Effect of ambient gas on the expansion dynamics of plasma plume formed by laser blow off of thin film

A study has been carried out to understand the influence of ambient gases on the dynamics of laser-blow-off plumes of multi-layered LiF–C thin film. Plume images at various time intervals ranging from 100 to 3000 ns have been recorded using an intensified CCD camera. Enhancement in the plume intensity and change in size and shape occurs on introducing ambient gases and these changes are highly dependent on the nature and composition of the ambient gas used. Velocity of the plume was found to be higher in helium ambient whereas intensity enhancement is greater in argon environment. The plume shapes have maximum size at 10−2 and 10−1 Torr of Ar and He pressures, respectively. As the background pressure increases further (>10−2 Torr: depending on the nature of gas), the plume gets compressed/focused in the lateral direction. Internal structure formation and turbulences are observed at higher pressures (>10−1 Torr) in both ambient gases.

Application of Imaging to Study the Evolution and Dynamics of Laser Produced Plasma from Solid and Thin Film Li Targets

The present thesis report the results obtained from the studies carried out on the laser blow off plasma (LBO) from LiF-C (Lithium Fluoride with Carbon) thin film target, which is of particular importance in Tokamak plasma diagnostics. Keeping in view of its significance, plasma generated by the irradiation of thin film target by nanosecond laser pulses from an Nd:YAG laser over the thin film target has been characterized by fast photography using intensified CCD. In comparison to other diagnostic techniques, imaging studies provide better understanding of plasma geometry (size, shape, divergence etc) and structural formations inside the plume during different stages of expansion.

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Spatially Adaptive Image Denoising Techniques Using Directionlets

The aim of the thesis was to design and develop spatially adaptive denoising techniques with edge and feature preservation, for images corrupted with additive white Gaussian noise and SAR images affected with speckle noise. Image denoising is a well researched topic. It has found multifaceted applications in our day to day life. Image denoising based on multi resolution analysis using wavelet transform has received considerable attention in recent years. The directionlet based denoising schemes presented in this thesis are effective in preserving the image specific features like edges and contours in denoising. Scope of this research is still open in areas like further optimization in terms of speed and extension of the techniques to other related areas like colour and video image denoising. Such studies would further augment the practical use of these techniques.