9527477 Prabhu This research is funded as an SGER (Small Grant for Exploratory Research). The objective of the project is to enhance the design, development, and implementation of a computer-aided nonlinear discriminant for pattern recognition. The approach will employ techniques from algebraic geometry and dynamic systems. Discriminant design uses a database of feature vectors and partitions the feature space into disjoint regions corresponding to different classes of patterns. Based on the methodology, an algorithm will be developed and implemented. The performance of the algorithm will be benchmarked through a series of tests problems in weather forecasting and production systems for which quick pattern recognition and control are required. To rapidly recognize and act on objects, especially in situations involving automatic assembly and part sortation, are essential qualities for the design and implementation of effective automated production systems. The technique, apart from its nonlinear discriminant nature, is expected to yield some improvement in both speed and accuracy in pattern recognition in general. The development of fast algorithms capable of processing large amounts of data in very short time and with high accuracy is essential for large scale adoption of automatic pattern recognition techniques in a host of applications. The outcome of this research has the potential to advance the state-of-the-art in this critical field.
