FPGA-based enhanced probabilistic convergent weightless network for human iris recognition

This paper investigates how human identification and identity verification can be performed by the application of an FPGA based weightless neural network, entitled the Enhanced Probabilistic Convergent Neural Network (EPCN), to the iris biometric modality. The human iris is processed for feature vectors which will be employed for formation of connectivity, during learning and subsequent recognition. The pre-processing of the iris, prior to EPCN training, is very minimal. Structural modifications were also made to the Random Access Memory (RAM) based neural network which enhances its robustness when applied in real-time

An advanced combination strategy for multi-classifiers employed in large multi-class problem domains

Traditional artificial neural architectures possess limited ability to address the scale problem exhibited by a large number of distinct pattern classes and limited training data. To address these problems, this paper explores a novel advanced encoding scheme, which reduces both memory demand and execution time, and provides improved performance. The novel advanced encoding scheme known as the engine encoding, have been implemented in a multi-classifier, which combines the scaled probabilities, configuration information, and the discriminating abilities of the associated component classifiers. The problems of overloading and saturation experienced by traditional networks are solved by training the base classifiers on differing sub-sets of the required pattern classes and allowing the combiner classifier to derive a solution. Current Multi-classifier Systems are easily biased when trained on one class more often than another class, when patterns representing a class are very large compared to the rest, or when the multi-classifier depends on a certain fixed order of arrangement of pattern classes. A unique statistical arrangement method is hereby presented which aims to solve the bias problem. This statistical arrangement method also enhances independence of component classifiers. The system is demonstrated on the exemplar of fingerprint identification and utilizes a Weightless Neural System called the Enhanced Probabilistic Convergent Neural Network (EPCN) in a Multi-classifier System. © 2010 Elsevier B.V. All rights reserved

A novel potential field algorithm and an intelligent multi-classifier for the automated control and guidance system (ACOS)

The ACOS project seeks to improve and develop novel robot guidance and control systems integrating Novel Potential Field autonomous navigation techniques, multi-classifier design with direct hardware implementation. The project development brings together a number of complementary technologies to form an overall enhanced system. The work is aimed at guidance and collision avoidance control systems for applications in air, land and water based vehicles for passengers and freight. Specifically, the paper addresses the generic nature of the previously presented novel Potential Field Algorithm based on the combination of the associated rule based mathematical algorithm and the concept of potential field. The generic nature of the algorithm allows it to be efficient, not only when applied to multi-autonomous robots, but also when applied to collision avoidance between a single autonomous agent and an obstacle displaying random velocity. In addition, the mathematical complexity, which is inherent when a large number of autonomous vehicles and dynamic obstacles are present, is reduced via the incorporation of an intelligent weightless multi-classifier system which is also presented