SEMANTIC EVOLUTION

Semantic evolution is a form of adaptation. Before we develop concepts for semantic evolution, we give a set of basic definitions for adaptation.
1. Adaptation definition
Adaptation of a software system (S) is caused by change (dE) from an old environment (E) to a new environment (E.), and results in a new system (S.) that ideally meets the needs of its new environment (E.). Adaptation involves three tasks:

· Ability to recognize dE.
· Ability to determine the change ds to be made to the system S according to dE.
· Ability to effect the change in order to generate the new system S.

Adaptability then refers to the ability of the system to make adaptation.
2. Semantic evolution
System S evolves semantically when the evolved system S. responds differently to the same input or accepts a different set of inputs. In this application domain, the inputs are the commands sent to the ES by EC and the responses are those strings received by EC from ES.

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Remotely Controlled Embedded Systems

Evolution of a software system is a natural process. In many systems evolution occurs during the working phase of their lifecycles. Such systems need to be designed to evolve, i.e., adaptable. Semantically adaptable systems are of particular interest to industry as such systems adapt themselves to environmental change with little or no intervention from their developers. Research in embedded systems is now becoming widespread but developing semantically adaptable embedded systems presents challenges of its own. Embedded systems usually have a restricted hardware configuration, hence techniques developed for other types of systems cannot be directly applied to embedded systems. This paper briefly presents the work done in semantic adaptation of embedded systems, using remotely controlled embedded systems as an application. In this domain, an embedded system is connected to an external controller via a communication link such as ethernet, serial, radio frequency, etc., and receives commands from, and sends responses to, the external controller. Techniques for semantic evolution in this application domain give a glimpse of the complexity involved in tackling the problem of semantic evolution in embedded systems. The techniques developed in this paper were validated by applying them in a real embedded system - a test instrument used for testing cell phones.

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ASYMMETRIC DIGITAL SUBSCRIBER LINE TECHNOLOGY

Asymmetric digital subscriber line (ADSL) uses existing twisted pair telephone lines to create access paths for high-speed data communications and transmits at speeds up to 8.1Mbps to a subscriber. This exciting technology is in the process of overcoming the technology limits of the public telephone network by enabling the delivery of high speed Internet access to the vast majority of subscribers’ homes at a very affordable cost. ADSL can literally transform the existing public information network from one limited to voice, text, and low-resolution graphics to a powerful, ubiquitous system capable of bringing multimedia, including full motion video, to every home this century. New broadband cabling will take decades to reach all prospective subscribers. Success of these new services will depend on reaching as many subscribers as possible during the first few years. By bringing movies, television, video catalogs, remote CD-ROMs, corporate LANs, and the Internet into homes and small businesses, ADSL will make these markets viable and profitable for telephone companies and application suppliers alike.

ADSL technology is asymmetric. It allows more bandwidth downstream---from an NSP's central office to the customer site---than upstream from the subscriber to the central office. This asymmetry combined with always-on access (which eliminates call setup), makes ADSL ideal for Internet/intranet surfing, video-on-demand, and remote LAN access. Users of these applications typically download much more information than they send. ADSL transmits more than 6 Mbps to a subscriber and as much as 640 kbps more in both directions (shown below). Such rates expand existing access capacity by a factor of 50 or more without new cabling.

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WORKING OF ARTIFICIAL VISION SYSTEM:

The main parts of this system are miniature video camera, a signal processor, and the brain implants. The tiny pinhole camera, mounted on a pair of eyeglasses, captures the scene in front of the wearer and sends it to a small computer on the patient's belt. The processor translates the image into a series of signals that the brain can understand, and then sends the information to the brain implant that is placed in patient’s visual cortex. And, if everything goes according to plan, the brain will "see" the image Light enters the camera, which then sends the image to a wireless wallet-sized computer for processing. The computer transmits this information to an infrared LED screen on the goggles. The goggles reflect an infrared image into the eye and on to the retinal chip, stimulating photodiodes on the chip. The photodiodes mimic the retinal cells by converting light into electrical signals, which are then transmitted by cells in the inner retina via nerve pulses to the brain. The goggles are transparent so if the user still has some vision, they can match that with the new information - the device would cover about 10° of the wearer’s field of vision.

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ARTIFICIAL VISION

Blindness is more feared by the public than any other ailment. Artificial vision for the blind was once the stuff of science fiction. But now, a limited form of artificial vision is a reality .Now we are at the beginning of the end of blindness with this type of technology. In an effort to illuminate the perpetually dark world of the blind, researchers are turning to technology. They are investigating several electronic-based strategies designed to bypass various defects or missing links along the brain's image processing pathway and provide some form of artificial sight.
HOW TO CREATE ARTIFICIAL VISION
The current path that scientists are taking to create artificial vision received a jolt in 1988, when Dr. Mark Humayun demonstrated that a blind person could be made to see light by stimulating the nerve ganglia behind the retina with an electrical current. This test proved that the nerves behind the retina still functioned even when the retina had degenerated. Based on this information, scientists set out to create a device that could translate images and electrical pulses that could restore vision. Today, such a device is very close to be available to the millions of people who have lost their vision to retinal disease. In fact, there are at least two silicon microchip devices that are being developed. The concept for both devices is similar, with each being:
Small enough to be implanted in the eye
Supplied with a continuous source of power
Biocompatible with the surrounding eye tissue

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SMART SENSOR FOR LABELING

In keeping pace with the fast emerging global trends, we have introduced stickers (self-adhesive) labeling machines, suitable for almost all sizes and types of containers.
The sticker labeling machines are the first of their king in India. The sticker labeling machine eliminates most of the disadvantages of the wet glue labeling machines. The sticker labeling machine are 100% user friendly, virtually maintenance free, do not require any data input/retrieval for label size and coding impression. The sticker labeling machines have built-in no bottle-no label system, production counter and synchronized on-line speed control system. The machines are compatible for any contact coding and inkjet coding system.
The machine settings are easy, and can be made by the operating staff with out much fuss. More over this sticker labeling machines will have speeds ranging from 50 to 400 labels per minute. In the following pages, we are going to describe the methods for proper setting and trouble shooting.
APPLICATIONS:
These can label nearly up to 400 containers per minute.
These sensors are very compact and can be fixed any where, it is of size 200mm-400mm.
These are controlled and sensed by simple photodiodes.
These sensors can label top, bottom, top/bottom, front/back, side and wrap around.
These sensors can label with width 8mm and maximum 90-180mm. These can handle up to outer diameter 400mm max and inner diameter 70/76mm
At last many of the systems were introduced by various companies but we introduce a smart technique for the sensor by which the labeling of the container makes task easier and effective. This smart sensor is very co-friendly and above all since its construction is very simple it is inexpensive. So this labeling sensor will be of very good use to the pharmaceutical industries which manufacture their chemicals and pack them in the containers which are to be labeled.

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CHARACTER RECOGNITION METHODS

1 Template Matching and Correlation Techniques

In 1929 Tausheck obtained a patent on OCR in Germany and this is the first conceived idea of an OCR. Their approach was, what is referred to as template matching in the literature. The template matching process can be roughly divided into two sub processes, i.e. superimposing an input shape on a template and measuring the degree of coincidence between the input shape and the template. The template, which matches most closely with the unknown, provides recognition. The two-dimensional template matching is very sensitive to noise and difficult to adapt to a different font. A variation of template matching approach is to test only selected pixels and employ a decision tree for further analysis. Peephole method is one of the simplest methods based on selected pixels matching approach. In this approach, the main difficulty lies in selecting the invariant discriminating set of pixels for the alphabet. Moreover, from an Artificial Intelligence perspective, template matching has been ruled out as an explanation for human performance [1, 2].

2 Features Derived from the Statistical Distribution of Points

This technique is based on matching on feature planes or spaces, which are distributed on an n-dimensional plane where n is the number of features. This approach is referred to as statistical or decision theoretic approach. Unlike template matching where an input character is directly compared with a standard set of stored prototypes. Many samples of a pattern are used for collecting statistics. This phase is known as the training phase. The objective is to expose the system to natural variants of a character. Recognition process uses this statistics for identifying an unknown character. The objective is to expose the system to natural variants of a character. The recognition process uses this statistics for partitioning the feature space. For instance, in the K-L expansion one of the first attempt in statistical feature extraction, orthogonal vectors are generated from a data set. For the vectors, the covariance matrix is constructed and its eigenvectors are solved which form the coordinates of the given pattern space. Initially, the correlation was pixel-based which led to large number of covariance matrices. This approach was further refined to the use of class-based correlation instead of pixel-based one which led to compact space size. However, this approach was very sensitive to noise and variation in stroke thickness. To make the approach tolerant to variation and noise, a tree structure was used for making a decision and multiple prototypes were stored for each class. Researchers for classification have used the Fourier series expansions, Walsh, Haar, and Hadamard series expansion.

3 Geometrical and Topological Features

The classifier is expected to recognize the natural variants of a character but discriminate between similar looking characters such as ‘k’ – ‘ph’, ‘p’ - ‘Sh’ etc. This is a contradicting requirement which makes the classification task challenging. The structural approach has the capability of meeting this requirement. The multiple prototypes are stored for each class, to take care of the natural variants of the character. However, a large number of prototypes for the same class are required to cover the natural variants when the prototypes are generated automatically. In contrast, the descriptions may be handcrafted and a suitable matching strategy incorporating expected variations is relied upon to yield the true class. The matching strategies include dynamic programming, test for isomorphism, inexact matching, relaxation techniques and multiple to-one matching. Rocha have used a conceptual model of variations and noise along with multiple to one mapping. Yet another class of structural approach is to use a phrase structured grammar for prototype descriptions and parse the unknown pattern syntactically using the grammar. Here the terminal symbols of the grammar are the primitives of strokes and non-terminals represent the pattern-classes. The production rules give the spatial relationships of the constituent primitives.

4 Hybrid Approach

The statistical approach and structural approach both have their advantages and shortcomings. The statistical features are more tolerant to noise (provided the sample space over which training has been performed is representative and realistic) than structural descriptions. Whereas, the variation due to font or writing style can be more easily abstracted in structural descriptions. Two approaches are complimentary in terms of their strengths and have been combined. The primitives have to be ultimately classified using a statistical approach. Combine the approaches by mapping variable length, unordered sets of geometrical shapes to fixed length numerical vectors. This approach, the hybrid approach, has been used for omni font, variable size character recognition systems.
5 Neural Networks
In the beginning, character recognition was regarded as a problem, which could be easily solved. But the problem turned out to be more challenging than the expectations of most of the researchers in this field. The challenge still exists and an unconstrained document recognition system matching human performance is still nowhere in the sight. The performance of a system deteriorates very rapidly with deterioration in the quality of the input or with the introduction of new fonts handwriting. In other words, the systems do not adapt to the changed environment easily. Training phase aims at exposing the system to a large number of fonts and their natural variants. The neural networks are based on the theory of learning from the known inputs. A back propagation neural network is composed of several layers of interconnected elements. Each element computes an output, which is a function of weighted sum of its inputs. The weights are modified until a desired output is obtained. The neural networks have been employed for character recognition with varying degree of success. The neural networks are employed for integrating the results of the classifiers by adjusting weights to obtain desired output. The main weakness of the systems based on neural networks is their poor capability for generality. There is always a chance of under training or over training the system. Besides this, a neural network does not provide structural description, which is vital from artificial intelligence viewpoint. The neural network approach has solved the problem of character classification no more than the earlier described approaches. The recent research results call for the use of multiple features and intelligent ways of combining them. The combination of potentially conflicting decisions by multiple classifiers should take advantage of the strength of the individual classifier, avoid their weaknesses and improve the classification accuracy. The intersection and union of decision regions are the two most obvious methods for classification combination.

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