Characterization of Photonic Materials Using Thermal Lens Technique

The subject of Photonics is concerned with the generation,control and utilization of photons for performing a variety of tasks.It came to existence as a consequence of the harmonious fusion of optical methods with electronic technology.Wide spread use of laser based methods in electronics is slowly replacing elecrtons with photons in the field of Communication,Control and Computing .Therefore,there is a need to promote the R & D activities in the area of Photonics and to generate well trained manpower in laser related fields.Development and characterization of photonic materials is an important subject of research in the field of Photonics.Optical and thermal characterization of photonic materials using thermal lens technique is a PhD thesis in the field of Photonics in which the author describes how thermal lens effect can be used to characterize themal and optical properties of photonic materials.Plausibility of thermal lens based logic gates is also presented in this thesis.

A Novel Slotless PMBLDC Motor for Precise Positioning Applications

This paper presents the design and analysis of a novel machine family of Siotiess Permanent Magnet Brushless DC motors (PMBLDC) for precise positioning applications of spacecrafts. Initial design, selection of major parameters and air gap magnetic flux density are estimated using the analytical model of the machine. The proportion of the halbach array in the machine was optimized using FE to obtain near trapezoidal flux pattern. The novel machine topology is found to deliver high torque density, high efficiency, zero cogging torque, better positional stability, high torque to inertia ratio and zero magnetic stiction suiting space requirements. The machine provides uniform air gap flux density along the radius thus avoiding circulating currents in stator conductors and hence reducing torque ripple

ECG Noise Removal using GA tuned Sign-Data Least Mean Square Algorithm

Adaptive filter is a primary method to filter Electrocardiogram (ECG), because it does not need the signal statistical characteristics. In this paper, an adaptive filtering technique for denoising the ECG based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This technique minimizes the mean-squared error between the primary input, which is a noisy ECG, and a reference input which can be either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Noise is used as the reference signal in this work. The algorithm was applied to the records from the MIT -BIH Arrhythmia database for removing the baseline wander and 60Hz power line interference. The proposed algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference which is better than the previous reported works

An Improved Technique for Evolving Wavelet Coefficients for Fingerprint Image Compression

In this paper, an improved technique for evolving wavelet coefficients refined for compression and reconstruction of fingerprint images is presented. The FBI fingerprint compression standard [1, 2] uses the cdf 9/7 wavelet filter coefficients. Lifting scheme is an efficient way to represent classical wavelets with fewer filter coefficients [3, 4]. Here Genetic algorithm (GA) is used to evolve better lifting filter coefficients for cdf 9/7 wavelet to compress and reconstruct fingerprint images with better quality. Since the lifting filter coefficients are few in numbers compared to the corresponding classical wavelet filter coefficients, they are evolved at a faster rate using GA. A better reconstructed image quality in terms of Peak-Signal-to-Noise-Ratio (PSNR) is achieved with the best lifting filter coefficients evolved for a compression ratio 16:1. These evolved coefficients perform well for other compression ratios also.