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.

Neural Network based Color Image Segmentation

Breast cancer is the most common cancer among women, and is the second leading cause of cancer
deaths in women today. Basically, the diagnosis procedure of breast cancer consists of two steps; (1) mammography
based breast abnormality detection; (2) biopsy based diagnosis. Biopsy is the only definitive way to determine
whether cancer is present.
In this project, biopsy color Image segmentation based on neural networks will be studied. The objective of breast
biopsy image segmentation is to segment cells and blood vessels in the image.
As the second part of the project, color image segmentation software will be developed.