Optimization of Preparation Techniques and Dielectric Study of Polyanilines in the Microwave & High Frequency Field

The thesis deals with the preparation and dielectric characterization of Poly aniline and its analogues in ISM band frequency of 2-4 GHz that includes part of the microwave region (300 MHz to 300 GHz) of the electromagnetic spectrum and an initial dielectric study in the high frequency [O.05MHz-13 MHz]. PolyaniIine has been synthesized by an in situ doping reaction under different temperature and in the presence of inorganic dopants such as HCl H2S04, HN03, HCl04 and organic dopants such as camphorsulphonic acid [CSA], toluenesulphonic acid {TSA) and naphthalenesulphonic acid [NSA]. The variation in dielectric properties with change in reaction temperature, dopants and frequency has been studied. The effect of codopants and microemulsions on the dielectric properties has also been studied in the ISM band. The ISM band of frequencies (2-4 GHz) is of great utility in Industrial, Scientific and Medical (ISM) applications. Microwave heating is a very efficient method of heating dielectric materials and is extensively used in industrial as well as household heating applications.

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.

Studies on lipid nutrition in larvae and juveniles of the indian white prawn penaeus indicus h. milne edwards

Recent studies with prawns indicate that their growth, metamorphosis, maturation and moulting are affected by the typo and level of lipids supplied in the diets. Despite the recognition of the importance of lipids in the diets of prawns there is no information on the essentiality and quantitative lipid requirements of Indian penaeid prawns. Therefore during the present study about 24 laboratory experiments were conducted to determine the essentiality and dietary requirements of total lipids, phoapholipida, fatty acids cholestrol, and to ascertain the nutritional value of natural lipid sources for the larvae, post-larvae tad juveniles of one of the most suitable cultivable species of panaeid prans. All the experiments were conducted in the laboratory following standard procedures, using isonitrogen and approaximately isocaloric purified diets. Changes were made in tha ingridients as required for specific requirements. For the larvae diets of particle size < 37) were fed, For the postlarvae and juveniles pellet feed was given. while data on survival and growth of larvae and pout-larvae 1-10 were recorded, data were collected on the survival, growth, food conversion ratio, protein efficiency ratio and biochemical composition at the body for post-larva 11-25 and juveniles. The influence ot fatty acid pattern of dietary lipid sources on the fatty acids profile of prawns were also studied in the case of juvenile prawns. Analysis of variance andleast significant differences test were employed to determine the significant differences between treatments in the observed parameters with the help of a newlett Packard master computer.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.