Ecological and Biochemical Studies On Cyanobacteria of Cochin Estuary and Their Application as Source of Antioxidants

The main objectives of the present investigation were to evaluate the qualitative and quantitative distribution of natural cyanobacterial population and their ecobiological properties along the Cochin estuary and their application in aquaculture systems as a nutritional supplement due to their nutrient-rich biochemical composition and antioxidant potential. This thesis presents a detailed account of the distribution of cyanobacteria in Cochin estuary, an assessment of physico-chemical parameters and the nutrients of the study site, an evaluation of the effect of physico-chemical parameters on cyanobacterial distribution and abundance, isolation, identification and culturing of cyanobacteria, the biochemical composition an productivity of cyanobacteria, and an evaluation of the potential of the selected cyanobacteria as antioxidants against ethanol induced lipid peroxidation. The pH, salinity and nutritional requirements were optimized for low-cost production of the selected cyanobacterial strains. The present study provides an insight into the distribution, abundance, diversity and ecology of cyanobacteria of Cochin estuary. From the results, it is evident that the ecological conditions of Cochin estuary support a rich cyanobacterial growth.

Ecological characteristics of prawn culture fields in the Cochin area

Prawn culture by traditional method forms an important occupation for the people in these areas, especially in the Vypeen island. Though short term studies have been made on various aspects of prawn culture field and its ecology, a study of detailed nature covering perennial, seasonal, fields and canals between coconut plantation is lacking from these areas. This study will also enable to assess the relative productivity of different systems during different seasons and the influence of the environment on the production potentials. Therefore the present study is taken upto throw more light on the ecological characteristics of these fields with special emphasis on its primary, secondary and tertiary production. The present area of investigation includes the prawn culture fields adjacent to Cochin backwater. The Cochin backwater (O9° 58'N 76° 28'E) is a shallow semi-enclosed body of water of tropical estuary. A narrow gut, about 450 M wide forms its main connection with the Arabian sea and this region is subjected to regular tidal influenceertiary production.

Fish Driven Bioturbation as a Viable Aquaculture Technology for Increasing Production and Improving Ecological Sustainability of Giant Tiger Shrimp Penaeus Monodon (FABRlClUS)

A general introduction to the problems faced in the shrimp culture due to waste formation and its consequent environmental hazards and production problems of Giant tiger shrimp, Penaeus monodon is highlighted by the author in this thesis. The objective of the present work was to assess the potential of brackish water finfish to improve bottom soil conditions and thereby increase the growth and production of Penaeus monodon. The salient findings of the present study are summarized in chapter 7. This is followed by the references cited in the thesis and list ofpublications originated from the present study.

Nature and Ecological significance of Nutrient Regeneration in Different Prawn Culture Fields

The present work "Nature and Ecological Significance of Nutrient Regeneration in different Prawn Culture Fields" was undertaken to understand the seasonal variation of nutrients, nutrient cycling and primary productivity of the prawn culture systems. The main emphasis was to find the qualitative and quantitative estimates of distribution of total phosphorus, inorganic phosphorus, organic phosphorus, total nitrogen and nitrogen fractions in the water. The effect of nutrient cycling on primary productivity and concentration of metals also form one part of the study. The entire thesis comprise of only one major chapter with subchapters such as, Introduction (I), Review of Literature (2), Material and Methods (3), Results (14), Discussion (5), Executive Summary (6) and Biblio~ graphy (7)

“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.

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

Effectiveness Of Feature Detection Operators On The Performance Of Iris Biometric Recognition System

Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.

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.

A Comparative Study of Wavelet Based Feature Extraction Techniques in Recognizing Isolated Spoken Words

Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. This work includes two speech recognition methods. First one is a hybrid approach with Discrete Wavelet Transforms and Artificial Neural Networks and the second method uses a combination of Wavelet Packet Decomposition and Artificial Neural Networks. Features are extracted by using Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results obtained show the efficiency of these techniques in recognizing speech

FEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING

Treating e-mail filtering as a binary text classification problem, researchers have applied several statistical learning algorithms to email corpora with promising results. This paper examines the performance of a Naive Bayes classifier using different approaches to feature selection and tokenization on different email corpora

Distribution and bioactive potential of soil actinomycetes from different ecological habitats

Emergence of drug resistance among pathogenic bacteria to currently available antibiotics has intensified the search for novel bioactive compounds from unexplored habitats. In the present study actinomycetes were isolated from two relatively unexplored and widely differing habitats such as mountain and wetlands and their ability to produce antibacterial substances were analyzed. Pure cultures of actinomycetes were identified by morphological and biochemical tests. Various genera of actinomycetes encountered included Nocardia, Pseudonocardia, Streptomyces, Nocardiopsis, Streptosporangium, Micromonospora, Rhodococcus, Actinosynnema, Nocardiodes, Kitasatosporia, Gordona, Intrasporangium and Streptoalloteichus. The frequency of occurrence of each genus was found to vary with sample. About 47% of wetland isolates and 33% of mountain isolates were identified as various species of Nocardia. The isolated strains differed among themselves in their ability to decompose proteins and amino acids and also in enzyme production potential. Antibiotic activities of these actinomycetes were evaluated against 12 test pathogenic bacteria by well diffusion method using agar wells in glycerol-yeast extract agar. About 95% of actinomycete isolates from wetland ecosystem and 75% of highland isolates suppressed in different degrees the growth of test pathogens. Relatively high antibacterial activity among these isolates underlined their potential as a source of novel antibiotics.

Antagonistic effect of Pseudomonas aeruginosa isolates from various ecological niches on Vibrio species pathogenic to crustaceans

This is a valuable research work in which authors have demonstrated the antagonistic effects of pseudomonas on the growth of vibrio

Ecological studies of the Parambikulam Tiger Reserve in the Western Ghats of India, using Remote Sensing and GIS

mbikulam Tiger Reserve of Western Ghats using Geospatial technology. The major objectives of the study are Land use land cover mapping (LULC) and Phytodiversity analysis. Satellite data was used to map the land use / land cover using supervised classification techniques in Erdas imagine. The change for a period of 32 years was assessed using the multi-temporal satellite datasets from Landsat MSS (1973), Landsat TM (1990), and IRS P6 LISS III (2005). A geospatial approach was used for the land cover analysis. Digital elevation models, Satellite imageries and SOI topo sheets were the data sets used in the analysis. Vegetation sampling plots distributed over the different forest types were enumerated and studied for Phytodiversity analysis.

Multifinger Feature Level Fusion Based Fingerprint Identification

Fingerprint based authentication systems are one of the cost-effective biometric authentication techniques employed for personal identification. As the data base population increases, fast identification/recognition algorithms are required with high accuracy. Accuracy can be increased using multimodal evidences collected by multiple biometric traits. In this work, consecutive fingerprint images are taken, global singularities are located using directional field strength and their local orientation vector is formulated with respect to the base line of the finger. Feature level fusion is carried out and a 32 element feature template is obtained. A matching score is formulated for the identification and 100% accuracy was obtained for a database of 300 persons. The polygonal feature vector helps to reduce the size of the feature database from the present 70-100 minutiae features to just 32 features and also a lower matching threshold can be fixed compared to single finger based identification