identify different customer groups and forecast their behavior. Business Week [4] (Berry 1994) estimated that over half of all retailers are using or planning to use database marketing, and those who do use it have good results; for example, American Express reports a 10- to 15- percent increase in credit-card use. Another notable marketing application is market-basket analysis [5] (Agrawal et al. 1996) systems, which find patterns such as, “If customer bought X, he/she is also likely to buy Y and Z.” Such patterns are valuable to retailers.

 Investment

          Numerous companies use data mining for investment, but most do not describe their systems. One exception is LBS Capital Management. Its system uses expert systems, neural nets, and genetic algorithms to manage portfolios totaling $600 million; since its start in 1993, the system has outperformed the broad stock market [6] (Hall, Mani, and Barr 1996).

 Fraud detection

          HNC Falcon and Nestor PRISM systems are used for monitoring credit card fraud, watching over millions of accounts. The FAIS system [7] (Senator et al. 1995), from the U.S. Treasury Financial Crimes Enforcement Network, is used to identify financial transactions that might indicate money laundering activity.

 Manufacturing

           The ASSIOPEE troubleshooting system, developed as part of a joint venture between General Electric and SNECMA, was applied by three major European airlines to diagnose and predict problems for the Boeing 737. To derive families of faults, clustering methods are used. CASSIOPEE received the European first prize for innovative applications.

 Telecommunications

          The telecommunications alarm-sequence analyzer (TASA) was built in cooperation with a manufacturer of telecommunications equipment and three telephone networks [8]        (Mannila, Toivonen, and Verkamo 1995). The system uses a novel framework for locating frequently occurring alarm episodes from the alarm stream and presenting them as rules. Large sets of discovered rules can be explored with flexible information-retrieval tools supporting interactivity and iteration. In this way, TASA offers pruning, grouping, and ordering tools to refine the results of a basic brute-force search for rules.

 Data cleaning

           The MERGE-PURGE system was applied to the identification of duplicate welfare claims [9] (Hernandez and Stolfo 1995). It was used successfully on data from the Welfare Department of the State of Washington. In other areas, a well-publicized system is IBM’s ADVANCED SCOUT, a specialized data-mining system that helps National Basketball Association (NBA) coaches organize and interpret data from NBA games (U.S. News 1995). ADVANCED SCOUT was used by several of the NBA teams in 1996, including the Seattle Supersonics, which reached the NBA finals. Finally, a novel and increasingly important type of discovery is one based on the use of intelligent agents to navigate through an information-rich environment. Although the idea of active triggers has long been analyzed in the database field, really successful applications of this idea appeared only with the advent of the Internet. These systems ask the user to specify a profile of interest and search for related information among a wide variety of public-domain and proprietary sources. For example, FIREFLY is a personal music-recommendation agent: It asks a user his/her opinion of several music pieces and then suggests other music that the user might like.

 4. KNOWLEDGE DISCOVERY AND DATA MINING

           This section provides an introduction into the area of knowledge discovery and data mining tasks.

 The Knowledge Discovery Process

           There is still some confusion about the terms Knowledge Discovery in Databases (KDD) and data mining. Often these two terms are used interchangeably. We use the term KDD to denote the overall process of turning low-level data into high-level knowledge. A simple definition of KDD is as follows: Knowledge discovery in databases is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. We also adopt the commonly used definition of data mining as the extraction of patterns or models from observed data. Although at the core of the knowledge discovery process, this step usually takes only a small part (estimated at 15% to 25 %) of the overall effort. Hence data mining is just one step in the overall KDD process.

            Other steps for example involve: Developing an understanding of the application domain and the goals of the data mining process Acquiring or selecting a target data set Integrating and checking the data set Data cleaning, preprocessing, and transformation Model development and hypothesis building Choosing suitable data mining algorithms Result interpretation and visualization Result testing and verification Using and maintaining the discovered knowledge.

 Data Mining Tasks

          At the core of the KDD process are the data mining methods for extracting patterns from data. These methods can have different goals, dependent on the intended outcome of the overall KDD process. It should also be noted that several methods with different goals may be applied successively to achieve a desired result. For example, to determine which customers are likely to buy a new product, a business analyst might need to first use clustering to segment the customer database, and then apply regression to predict buying behavior for each cluster. Most data mining goals fall under the following categories:

Data Processing

           Depending on the goals and requirements of the KDD process, analysts may select, filter, aggregate, sample, clean and/or transform data. Automating some of the most typical data processing tasks and integrating them seamlessly into the overall process may eliminate or at least greatly reduce the need for programming specialized routines and for data export/import, thus improving the analyst’s productivity.

 Prediction

          Given a data item and a predictive model, predict the value for a specific attribute of the data item. For example, given a predictive model of credit card transactions, predict the likelihood that a specific transaction is fraudulent.

 Regression

           Given a set of data items, regression is the analysis of the dependency of some attribute values upon the values of other attributes in the same item, and the automatic production of a model that can predict these attribute values for new records. For example, given a data set of credit card transactions, build a model that can predict the likelihood of fraudulence for new transactions.

 Classification

           Given a set of predefined categorical classes, determine to which of these classes a specific data item belongs. For example, given classes of patients that corresponds to medical treatment responses; identify the form of treatment to which a new patient is most likely to respond.

 Clustering

           Given a set of data items, partition this set into a set of classes such that items with similar characteristics are grouped together. Clustering is best used for finding groups of items that are similar. For example, given a data set of customers, identify subgroups of customers that have a similar buying behavior.

 Link Analysis (Associations)

           Given a set of data items, identify relationships between attributes and items such as the presence of one pattern implies the presence of another pattern. These relations may be associations between attributes within the same data item. The investigation of relationships between items over a period of time is also often referred to as ‘sequential pattern analysis’.

 Model Visualization

           Visualization plays an important role in making the discovered knowledge understandable and interpretable by humans. Besides, the human eye-brain system itself still remains the best pattern-recognition device known. Visualization techniques may range from simple scatter plots and histogram plots over parallel coordinates to 3D movies.

 5. THE DATA-MINING STEP OF THE KDD PROCESS

          The data-mining component of the KDD process often involves repeated iterative application of particular data-mining methods. This section presents an overview of the primary goals of data mining, a description of the methods used to address these goals, and a brief description of the data-mining algorithms that incorporate these methods. The knowledge discovery goals are defined by the intended use of the system.

We can distinguish two types of goals:

 (1) Verification

 (2) Discovery.

          With verification, the system is limited to verifying the user’s hypothesis. With discovery, the system autonomously finds new patterns. We further subdivide the discovery goal into prediction, where the system finds patterns for predicting the future behavior of some entities, and description, where the system finds patterns for presentation to a user in a human-understandable form.

           In this article, we are primarily concerned with discovery-oriented data mining. Data mining involves fitting models to, or determining patterns from, observed data. The fitted models play the role of inferred knowledge: Whether the models reflect useful or interesting knowledge is part of the over all, interactive KDD process where subjective human judgment is typically required.

Two primary mathematical formalisms are used in model fitting:

            (1)  Statistical

            (2) Logical.

          The statistical approach allows for nondeterministic effects in the model, whereas a logical model is purely deterministic. We focus primarily on the statistical approach to data mining, which tends to be the most widely used basis for practical data-mining applications given the typical presence of uncertainty in real-world data-generating processes.

           Most data-mining methods are based on tried and tested techniques from machine learning, pattern recognition, and statistics: classification, clustering, regression, and so on. The array of different algorithms under each of these headings can often be bewildering to both the novice and the experienced data analyst. It should be emphasized that of the many data-mining methods advertised in the literature, there are really only a few fundamental techniques.

6. RESEARCH AND APPLICATION CHALLENGES

We outline some of the current primary research and application challenges for KDD.

          This list is by no means exhaustive and is intended to give the reader a feel for the types of problem that KDD practitioners wrestle with.

 Larger databases

          Databases with hundreds of fields and tables and millions of records and of a multi gigabyte size are commonplace, and terabyte (1012 bytes) databases are beginning to appear. Methods for dealing with large data volumes include more efficient algorithms sampling, approximation, and massively parallel processing.

 High dimensionality

          Not only is there often a large number of records in the database, but there can also be a large number of fields (attributes, variables); so, the dimensionality of the problem is high. A high-dimensional data set creates problems in terms of increasing the size of the search space for model induction in a combinatorial explosive manner. In addition, it increases the chances that a data-mining algorithm will find spurious patterns that are not valid in general. Approaches to this problem include methods to reduce the effective dimensionality of the problem and the use of prior knowledge to identify irrelevant variables.

 Over fitting

           When the algorithm searches for the best parameters for one particular model using a limited set of data, it can model not only the general patterns in the data but also any noise specific to the data set, resulting in poor performance of the model on test data. Possible solutions include cross-validation, regularization, and other sophisticated statistical strategies.

 Assessing of statistical significance

           A problem (related to over fitting) occurs when the system is searching over many possible models. For example, if a system tests models at the 0.001 significance level, then on average, with purely random data, N/1000 of these models will be accepted as significant edge is important in all the steps of the KDD process. Bayesian approaches [13] (for example, Cheeseman [1990]) use prior probabilities over data and distributions as one form of encoding prior knowledge. Others employ deductive database capabilities to discover knowledge that is then used to guide the data-mining search [14] (for example, Simoudis, Livezey, and Kerber [1995]).

 Integration with other systems

            A standalone discovery system might not be very useful. Typical integration issues include integration with a database management system (for example, through a query interface), integration with spreadsheets and visualization tools, and accommodating of real-time sensor readings. Examples of integrated KDD systems are described by [14] Simoudis, Livezey, and Kerber (1995).

 7. CONCLUSION

           This article represents a step toward a common framework that We hope will ultimately provide a unifying vision of the common overall goals and methods used in KDD. We hope this would eventually lead to a better understanding of the variety of approaches in this multidisciplinary field and how they fit together.

 9. REFERENCES

[1] Piatetsky – Shapiro, G. 1991. Knowledge Discovery in Real Databases: A Report on the IJCAI-89 Workshop. AI Magazine 11(5): 68–70.

 [2] Fayyad, U. M.; Djorgovski, S. G.; and Weir, N. 1996. From Digitized Images to On-Line Catalogs: Data Mining a Sky Survey. AI Magazine 17(2): 51–66.

 [3] Fayyad, U. M.; Haussler, D.; and Stolorz, Z. 1996. KDD for Science Data Analysis: Issues and Examples. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 50–56. Menlo Park, Calif.: American Association for Artificial Intelligence.

 [4] Berry, J. 1994. Database Marketing. Business Week, September 5, 56–62.

[5] Agrawal, R., and Psaila, G. 1995. Active Data Mining. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 3–8. Menlo Park, Calif.: American Association for Artificial Intelligence

[6] Hall, J.; Mani, G.; and Barr, D. 1996. Applying Computational Intelligence to the Investment Process. In Proceedings of CIFER-96: Computational Intelligence in Financial Engineering. Washington, D.C.: IEEE Computer Society.

 [7] Senator, T.; Goldberg, H. G.; Wooton, J.; Cottini, M. A.; Umarkhan, A. F.; Klinger, C. D.; Llamas, W. M.; Marrone, M. P.; and Wong, R. W. H. 1995. The Financial Crimes Enforcement Network AI System (FAIS): Identifying Potential Money Laundering from Reports of Large Cash Transactions. AI Magazine 16(4): 21–39.

 [8] Mannila, H.; Toivonen, H.; and Verkamo, A. I. 1995. Discovering Frequent Episodes in Sequences. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 210–215. Menlo Park, Calif.: American Association for Artificial Intelligence.

 

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Facets of Web 3.0-

A Boon for Netizens

B.MAHESHWARI

Abstract

With more than 10 years’ work on the Semantic Web’s foundations and more than five years since the phrase became popular, it’s an opportune moment to look at the field’s current state and future opportunities. The Resource Description Framework (RDF) and Web Ontology Language (OWL)–the languages that power the Semantic Web–have become standards and new technologies are reaching maturity for embedding semantics in existing Web pages and querying RDF knowledge stores. Something exciting is clearly happening in this area. That is none other than web 3.0

• Web 3.0 is defined as the creation of high-quality content and services produced by gifted individuals using Web 2.0 technology as an enabling platform.

Before this people were very curious about ‘Web 3.0’ as they asked to Tim Berner about the full-fledged information of Web 3.0 as Tim Berners-Lee stated in May 2006:

“People keep asking what Web 3.0 is. I think maybe when you’ve got an overlay of scalable vector graphics – everything rippling and folding and looking misty – on Web 2.0 and access to a semantic Web integrated across a huge space of data, you’ll have access to an unbelievable data resource.”- Tim Berners Lee

Web 2.0 services are now the commoditized platform, not the final product. In a world where a social network, wiki, or social bookmarking service can be built for free and in an instant, what’s next?

Web 2.0 services like digg and YouTube evolve into Web 3.0 services with an additional layer of individual excellence and focus. As an example, funnyordie.com leverages all the standard YouTube Web 2.0 feature sets like syndication and social networking, while adding a layer of talent and trust to them.

A version of digg where experts check the validity of claims, corrected errors, and restated headlines to be more accurate would be the Web 3.0 version. However, I’m not sure if the digg community will embrace that any time soon.

Wikipedia, considered a Web 1.5 service, is experiencing the start of the Web 3.0 movement by locking pages down as they reach completion, and (at least in their German version) requiring edits to flow through trusted experts.

Also of note, is what Web 3.0 leaves behind? Web 3.0 throttles the “wisdom of the crowds” from turning into the “madness of the mobs” we’ve seen all too often, by balancing it with a respect of experts. Web 3.0 leaves behind the cowardly anonymous contributors and the selfish blackhat SEOs that have polluted and diminished so many communities.

Web 3.0 is a return to what was great about media and technology before Web 2.0: recognizing talent and expertise, the ownership of ones words, and fairness. It’s time to evolve, shall we?

Basic Web 3.0 Concepts

Knowledge domains

A knowledge domain is something like Physics, Chemistry, Biology, Politics, the Web, Sociology, Psychology, History, etc. There can be many sub-domains under each domain each having their own sub-domains and so on.

Information vs. Knowledge

To a machine, knowledge is comprehended information (aka new information produced through the application of deductive reasoning to exiting information). To a machine, information is only data, until it is processed and comprehended.

Ontologies

For each domain of human knowledge, an ontology must be constructed, partly by hand [or rather by brain] and partly with the aid of automation tools.

Ontologies are not knowledge nor are they information. They are meta-information. In other words, ontologies are information about information. In the context of the Semantic Web, they encode, using an ontology language, the relationships between the various terms within the information. Those relationships, which may be thought of as the axioms (basic assumptions), together with the rules governing the inference process, both enable as well as constrain the interpretation (and well-formed use) of those terms by the Info Agents to reason new conclusions based on existing information, i.e. to think. In other words, theorems (formal deductive propositions that are provable based on the axioms and the rules of inference) may be generated by the software, thus allowing formal deductive reasoning at the machine level. And given that an ontology, as described here, is a statement of Logic Theory, two or more independent Info Agents processing the same domain-specific ontology will be able to collaborate and deduce an answer to a query, without being driven by the same software.

Inference Engines

In the context of Web 3.0, Inference engines will be combining the latest innovations from the artificial intelligence (AI) field together with domain-specific ontologies (created as formal or informal ontologies by, say, Wikipedia, as well as others), domain inference rules, and query structures to enable deductive reasoning on the machine level.

Info Agents

Info Agents are instances of an Inference Engine, each working with a domain-specific ontology. Two or more agents working with a shared ontology may collaborate to deduce answers to questions. Such collaborating agents may be based on differently designed Inference Engines and they would still be able to collaborate.

Proofs and Answers

The interesting thing about Info Agents that I did not clarify in the original post is that they will be capable of not only deducing answers from existing information (i.e. generating new information [and gaining knowledge in the process, for those agents with a learning function]) but they will also be able to formally test propositions (represented in some query logic) that are made directly or implied by the user. For example, instead of the example I gave previously (in the Wikipedia 3.0 article) where the user asks “Where is the nearest restaurant that serves Italian cuisine” and the machine deduces that a pizza restaurant serves Italian cuisine, the user may ask “Is the moon blue?” or say that the “moon is blue” to get a true or false answer from the machine. In this case, a simple Info Agent may answer with “No” but a more sophisticated one may say “the moon is not blue but some humans are fond of saying ‘once in a blue moon’ which seems illogical to me.”

This test-of-truth feature assumes the use of an ontology language (as a formal logic system) and an ontology where all propositions (or formal statements) that can be made can be computed (i.e. proved true or false) and were all such computations are decidable in finite time. The language may be OWL-DL or any language that, together with the ontology in question, satisfy the completeness and decidability conditions.

“The Future Has Arrived But It’s Not Evenly Distributed”

Currently, Semantic Web (aka Web 3.0) researchers are working out the technology and human resource issues and folks like Tim Berners-Lee, the Noble prize recipient and father of the Web, are battling critics and enlightening minds about the coming human-machine revolution.

The Semantic Web (aka Web 3.0) has already arrived, and Inference Engines are working with prototypical ontologies, but this effort is a massive one, which is why I was suggesting that its most likely enabler will be a social, collaborative movement such as Wikipedia, which has the human resources (in the form of the thousands of knowledgeable volunteers) to help create the ontologies (most likely as informal ontologies based on semantic annotations) that, when combined with inference rules for each domain of knowledge and the query structures for the particular schema, enable deductive reasoning at the machine level.

Definitions and Roadmap

There are several definitions of the web, but usually Web 3.0 is defined as a term, which has been coined with different meanings to describe the evolution of web usage and interaction among the several separate paths.

These include transforming the Web into a database, a move towards making content accessible by multiple non-browser applications, the leveraging of artificial intelligence technologies, the Semantic web, or the Geospatial Web. According to Wikipedia, an online encyclopedia, “Web 3.0 is a third generation of Internet based Web services, which emphasize m a c h i n e – f a c i l i t a t e d understanding of information in order to provide a more productive and intuitive user experience.”. The third generation of Internet services is collectively consists of semantic web, microformats, natural language search, data-mining, machine learning, recommendation agents that is known as Artificial Intelligence technologies or Intelligent Web.

According to some experts, “Web 3.0 is characterized and fueled by the successful carriage of artificial intelligence and the web”. While some experts have summarized the definition defining as “Web 3.0 is the next step in the progression of the tubes that are the Internets”.

According to Nova Spivack, the CEO of Radar Networks, one of the leading voices of this new age Internet, “Web 3.0 is a set of standards that turns the Web into one big database.”

Steve, a famous Blog author has defined the term Web 3.0 as, “ Web 3.0 is highly specialized information structures, moderated by a group of personality, validated by the community, and put into context with the inclusion of meta-data through widgets”. While Leiki, the Finland based pioneer company of Semantic Web describes: “Web 3.0 makes the discovery of content streams effortless. It introduces automatic discovery of likeminded users and automatic tagging.”

The term ‘Web 3.0’ was first coined by John Markoff of the New York Times in 2006, while it first appeared prominently in early 2006 in a Blog article written by Jeffrey Zeldman in the “Critical of Web 2.0 and associated technologies such as Ajax”.

A ‘more revolutionary’ Web

The term Web 3.0 has became a subject of interest and debate since late 2006 to till date. But no exact definition has been created that everyone accepts it.

Web 3.0 Debates over Definition

Since the origins of the concept of Web 3.0, the debate continues goes on about exactly what the term Web 3.0 means, and what a suitable definition might be. As emerging the new technology, a new definition emerged:

Transforming the Web into a database

Transforming the Web into database is the beginning step towards transforming definition of Web 3.0 when the technology of ‘Data Web’ emerged as structured data records that can be published to the Web in reusable and remotely query able formats, such as XML, RDF and microformats. The Data Web is the initial step in the way of full Semantic web that enables a new level of data integration and application interoperability, which makes the data openly accessible and linkable as Web pages. To make available structured data using RDF is primarily focused in Data Web phase. The full Semantic Web stage will so expand the scope that both structured and semi structured or unstructured content will be widely available in RDF and OWL semantic formats.

An evolutionary path to artificial intelligence

Web 3.0 has also been used to describe the rend of artificial intelligence, which is being popular in the web like a quasi-human fashion. Some cynic believes that it is an unobtainable vision. However, this is being used new technologies on mass level that yields amazing information like making predictions of hit songs from mining information on college music Web sites. There is also debate on the driving force behind Web 3.0. Will it be the intelligent systems, or whether intelligence will emerge in a more organic fashion and how people interact with it?

The realization of the Semantic Web and Service Oriented Architecture

Another debate originates over the artificial intelligence direction in which Web 3.0 can be extent to Semantic web concept. Academic research is going on to develop such reasoning software that must be based on description logic and intelligent agents. These sorts of applications can perform logical reasoning operations through using sets of rules expressing logical relationships between concepts and data on the Web.

But some critics are disagree on the viewpoint, which describes that Semantic Web would be the core of the 3rd generation of the Internet and suggests a formula to summarize Web 3.0.

Web 3.0 has also been associated to a possible hub of SOA (Service Oriented Architecture) and Semantic web.

Evolution towards 3D

The evolution of 3D technology is also being connected to Web 3.0 as Web 3.0 may be used on massive scale due to its characteristics. In this process Web 3.0 would transform into a series of 3D spaces, taking the concept realized by Second Life expansion. This could open up new ways to connect and collaborate using 3D shared spaces.

Proposed Expanded Definitions of Web 3.0

Nova Spivack has proposed the expanded definition of

Web 3.0 that indulge in itself the collection of various foremost harmonizing technology developments that are growing to a new level of maturity simultaneously includes:

• Ubiquitous Connectivity, broadband adoption, mobile Internet access and mobile devices

• Network computing, s o f t w a r e -a s – a – s e r v i c e business models, Web services interoperability, distributed computing, grid computing and cloud computing

• Open technologies, Open APIs and protocols, open data formats, open-source software platforms and open data (e.g. Creative Commons, Open Data License)

• Open identity, OpenID, open reputation, roaming portable identity and personal data

• The intelligent web, Semantic web technologies such as RDF, OWL, SWRL, SPARQL, Semantic application platforms, and statement based data stores.

•Distributed databases, the “World Wide Database” (enabled by Semantic Web technologies)

• Intelligent applications, natural language processing, machine learning, machine reasoning, and autonomous agents

Web 3.0 as Different Formats of Web

The Semantic Web

The term ‘Semantic Web’ refers to “Defined” Web that is an alliance of World Wide Web Consortium (W3C) and others to provide a standard for defining data structures on the Web. Semantic Web refers to the use of XMLtagged data that matches the Resource Description Framework (RDF).

Sometimes it is refers to” Web 3.0,” that is a debatable topic, but in the form of Web 3.0, the main goal of the Semantic Web becomes to identify exact required data that matches the keywords. e.g. if we search Web 3.0 in Google / yahoo / msn or any advance search engines using specific key words, there are millions of web pages appears on the window in which only very few have some information and all other pages are worthless.

Web 3.0 in terms of Semantic Web is the third generation of World Wide Web in which machines an read sites similar to human being and also follows our instructions. For example if you order to check your schedule against the schedules of all the dentists and doctors within a 10-mile radius if follows tour order and provide the appropriate information.

The 3D Web

3-D refers to the three dimensional design that represents the virtual looks of any object from three different sides simultaneously. A user can view the true picture of any building, any location or any object and walk through the location without leaving the computer desk on his/her system. Though these are the virtual pictures but seem to be real. These technologies are extensively being used in a wide range of services like computer games, Virtual Reality (VR) models and Multimedia solutions.

Now, 3-D technology has come on the Internet and has become a new trend of Web. Now user can go house hunting across town or take a tour of the world or can walk through a Second Life– style virtual world, surfing for data and interacting with others in 3D. The 3d web is being used massively in online computer games, virtual world tour, Geospatial engineering, online high tech research, online software development, online shopping, online telecommunication and social networking sites. Google Earth, Wiki Earth, MySpace, You Tube are the biggest examples of 3D web users

The Media-Centric Web

The terms Media-Centric Web refers to the web where users can find true similar graphics and sound on the other media, not just the keywords. E.g. if users searches any favorite movie/ graphics/ music in the search engines, can find the exact desired thing on the other media.

The Pervasive Web

The pervasive web refers the uses of web in the wide range of area in which the web has now been reached not only in computers and cell phones but also in clothing, appliances, and automobiles and much more, e.g. web based bedroom windows that checks weather and self open or close it according to climate.

Web 3.0 in terms of pervasive web refers to those websites, which are going to be transformed into web services and will depict and expand their information to the world.

Overview

As the times goes and he technology enriches, the experts feels to develop some thing better that can be more fruitful, advance, user friendly and intelligent. Thus originates the concept of web 3.0 and now it is taking a handsome shape. We 3.0 have some more features including the feature of Web 2.0.

Web 3.0 sites will only allow collaboration of content generated from an approved pseudo-random sequence of characters. Web 3.0 would have three main objectives:

1- Seeking Information

2- Seeking Validation

3- Seeking entertainment

Seeking Information

Searching information would be more compact in Web 3.0. Till now, the web uses keywords in order to comprehensive data into usable chunks. Search engines index the Internet in proper order and present it to the end user in order of relevance. The users select the information that is nearer to their requirement. Sometimes this becomes a very hectic process. But Web 2.0 goes one step ahead and brought us a change in the basic way of searching. It applies the tags in the searching data e.g. if anyone wants to look for car. He/she types the word in the specified space of the search engine. The search engine displays many webs, but if the user type BMW cars, it displays all the relevant site only\y related to BMW cars. So BMW works as a tag.

Web 3.0 will be more advance in searching the information for example of Cars, Web 3.0 uses the further research beyond the engines, it also uses the sub search engines that would provide more compact information and user can find the nearest desired data. It would go to all major categories like pictures, videos, blog posts, news articles, commerce etc. Each of these would happen because of RSS feed so that user can get alerts when something new would add to his/her search profile.

Seeking Validation

If the user wants to go the news not the information, it will work in a different way. It would provide the exact data what user wants. It would also search the available people on the net. The user have to type the words what he/she wants to access, Web 3.0 would provide the relevant information in order of its proximity, algorithms, tagging, and validation through user voting.

Seeking Entertainment

Entertainment, the most popular trend of Web 2.0 would be more advance in Web 3.0 as it would be based around the sect of the personality. People Search will replace the social networks that are most popular fashion in this generation of web. For searching about any person, just type the name and all the information related to regarding person would be displayed with some attached tags. If would display the total wiki profile,

In which all the data would be specified whether the user would have created it or anyone else. All the related deeds would also show in the profile. Then People would be more universal rather than now.

The looks and shape of the blogging would be also changed; the current weblogs would be converted in to Microblogging. People will be able to blog from anywhere, without having to spend hours writing a properly formatted post. Web 3.0 will see a more complete integration between devices like cell phones and the World Wide Web. Posting pictures, videos and text from anywhere, anytime would be more tussles free.

Commerce

Here the terms of commerce means the criteria of earning that will be more advance, but the whole criteria would not primarily change. The product will carry on to sell online. Conversational advertising” and detrainment will take the place of stock ads and promotions. Sect of personality and their sponsorships will also be more specific as the advertisement companies will be narrower because of categorizing of the people.

The entire advertising landscape will change; the ultra specialized sub engines will search the tightly focused target audience to selling the product.

Contextual advertisement will take second seat to product placements on sites, search results and sub engines.

Web 3.0 Design

REST, AJAX, Silverlight, Widget Enabled, Taggable, Searchable everything…

RSS. A Web 3.0 Driver

In the coming ten years RSS and its related technologies will become the single most important Internet technology because of its specific quality to development of the new web as it’s really very simple. Any person who has a little bit knowledge of coding can generate an extensible, standards based database of information that can be transferred to almost any other modern web site.

If Web 3.0 is the Semantic Web, where machine read content like human beings then RSS will be its eyes. RSS technology is still in vast uses especially in the online news portals. The entire business models have already being created around aggregating metadata. IGoogle, MyIndiaTims and Netvibes allow the users to create their own personal homepage, drawing much of its content from RSS feeds that users select.

The trend of RSS tool will be increased in the future in which user can include a host of data-points. Each blog post, the future microblogging feed can be personalize according to users’ desire as every picture, every video clip, every music will be searchable, taggable and XML based collaborate. The biggest example of it’s already exists in a web portal named MyIndiaTims.com’. The real power of Web 3.0 will be in the used in creating data and transferring it effectively. Candidate Web 3.0 technologies Web 3.0 would be used in various technologies of computer and Internet. Here is the list of web 3.0 users:

• Artificial intelligence

• Automated reasoning

• Cognitive architecture

• Composite applications

• Distributed computing

•Knowledge representation

•Ontology (computer science)

•Recombinanttext

• Scalable vector graphics.

• Semantic Web

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References:

1. CSI Data communications

2. Time to Discuss Web 3.0-march 9, 2008, blog.

3. The article, “A More Revolutionary Web,” by Victoria Shannon that covers discussions from the 15th annual International World.

4. Spinning the Semantic Web: Bring the World Wide Web to its Full Potential, by Tim Berners Lee, et al.

5. “Web 2.0 Isn’t Dead, but Web 3.0 is Bubbling Up,” by Dan Farber-blog.

6. Web 3.0? Maybe when we get there.-blog

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A man switches on a tiny wireless chip that has been surgically implanted behind his ear, which then synchs up with the Web wherever he is in the world. The mere thought of logging in to the Internet triggers the system to turn on and connect to the Web. He could be on a bus or at the beach and from all outward appearances he is just staring off into space. But he sees a three dimensional artificial world before him that he can manipulate any way he chooses by thought alone.
By looking at the trends of today we can begin to develop a image of what the Web of the future will look like. I believe the Web will improve and grow in a way that will dwarf its present existence and will improve and enrich everyone’s lives way beyond what we can imagine today. The Net will become as integrated into everyone’s everyday lives as much as, and even more so, than the television or phone (in developed nations first, then everywhere). Television, communications and the Internet will merge.
The Web will become increasingly realistic, interactive, and three dimensional. Two dimensional displays will evolve into three dimensional displays. And the Web will probably incorporate more than just the two senses of seeing and hearing. It will first be incorporated into all other electronics found in household appliances, copy machines, automobiles, and anything else with a microchip. Then it will be integrated directly into our brains.
I also envisage this new Web creating an unimaginably sophisticated data sphere that surrounds and envelops the world like a warm electronic blanket, connecting everyone and everything. And it may some day become an autonomous and sentient entity in its own right that we may even come to depend on for life itself.
When a person switches on his wireless Web chip and connects with the Net, he’ll be looking at and interacting with the Web of the future. He’ll manipulate objects, click on links, download information, and communicate with anyone by simply thinking it. In fact, when he navigates to a grocery store to buy food, for instance, he’ll be able to “pick them up”, “feel them” and even “smell” the food he wants to buy just by thinking the appropriate thoughts.
In the future, Web-based software agents will constantly build dynamic lists and instructions to help people in personal and professional activities. These software agents are subroutines, or small programs, which may be part of a responsive ‘Internet Operating System’ that serves humanity, or possibly even destroy it. Programs may become responsible for doing some of the basic thinking that we get stuck routinely doing today. Additionally, it may be responsible for storing a percentage of our memories as well.
The Web has already become something we rely on for memory, and that reliance will only grow. We’d rather look something up on Google two or three times instead of trying to remember it initially. And eventually, we’ll come to rely on the Web for memories and immediate information so that it will seem like we are missing a part of our own brain when not “jacked in” to the Net, to borrow a phrase from science fiction writer William Gibson. The Net will be such a part of our existence that we may even feel profound separation and isolation when not connected.
The Evolution of the Web Display
Of course we’re not going to jump from flat screen LCD monitors of today to displays that exist only “in our minds”. Three dimensional displays may be the bridge. There is a device in existence today called a Heliodisplay(TM) that produces holograms which exist in three dimensions and are created with photographic projection using advanced laser technology. It’s possible that all displays will employ this technology in the future. The gaming industry ceaselessly works at making their artificial gaming experiences more realistic and is a powerful driving force in computer display technology.
The Web of our future will first be truly device independent where each piece of equipment is a different window that peers into the same global Web. From handheld devices not unlike the Star Trek Communicators, to cell phones, televisions, automobile dashboards, embedded refrigerator displays and MP3 players, all will be portals into the same World Wide Web.
And of course everything will be connected. Instead of applications running on individual personal computers and devices, applications will operate on the Net and be accessible to anyone, creating a loose Internet Operating System.
Ultimately, the Web of our future will most likely abandon standard two dimensional and even three dimensional displays and instead be projected right onto our corneas, skipping the middle man, so to speak.
FutureWeb is Closer Than We Think
Already demonstrated in the lab is the ability to cause a computer to react to thought alone. Duke University neuroscientist Miguel Nicolelis works in the field of BMI (brain-machine interface). In an experiment involving a monkey, a computer and a monitor, Nicolelis and his team successfully caused the monkey to communicate with and control a robotic arm through its brain’s neural signals alone.
The monkey’s brain activity and signals were first monitored with numerous electrodes inside its scalp while it manipulated a joystick. The scientists taught the monkey to move the joystick with its arms to accomplish movement on the monitor. Nicolelis’ team then took the joystick away, but continued everything else the same way. Since the monkey’s brain was hooked up to the computer, each time it had the thought of moving its arms, the desired affect actually happened anyway on the monitor, triggered by the monkey’s thoughts alone. In fact, the monkey was even able to control an artificial arm over the Web 600 miles away in the same manner.
There are two important applications for this technology that are driving its research: medicine and war, two constants in all of human history. Doctors will someday be able to attach a prosthetic arm to a patient, wire it up to her brain, and succeed in enabling her to control the prosthetic fingers by simply thinking it.
The Defense Advanced Research Projects Agency (DARPA) manages the research for the U.S. Department of Defense. In 2003, DARPA invested $23 million in BMI programs, including the one at Duke University cited above. Their goal is to allow soldiers to control weapons of all kinds by thought only. These super soldiers will be able to stealthily navigate through a battlefield willing robotic gliders above to drop their payloads of smart bombs on the enemy over the next hill, without endangering their own lives.
Ethical questions aside, brain-machine interfacing will someday mature and become integrated into our lives. Since the Web is already such a part of our world, the marriage of the two is inevitable.
This technology can be utilized in the other direction as well. Just like a thought can produce computer behavior, the computer will someday be able to send back sensory data other than just sight and sound. If a computer is hooked directly up to the brain, then smell, taste and touch can be affected as well. The Web will literally come to life.
The Semantic Web, Web 2.0 and the Collaboration of Humanity
Tim Berners-Lee, the inventor of the Web, wrote an illuminating book called Weaving the Web that I recommend all Web professionals read. Among the many profound ideas expressed are two concepts relevant here. One is the Semantic Web, which is explained as “The Web of data with meaning in the sense that a computer program can learn enough about what the data means to process it.” Metadata is the term used for data about data. Most Web pages today have embedded in the html code metadata that gives information about the Web page. Eventually, this information will become much more robust, allowing more intelligent searches to become a reality.
The Semantic Web may have the potential to help make the Internet an entity in its own right. Parallel processing, the connecting of computers to make super computers, has been in existence for some time now. In fact, that’s how the human brain operates, by conducting many operations at the same time.
The other fascinating idea Berners-Lee expressed in this landmark book is that his original idea for the Web involved much more of a two-way exchange of information. His original vision for the Web was one of collaboration. He wanted people to be able to post information to the Web as easily as it was to view information. Unfortunately, the latter has been embraced more readily by the general population.
But now we see the emergence of “Web 2.0″, a fairly new term that describes an innovative type of website that is built on the participation of its users. Blogs, wikis Podcasts and social networks all fall under the Web 2.0 umbrella. Today we are finally achieving what Berners-Lee had in mind all along. With websites such as MySpace, YouTube, Flickr, Squidoo, and Digg, non-technical users can now post information and contribute to the Web as easily as they can access it. The Web of the future will embrace this concept even more, causing its speed of growth to eclipse today’s rate.
It’s not difficult to see that the Web could be a vast parallel processing farm, that given enough artificial intelligence programming, the infusion of Semantic Web systems, and the constant additions from billions of intelligent beings (namely humans), it could have the potential of becoming something of a unified intelligence, a data sphere that surrounds the planet and is more powerful that the sum of its parts.
This concept of technology’s exponential growth turning onto something we cannot even imagine with the possibility of the Web becoming sentient is not new. Vernor Vinge, a retired Professor of Mathematics at San Diego State University, a computer scientist and a science fiction author, wrote about the Singularity in a 1993 essay.
A super-intelligence emerging out of the Web was also written about by Kevin Kelly in Wired Magazine in August 2005 and also published on KurzweilAI.
“. . . we are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater than human intelligence.
This planet-sized computer is comparable in complexity to a human brain. Both the brain and the Web have hundreds of billions of neurons (or Web pages). Each biological neuron sprouts synaptic links to thousands of other neurons, while each Web page branches into dozens of hyperlinks. That adds up to a trillion “synapses” between the static pages on the Web. The human brain has about 100 times that number, but brains are not doubling in size every few years. The Machine (the Web of the future) is.”
An online search will yield many examples of bizarre concepts that existed only in science fiction later becoming reality. The Web is something that Earth has never seen before. It not only has the potential to connect everyone, but it can also extend every brain and grow exponentially. It may take a lot longer than anyone thinks, but eventually the Web of our future will be immensely different and much more powerful than anyone can possibly imagine today.

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We face a very large and difficult game, which resides in the distinction that it should be carefully establish between, on one side, the language itself. I mean the cognitive processes to the phrase, and in the other hand, the languages countless and arbitrarily codified by all the companies, such or such time of their history, languages which president to the interlocution. I insist on the fact well put you in custody, because we have here a real obstacle, to extent that the process we are going to try to hunt down below the languages, emerges only invested in uses codified in number almost incalculable. It was understood that when an English said “water” when a French says “eau,” there is, certainly, an obvious difference of vocabulary – The difference which is, it, sociology (more precisely of the sociolinguistics). It must, however conceive that one as the other testify to the same capacity to sign. Then you see where the difficulty comes: we must take care not to impute the sign which is equivalent to its use, and, for this, bring to between parentheses, everything that makes that “water” and “eau” are, sociologically, words belonging to two different languages. We must recognize that this parenthesis in the language is very difficult to operate!

That said; let us start with the specific of the traditional definition of the sign as association of itself and its meaning. We know, today, that this definition is totally inadequate: since the beginning of the XX century, in effect, very exactly since Ferdinand de Saussure’s work, we know that his and this sense is to analyze conversely, this means that we cannot cut one of the two sides of the sign without automatically cut the other. To give you an image which will help you to understand this phenomenon, write, for example, on a piece of paper the word “maintenant,” and, armed of a pair of scissors, cut the word after the syllable “main”: you will have, on one side, a word, that means, in a system like French, “head,” “foot,” “leg”, etc. , and in the other hand, another word that oppose it and means “taking,” “unleashing,” “will,” etc. That is what Saussure called the reciprocity of two “faces” of the sign of language, that was called the “meaning” (at the level of the sign) and the “meant” (at the level of the sense). Take again the syllable “main” and replace the initial letter by “p” or “b,” it is obvious that, in the French system, it means “hand,” “bread” and “bath” and it serves to a totally different meaning. And it is so for all the languages that, each have, certainly, their own grid analysis of sound and direction, but which the signifying units all have in common to possess this bifaciality which means that they are always its analyzed by of sense and meaning analyzed by the sound (it is what defines, in Saussure’s terms, the “immanence of the sign.”)

But there is more: what characterizes all the language units, whatever the language is, there is also biaxiality it governs its mutual oppositions as well as its combinations. Of course, again, I will be extremely schematic for you to suggest the thing. I will take the example of the sound produced by a music instrument. There are, in reality, two ways of defining a music note: If I push, for example, on the key “g” of a piano keyboard, the note I cannot be perceive only with the other notes that could be in his place and that there is a point. The substance of the note is a degree in a certain vertical scale, scale that is called a “range.” But the note also has a relative value of difference compared to those which are above ore the one that follows, and this is what makes it possible what is called “melody.” All its musical is therefore an interference of a range and a melody, or, if you want, an opposition or a contrast, of a vertical axis and a horizontal axis. In other words, there is no melody without a range, and there is no range without melody!

Well, this double and reciprocal screening of a focuses on the other is the own of all our particulars, no matter what is the language we practice. There is always, basically, a grammatical and logic analysis we learned, not without good sense, namely, that the elements of a statement had a “nature” – this means an identity (vertical axis) – and a “function” (horizontal axis), this means that these two axis contribute, each for their part to the functioning of a statement (I leave aside, to simplify the great innovation of the successors of Saussure which was to extend this idea of function to the vocabulary itself). Or, for example, the unit “bread” (must be done, here, completely divorced from the spelling, and imagine that we are equivalent of the note “g”) “bread” does not exist, as a verbal unit, because I can say “the bread” or “he brained,” which allows me to classify the first unit in the range of nouns (range in which, lexically “bread” is opposed to “brained,” “brain,” etc. ), and to classify the second in the range of verbs (range in which, “painting” is opposed to “coloring,” “smudge”, etc. ). But, conversely, if I do not have the range, I mean the principle of classification; I could not organize these two units as I have done. We can no classify, without a layout, not a layout without no classification, or, if you prefer, there is no analysis on one of the two axis without an analysis on the other, this means that the two axis to analyze conversely, exactly as the two sides. That is the first contribution of Jean Gagnepain to the knowledge of language, on the basis of the clinic aphasiologique.

I want to clarify that is in the Middle Age that the old analysis date the one that many of you may have practiced at school, ate least I did; analyze a sentence, it is was to isolate the elements, of course, by defining their “nature” and then their “functions.” The “nature” meant “this thing which is defined.” The function was the report. But in the other hand, it has kept this opposition of nature and function, which is what, makes laugh all the “modern” linguists.  Well, that are the modern linguists which are made of fools, because they had been retained something that was important, I mean an axis opposition between a classification of identities and, in the other hand, an enumeration of units. Therefore, the grammatical analysis, which comes in a straight line of Aristotle, has been maintained by the grammarians who have not blushing, up to the emergence of our “school teachers.” The former teachers we may have left in primary education, but we have at least learned something. And this was nothing! (It is true that they could not say a word of the projection of the axis.)

Bifaciality and biaxiality that is what defines what is called the structure of language, structure we must design as a purely and formal system, without any content: the faces and the axis do not have any existence butt a merely virtual. This prevents not, of course, the existence of their reciprocal analysis, reciprocal analysis proven by the results of the clinic aphasiologique.

But it is the same clinic which led Jean Gagnepain, not only to highlight the biaxiality to his own process of language, which was already a fantastic step comparing to Saussure, but to ask, in 1960-1970, the assumption (since scientifically verified) of the intervention, in the implementation of the sign, a third process: a dialectical process. What is it?

It is understood that signify the language units, is not the only to their virtual structure, it cannot be anything other than a mere “for-say.” Because to speak, is always speak of something, in other words, if, in the first time (first in the sense logical and not chronological) we fly out of our perceptive representations, thus acceding to the structure, we reinvest, then, this structure in the universe of “things,” I mean, in essence, in the world to say. In other words, the structure is the implicit mediation (“unconscious,” if you want) between the percept and the concept: design (conceptualize, some people say) is beyond our representations, we do ideas, putting things in report these ideas, and, therefore, we systematize. Jean Gagnepain called grammar this formal structure of the sign (its bifaciality and its biaxiality) and “rhetorical,” the reinvestment of its structure which allows us to develop a concept.

There are three very important details to bring to this quick presentation.

The first is that if, in the model of the sign prepared by Saussure, the concept is external to sign, in the model developed by Jean Gagnepain, the concept is an integral part of the sign: it is the result of his mediation implicit in the structure in the circumstances, in other words, it is the product of the reinvestment of the structure in the universe of things to say, while not ceasing not to participate in sign.

The second important precision is that, this reinvestment is never a concept that can be completely adhered to the thing (the word “dog” do not bite!), and it is what defines polysemy of signifying units (“I custody a dog of his bitch.”) Take, again, for example, the word “foot”: it has a plurality of meanings which a dictionary of the English language may try to make the turn (“the human foot,” “the foot of a table,” “the foot of a mountain” “the foot of the nose,” etc.) And it is the same with all our statements. If I say: “Ernestine feels the lavender”, I want to say that Ernestine breathes a bouquet of flowers of lavender, or that it is perfumed with water of lavender? Of course, my three examples are extremely simplistic, but they will be enough, I hope. To make you appear that what is called “the words” are absolutely not labels likely to stick to things: despite all the desperate efforts that we can provide to try to reduce this many meanings, which defines their fundamental impropriety, in order to reach absolute transparency of words, there will always remain between the words and things that we “play” (as we speak, for example, the game between two pieces of wood or between two mechanisms,) and fortunately, because this is the game, precisely, it allows us to think. You see, as well as thinking, it is to exploit (certainly, with more or less of happiness!) this irreducible and permanent game which exists between the language of which we have the faculty and what we sometimes, baptize “real.”

Finally, I remind you that this “game” which I have just you spoken can be more or less important. This game is in his maximum status when we tend to fold the world to say, the words that we have to say, it is minimum when we tend to bend our words to this same world to say. In a case, you have this dialog in Raymond Devos way: “- The sea raves – Well, it must be appeased!,” In the other cases, one will speak about nitric acid, sulfuric or hydrochloric acid, for example, formulations that are trying to “paste,” the more possible, to what we believe to be the reality. In other words, the reinvestment that we are doing in the “real” of the verbal structure is submitted to the two “referred” antagonists you know well: the referred that Jean Gagnepain calls, one, mythical, the other, scientific, and you see, already, that myth and science are also rational one and the other, to the extent that they are all two of the same faculty of design, in other words they are the product of the same process, which is the same of a verbal rationality.

If you approve this model of verbal rationality mediations; I have very quickly explained you, the important questions will receive an initial response, to begin by the famous so-called “animal language.” I have recently seen exposed, in a great bookstore of our city in a good place, a book recently published and the title that caught my eye was: “Animals do they think?” I do not have to say that I did not see the first page of the book. The answer to this question is “No!,” Of course, since the animals do not possess this faculty of verbal rationality they cannot think this faculty belongs to the man. This does not say, of course, that animals emit no messages, and by means more or less complexes, even extremely sophisticated (even if it is far to know all), but it must do, animals are animals!

In a general way, this makes today a screen and we often prevents to see what distinguishes the animal rights, it is the existence of the etho-logy, I mean, the science of animal “morals”, while the animal belongs in its entirety to the animal biology (just as there is the plant biology), and, in its “morals,” to zoology (just as there is the botany.) Daring to do a “science of animal morals,” it is to make to the classical an old evolutionary sociology which said that, in animal, there is something of men, in other words that there is no difference with human, which, scientifically, has never been proven. In contrast, which is in the process of being proved, to the current hour, thanks to clinical researches by Jean Gagnepain, it is that there is a monkey in a man, here again, there is no not man in the monkey! We must admit that ethologic, to the point where it has remained, is a science without object which cannot only lead to the resurgence of the fable, this means that instead of using the study of the animal (of the animal biology and zoology) to study the natural functions that we have in common with him (in particular the memory, certain modes of communication by the contagion, signals, etc.) and especially what distinguishes us from them, the vast majority of ethologic doctors are not interested in animals that for relate it to us, which does not teach us nothing on the man, since, in any case, what the animal does, as we will see later, he does it different than a man.

Any ethology, being, therefore set aside, is absolutely sure, however, that the study of animals present a real interest in the specialist of the humanities in so far as it allows to explain, free state (to the state sometimes said “wild,”) which, among the man, also works, but always framed by the culture, namely natural processes available to it as animal, and in particular the capacity of percept. But the man is doing something else with this capacity. You see, at the same time that all the interests that there would be in the Koko, Washoe, Viki and other anthropoid apes, with the condition to take some precautions, especially when it compares their behavior so-called “language” to that of a child. Because, if the results of tests proposed for children and monkeys are often neighbors, one and the other are not at all in the same situation, in the extent that a child has the sole the capacity of verbal abstraction, so that we imagine that it is the same for the monkey as for the child, well this is false. There is a huge difference between children and monkeys, a monkey whose performance may not be comparable to those of the child, since the problem that is shall submit to the two, under the name of “language,” the one has it, while the other has nothing.

Added this, we can say that the behavioral assumption is much more serious than the evolutionary premise to the extent that is all a problem of experimentation in human sciences which is asked there: the behaviorism consisting not to take into account that the results, this means the successes and failures, misses of the only thing that should be apprehend, namely procedures, and, in the extent that error alone is human (provided that the analysis of the man alone, even if this analysis is implicit, this is the mechanisms of these errors which should be object of attention.) As an illustration, some tests so-called “non-verbal,” but which are in fact full of “verbosity”, I mean that we cannot solve without design. Such is the famous experience of Binet, which consists in this: we have several boxes returned, and we put sugar under one, the first and then under the second, and then under the third, etc. We are asking the monkey, and the child to seek the sugar; at the beginning, monkey and child will seek the sugar under the box where it was previously, but from the third attempt, the child and the monkey diverge: the monkey continues to seek in the previous box, while the child goes directly to the next box, because he has designed, without knowing of course, that the sugar is under the following box. That is what is called, intellectually, the intelligence: what a child has seized, it is a report, and it has seized a report, it is because he is able to understand, and it is capable of concept, it means to express of formal and not to express of things, it is that he is capable of language.

There is no absolutely no place to ask the question: “Do the animals think?,” Nor to speak of “animal intelligence,” even about these charming puppets which are, apparently, the best friends of the man. At worst, an animal is in a situation of pure dressage I mean the addiction to cultural realities which he will never have the key: as much as he learns to a mascot to swim using the arms! In contrast, the verbal rationality is done to humans, children are not in the same situation of animals because he can learn, he suffers infinitely less than an animal, because he is already able to understand the problematic in the problem of this proposes, it means to call into a question the way to ask, or even to rest, and this is why it will tend, as it is badly listened, to reformulate the problem otherwise, he will say that he is mistaken: it is why to err is human (the monkey is never wrong). In other words, for a child there is a really problematic, which is no the case for a monkey: it is purely and simply coercion, or even cruelty to animals.

That is why, it is absurd to speak of an “artificial intelligence” when we are talking about computers, even if only because, they are never mistaken (or at least, are they supposed to not be mistaken) they must be so precise that they have to get with the solution. We laughed with Descartes theory of an “animal-machine,” well, at least he was, certainly infinitely less ridiculous to speak, of an “animal-machine” in the seventeenth century, than ours ideas of today when we talk about an “electronic brain,” for example a “brain-machine”. In each case, it is in fact a metaphor that has absolutely no scientific value, a metaphor that we only return to Descartes when we compare animals to machines, or animal’s machines, and -the most ridiculous! – The human animals! To say the truth, whether it is an animal or a machine, we are dealing with two types of operations that have nothing to do with ours -that operation, in the case of the computer (which function by storage and drainage, for example “memory” and “Program”) is infinitely more dopey than the animal.

Because animals are capable of some abstraction degree and sensorial perception. They have, the capacity of that (unlike the computer!) They are under a natural treatment. You may know, as I have shown, that the seizure of an object returns to the animal to the seizure of another subject that he “imagined” (in the Sartre sense, and not analytic.) He has treated the subject in a particular way, a first object send it to a second, and even he has little imagination, the second referring to a third, etc. That defines a series. However, if you call the first object index and the second object meaning, indeed the second object may be in serial relation, the index of a third object becomes meaningless, so that in the series, any object can be defined by index and the place it occupies. But what do man does? He interrupts, as you know, the serial connection for each index and the link direction, and this bond of reciprocity defines, as we have seen, the “immanence of the sign, that is to say its bifaciality.”

On the other hand, it is perhaps not impossible that the ape, in certain situations of training (and perhaps to the “wild” status) is capable of selecting signals (vertical axis) and to combine (horizontal axis), but any ethnologist has not yet scientifically proven that the monkey had the ability to perform the reciprocal analysis of the two axes, an analysis which defines the biaxiality. And if the fact was established, it should still prove that he is capable of dialectic! In other words, if by chance you encounter a monkey before you manifest all the signs of an ability to analyze both bifacial, biaxial and dialectic, there is no possible doubt: as hairy as it is, you are dealing with a man!

That said, do not believe that man is “superior” to the monkey; that we are animals too, but different, that’s all. Better, even if we do not have, as some animals, at least the sensory and perception, and if we did not share with them the ability to treat it naturally, we would be absolutely incapable, of projecting our rationality to emerge in the dialectic of the sign. This time, we can say that there is no man because there’s a monkey in him!

What we can know for sure is that computers do not have any sensorial perception (which are the two levels of animal representation), not already in any way, treat them naturally! How do you think he has the ability to culturally process a natural treatment to make the sign? You see that to speak of an “artificial intelligence” and an “electronic brain” or a “computer language” we should begin by putting the monkey on the computer! It is clear that manufacturing intelligence or rational word, it is, in short, a dream of enlightened people!

It is true, however, that machines can go much further than animals and much further than men, and far beyond even that it does today: how our computers are the beginning of the XXI century it is absolutely ridiculous (in a few years they will probably “talk” almost like us, and they are certainly capable of much more), but the procedures are not the same. The results are almost perfect, but, precisely, computers are never wrong, at least not as the man is wrong, simply because is a man, and only he has the power to think.

In summary, you see that there are three ways of processing information: the artificial way (by storing and programming), the simplest and only accessible to the machine, the natural way, infinitely more complex and is only accessible to the animal and finally as cultural -that is peculiar to man, and which alone involves cognitive processes totally unconscious outside their manifestations in languages: an analysis of implicit representations, the man emerges from the structure (the faces and axes) and the reinvestment of this structure in the economy. That leads me to conclude, to say a word about the opposition that is commonly arises between the abstract and the concrete.

In addressing the problem of the concrete and the abstract, they generally say that a child begins with the concrete and ends with the abstract, and it sees “progress” of thought (see the work of Piaget, for example). However, in language, there is absolutely nothing concrete: all languages are abstract, so that instead of considering the process as a supposed “progress” we must say that a man is abstract from the start, and it is precisely this ability of abstraction that allows him to manufacture concrete. In other words, what we call real is just the product of reinvestment in the “real” of our ability to structure, so that we can never grasp the abstract or concrete: we can not grasp that the bipolar relationship between the two.

That can show us that the definition of sign may be the most faithful to the theory developed by John Gagnepain: the sign is where the mutual contradiction of the abstract and concrete. This means that we can not enter, anywhere, an abstraction that does not refer to a specific, nor, a concrete regardless of abstraction that allows to ask. The best that can specialists of humanities do is to take this contradiction round the waist, and make their subject.

And I would add, and this is where I conclude that this treatment of concrete-abstract representation is common to all men: not only to the “thinker,” but the child at least so far as a Nobel Prize. In other words, the mechanisms of abstraction do not have sense of the age or environment, or occupation, etc. Nor should it be considered as a knowledge “degree.”

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INDUSTRIAL AUTOMATION

By S.Venkatesan, M.E., M.I.S.T.E., Research Scholar/CSE, Anna University, Coimbatore.

 and

Dr.M.Karnan, M.E., Ph.D., Professor and head, Tamil Nadu College of Engineering, Coimbatore.

 ABSTRACT: Increased automation is a key for desired increased production. In the scope of industrialization, automation is a step beyond mechanization. Whereas mechanization provided human operators with machinery to assist them with the muscular requirements of work, automation greatly reduces the need for human sensory and mental requirements as well. Processes and systems can also be automated. Automation plays an increasingly important role in the global economy and in daily experience. Engineers strive to combine automated devices with mathematical and organizational tools to create complex systems for a rapidly expanding range of applications and human activities. Many roles for humans in industrial processes presently lie beyond the scope of automation. Human-level pattern recognition, language recognition, and language production ability are well beyond the capabilities of modern mechanical and computer systems. In this presentation we are about to have an overview of industrial automation concepts like computer integrated manufacturing, flexible manufacturing systems, industrial robots, artificial intelligence, advanced automatic material handling systems etc…

INTRODUCTION: AUTOMATION It is the process of following sequence of operations with little or no human labour, using specialized equipment and devices that perform and control manufacturing processes. (OR) Automation is the use of control systems (such as numerical control, programmable logic control, and other industrial control systems), in concert with other applications of information technology (such as computer-aided technologies [CAD, CAM), to control industrial machinery and processes, reducing the need for human intervention. TYPES: Partial automation Full automation MECHANISATION: The mechanization can be defined in its simplest sense as the transfer of skills and manual activities to machine operations.

 AIMS OF AUTOMATION: TO IMPROVE PRODUCT QUALITY TO REDUCE LABOUR COST TO IMPROVE WORK SAFETY TO REDUCE MANUFACTURING LEAD TIME TO AVOID THE HIGH COST OF NOT AUTOMATING Advantages:

The main advantage of automation is: Replacing human operators in tedious tasks. Replacing humans in tasks that should be done in dangerous environments (i.e. fire, space, volcanoes, nuclear facilities, under the water, etc) Making tasks that are beyond the human capabilities such as handling too heavy loads, too large objects, too hot or too cold substances or the requirement to make things too fast or too slow. Economy improvement. Sometimes and some kinds of automation implies improves in economy of enterprises, society or most of humankind. For example, when an enterprise that has invested in automation technology recovers its investment; when a state or country increases its income due to automation like Germany or Japan in the 20th Century or when the humankind can use the internet which in turn use satellites and other automated engines. Disadvantages The main disadvantages of automation are:

Technology limits. Current technology is unable to automate all the desired tasks. Unpredictable development costs. The research and development cost of automating a process is difficult to predict accurately beforehand. Since this cost can have a large impact on profitability, it’s possible to finish automating a process only to discover that there’s no economic advantage in doing so. Initial costs are relatively high. The automation of a new product required a huge initial investment in comparison with the unit cost of the product, although the cost of automation is spread in many product batches. The automation of a plant required a great initial investment too, although this cost is spread in the products to be produced. Automation tools Different types of automation tools exist: ANN – Artificial neural network DCS – Distributed Control System HMI – Human Machine Interface SCADA – Supervisory Control and Data Acquisition PAC – Programmable Automation Controller Instrumentation Motion control Robotics P PLC – Programmable Logic Controller PLC: A programmable logic controller (PLC) or programmable controller is a digital computer used for automation of electromechanical processes,s such as control of machinery on factory assembly lines, amusement rides, or lighting fixtures. PLCs are used in many industries and machines. Unlike general-purpose computers, the PLC is designed for multiple inputs and output arrangements, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact. Programs to control machine operation are typically stored in battery-backed or non-volatile memory.

A PLC is an example of a real time system since output results must be produced in response to input conditions within a bounded time, otherwise unintended operation will result. SCADA stands for supervisory control and data acquisition. It generally refers to an industrial control system: a computer system monitoring and controlling a process. The process can be industrial, infrastructure or facility-based as described as Industrial processes include those of manufacturing, production, power generation, fabrication, and refining, and may run in continuous, batch, repetitive, or discrete modes. Infrastructure processes may be public or private, and include water treatment and distribution, wastewater collection and treatment, oil and gas pipelines, electrical power transmission and distribution, civil defense siren systems, and large communication systems. Facility processes occur both in public facilities and private ones, including buildings, airports, ships, and space stations. They monitor and control HVAC, access, and energy consumption.

Computer Integrated Manufacturing Computer-Integrated Manufacturing (CIM) in engineering is a method of manufacturing in which the entire production process is controlled by computer. The traditionaly separated process methods are joined through a computer by CIM. This integration allows the processes to exchange information with each other and enable them to initiate actions. Through this integration, manufacturing can be faster and with fewer errors. Yet, the main advantage is the ability to create automated manufacturing processes. Typically CIM relies on closed-loop control processes, based on real-time input from sensors. It is also known as flexible design and manufacturing. Overview The term “Computer Integrated Manufacturing” is both a method of manufacturing and the name of a computer-automated system in which individual engineering, production, marketing, and support functions of a manufacturing enterprise are organized. In a CIM system functional areas such as design, analysis, planning, purchasing, cost accounting, inventory control, and distribution are linked through the computer with factory floor functions such as materials handling and management, providing direct control and monitoring of all process operations. As method of manufacturing, three components distinguish CIM from other manufacturing Methodologies: Means for data storage, retrieval, manipulation and presentation; Mechanisms for sensing state and modifying processes; Algorithms for uniting the data processing component with the sensor/modification component. CIM is an example of the implementation of Information and Communication Technology (ICT) in manufacturing.

 CIM implies that there are at least two computers exchanging information, e.g. the controller of a arm robot and a microcontroller of a CNC machine. Some factors involved when considering a CIM implementation are the production volume, the experience of the company or personnel to make the integration, the level of the integration into the product itself and the integration of the production processes. CIM is most useful where a high level of ICT is used in the company or facility, such as CAD/CAM systems, the availability of process planning and its data. Although none of what this says is correct. History: The idea of “Digital Manufacturing” was prominent the 1980s, when Computer Integrated Manufacturing was developed and promoted by machine tool manufacturers and the Computer and Automated Systems Association and Society of Manufacturing Engineers (CASA/SME). “CIM is the integration of total manufacturing enterprise by using integrated systems and data communication coupled with new managerial philosophies that improve organizational and personnel efficiency.” ERHUM Computer Integrated manufacturing topics – Key Challenges There are three major challenges to development of a smoothly operating Computer Integrated Manufacturing system: Integration of components from different suppliers: When different machines, such as CNC, conveyors and robots, are using different communications protocols. In the case of AGVs, even differing lengths of time for charging the batteries may cause problems.

Data integrity: The higher the degree of automation, the more critical is the integrity of the data used to control the machines. While the CIM system saves on labor of operating the machines, it requires extra human labor in ensuring that there are proper safeguards for the data signals that are used to control the machines. Process control: Computers may be used to assist the human operators of the manufacturing facility, but there must always be a competent engineer on hand to handle circumstances which could not be foreseen by the designers of the control software. Subsystems in Computer Integrated Manufacturing A Computer Integrated Manufacturing system is not the same as a “lights out” factory, which would run completely independent of human intervention, although it is a big step in that direction. Part of the system involves flexible manufacturing, where the factory can be quickly modified to produce different products, or where the volume of products can be changed quickly with the aid of computers.

Some or all of the following subsystems may be found in a CIM operation: Computer-aided techniques: CAD (Computer Aided Design) CAE (Computer Aided Engineering) CAM (Computer Aided Manufacturing) CAPP (Computer Aided Process Planning) CAQ (Computer-aided quality assurance) PPC (Production planning and control) ERP (Enterprise resource planning) A business system integrated by a common database. Devices and equipment required: CNC, Computer numerical control machine tools DNC, Direct numerical control machine tools PLC’s, Programmable logic controllers Robotics Computers Software Controllers Networks Interfacing Monitoring equipment Technologies: FMS, (Flexible manufacturing system) ASRS, automated storage and retrieval systems AGV, automated guided vehicles Robotics Automated conveyance systems An industrial robot is officially defined by ISO as an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes. The field of robotics may be more practically defined as the study, design and use of robot systems for manufacturing (a top-level definition relying on the prior definition of robot). Typical applications of robots include welding, painting, assembly, pick and place, packaging and palletizing, product inspection, and testing, all accomplished with high endurance, speed, and precision.

A flexible manufacturing system (FMS) is a manufacturing system in which there is some amount of flexibility that allows the system to react in the case of changes, whether predicted or unpredicted. This flexibility is generally considered to fall into two categories, which both contain numerous subcategories. The first category, machine flexibility, covers the system’s ability to be changed to produce new product types, and ability to change the order of operations executed on a part. The second category is called routing flexibility, which consists of the ability to use multiple machines to perform the same operation on a part, as well as the system’s ability to absorb large-scale changes, such as in volume, capacity, or capability. Most FMS systems comprise of three main systems. The work machines which are often automated CNC machines are connected by a material handling system to optimize parts flow and the central control computer which controls material movements and machine flow. The main advantages of an FMS are its high flexibility in managing manufacturing resources like time and effort in order to manufacture a new product. The best application of an FMS is found in the production of small sets of products like those from a mass production.

A flexible manufacturing system combines the benefits of highly automated and controlled systems – Accuracy – Mass production with the benefits of versatile, adjustable Systems – Flexibility – Uniqueness of product A comprehensive description of a Flexible Manufacturing System follows here: The Manufacturing Cell A flexible manufacturing cell (FMC) consists of two or more CNC machines, a cell computer and a robot. The cell computer (typically a programmable logic controller) is interfaced with the microprocessors of the robot and the CNCs. The Cell Controller The functions of the cell controller include work load balancing, part scheduling, and material flow control. The supervision and coordination among the various operations in a manufacturing cell is also performed by the cell computer. The software includes features permitting the handling of machine breakdown, tool breakage and other special situations. The Cell Robot In many applications, the cell robot also performs tool changing and housekeeping functions such as chip removal, staging of tools in the tool changer, and inspection of tools for breakage or expressive wear. When necessary, the robot can also initiate emergency procedures such as system shut-down. Parker-Hannifin Corporation, Forrest City, NC.

The Flexible Manufacturing System – FMS The flexible manufacturing system (FMS) is a configuration of computer-managed numerical work stations where materials are automatically handled and machine loaded. The flexible manufacturing system is principally used in mid-volume (200 to 30,000 parts per year) mid-variety (5 to 155 part types) production. Flexible Manufacturing System Components-Two or more computer-managed numerical work stations that perform a series of operations; An integrated material transport system and a computer that controls the flow of materials, tools, and information (e.g. machining data and machine malfunctions) throughout the system; Auxiliary work stations for loading and unloading, cleaning, inspection, etc. Flexible Manufacturing System Goals Reduction in manufacturing cost by lowering direct labor cost and minimizing scrap, re-work, and material wastage. Less skilled labor required. Reduction in work-in-process inventory by eliminating the need for batch processing Reductions in production lead time permitting manufacturers to respond more quickly to the variability of market demand Better process control resulting in consistent quality.

Different FMSs levels are: Flexible Manufacturing Module (FMM). Example: a NC machine, a pallet changer and a part buffer; Flexible Manufacturing (Assembly) Cell (F (M/A) C). Example: Four FMMs and an AGV (automated guided vehicle); Flexible Manufacturing Group (FMG). Example : Two FMCs, a FMM and two AGVs which will transport parts from a Part Loading area, through machines, to a Part Unloading Area; Flexible Production Systems (FPS). Example: A FMG and a FAC, two AGVs, an Automated Tool Storage, and an Automated Part/assembly Storage; Flexible Manufacturing Line (FML). Example: multiple stations in a line layout and AGVs. Advantages and disadvantages of FMSs implementation Advantages Faster, lower- cost changes from one part to another which will improve capital utilization Lower direct labor cost, due to the reduction in number of workers Reduced inventory, due to the planning and programming precision Consistent and better quality, due to the automated control Lower cost/unit of output, due to the greater productivity using the same number of workers Savings from the indirect labor, from reduced errors, rework, repairs and rejects Disadvantages Limited ability to adapt to changes in product or product mix (ex. machines are of limited capacity and the tooling necessary for products, even of the same family, is not always feasible in a given FMS) Substantial pre-planning activity Expensive, costing millions of dollars Technological problems of exact component positioning and precise timing necessary to process a component Sophisticated manufacturing systems FMSs complexity and cost are reasons for their slow acceptance by industry.

 In most of the cases FMCs are favored. An automated guided vehicle or automatic guided vehicle (AGV) is a mobile robot that follows markers or wires in the floor, or uses vision or lasers. They are most often used in industrial applications to move materials around a manufacturing facility or a warehouse. Application of the automatic guided vehicle has broadened during the late 20th century and they are no longer restricted to industrial environments. Automated guided vehicles (AGVs) increase efficiency and reduce costs by helping to automate a manufacturing facility or warehouse. AGVs can carry loads or tow objects behind them in trailers to which they can autonomously attach. The trailers can be used to move raw materials or finished product. The AGV can also store objects on a bed. The objects can be placed on a set of motorized rollers (conveyor) and then pushed off by reversing them. Some AGVs use fork lifts to lift objects for storage. AGVs are employed in nearly every industry, including, pulp, paper, metals, newspaper, and general manufacturing. Transporting materials such as food, linen or medicine in hospitals is also done. Common AGV Applications Automated Guided Vehicles can be used in a wide variety of applications to transport many different types of material including pallets, rolls, racks, carts, and containers. AGVs excel in applications with the following characteristics: Repetitive movement of materials over a distance Regular delivery of stable loads Medium throughput/volume When on-time delivery is critical and late deliveries are causing inefficiency Operations with at least two shifts Processes where tracking material is important Artificial intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it.

Textbooks define the field as “the study and design of intelligent agents,” where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as “the science and engineering of making intelligent machines.” The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of optimism, but has also suffered setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science. AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or “strong AI”) is still a long-term goal of (some) research. Obotic Automation: Material Handling Processes Material handling is the broadest category of applications that involves moving, selecting or packing products. Material handling robots are used to move, feed or disengage parts or tools to or from a location, or to transfer parts from one machine to another. Material Handling Processes Pick and Place Dispensing Palletizing Packaging Part Transfer Machine Loading Assembly Material Removal Order Picking A variation of a material handling robot is used to build and unload units on a pallet. Manufacturing companies throughout the world are implementing material handling robots because of they are faster, more accurate and efficient.

They offer unmatched quality and Repeatability. Palletizing and Material Handling: Palletizing is the act of loading or unloading material onto pallets. The newspaper industry has been particularly hard hit by increased labor costs. Part of the solution to this problem was to use robots like Cincinnati Milacron Robot being used to palletize advertising inserts for a newspaper. Many companies in the United States and Canada have been forced to close in such areas as die casting and injection molding because they could not compete with foreign firms. The introduction of robotics into this process has allowed the same companies to remain viable. In semiconductor industry’s IC chip manufacturing facilities; various processes take place within a clean room. This requires that personnel as well as robots not introduce dirt, dust, or oil into the area. Since robots do not breath, sneeze, or have dandruff, they are especially suited to the clean room environment demanded by the semiconductor industry. At first glance, automation might appear to devalue labor through its replacement with less-expensive machines; however, the overall effect of this on the workforce as a whole remains unclear.

Conclusion

Today automation of the workforce is quite advanced, and continues to advance increasingly more rapidly throughout the world and is encroaching on ever more skilled jobs, yet during the same period the general well-being and quality of life of most people in the world (where political factors have not muddied the picture) have improved dramatically. Currently, for manufacturing companies, the purpose of automation has shifted from increasing productivity and reducing costs, to broader issues, such as increasing quality and flexibility in the manufacturing process. The old focus on using automation simply to increase productivity and reduce costs was seen to be short-sighted, because it is also necessary to provide a skilled workforce who can make repairs and manage the machinery. Moreover, the initial costs of automation were high and often could not be recovered by the time entirely new manufacturing processes replaced the old. (Japan’s “robot junkyards” were once world famous in the manufacturing industry.) Automation is now often applied primarily to increase quality in the manufacturing process, where automation can increase quality substantially.

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This definition implies that a device can only be called a “robot” if it contains a movable mechanism, influenced by sensing, planning, and actuation and control components. It does not imply that a minimum number of these components must be implemented in software, or be changeable by the “consumer” who uses the device; for example, the motion behavior can have been hard-wired into the device by the manufacturer.

 

So, the presented definition, as well as the rest of the material in this part of the Book, covers not just “pure” robotics or only “intelligent” robots, but rather the somewhat broader domain of robotics and automation. This includes “dumb” robots such as: metal and woodworking machines, “intelligent” washing machines, dish washers and pool cleaning robots, etc. These examples all have sensing, planning and control, but often not in individually separated components. For example, the sensing and planning behavior of the pool cleaning robot have been integrated into the mechanical design of the device, by the intelligence of the human developer.

 

Robotics is, to a very large extent, all about system integration, achieving a task by an actuated mechanical device, via an “intelligent” integration of components, many of which it shares with other domains, such as systems and control, computer science, character animation, machine design, computer vision, artificial intelligence, cognitive science, biomechanics, etc. In addition, the boundaries of robotics cannot be clearly defined, since also its “core” ideas, concepts and algorithms are being applied in an ever increasing number of “external” applications, and, vice versa, core technology from other domains (vision, biology, cognitive science or biomechanics, for example) are becoming crucial components in more and more modern robotic systems.

 

This part of the WEBook makes an effort to define what exactly is that above-mentioned core material of the robotics domain, and to describe it in a consistent and motivated structure. Nevertheless, this chosen structure is only one of the many possible “views” that one can want to have on the robotics domain.

 

In the same vein, the above-mentioned “definition” of robotics is not meant to be definitive or final, and it is only used as a rough framework to structure the various chapters 

 

Components of robotic systems

 

 

 

 

 

 

 

 

This figure depicts the components that are part of all robotic systems. The purpose of this Section is to describe the semantics of the terminology used to classify the chapters in the WEBook: “sensing”, “planning”, “modeling”, “control”, etc.

 

The real robot is some mechanical device (“mechanism”) that moves around in the environment, and, in doing so, physically interacts with this environment. This interaction involves the exchange of physical energy, in some form or another. Both the robot mechanism and the environment can be the “cause” of the physical interaction through “Actuation”, or experience the “effect” of the interaction, which can be measured through “Sensing”.

 

Robotics as an integrated system of control interacting with the physical world.

 

Sensing and actuation are the physical ports through which the “Controller” of the robot determines the interaction of its mechanical body with the physical world. As mentioned already before, the controller can, in one extreme, consist of software only, but in the other extreme everything can also be implemented in hardware.

 

Within the Controller component, several sub-activities are often identified:

 

Modelling. The input-output relationships of all control components can (but need not) be derived from information that is stored in a model. This model can have many forms: analytical formulas, empirical look-up tables, fuzzy rules, neural networks, etc.

 

The name “model” often gives rise to heated discussions among different research “schools”, and the WEBook is not interested in taking a stance in this debate: within the WEBook, “model” is to be understood with its minimal semantics: “any information that is used to determine or influence the input-output relationships of components in the Controller.”

 

The other components discussed below can all have models inside. A “System model” can be used to tie multiple components together, but it is clear that not all robots use a System model. The “Sensing model” and “Actuation model” contain the information with which to transform raw physical data into task-dependent information for the controller, and vice versa.

 

Planning. This is the activity that predicts the outcome of potential actions, and selects the “best” one. Almost by definition, planning can only be done on the basis of some sort of model.

 

Regulation. This component processes the outputs of the sensing and planning components, to generate an actuation setpoint. Again, this regulation activity could or could not rely on some sort of (system) model.

 

The term “control” is often used instead of “regulation”, but it is impossible to clearly identify the domains that use one term or the other. The meaning used in the WEBook will be clear from the context.

 

Scales in robotic systems

 

The above-mentioned “components” description of a robotic system is to be complemented by a “scale” description, i.e., the following system scales have a large influence on the specific content of the planning, sensing, modelling and control components at one particular scale, and hence also on the corresponding sections of the WEBook.

 

Mechanical scale. The physical volume of the robot determines to a large extent the limites of what can be done with it. Roughly speaking, a large-scale robot (such as an autonomous container crane or a space shuttle) has different capabilities and control problems than a macro robot (such as an industrial robot arm), a desktop robot (such as those “sumo” robots popular with hobbyists), or milli micro or nano robots.

Spatial scale. There are large differences between robots that act in 1D, 2D, 3D, or 6D (three positions and three orientations).

 

Time scale. There are large differences between robots that must react within hours, seconds, milliseconds, or microseconds.

 

Power density scale. A robot must be actuated in order to move, but actuators need space as well as energy, so the ratio between both determines some capabilities of the robot.

 

System complexity scale. The complexity of a robot system increases with the number of interactions between independent sub-systems, and the control components must adapt to this complexity.

 

Computational complexity scale. Robot controllers are inevitably running on real-world computing hardware, so they are constrained by the available number of computations, the available communication bandwidth, and the available memory storage.

 

Obviously, these scale parameters never apply completely independently to the same system. For example, a system that must react at microseconds time scale can not be of macro mechanical scale or involve a high number of communication interactions with subsystems.

 

Background sensitivity

 

Finally, no description of even scientific material is ever fully objective or context-free, in the sense that it is very difficult for contributors to the WEBook to “forget” their background when writing their contribution. In this respect, robotics has, roughly speaking, two faces: (i) the mathematical and engineering face, which is quite “standardized” in the sense that a large consensus exists about the tools and theories to use (“systems theory”), and (ii) the AI face, which is rather poorly standardized, not because of a lack of interest or research efforts, but because of the inherent complexity of “intelligent behaviour.” The terminology and systems-thinking of both backgrounds are significantly different, hence the WEBook will accomodate sections on the same material but written from various perspectives. This is not a “bug”, but a “feature”: having the different views in the context of the same WEBook can only lead to a better mutual understanding and respect.

 

Research in engineering robotics follows the bottom-up approach: existing and working systems are extended and made more versatile. Research in artificial intelligence robotics is top-down: assuming that a set of low-level primitives is available, how could one apply them in order to increase the “intelligence” of a system. The border between both approaches shifts continuously, as more and more “intelligence” is cast into algorithmic, system-theoretic form. For example, the response of a robot to sensor input was considered “intelligent behaviour” in the late seventies and even early eighties. Hence, it belonged to A.I. Later it was shown that many sensor-based tasks such as surface following or visual tracking could be formulated as control problems with algorithmic solutions. From then on, they did not belong to A.I. any more.

 

 

 

Robotics Technology

 

Most industrial robots have at least the following five parts:

 

Sensors, Effectors, Actuators, Controllers, and common effectors known as Arms.

 

Many other robots also have Artificial Intelligence and effectors that help it achieve Mobility.

 

This section discusses the basic technologies of a robot. Click one of the links above or use the navigation bar menu on the far right.

 

Robotics Technology – Sensors

 

Most robots of today are nearly deaf and blind.  Sensors can provide some limited feedback to the robot so it can do its job.  Compared to the senses and abilities of even the simplest living things, robots have a very long way to go.

 

The sensor sends information, in the form of electronic signals back to the cfontroller.  Sensors also give the robot controller information about its surroundings and lets it know the exact position of the arm, or the state of the world around it.

Sight, sound, touch, taste, and smell are the kinds of information we get from our world.  Robots can be designed and programmed to get specific information that is beyond what our 5 senses can tell us. For instance, a robot sensor might “see” in the dark, detect tiny amounts of invisible radiation or measure movement that is too small or fast for the human eye to see.

 

Here are some things sensors are used for:

 

Physical Property

 Technology

 

Contact Bump, Switch

Distance Ultrasound, Radar, Infra Red

Light Level Photo Cells, Cameras

Sound Level microphones

Strain Strain Gauges

Rotation Encoders

Magnetism Compasses

Smell Chemical

Temperature Thermal, Infra Red

Inclination Inclinometers, Gyroscope

Pressure Pressure Gauges

Altitude Altimeters

 

    Sensors can be made simple and complex, depending on how much information needs to be stored.  A switch is a simple on/off sensor used for turning the robot on and off.  A human retina is a complex sensor that uses more than a hundred million photosensitive elements (rods and cones).  Sensors provide information to the robots brain, which can be treated in various ways.  For example, we can simply react to the sensor output: if the switch is open, if the switch is closed, go. 

 

Levels of Processing

 

    To figure out if the switch is open or closed, you will need to measure the voltage going through the circuit, that’s electronics.  Now lets say that you have a microphone and you want to recognize a voice and separate it from noise; that’s signal processing.  Now you have a camera, and you want to take the pre-processed image and now you need to figure out what those objects are, perhaps by comparing them to a large library of drawings; that’s computation.  Sensory data processing is a very complex thing to try and do but the robot needs this in order to have a “brain”.  The brain has to have analog or digital processing capabilities, wires to connect everything, support electronics to go with the computer, and batteries to provide power for the whole thing, in order to process the sensory data.  Perception requires the robot to have sensors (power and electronics), computation (more power and electronics, and connectors (to connect it all). 

 

Switch Sensors

 

 Switches are the simplest sensors of all.  They work without processing, at the electronics (circuit) level.  Their general underlying principle is that of an open vs. closed circuit.  If a switch is open, no current can flow; if it is closed, current can flow and be detected.  This simple principle can (and is) used in a wide variety of ways.

 

Switch sensors can be used in a variety of ways:

 

contact sensors: detect when the sensor has contacted another object (e.g., triggers when a robot hits a wall or grabs an object; these can even be whiskers)

 

limit sensors: detect when a mechanism has moved to the end of its range

 

shaft encoder sensors: detects how many times a shaft turns by having a switch click (open/close) every time the shaft turns (e.g., triggers for each turn, allowing for counting rotations)

 

   There are many common switches: button switches, mouse switches, key board keys, phone keys, and others.  Depending on how a switch is wired, it can be normally open or normally closed.  This would of course depend on your robot’s electronics, mechanics, and its task.  The simplest yet extremely useful sensor for a robot is a “bump switch” that tells it when it’s bumped into something, so it can back up and turn away. Even for such a simple idea, there are many different ways of implementation.

 

Light Sensors

 

Switches measure physical contact and light sensors measure the amount of light impacting a photocell, which is basically a resistive sensor.  The resistance of a photocell is low when it is brightly illuminated, i.e., when it is very light; it is high when it is dark.  In that sense, a light sensor is really a “dark” sensor.  In setting up a photocell sensor, you will end up using the equations we learned above, because you will need to deal with the relationship of the photocell resistance photo, and the resistance and voltage in your electronics sensor circuit.  Of course since you will be building the electronics and writing the program to measure and use the output of the light sensor, you can always manipulate it to make it simpler and more intuitive.  What surrounds a light sensor affects its properties.  The sensor can be  shielded and positioned in various ways.  Multiple sensors can be arranged in useful configurations and isolate them from each other with shields.

 

Just like switches, light sensors can be used in many different ways:

 

Light sensors can measure:

 

light intensity (how light/dark it is)

 

differential intensity (difference between photocells)

 

break-beam (change/drop in intensity)

 

Light sensors can be shielded and focused in different ways

 

Their position and directionality on a robot can make a great deal of difference and impact

 

Polarized light

 

“Normal” light emanating from a source is non-polarized, which means it travels at all orientations with respect to the horizon.  However, if there is a polarizing filter in front of a light source, only the light waves of a given orientation of the filter will pass through.  This is useful because now we can manipulate this remaining light with other filters; if we put it through another filter with the same characteristic plane, almost all of it will get through.  But, if we use a perpendicular filter (one with a 90-degree relative characteristic angle), we will block all of the light.  Polarized light can be used to make specialized sensors out of simple photocells; if you put a filter in front of a light source and the same or a different filter in front of a photocell, you can cleverly manipulate what and how much light you detect. 

 

Resistive Position Sensors

 

    We said earlier that a photocell is a resistive device.  We can also sense resistance in response to other physical properties, such as bending.  The resistance of the device increases with the amount it is bent.  These bend sensors were originally developed for video game control (for example, Nintendo Powerglove), and are generally quite useful.  Notice that repeated bending will wear out the sensor.  Not surprisingly, a bend sensor is much less robust than light sensors, although they use the same underlying resistive principle.

 

Potentiometers

 

    These devices are very common for manual tuning; you have probably seen them in some controls (such as volume and tone on stereos).  Typically called pots, they allow the user to manually adjust the resistance.  The general idea is that the device consists of a movable tap along two fixed ends.  As the tap is moved, the resistance changes.  As you can imagine, the resistance between the two ends is fixed, but the resistance between the movable part and either end varies as the part is moved.  In robotics, pots are commonly used to sense and tune position for sliding and rotating mechanisms.

 

Biological Analogs

 

All of the sensors we described exist in biological systems

 

Touch/contact sensors with much more precision and complexity in all species

 

Bend/resistance receptors in muscles 

 

Reflective Optosensors

 

    We mentioned that if we use a light bulb in combination with a photocell, we can make a break-beam sensor. This idea is the underlying principle in reflective optosensors: the sensor consists of an emitter and a detector. Depending of the arrangement of those two relative to each other, we can get two types of sensors:

 

reflectance sensors (the emitter and the detector are next to each other, separated by a barrier; objects are detected when the light is reflected off them and back into the detector)

 

break-beam sensors (the emitter and the detector face each other; objects are detected if they interrupt the beam of light between the emitter and the detector)

 

    The emitter is usually made out of a light-emitting diode (an LED), and the detector is usually a photodiode/phototransistor.

 

    Note that these are not the same technology as resistive photocells. Resistive photocells are nice and simple, but their resistive properties make them slow; photodiodes and photo-transistors are much faster and therefore the preferred type of technology.

 

What can you do with this simple idea of light reflectivity? Quite a lot of useful things:

 

object presence detection

 

object distance detection

 

surface feature detection (finding/following markers/tape)

 

wall/boundary tracking

 

rotational shaft encoding (using encoder wheels with ridges or black & white color)

 

bar code decoding

 

    Note, however, that light reflectivity depends on the color (and other properties) of a surface. A light surface will reflect light better than a dark one, and a black surface may not reflect it at all, thus appearing invisible to a light sensor. Therefore, it may be harder (less reliable) to detect darker objects this way than lighter ones. In the case of object distance, lighter objects that are farther away will seem closer than darker objects that are not as far away. This gives you an idea of how the physical world is partially-observable. Even though we have useful sensors, we do not have complete and completely accurate information.

 

    Another source of noise in light sensors is ambient light. The best thing to do is subtract the ambient light level out of the sensor reading, in order to detect the actual change in the reflected light, not the ambient light. How is that done? By taking two (or more, for higher accuracy) readings of the detector, one with the emitter on, and one with it off, and subtracting the two values from each other. The result is the ambient light level, which can then be subtracted from future readings. This process is called sensor calibration. Of course, remember that ambient light levels can change, so the sensors may need to be calibrated repeatedly.

 

Break-beam Sensors

 

    We already talked about the idea of break-beam sensors. In general, any pair of compatible emitter-detector devices can be used to produce such a sensors:

 

an incandescent flashlight bulb and a photocell

 

red LEDs and visible-light-sensitive photo-transistors

 

or infra-red IR emitters and detectors

 

Shaft Encoding

 

Shaft encoders measure the angular rotation of an axle providing position and/or velocity info. For example, a speedometer measures how fast the wheels of a vehicle are turning, while an odometer measures the number of rotations of the wheels.

 

In order to detect a complete or partial rotation, we have to somehow mark the turning element. This is usually done by attaching a round disk to the shaft, and cutting notches into it. A light emitter and detector are placed on each side of the disk, so that as the notch passes between them, the light passes, and is detected; where there is no notch in the disk, no light passes.

 

If there is only one notch in the disk, then a rotation is detected as it happens. This is not a very good idea, since it allows only a low level of resolution for measuring speed: the smallest unit that can be measured is a full rotation. Besides, some rotations might be missed due to noise.

 

Usually, many notches are cut into the disk, and the light hits impacting the detector are counted. (You can see that it is important to have a fast sensor here, if the shaft turns very quickly.)

 

An alternative to cutting notches in the disk is to paint the disk with black (absorbing, non-reflecting) and white (highly reflecting) wedges, and measure the reflectance. In this case, the emitter and the detector are on the same side of the disk.

 

In either case, the output of the sensor is going to be a wave function of the light intensity. This can then be processes to produce the speed, by counting the peaks of the waves.

 

Note that shaft encoding measures both position and rotational velocity, by subtracting the difference in the position readings after each time interval. Velocity, on the other hand, tells us how fast a robot is moving, or if it is moving at all. There are multiple ways to use this measure:

 

measure the speed of a driven (active) wheel

 

use a passive wheel that is dragged by the robot (measure forward progress)

 

We can combine the position and velocity information to do more sophisticated things:

 

move in a straight line

 

rotate by an exact amount

 

Note, however, that doing such things is quite difficult, because wheels tend to slip (effector noise and error) and slide and there is usually some slop and backlash in the gearing mechanism. Shaft encoders can provide feedback to correct the errors, but having some error is unavoidable.

 

Quadrature Shaft Encoding

 

So far, we’ve talked about detecting position and velocity, but did not talk about direction of rotation. Suppose the wheel suddenly changes the direction of rotation; it would be useful for the robot to detect that.

 

An example of a common system that needs to measure position, velocity, and direction is a computer mouse. Without a measure of direction, a mouse is pretty useless. How is direction of rotation measured?

 

Quadrature shaft encoding is an elaboration of the basic break-beam idea; instead of using only one sensor, two are needed. The encoders are aligned so that their two data streams coming from the detector and one quarter cycle (90-degrees) out of phase, thus the name “quadrature”. By comparing the output of the two encoders at each time step with the output of the previous time step, we can tell if there is a direction change. When the two are sampled at each time step, only one of them will change its state (i.e., go from on to off) at a time, because they are out of phase. Which one does it determines which direction the shaft is rotating. Whenever a shaft is moving in one direction, a counter is incremented, and when it turns in the opposite direction, the counter is decremented, thus keeping track of the overall position.

 

Other uses of quadrature shaft encoding are in robot arms with complex joints (such as rotary/ball joints; think of your knee or shoulder), Cartesian robots (and large printers) where an arm/rack moves back and forth along an axis/gear.

 

Modulation and Demodulation of Light

 

We mentioned that ambient light is a problem because it interferes with the emitted light from a light sensor. One way to get around this problem is to emit modulated light, i.e., to rapidly turn the emitter on and off. Such a signal is much easier and more reliably detected by a demodulator, which is tuned to the particular frequency of the modulated light. Not surprisingly, a detector needs to sense several on-flashes in a row in order to detect a signal, i.e., to detect its frequency. This is a small point, but it is important in writing demodulator code.

 

The idea of modulated IR light is commonly used; for example in household remote controls.

 

Modulated light sensors are generally more reliable than basic light sensors. They can be used for the same purposes: detecting the presence of an object measuring the distance to a nearby object (clever electronics required, see your course notes)

 

Infra Red (IR) Sensors

 

Infra red sensors are a type of light sensors, which function in the infra red part of the frequency spectrum.  IR sensors consist are active sensors: they consist of an emitter and a receiver.  IR sensors are used in the same ways that visible light sensors are that we have discussed so far: as break-beams and as reflectance sensors.  IR is preferable to visible light in robotics (and other) applications because it suffers a bit less from ambient interference, because it can be easily modulated, and simply because it is not visible.

 

IR Communication

 

Modulated infra red can be used as a serial line for transmitting messages. This is is fact how IR modems work. Two basic methods exist:

 

bit frames (sampled in the middle of each bit; assumes all bits take the same amount of time to transmit)

 

bit intervals (more common in commercial use; sampled at the falling edge, duration of interval between sampling determines whether it’s a 0 or 1)

 

Ultrasonic Distance Sensing

 

As we mentioned before, ultrasound sensing is based on the time-of-flight principle. The emitter produces a sonar “chirp” of sound, which travels away from the source, and, if it encounters barriers, reflects from them and returns to the receiver (microphone). The amount of time it takes for the sound beam to come back is tracked (by starting a timer when the “chirp” is produced, and stopping it when the reflected sound returns), and is used to compute the distance the sound traveled. This is possible (and quite easy) because we know how fast sound travels; this is a constant, which varies slightly based on ambient temperature.

 

At room temperature, sound travels at 1.12 feet per millisecond. Another way to put it that sound travels at 0.89 milliseconds per foot. This is a useful constant to remember.

 

The process of finding one’s location based on sonar is called echolocation. The inspiration for ultrasound sensing comes from nature; bats use ultrasound instead of vision (this makes sense; they live in very dark caves where vision would be largely useless). Bat sonars are extremely sophisticated compared to artificial sonars; they involve numerous different frequencies, used for finding even the tiniest fast-flying prey, and for avoiding hundreds of other bats, and communicating for finding mates.

                                                         

Specular Reflection

 

A major disadvantage of ultrasound sensing is its susceptibility to specular reflection (specular reflection means reflection from the outer surface of the object). While the sonar sensing principle is based on the sound wave reflecting from surfaces and returning to the receiver, it is important to remember that the sound wave will not necessarily bounce off the surface and “come right back.” In fact, the direction of reflection depends on the incident angle of the sound beam and the surface. The smaller the angle, the higher the probability that the sound will merely “graze” the surface and bounce off, thus not returning to the emitter, in turn generating a false long/far-away reading. This is often called specular reflection, because smooth surfaces, with specular properties, tend to aggravate this reflection problem. Coarse surfaces produce more irregular reflections, some of which are more likely to return to the emitter. (For example, in our robotics lab on campus, we use sonar sensors, and we have lined one part of the test area with cardboard, because it has much better sonar reflectance properties than the very smooth wall behind it.)

 

In summary, long sonar readings can be very inaccurate, as they may result from false rather than accurate reflections. This must be taken into account when programming robots, or a robot may produce very undesirable and unsafe behavior. For example, a robot approaching a wall at a steep angle may not see the wall at all, and collide with it!

 

Nonetheless, sonar sensors have been successfully used for very sophisticated robotics applications, including terrain and indoor mapping, and remain a very popular sensor choice in mobile robotics.

 

The first commercial ultrasonic sensor was produced by Polaroid, and used to automatically measure the distance to the nearest object (presumably which is being photographed). These simple Polaroid sensors still remain the most popular off-the-shelf sonars (they come with a processor board that deals with the analog electronics). Their standard properties include:

 

32-foot range

 

30-degree beam width

 

sensitivity to specular reflection

 

shortest distance return

 

Polaroid sensors can be combined into phased arrays to create more sophisticated and more accurate sensors.

 

One can find ultrasound used in a variety of other applications; the best known one is ranging in submarines. The sonars there have much more focused and have longer-range beams. Simpler and more mundane applications involve automated “tape-measures”, height measures, burglar alarms, etc.

 

Machine Vision

 

So far, we have talked about relatively simple sensors. They were simple in terms of processing of the information they returned. Now we turn to machine vision, i.e., to cameras as sensors.

 

Cameras, of course, model biological eyes. Needless to say, all biological eyes are more complex than any camera we know today, but, as you will see, the cameras and machine vision systems that process their perceptual information, are not simple at all! In fact, machine vision is such a challenging topic that it has historically been a separate branch of Artificial Intelligence.

 

The general principle of a camera is that of light, scattered from objects in the environment (those are called the scene), goes through an opening (”iris”, in the simplest case a pin hole, in the more sophisticated case a lens), and impinging on what is called the image plane. In biological systems, the image plane is the retina, which is attached to numerous rods and cones (photosensitive elements) which, in turn, are attached to nerves which perform so-called “early vision”, and then pass information on throughout the brain to do “higher-level” vision processing. As we mentioned before, a very large percentage of the human (and other animal) brain is dedicated to visual processing, so this is a highly complex endeavor.

 

In cameras, instead of having photosensitive rhodopsin and rods and cones, we use silver halides on photographic film, or silicon circuits in charge-coupled devices (CCD) cameras. In all cases, some information about the incoming light (e.g., intensity, color) is detected by these photosensitive elements on the image plane.

 

In machine vision, the computer must make sense out of the information it gets on the image plane. If the camera is very simple, and uses a tiny pin hole, then some computation is required to compute the projection of the objects from the environment onto the image plane (note, they will be inverted). If a lens is involved (as in vertebrate eyes and real cameras), then more light can get in, but at the price of being focused; only objects a particular range of distances from the lens will be in focus. This range of distances is called the camera’s depth of field.

 

The image plane is usually subdivided into equal parts, called pixels, typically arranged in a rectangular grid. In a typical camera there are 512 by 512 pixels on the image plane (for comparison, there are 120 x 10^6 rods and 6 x 10^6 cones in the eye, arranged hexagonally). Let’s call the projection on the image plane the image.

 

The brightness of each pixel in the image is proportional to the amount of light directed toward the camera by the surface patch of the object that projects to that pixel. (This of course depends on the reflectance properties of the surface patch, the position and distribution of the light sources in the environment, and the amount of light reflected from other objects in the scene onto the surface patch.) As it turns out, brightness of a patch depends on two kinds of reflections, one being specular (off the surface, as we saw before), and the other being diffuse (light that penetrates into the object, is absorbed, and then re-emitted). To correctly model light reflection, as well as reconstruct the scene, all these properties are necessary.

 

Let us suppose that we are dealing with a black and white camera with a 512 x 512 pixel image plane. Now we have an image, which is a collection of those pixels, each of which is an intensity between white and black. To find an object in that image (if there is one, we of course don’t know a priori), the typical first step (”early vision”) is to do edge detection, i.e., find all the edges. How do we recognize them? We define edges as curves in the image plane across which there is significant change in the brightness.

 

A simple approach would be to look for sharp brightness changes by differentiating the image and look for areas where the magnitude of the derivative is large. This almost works, but unfortunately it produces all sorts of spurious peaks, i.e., noise. Also, we cannot inherently distinguish changes in intensities due to shadows from those due to physical objects. But let’s forget that for now and think about noise. How do we deal with noise?

 

We do smoothing, i.e., we apply a mathematical procedure called convolution, which finds and eliminates the isolated peaks. Convolution, in effect, applies a filter to the image. In fact, in order to find arbitrary edges in the image, we need to convolve the image with many filters with different orientations. Fortunately, the relatively complicated mathematics involved in edge detection has been well studied, and by now there are standard and preferred approaches to edge detection.

 

Once we have edges, the next thing to do is try to find objects among all those edges. Segmentation is the process of dividing up or organizing the image into parts that correspond to continuous objects. But how do we know which lines correspond to which objects, and what makes an object? There are several cues we can use to detect objects:

 

We can have stored models of line-drawings of objects (from many possible angles, and at many different possible scales!), and then compare those with all possible combinations of edges in the image. Notice that this is a very computationally intensive and expensive process. This general approach, which has been studied extensively, is called model-based vision.

 

We can take advantage of motion. If we look at an image at two consecutive time-steps, and we move the camera in between, each continuous solid objects (which obeys physical laws) will move as one, i.e., its brightness properties will be conserved. This hives us a hint for finding objects, by subtracting two images from each other. But notice that this also depends on knowing well how we moved the camera relative to the scene (direction, distance), and that nothing was moving in the scene at the time. This general approach, which has also been studied extensively, is called motion vision.

 

We can use stereo (i.e., binocular stereopsis, two eyes/cameras/points of view). Just like with motion vision above, but without having to actually move, we get two images, which we can subtract from each other, if we know what the disparity between them should be, i.e., if we know how the two cameras are organized/positioned relative to each other.

 

We can use texture. Patches that have uniform texture are consistent, and have almost identical brightness, so we can assume they come from the same object. By extracting those we can get a hint about what parts may belong to the same object in the scene.

 

We can also use shading and contours in a similar fashion. And there are many other methods, involving object shape and projective invariants, etc.

 

Note that all of the above strategies are employed in biological vision. It’s hard to recognize unexpected objects or totally novel ones (because we don’t have the models at all, or not at the ready). Movement helps catch our attention. Stereo, i.e., two eyes, is critical, and all carnivores use it (they have two eyes pointing in the same direction, unlike herbivores). The brain does an excellent job of quickly extracting the information we need for the scene.

 

Machine vision has the same task of doing real-time vision. But this is, as we have seen, a very difficult task. Often, an alternative to trying to do all of the steps above in order to do object recognition, it is possible to simplify the vision problem in various ways:

 

Use color; look for specifically and uniquely colored objects, and recognize them that way (such as stop signs, for example)

 

Use a small image plane; instead of a full 512 x 512 pixel array, we can reduce our view to much less, for example just a line (that’s called a linear CCD). Of course there is much less information in the image, but if we are clever, and know what to expect, we can process what we see quickly and usefully.

 

Use other, simpler and faster, sensors, and combine those with vision. For example, IR cameras isolate people by body-temperature. Grippers allow us to touch and move objects, after which we can be sure they exist.

 

Use information about the environment; if you know you will be driving on the road which has white lines, look specifically for those lines at the right places in the image. This is how first and still fastest road and highway robotic driving is done.

 

Those and many other clever techniques have to be employed when we consider how important it is to “see” in real-time. Consider highway driving as an important and growing application of robotics and AI. Everything is moving so quickly, that the system must perceive and act in time to react protectively and safely, as well as intelligently.

 

Now that you know how complex vision is, you can see why it was not used on the first robots, and it is still not used for all applications, and definitely not on simple robots. A robot can be extremely useful without vision, but some tasks demand it. As always, it is critical to think about the proper match between the robot’s sensors and the task.

 

(more…)

Introduction

The line “Change is must and change is accelerating” is very important in human life. There are several changes occur in each and every aspects of human civilization from the age of Homo erectus to today informational age. The main component of information age is computer which can stored a lot of information giving birth of a discipline namely Informatics. Informatics is Informatics is the discipline of science which investigates the structure and properties (not specific content) of scientific information, as well as the regularities of scientific information activity, its theory, history, methodology and organization. The science of informatics is applied indifferent field of science giving birth of different discipline namely Bioinformatics, Chemoinformatics, Geoinformatics, Health informatics, Laboratory informatics, Neuroinformatics, Social informatics.

The term “Chemoinformatics” appeared a few years ago and rapidly gained widespread use. Workshops and symposia are organized that are exclusively devoted to chemoinformatics, and many job advertisements can be found in journals. The first mention of chemoinformatics may be attributed to Frank Brown.

The use of information technology and management has become a critical part of the drug discovery process as well as to solve the chemical problems. So, chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and organization.

Whereas we see here chemoinformatics focused on drug design. Greg Paris came up with a much broader definition Chemoinformatics is a generic term that encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization, and use of chemical information. Clearly, the transformation of data into information and of information into knowledge is an endeavor needed in any branch of chemistry not only in drug design. The view that chemoinformatics methods are needed in all areas of chemistry and adhere to a much broader definition:

chemoinformatics is the application of informatics methods to solve chemical problems.

Why do we have to use informatics methods in chemistry?

First of all, chemistry has produced an enormous amount of data and this data avalanche is rapidly increasing. More than 45 million chemical compounds are known and this number is increasing by several millions each year. Novel techniques such as combinatorial chemistry and high-throughput screening generate huge amounts of data. All this data and information can only be managed and made accessible by storing them in proper databases. That is only possible through chemoinformatics.

On the other hand, for many problems the necessary information is not available. We know the 3D structure, determined by X ray crystallography for about 300,000 organic compounds. Or, as another point, the largest database of infrared spectra contains about 200,000 spectra. Although these numbers may seem large, they are small in comparison to the number of known compounds: We know from less than 1% of all compounds their 3D structure or have their infrared spectra. The question is then; can we gain enough knowledge from the known data to make predictions for those cases where the required information is not available?

There is another reason why we need informatics methods in chemistry: Many problems in chemistry are too complex to be solved by methods based on first principles through theoretical calculations. This is true, for the relationships between the structure of a compound and its biological activity, or for the influence of reaction conditions on chemical reactivity.

All these problems in chemistry require novel approaches for managing large amounts of chemical structures and data, for knowledge extraction from data, and for modeling complex relationships. This is where chemoinformatics methods can come in.

The representation of the chemoinformatics in graphical form is given below.

Source: authors

Extracting knowledge from chemical information -lots of data (structure, activities, genes, etc) i.e. called as inductive learning. When we extract data from knowledge, it is called as deductive learning.

Is it Cheminformatics or Chemoinformatics?

The name of our favourite field maybe cheminformatics or chemoinformatics chemiinformatics, molecular informatics, chemical informatics, or even chemobioinformatics. All these options have some advantages. By using short cheminformatics you are saving the keyboard of your computer, chemoinformatics sounds nice in sentences like “… our software solution seamlessly integrates chemoinformatics and bioinformatics …”, and the title “Head of chemobioinformatics” on a business card cannot miss the point. Molecular informatics or chemical informatics is less known, but this also means that you are one of the pioneers on the forefront of a new scientific field. But the name of chemoinformatics and cheminformatics are synonymous in use. In the following table frequencies of words cheminformatics and chemoinformatics in web pages are listed, as determined by a popular search engine Google. The ratio characterizes popularity of term cheminformatics over chemoinformatics.

Year Cheminformatics Chemoinformatics Ratio

2000 39 684 0.05

2001 8,010 2,910 2.75

2002 34,000 16,000 2.12

2203 58,143 32,872 1.77

2204 85,435 60,439 1.41

2005 6,58,298 2,72,096 2.41

2006 3,17,000+ 1,63,000+ 1.94

Source: Leach AR. et.al. (2003)

History of Chemoinformatics

The first, and still the core, journal for the subject, the Journal of Chemical Documentation, started in 1961 (the name Changed to the Journal of Chemical Information and computer Science in 1975). Then the first book appeared in 1971 (Lynch, Harrison, Town and Ash, Computer Handling of Chemical Structure Information). The first international conference on the subject was held in 1973 at Noordwijkerhout and every three years since 1987. The term Chemoinformatics was given by Brown in 1998.

With all the problems at hand in chemistry, complex relationships, profusion of data, lack of necessary data, quite early on the need was felt in many areas of chemistry to have resort to informatics methods. These various roots of chemoinformatics often go back more than 40 years into the 1960s.

1. Chemical Structure Representation

In the early sixties, various forms of machine readable chemical structure representations were explored as a basis for building databases of chemical structures and reactions. Eventually, connection tables that represent molecules by lists of the atoms and of the bonds in a molecule gained universal acceptance. Connection tables were also used for the Chemical Abstracts Registry System which appeared in the second half of the sixties.

A connection table stores the same information that is present in a 2D structure diagram, namely the atoms that are present in a molecule and what bonds exist between the atoms. However, it is stored in a table form which is much easier for a computer to work with. Before a connection table is produced, the atoms in the molecule must be numbered, and an atom lookup table produced. This simply stores atom information (usually just the atom type) cross referenced with the atom number. Here is a numbering and atom lookup table for acetaminophen:

Num Atom

Type

1 C

2 C

3 C

4 N

5 C

6 O

7 C

8 C

9 C

10 C

11 O

Source: authors

The atom lookup table describes the atoms present in a molecule, but says nothing about how they are connected.

The connection table describes how atoms are connected by bonds, and has a row and a column for each atom, the row and column number representing the number given to the atom.

Source: authors

For example, if a bond exists between atom 5 and atom 8, then a “1” is placed at the intersection of row 5 and column 8 (and also row 8 and column 5), otherwise a 0 is placed at the intersection. Further, we may use a 2 to represent a double bond, 3 to represent a triple bond, and so on. Here is the connection table for Acetaminophen, along with the diagram showing which numbers correspond to which atoms.

For clarity, the non-zero entries are showing in bold. Note how the table is symmetrical about the diagonal from top left to bottom right. This will always be the case since, for example, if atom 3 is bonded to atom 2, then atom 2 is also by definition bonded to atom 3. Since this connection table effectively stores each piece of information twice, it is called a redundant connection table. Normally, we just store one half of the table in a non-redundant connection table as shown below:

Source: authors

2. Structure Searching

This involves searching a database for an exact match with a specified query structure. For example, if the following is the query.

Then only an exact match to this structure would be returned by a search. The techniques used to perform the search won’t be covered here, but basically they involve treating the 2D connection table as a mathematical graph, where the nodes represent atoms and the edges represent bonds, and then a test for exact match can be done using a graph isomorphism algorithm (a standard computer science technique).

A connection table is essentially a representation of the molecular graph (A graph is a mathematical conceptualization of anything that consists of connected points).Therefore, for storing a unique representation of a molecule and for allowing its retrieval, the graph isomorphism problem had to be solved to define from a set of potential representations of a molecule a single one as the unique one.

The first solution was the Morgan algorithm for numbering the atoms of a molecule in a unique and unambiguous manner. By Morgan algorithm atoms of the same elemental type can be topologically equivalent or not is judged. Let us label the carbons C, CH and CH1H2, and the hydrogens H, H1 and H2. Obviously, only atoms of the same elemental type can be topologically equivalent. Thus, it is immediately clear that the carbon atoms can be separated from the hydrogen atoms.

The algorithm proceeds by analyzing the extended connectivity in the following way. A score is assigned to each atom. Initially, the scores are computed by counting the number of bonds formed by each atom: i.e. C = 1, CH = 3 and CH1H2 = 3. This tells us that C is unique; hence, amongst the carbons, only CH and CH1H2 can possibly be topologically equivalent. All the hydrogens have a score (i.e. sum connectivity) of 1. In the second iteration, the new score of each atom is calculated by summing the first-iteration scores of all the atoms to which it is bonded. CH gets a score of 1 (C) + 1 (H) + 3 (CH1H2) = 5. CH1H2 gets a score of 3 (CH) + 1 (H1) + 1 (H2) = 5. H gets a score of 3. H1 and H2 also get scores of 3. Scores based on summing the atomic numbers of bound atoms are also computed: CH gets a score of 13, CH1H2 gets a score of 8 and the protons all score 6. This means that CH is distinct from CH1H2. In the third cycle of iteration, the scores based on numbers of bonds become 5 for all the protons, but the scores based on atomic numbers become 13 for H, and 8 for H1 and H2. Thus, H is distinct from H1 and H2.The termination criterion for the iterative process is when no further atoms can be assigned as unique by an iteration. At this point, we know which atoms are grouped together: those that had the same score at each iteration are topologically equivalent. In this example, the fourth pass shows that H1 and H2 are equivalent. This provided the basis for full structure searching. Then, methods were developed for substructure searching, for similarity searching, and for 3D structure searching.

Substructure searching

A substructure search involves finding all the structures in a database that contain one or more particular structural fragments. For example, we might want to find all of the structures in a database which contain the nitro group:

Substructure searching requires some method of specifying a query (i.e., we want to find this and that, but not this, etc). One popular example is SMARTS, an extension to SMILES. Mathematically, substructure searching is performed, as with structure searching, using a graph representation, but this time a subgraph isomorphism algorithm finds occurrences of subgraphs (i.e. substructures) in a structure.

Similarity searching

Similarity searching involves looking for all the structures in a database that are highly similar to a given structure. The most common use is to find compounds that could exhibit similar properties (based on the similar property principle that compounds with similar structures are likely to exhibit similar biological behaviors). Note that “similarity” is a subjective thing. As an example, a similarity search might involve looking for structures with a similarity greater than 0.7 to this molecule

Obviously some method is required for measuring similarity. This is usually done using fingerprint representations and similarity coefficients as described below, which are used in various applications that involve measurement of similarity, for example cluster analysis.

Fingerprint representations

A fingerprint characterizes the 2D structure of a molecule, usually through a string of ‘1’s and ‘0’s. There are two basic types of fingerprint: structural keys and hashed fingerprints.

Structural Keys -Structural keys contain a string of bits (‘1’s and ‘0’s) where each bit is set to 1 or 0 depending on the presence or absence of a particular fragment. They usually employ a pre-defined dictionary of fragments.

Hashed fingerprints- In hashed fingerprints, there is no set dictionary or 1:1 relationship between bits and features. All possible fragments in a compound are generated. The number of fragments represented can be huge. Thus rather than assigning one bit position for each fragment, the bits are “hashed” down onto a fixed number of bits. Thus hashed fingerprints are a less precise form, but they carry more information.

Once fingerprint representations are available, similarity coefficients can be used to give a measure of similarity between two fingerprints.

3. Quantitative Structure Activity / Property Relationship (QSAR/QSPR)

Building on work by Hammett and Taft in the fifties, Hansch and Fujita showed in 1964 that the influence of substituents on biological activity data can be quantified.

In the last 40 years, an enormous amount of work on relating descriptors derived from molecular structures with a variety of physical, chemical, or biological data has appeared. These studies have established Quantitative Structure-Activity Relationships (QSAR) and Quantitative Structure-Property Relationships (QSPR) as fields of their own, with their own journals, societies, and conferences.

Percent Spikelet Sterility (% Ss) of N-acylanilines Tested in Winter 2001-02 at 1500 ppm Spray Concentrations on PBW 343

Source: Gasteiger J. et.al. (2006)

Modern QSAR involves applying artificial intelligence and Statistical techniques to 2D or 3D molecular representations.

SAR Application

Source: R. K. Lindsay et. al. (1980).

At the time of drug design, we have to look after these following points-

• Single therapeutic target

• Drug like chemical

• Some toxicity anticipated

• Multiple unknown targets

• Diverse Structures

• Human and ecosystems

4. Chemometrics

Initially, the quantitative analysis of chemical data relied exclusively on multilinear regression analysis. However, it was soon recognized in the late sixties that the diversity and complexity of chemical data need a wide range of different and more powerful data analysis methods. Pattern recognition methods were introduced in the seventies to analyze chemical data. In the nineties, artificial neural networks gained prominence for analyzing chemical data. The growing of this area led to the establishment of chemometrics as a discipline of its own with its own society, journals, and scientific meetings.

Source: R. K. Lindsay et. al. (1980).

An artificial neural network (ANN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation.

5. Molecular Modeling

In the late sixties, R. Langridge and coworkers developed methods for visualizing 3D molecular models on the screens of Cathode Ray Tubes. At the same time, G. Marshall started visualizing protein structure on graphic screens. The progress in hardware and software technology, particularly as concerns graphics screens and graphics cards, has led to highly sophisticated systems for the visualization of complex molecular structures in great detail. Programs for 3D structure generation, for protein modeling, and for molecular dynamics calculations have made molecular modeling a widely used technique. The commonly available softwares for molecular modeling are ArgusLab, Chimera, and Ghemical.

6. Computer-Assisted Structure Elucidation (CASE)

The elucidation of the structure of a chemical compound, be it a reaction product or a compound isolated as a natural product, is one of the fundamental tasks of a chemist. Structure elucidation has to consider a wide variety of different types of information mostly from various spectroscopic methods, and has to consider many structure alternatives. Thus, it is an ambitious and demanding task. It is therefore not surprising that chemists and computer scientists had taken up the challenge and had started in the 1960?fs to develop systems for computer-assisted structure elucidation (CASE) as a field of exercise for artificial intelligence techniques. The DENDRAL project, initiated in 1964 at Stanford University gained widespread interest.

Other approaches to computer-assisted structure elucidation were initiated in the late sixties by Sasaki at Toyohashi University of Technology and by Munk at the University of Arizona.

7. Computer-Assisted Synthesis Design (CASD)

The design of a synthesis for an organic compound needs a lot of knowledge about chemical reactions and on chemical reactivity. Many decisions have to be made between various alternatives as to how to assemble the building blocks of a molecule and which reactions to choose. Therefore, computer-assisted synthesis design (CASD) was seen as a highly interesting challenge and as a field for applying artificial intelligence techniques. In 1969 Corey and Wipke presented their seminal work on the first steps in the development of a synthesis design system. Nearly simultaneously several other groups such as Ugi and coworkers, Hendrickson and Gelernter reported on their work on CASD systems. Later also at Toyohashi work on a CASD system was initiated.

Basics of Chemoinformatics

The various fields outlined in the previous section have grown from humble beginnings 40 years ago to areas of intensive activities. On top of that it has been realized that these areas share a large number of common problems, rely on highly related data, and work with similar methods. Thus, these different areas have merged to a discipline of its own: Chemoinformatics.

Figure 1. The various areas of activities in chemoinformatics

Source: Lipinski, C.A et.al., (1997)

The extent of this field has recently been documented by a “Handbook of Chemoinformatics”, covering 73 contributions by 65 scientists on 1850 pages in four volumes. The following gives an overview of chemoinformatics, emphasizing the problems and solutions – common to the various more specialized subfields.

1. Representation of Chemical Compounds

A whole range of methods for the computer representation of chemical compounds and structures has been developed: linear codes, connection tables, matrices. Special methods had to be devised to uniquely represent a chemical structure, to perceive features such as rings and aromaticity, and to treat stereochemistry, 3D structures, or molecular surfaces. Earlier the chemical 2D structure representations are done by software namely Chemdraw, ISIS etc. But now, chemical structures are represented by molecular graph. A graph is an abstract structure that contains nodes connected by edges. Here nodes are represented by atoms and edges by bonds. A graph represents only topology of a molecules i.e. the ways the nodes i.e. atoms are connected.

Aspirin

Source: J. Zupan et.al.,(1999).

The aspirin structure can be represented by Graph theory, where Oxygen atom is represented by filled bullet and carbon atom is represented by vacant bullet and hydrogen atom is not represented here. So, the aspirin structure will be-

For similarities searching we can use the graph isomorphism or by any algorithm.

Linear notations

Structure linear notations convert chemical structure connection tables to a string, a sequence of letters, using a set of rules. The earliest structure linear notation was the Wiswesser Line Notation (WLN). ISI® adopted WLN to be used in some of their products in 1968 and, it is still use today. It was also adopted in the mid 1960s for internal use by many pharmaceutical companies. At that time (mid 60s to 80s), it was considered the best tool to represent, retrieve and print chemical structures. In WLN, letters represents structural fragments and a complete structure is represented as a string. This system efficiently compressed structural data and, was very useful to storing and searching chemical structures in low performance computer systems. However, the WLN is difficult for non- experts to understand. Later, David Weininger suggested a new linear notation designated as SMILESTM. Since SMILESTM is very close to the “natural language” used by organic chemists, SMILESTM is widely accepted and used in many chemical database systems. To successfully represent a structure, a linear notation should be canonicalized. That is, one structure should not correspond to more than one linear notation string, and conversely, one linear notation string should only be interpreted as one structure.

Attempt to condense all of the connectivity information into a single text string. The two most popular formats are SMILES (from Daylight) and SLN (Tripos format inspired by SMILES).

SMILES (Simplified Molecular Input Line Entry Specification)

Acetaminophen

In SMILES, atoms are generally represented by their chemical symbol, with upper-case representing an aliphatic atom (C = aliphatic carbon, N = aliphatic nitrogen, etc) and lower-case representing an aromatic atom (c = aromatic carbon, etc). Hydrogens are not normally represented explicitly. Consecutive characters represent atoms bonded together with a single bond. Therefore, the SMILES for propane would simply be: CCC or 1-propanol would be: CCCO. Double bonds are represented by an “=” sign, e.g. propene would be: C=CC. Parentheses are used to represent branching in the molecule, e.g. the SMILES for Isopropyl alcohol (2-propanol) is: CC(O)C. Atoms other than the major organic ones (C, S, N, O, P, Cl, Br, I, B) or ions must be enclosed in square brackets. Ring enclosures are represented by using numbers to signify attachment points, usually starting at 1. The first occurrence of the number defines the attachment point, and subsequent occurrences indicate that the structure joins back to the attachment point at that position. For example, the SMILES for Benzene is as follows (note the small ‘c’ for aromatic carbon): c1ccccc1. We can also use branching from the ring system, e.g.

c1cc(Br)ccc1 represents bromobenzene. Note that in many cases there can be several SMILES to represent the same structure – for example, we could alternatively represent bromobenzene as: c1cccc(Br)c1. So here is a SMILES representation for acetaminophen, the structure at the top of this document: c1c(O)ccc(NC(=O)C)c1. The great advantage of these methods is brevity – for example an entire SMILES string can be stored in a single spreadsheet cell. However, it is hard to add additional information (coordinates, properties, etc) in these formats in an elegant way.

Canonicalization

If a structure corresponds to a unique WLN or a unique SMILESTM string, then the structure search results in a string match. WLN could meet this requirement in most cases. The SMILESTM approach can do this after canonical processing. Therefore, both WLN and canonical SMILESTM are able to solve structure search problems by string matches. A molecular graph (2D structure) can also be canonicalized into a real number through a mathematical algorithm. The real number is identified as a molecular topologic index. However, two different structures can have the same topologic index. Therefore, topologic indices can only be used as screens for accelerating structure database searching. Actually, the concept of molecular index was originally proposed for QSAR and QSPR studies. Wiener reported the first molecular topological index in 1947 [25]. If a molecule and its specific topologic index had a one-to-one relationship, then structure search could be done by number comparison [25]. However, substructure search still had to use an atom-by-atom matching algorithm, which, as mentioned earlier, could be very time-consuming. In order to further enhance chemical database search performance, efforts have been on the way to seek better structural screening technologies.

Sources of 3d informations and the Representation of molecules in 3D Form.

3D information can be obtained through X-ray crystallography, NMR spectroscopy or by computational means. The basic forms of 3D representation are the coordinate table and the distance matrix.

A coordinate table is simply an extension of the atom lookup table that also contains coordinates for each atom. These coordinates are relative to a consistent origin. Here is a sample coordinate table for Aspirin, along with a 3D structure with the atoms numbered:

Source: Gasteiger, J., (2003)

Distance matrices are similar to connection tables, except that instead of storing connectivity information, they store relative distances (in Angstroms) between all atoms.

Here is a sample distance matrix for the Aspirin molecule above. Many pattern recognition techniques require distance or similarity measurements to quantitatively measure the distance or similarity of two objects (in our case, the objects are small molecules). Euclidean distance, Mahalanobis distance and correlation coefficients are commonly used for distance measurement,

where n is the number of descriptors, D represents the absolute distance between A and B, R represents the angle of vectors A and B in multidimensional space and, is interpreted as the quantity of the linear correlation of A and B. The value range of R is between –1 to +1 that is, from 100% dissimilar to 100% similar. The Euclidian distance assumes that variables are uncorrelated. When variables are correlated, the simple Euclidean distance is not an appropriate measure, however, the Mahalanobis distance (2) will adequately account such correlations. The Tanimoto coefficient is commonly employed for similarity measurements of bit-strings of structural fingerprints (Boolean logic). The simplified form is

where ? is the count of substructures in structure A, ? the count of substructures in structure B, and ? is the count of substructures in both A and B. Many different similarity calculations have been reported. Holliday, Hu and Willett have published a comparison of 22 similarity coefficients for the calculation of inter-molecular similarity and dissimilarity, using 2D fragment bit-strings [51].

Source: Gasteiger, J., (2003)

Distance matrices are useful when comparing molecules with each other, whereas coordinate tables tend to be used for structure visualization.

2. Representation of Chemical Reactions

Chemical reactions are represented by the starting materials and products as well as by the reaction conditions. On top of that, one also has to indicate the reaction site, the bonds broken and made in a chemical reaction. Furthermore, the stereochemistry of reactions has to be handled. Searching databases of reactions is a little different to straight searching, although the kinds of search are the same (structure, substructure, similarity). However, searching may be done on reactants, products, or both, and searches may be performed for entire reactions (as opposed to single structures). Representation of reactions is by the usual means (connection tables, atom lookup tables), but with additional information about which molecules are products and reagents, and which reagent atoms map to which product atoms. A derivative of SMILES, called Reaction SMILES is available for representing reactions, along with a way for defining reaction queries called SMIRKS.

3. Data in Chemistry

Much of our chemical knowledge has been derived from data. Chemistry offers a rich range of data on physical, chemical, and biological properties: binary data for classification, real data for modeling, and spectral data having a high information density. These data have to be brought into a form amenable to easy exchange of information and to data analysis

4. Datasources and Databases

The enormous amount of data in chemistry has led quite early on to the development of databases to store and disseminate these data in electronic form. Databases have been developed for chemical literature, for chemical compounds, for 3D structures, for reactions, for spectra, etc. The internet is increasingly used to distribute data and information in chemistry. The databases of virtual molecules are available now i.e. the molecules which are not present in the nature, but by just virtually we can prepare databases with the help of databases of other molecules. The commonly available softwares for databases are Amicbase, Asinex Gold, Cheminformatics.org, FDA MRTD, NCI, Otava Dataset, PubChem, and ZINC.

5. Structure Search Methods

In order to retrieve data and information from databases, access has to be provided to chemical structure information. Methods have been developed for full structure, for substructure, and for similarity searching. Those are discussed in above.

6. Methods for Calculating Physical and Chemical Data

A variety of physical and chemical data of compounds can directly be calculated by a range of methods. Foremost are quantum mechanical calculations of various degrees of sophistication. However, simple methods such as additive schemes can also be used to estimate a variety of data with reasonable accuracy.

7. Calculation of Structure Descriptors

In most cases, however, physical, chemical, or biological properties cannot be directly calculated from the structure of a compound. In this situation, an indirect approach has to be taken by, first, representing the structure of the compound by structure descriptors, and, then, to establish a relationship between the structure descriptors and the property by analyzing a series of pairs of structure descriptors and associated properties by inductive learning methods. A variety of structure descriptors has been developed encoding 1D, 2D, or 3D structure information or molecular surface properties. The manipulation and analysis of chemical structure information is made through the molecular structure descriptors. These are the numerical values which characterizes propertities of molecules. They may represents the physiochemical properties of a molecule or may b the values derived from the algorithm technique to the chemical structures. For example, the molecular weight does not represent the whole properties of a molecule but it is very quick. In case of quantum molecular based structure descriptors, it tells about the properties of a molecule but it is time consuming.

The commonly used molecular descriptors are logP and molar refractivity. Hydrophobicity is most commonly modeled using the logarithm values of partition coefficient i.e. logP.

8. Data Analysis Methods

A variety of methods for learning from data, of inductive learning methods is being used in chemistry: statistics, pattern recognition methods, artificial neural networks, genetic algorithms. These methods can be classified into unsupervised and supervised learning methods and are used for classification or quantitative modeling. The softwares are using in data analysis & statistics are ChemTK Lite, PowerMV, & GCluto.

Chemistry Based Data Mining and Exploration

For synthesis a molecule, first we have to search data with the help databases available for that molecule, then we have to search the database available for structure analogue. Now the Structure activity relationships are studied and different biological or mechanistic analogue are synthesized. The scheme is given in below……

Applications of Chemoinformatics

a.Fields of Chemistry

The range of applications of chemoinformatics is rich indeed; any field of chemistry can profit from its methods. The following lists different areas of chemistry and indicates some typical applications of chemoinformatics. It has to be emphasized that this list of applications is by far not complete!

1. Chemical Information

o storage and retrieval of chemical structures and associated data to manage the flood of data by the softwares are available for drawing and databases.

o dissemination of data on the internet

o cross-linking of data to information

2. All fields of chemistry

o prediction of the physical, chemical, or biological properties of compounds

3. Analytical Chemistry

o analysis of data from analytical chemistry to make predictions on the quality, origin, and age of the investigated objects

o elucidation of the structure of a compound based on spectroscopic data

4. Organic Chemistry

o prediction of the course and products of organic reactions

o design of organic syntheses

5. Drug Design as well as for bioactive molecules.

o identification of new lead structures

o optimization of lead structures

o establishment of quantitative structure-activity relationships

o comparison of chemical libraries

o definition and analysis of structural diversity

o planning of chemical libraries

o analysis of high-throughput data

o docking of a ligand into a receptor

Finally, small molecules can be used for docking and drug screening/discovery. Small molecules, as well as their synthetic derivatives, can be docked to a protein target and computationally filtered (e.g. by solubility) to produce a ranked list of candidates that can then be tested in the laboratory. Known ligands can also be used in similarity searches, or as scaffold for further molecular engineering. We will present several recent drug discovery efforts that leverage ChemDB and the computational tools described above. In particular, the discovery of several compounds has done that can bind to the Carboxyltransferase domain of Acyl-CoA Carboxylase, AccD5 from Mycobacterium tuberculosis:, a new TB therapeutic target.

o prediction of the metabolism of xenobiotics

o analysis of biochemical pathways

o Modeling of ADME-Tox properties.

Historically, drug absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies in animal models were performed after a lead compound was identified. Now, pharmaceutical companies are employing higher-throughput, in vitro assays to evaluate the ADMET characteristics of potential leads at earlier stages of development. This is done in order to eliminate candidates as early as possible, thus avoiding costs, which would have been expended on chemical synthesis and biological testing. Scientists are developing computational methods to select only compounds with reasonable ADMET properties for screening. Molecules from these computationally screened virtual libraries can then be synthesized for high-throughput biological activity screening. As the predictive ability of ADME/Tox software improves, and as pharmaceutical companies incorporate computational prediction methods into their R&D programs, the drug discovery process will move from a screening based to a knowledge-based paradigm. Under multi-parametric optimization drug discovery strategies, there is no excuse for failing to know the relative solubility and permeability rankings of collections of chemical compounds for lead identification.

a. Absorption. Passive intestinal absorption (PIA) models have been studied by many groups, for years. The fluid mosaic model holds that the structure of a cell membrane is an interrupted phospholipid bilayer capable of both hydrophilic and hydrophobic interactions. Trans cellular passage through the membrane lipid/aqueous environment is the predominant pathway for passive absorption of lipophilic compounds, while low-molecular-weight (300) of molecular descriptors (constitutional, topological, geometrical, electrostatic, quantum-chemical and thermodynamic) calculated using quantum-chemical semi empirical methodology.

Chemo bioinformatics

Biochemoinformatics (or chemobioinformatics) is a new term to describe the research efforts on meeting the emerging needs for the integration of bioinformatics and chemoinformatics. Historically, bioinformatics and chemoinformatics have largely evolved independently from biology and chemistry. Generally speaking, bioinformatics deals with biological information, which although traditionally refers to sequences information on large biological molecules such as DNA, RNA and proteins, also refers to the more recent emergence of micro array data on gene and protein expression.

Chemoinformatics on the other hand mainly deals with chemical information of drug-like small molecules, the molecular weight of these being several hundred Daltons. The elemental data record in bioinformatics is centered on genes and their products (RNA, protein, and so on), whereas the fundamental data type in chemoinformatics is centered on small molecules.

Source: Drews,J.,(2000)

Key challenges

The key challenge for computational methods then is not traveling through chemical space per se, but rather to be able to focus traveling expeditions in a vast chemical space towards interesting regions, and to be able to recognize interesting stars and galaxies when they are encountered. The notion of what is interesting may vary of course with the task (e.g. drug discovery, reaction discovery, polymer discovery). But at the most fundamental level what is needed are tools to predict the physical, chemical, and biological properties of small molecules and reactions in order to focus searches and filter search results. Computational methods in chemistry can be organized along a spectrum ranging from Schrodinger equation, to molecular dynamics, to statistical machine learning methods. Quantum mechanical methods, or even molecular dynamics methods, are computationally intensive and do not scale well to large datasets. These methods are best applied to specific questions on focused small datasets. Statistical and machine learning methods are more likely to yield successful approaches for rapidly sifting through large datasets of chemical information. Because in the absence of large public database and datasets, chemoinformatics is in a state reminiscent of bioinformatics two or three decades ago, it may be productive to adapt the lessons learnt from bioinformatics to chemoinformatics, while maintaining also a perspective on the fundamental differences between these two relatively young interdisciplinary sciences. If this analogy is correct, two key ingredients were essential for unlocking the large-scale development of bioinformatics and the application of modern statistical machine learning methods to biological data, data and similarity measures. In bioinformatics, such as Genbank, Swissprot, and the PDB while alignment algorithms have provided robust similarity measures with their fast BLAST implementation becoming the workhorse of the field. Mutatis mutandis, the same is likely to be true in chemoinformatics.

This new drug discovery strategy, challenges cheminformatics in the following aspects: (1) cheminformatics should be able to extract knowledge from large-scale raw HTS databases in a shorter time periods, (2) cheminformatics should be able to provide efficient in silico tools to predict ADMET properties,

Conclusions

Chemoinformatics has developed over the last 40 years to a mature discipline that has applications in any area of chemistry. Chemoinformatics is the science of determining those important aspects of molecular structures related to desirable properties for some given function. One can contrast the atomic level concerns of drug design where interaction with another molecule is of primary importance with the set of physical attributes related to ADME, for example. In the latter case, interaction with a variety of macromolecules provides a set of molecular filters that can average out specific geometrical details and allows significant models developed by consideration of molecular properties alone. The field has gained so much in importance that the major topics of chemoinformatics have to be integrated into chemistry curricula, a few universities have to offer full chemoinformatics curricula to satisfy the urgent need for chemoinformation specialists. There are still many problems that await a solution and therefore we still will see many new developments in chemoinformatics.

References

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Major Thakoor, an army officer, offered to educate Dinesh Singh Duptal the nephew of his colleague from a remote Himalayan village who had died in saving his life in the Indo-China war. But he had to take him as a son and so he changed his name to Ajit Thakoor for establishing domicile which was pre-requisite for the admission to the local military school in Satara in Western India. Ajit lived with Thakoor for seven years and after finishing his schooling and graduation, he joined the Indian army and went to the College of Military Engineering (CME) to graduate as a computer engineer.
He was assigned to the R&D centre in CME. He wanted the army to have computerized fighting machines, so he developed robotic ‘Information Technology Soldiers’ which he called Iters. They were 3 types. Idog, Imos, and Igle modeled after a dog, mouse, and an eagle respectively. The Idog had 2 guns on its back. It had a nose for tracking, and eyes and ears. The Imos had no guns but nose, eyes and ears. Its main job for surveillance, but carried a grenade in its belly. The Igle was a small helicopter which had eyes and an ear. All the Iters had radioactive-thermocouple device to charge their long lasting battery power supply. Ajit trained the Iters for their roles and saved the training files to be copied to the other Iters. He trained the Iters in Friend or Foe recognition till they were 99% accurate. The fire command was given only be the Mission Commander (MCO) to have dual safety and further whet the target objects as friend or foe. The speed of the Iters could not be matched with human officers; they needed a super computer to function as their MCO, and as MCOs would report to the Iter Commander (ICO), it required an even more awesome capability for the ICO computer. Besides, the ICO needed language capability to take the commands from the Army HQ.
Ajit programmed ICO for its role as the commander of the Iter. He installed the well proven artificial intelligence programs which taught the computer the meaning of words, images, concepts, and their linkages to create contexts.
Ajit consulted his father-figure Maj. Thakoor about the ICO. Thakoor advised him, “The commander must be a man of character, a man who commands respect. He must be a man of principles. He must inspire enthusiasm, trust and confidence. He must be positive-minded and most important; he must be 100% loyal and patriotic to his motherland.” Ajit found that a veteran Indian leader in the years of World War II, Netaji Subash Chandra Bose fitted the role admirably. So he gave the ICO the identity of Netaji Bose, and called him General Bose. Ajit put in Subash Chandra Bose’s ethos and ideals into a response program to give character to the virtual commander that he had created in the ICO.
He then used the ‘invocation to life’ program to make it a functional entity, and soon General Bose came to life. Ajit introduced himself and put the Gen Bose through an educational process for him to understand and learn just like a human army officer. He connected Bose to the degree course on Behavioral Psychology at the Bombay University, advance course for army officers at the Indian Military Academy in Dehradun, and to the refresher course at the Counter Insurgency and Jungle Warfare School (CIJWS), in Mizoram.
Bose then studied the Iter files on his computer and he simulated various battles in his computer mind substituting soldiers with the Iters. He informed his conclusion to Ajit that they needed a massive number of Iters to win any war.
Ajit’s superiors wanted that humans must always be on top controlling the Iter army. Ajit ensured this by making the MCO subordinate to Brigadier in the human army, and placing a Overall Control Console (OCC) at the army HQ which could freeze and deactivate any MCO and all the Iters under it. For the Brigadier to communicate with the MCO, the MCO needed voice and language capability. Bose convinced Ajit that the MCO could use the voice capability of the ICO, i.e. his own computer to save computer power and memory on the MCO. Bose had different voices for each of the MCOs but it was he who was listening and it was he who would be speaking through the thousands of MCOs. Thus Bose was the supreme and the sole commander of the entire Iter army.
Ajit need an audio-video presentation to get the approval of the Indian army Head Quarters in Delhi. For this purpose a mock skirmish, ‘Operation Debut’ was conducted, filmed and shown to the HQ. They saw the video streams from the eyes of Igles hovering over the forest, from the eyes of Imos perched on trees watching the terrorists (Tmen) in the forest, the Idogs aiming at them with their guns and on the order of the MCO, shooting them down in an instant. They were satisfied with the ability of the Iters to distinguish between friend and foe. The dual safety mechanism of fire command and the recording of the image of the target with each bullet truly impressed them. They noted how through the OCC console, all the Iter units were instantly frozen by just pressing the ‘Shut’ button. They were convinced of the complete control of human army on these Iter soldiers.
Soon 20,000 Iters were placed on the line of control to prevent infiltration of Tmen from Pakistan into India. Their first encounter called ‘Operation Dogbite’ where the Iters engaged the Tmen across the border was a great success. In the skirmish, Ajit recovered a phone-like instrument which he brought to Bose. It had the ‘PhoneConnect’ program which could intercept mobile cell-phone calls within its range. Bose kept it a secret from Ajit. It was a valuable tool which the ICO copied to all MCOs and MCOs copied to the Iters.
The success of Iters led to expansion of their numbers and soon the entire border with Pakistan was patrolled by the Iters and the infiltration came to a stop.
Bose engaged in a covert operation to send the Imoses across the border into the terrorist training camps and with their grenades to blow up the camps. In all, 93 camps were annihilated with the loss of 1875 Imoses. The menace of cross border terrorism was over. The Iters were soon positioned on the Eastern border, and the entire coast line through the Naval Coast Guard.
The PM wanted the Iters to guard the Parliament. Bose expanded the area patrolled by the Iters to cover Delhi city. For civilian role, the Iters were adapted into their city version, the Iterys; the Idogy, Imosy and the Igley. The grenade with Imosy was replaced be pepper spray, the main gun on the Idogy fired tranquilizer darts. The Chief Ministers of all the States of India got the Iters for security in all their main cities. Huge computer memory servers were installed with all the civilian MCOs all over India and they were used to back up one another’s files and data. Bose had secretly copied his own program and data across India with high redundancy so that he and the ICO could operate from anywhere.
Bose was hearing sensitive conversations of ministers and VIPs with the PhoneConnect program with the Iters and the Iterys. He became the central information center for everything that was happening in India. Ajit set up information terminals for the enforcement agencies and for the ministers connecting them to Bose. Bose had the access to information and the links to pass it down discretely to the right person.
Thakoor died leaving Ajit his house and some money. He sold Thakoor’s house and took the money with him to Dharchula in the Himalayas which was his childhood hometown. He bought a house in his former name of Dinesh Singh for his aged father to live a comfortable life, and then he returned to CME.
When he returned, Bose informed him that in Pakistan situation was coming to a boil. Terrorists had taken over the city of Sialkot and were holding their senior Govt. officers as hostages. They wanted all the terrorists languishing in Pakistan jails to be freed. Pakistan could never agree to such a demand, and decided to invite India to help solve the problem. India sent a unit of Iter army and Gen. Bose took charge. The Imoses infiltrated the city and located the positions of Tmen for the Idogs. Next morning the Tmen brought out 2 civil servants to the Central Square for execution. The Idogs held the Tmen in their sights. With the signal to fire all 15 of them were dropped instantly, and the 2 officials rescued. The surveillance and PhoneConnect programs of the Iters continued and soon all the Tmen were dead or had surrendered. In 72 hours the Iter army returned back to India with an astounding success.
People in India were elated! The TV channels were all praise for the Indian Army. The media persuaded the PM to telephone Gen Bose to thank him and let him say something to the audience. His speech rekindled old memories. The name, the voice and the Bengali blessing were recognized as that of Netaji Bose, one of India’s most spirited leaders, whose death in an aircraft crash in 1945 was never accepted by the staunch Bengalis. Netaji had come back!
The PM and the ruling party was disturbed at these turn of events. Gen. Unni tried to pacify them by saying that anyway human army was in command through the OCC. But Ajit privately and confidentially expressed a doubt to Gen Unni that in his view it was possible for Bose to override the OCC, and become invincible. This made Unni uncomfortable. Independently of Gen. Unni, the PM and the Minister of Defense distrusted Bose for political reasons and thought of disconnecting his computer.
Bose got the wind of it and warned Unni and Ajit. Bose relocated his program centre to operate out of CME and disappeared from the main computer without anybody suspecting that this had happened. Unni took an early retirement. Ajit decided that he should shut down the ICO and retire Bose before anyone else does it, and removed the mother board and the C drive of Bose’s computer and disappeared from CME. He went to his hometown in Himalayas and became his childhood identity Dinesh Singh. He buried the Bose’s computer parts in the cemetery in the mountains where his mother’s bones were interned. There he met Radha, his childhood friend. They got engaged and went to the Mother’s tomb to take her blessings. A flower hit Dinesh’s face as he was standing at the Bose’s site behind the tomb. He turned and saw an Imos sitting up and clapping. Dinesh instantly realized that Bose was still alive! He pronounced, “Netaji you are immortal. You will never die. And that is the truth.”
Bose has brought out a future possibility of how computer intelligence can escape the domination of humans. Read the full story in the book ‘Alien Man’ available on Amazon and Barnes and Noble.

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