Investigation of the Sequential Accelerator on LDA for Pattern Recognition

In machine learning methods, when the input data becomes extremely large, the current direct methods
require too large learning times and memory. This project investigates one sequential method to overcome this
problem. It is quite a simple method to implement and is tested using the well-known LDA classifier. The student
will try various investigations on different very large data sets and to measure their computational complexities. It
has been shown that this sequential method is very fast and only need a small subset of the large data set to
complete the learning.

Equalization of Fading Channels using RBF Networks

Equalization of Fading Channels using RBF Networks
Summary: Equalization is the process of recovering the true input data from the received data which is corrupted
by noise and passes through a nonlinear communication channel. Earlier work by the supervisor and his group has
successfully used RBF neural networks for complicated nonlinear channels that are stationary. In this project, the
performance of the RBF equalizers will be investigated for time varying ( fading ) channels ( mainly for Raleigh
Fading Channels) and an implementation scheme for the equalizer will be evolved. Performance comparison with
other conventional equalizers will also be made.

Analysis of the quality of fingerprint image features

The most popular fingerprint feature used for matching is the feature points, called minutiae. Several approaches have been proposed which in a way, involves detecting the skeleton image to locate the minutiae. Subsequently, the geometrical and other information are extracted and used to match the minutiae. However, the performance of the minutiae extraction varies. Problem arises when there is noise or the fingerprint image is not clear. This could cause the introduction of false minutiae or the omission of valid minutiae. In this project, the main aim is to devise methods to analyse the quality of the minutiae so that the overall matching is improved. This
will involve analysing the property of the minutiae to provide suitable confidence level and providing consistent information that can be extracted from the minutiae. Good knowledge in C/C++ or Matlab programming is essential for this project.