Abstract-Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. It's well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure.Index Terms-Stationary wavelet transform, microarray images, denoising.

The report addresses the digital controller structure problem for the closed loop stability of a feedback digital control system subject to Finite Word Length (FWL). A new method of maximizing the stability subject to perturbations in the digital controller implementation is proposed. The approach is based on structured perturbation theory in an`1 framework. It can be simply extended to consider closed loop performance and robustness. The method is demonstrated with application examples.

The objective of this correspondence is to present an overview of some of the current development and some of the ongoing telemedicine programs in the United Kingdom. The issues of the future integration of telemedicine activities within the National Health Service that promises better access to healthcare with higher efficiency, mobility, and lower cost are also discussed.

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A new de-noising and compression method for ECG sigmls has been developed based on the wavelet transform.It has been designed for mobile telecardiology scenarios, where reliability as well as spectral eficiency are essential. The signal is segmented into beats and a beat template is subtracted to them. Beat templates as well as residual signals are coded with o wavelet expansion. De-noising and compression are achieved by selecting a subset of wavelet coefficients. The number of coefficients depends on the noise level. A SNR impmvement about 5 dB was obtained. Compression performance has been tested using a subset of ECG records from MIT-BlHArrhythmia database. For example, a compression ratio of 35 with a PRD as low as 3.6 % was achievedfor record 119. o z 7~7 r n 3 ~1 7 .~1 o 2w3 IEEE