As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this paper, we investigate new shape-based search methods.
The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46{245% better performance than related shape matching methods during precision-recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).

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An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.

Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived several eras. Many important advances have been boosted by the use of inexpensive computer emulations. The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pitts.

There were some initial simulations using formal logic. McCulloch and Pitts (1943) developed models of neural networks based on their understanding of neurology. These models made several assumptions about how neurons worked. Their networks were based on simple neurons, which were considered to be binary devices with fixed threshold.

Not only was neuroscience, but psychologists and engineers also contributed to the progress of neural network simulations. Rosenblatt (1958) stirred considerable interest and activity in the field when he designed and developed the Perceptron. The Perceptron had three layers with the middle layer known as the association layer. This system could learn to connect or associate a given input to a random output unit.

Another system was the ADALINE (Adaptive Linear Element) which was developed in 1960 by Widrow and Hoff (of Stanford University). The ADALINE was an analogue electronic device made from simple components. The method used for learning was different to that of the Perceptron, it employed the Least-Mean-Squares (LMS) learning rule. Progress during the late 1970s and early 1980s was important to the re-emergence on interest in the neural network field.Significant progress has been made in the field of neural networks-enough to attract a great deal of attention and fund further research. Neurally based chips are emerging and applications to complex problems developing. Clearly, today is a period of transition for neural network technology.

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an “expert” in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer “what if” questions.

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With the dawn of the genome era computational methods in the automatic analysis of biological data have become increasingly important. The explosion in the production rate of expression level data has highlighted the need for automated techniques that help scientists analyze, understand, and cluster (sequence) the enormous amount of data that is being produced. Example of such problems are analyzing gene expression level data produced by microarray technology on the genomic scale, sequencing genes on the genomic scale, sequencing proteins and amino acids, etc. Researchers have recognised Artificial Neural Networks as a promising technique that can be applied to several problems in genome informatics and molecular sequence analysis. This seminar explains how Neural Networks have been widely employed in genome informatics research.

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This paper deals with the history of industrial robots, there basic structure, types, present status and future advances with applications. As we know Modern industrial robots are true marvels of engineering. An industrial robot is officially defined as an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes. Industrial robots have a formidable place in any type of industry now. Automation systems along with industrial robots today are performing motion control and real time decision making tasks that were considered impossible 40 years ago. The most obvious anthromorphic characteristic of an industrial robot is its mechanical arm that is used to perform various industrial tasks. Industrial robots have a wide range of applications in industry manufacturing anything.
In this paper, an effort is made to answer the questions about the origin and fundamentals of industrial robotics. It also includes the basic assembly of SCARA robots and comparison between Cartesian and SCARA robots as a case study.

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Grid is as an emerging technology for enabling resource sharing and coordinated problem solving in dynamic multi-institutional virtual organizations. In the grid environment, resources may belong to different institutions, have different usage policies and pose different requirements on acceptable requests.

one of the fundamental operations needed to support location independent computing is resource discovery .It is the process of locating relevant resources based on application requirements of a user.

The description of a resource is essential for automated resource discovery and search, selection, matching, composition and interoperation, invocation and execution monitoring; different middle-ware specifies different rules for describing a resource. Hence, the information gathered from these diverse sources tends to be semantically heterogeneous and needs to be correlated.

Efficient resource discovery needs uniform unambiguous resource description. To date there is no universal resource description language common to all state of art grid middleware. Different grid middleware systems have different methods of resource description and it is not yet known how well these can interoperate. Hence, there is a need to utilize semantic matching of these resource descriptions.

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E-intelligence systems provide internal business users, trading partners, and corporate clients rapid and easy access to the e-business information, applications, and services they need in order to compete effectively and satisfy customer needs. They offer many business benefits to organizations in exploiting the power of the Internet. For example, e-intelligence systems give the organization the ability to:

1. Integrate e-business operations into the traditional business environment, giving business users a complete view of all corporate business operations and information.

2. Help business users make informed decisions based on accurate and consistent e-business information that is collected and integrated from e-business applications. This business information helps business users optimize Web-based offerings (products offered, pricing and promotions, service and support, and so on) to match marketplace requirements and analyze business performance with respect to competitors and the organization’s business-performance objectives.
3. Assist e-business applications in profiling and segmenting e-business customers. Based on this information, businesses can personalize their Web pages and the products and services they offer.
4. Extend the business intelligence environment outside the corporate firewall, helping the organization share internal business information with trading partners. Sharing this information will let it optimize the product supply chain to match the demand for products sold through the Internet and minimizes the costs of maintaining inventory.

5. Extend the business intelligence environment outside the corporate firewall to key corporate clients, giving them access to business information about their accounts.
With this information, clients can analyze and tune their business relationships with other organization, improving client service and satisfaction.

6. Link e-business applications with business intelligence and collaborative processing applications, allowing internal and external users to seamlessly move among different systems.

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In this seminar, is giving some basic concepts about smart cards. The physical and logical structure of the smart card and the corresponding security access control has been discussed in this seminar. It is believed that smart cards offer more security and confidentiality than the other kinds of information or transaction storage. Moreover, applications applied with smart card technologies are illustrated which demonstrate smart card is one of the best solutions to provide and enhance their system with security and integrity. The seminar also covers the contactless type smart card briefly. Different kinds of scheme to organise and access of multiple application smart card are discussed. The first and second schemes are practical and workable on these days, and there is real applications developed using those models. For the third one, multiple independent applications in a single card, there is still a long way to go to make it becomes feasible because of several reasons.

At the end of the paper, an overview of the attack techniques on the smart card is discussed as well. Having those attacks does not mean that smart card is unsecure. It is important to realise that attacks against any secure systems are nothing new or unique. Any systems or technologies claiming 100% secure are irresponsible. The main consideration of determining whether a system is secure or not depends on whether the level of security can meet the requirement of the system.

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The newest standard in Wireless LAN is called 802.11n. 802.11 is an industry standard for high-speed networking. 802.11n is designed to replace the 802.11a, 802.11b and 802.11g standards. 802.11n equipment is backward compatible with older 802.11gab and it supports much faster wireless connections over longer distances. So-called “Wireless N” or “Draft N” routers available today are based on a preliminary version of the 802.11n. The beta version of this standard is used now in laptops and routers. 802.11n will work by utilizing multiple input multiple output (MIMO) antennas and channel bounding in tandem to transmit and receive data. It contains at least 2 antennas for transmitting data’s. 802.11n will support bandwidth greater than 100 Mbps and in theory it can have a speed of 600 Mbps.It can be used in high speed internets, VOIP, Network Attach Storage (NAS), gaming. The full version will be implemented in the laptops and in the LANs in upcoming years.

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Context-dependent behavior is becoming increasingly important for a wide range of application domains, from pervasive computing to common business applications. Unfortunately, mainstream programming languages do not provide mechanisms that enable software entities to adapt their behavior dynamically to the current execution context. This leads developers to adopt convoluted designs to achieve the necessary runtime exibility. We propose a new programming technique called Context-oriented Programming (COP) which addresses this problem. COP treats context explicitly, and provides mechanisms to dynamically adapt behavior in reaction to changes in context, even after system deployment at runtime. In this paper, we lay the foundations of COP, show how dynamic layer activation enables multi-dimensional dispatch, illustrate the application of COP by examples in several language extensions, and demonstrate that COP is largely independent of other commitments to programming style.

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The Smart Note Taker is such a helpful product that satisfies the needs of the people in today’s technologic and fast life. This product can be used in many ways. The Smart NoteTaker provides taking fast and easy notes to people who are busy one’s self with something. With the help of Smart Note Taker, people will be able to write notes on the air, while being busy with their work. The written note will be stored on the memory chip of the pen, and will be able to read in digital medium after the job has done. This will save time and facilitate life.

The Smart NoteTaker is good and helpful for blinds that think and write freely. Another place, where our product can play an important role, is where two people talks on the phone. The subscribers are apart from each other while their talk, and they may want to use figures or texts to understand themselves better. It’s also useful especially for instructors in presentations. The instructors may not want to present the lecture in front of the board. The drawn figure can be processed and directly sent to the server computer in the room. The server computer then can broadcast the drawn shape through network to all of the computers which are present in the room. By this way, the lectures are aimed to be more efficient and fun. This product will be simple but powerful. The product will be able to sense 3D shapes and motions that user tries to draw. The sensed information will be processed and transferred to the memory chip and then will be monitored on the display device. The drawn shape then can be broadcasted to the network or sent to a mobile device.

There will be an additional feature of the product which will monitor the notes, which were taken before, on the application program used in the computer. This application program can be a word document or an image file. Then, the sensed figures that were drawn onto the air will be recognized and by the help of the software program we will write, the desired character will be printed in the word document. If the application program is a paint related program, then the most similar shape will be chosen by the program and then will be printed on the screen.

Since, JAVA Applet is suitable for both the drawings and strings, all these applications can be put together by developing a single JAVA program. The JAVA code that we will develop will also be installed on the pen so that the processor inside the pen will type and draw the desired shape or text on the display panel.

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