Speaker identification using models for phonemes

Motivation for Speaker recognition work is presented in the first part of the thesis. An exhaustive survey of past work in this field is also presented. A low cost system not including complex computation has been chosen for implementation. Towards achieving this a PC based system is designed and developed. A front end analog to digital convertor (12 bit) is built and interfaced to a PC. Software to control the ADC and to perform various analytical functions including feature vector evaluation is developed. It is shown that a fixed set of phrases incorporating evenly balanced phonemes is aptly suited for the speaker recognition work at hand. A set of phrases are chosen for recognition. Two new methods are adopted for the feature evaluation. Some new measurements involving a symmetry check method for pitch period detection and ACE‘ are used as featured. Arguments are provided to show the need for a new model for speech production. Starting from heuristic, a knowledge based (KB) speech production model is presented. In this model, a KB provides impulses to a voice producing mechanism and constant correction is applied via a feedback path. It is this correction that differs from speaker to speaker. Methods of defining measurable parameters for use as features are described. Algorithms for speaker recognition are developed and implemented. Two methods are presented. The first is based on the model postulated. Here the entropy on the utterance of a phoneme is evaluated. The transitions of voiced regions are used as speaker dependent features. The second method presented uses features found in other works, but evaluated differently. A knock—out scheme is used to provide the weightage values for the selection of features. Results of implementation are presented which show on an average of 80% recognition. It is also shown that if there are long gaps between sessions, the performance deteriorates and is speaker dependent. Cross recognition percentages are also presented and this in the worst case rises to 30% while the best case is 0%. Suggestions for further work are given in the concluding chapter.

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

Salient Features of the North Indian Ocean Associated with the Indian summer Monsoon

This study deals with the salient features of the north Indian ocean associated with the summer monsoon. The focus is given on the Arabian sea mini warm pool, which is a part of the Indian ocean. It primarily study the certain aspects of the atmosphere and ocean variability in the north Indian ocean. The attempt were made to understand various aspects of time –scale variability of major features occurring in the Indian summer monsoon. The result from the thesis can be utilized as an input for model studies for prediction of monsoon, understanding ocean dynamics, radar tracking and ranging etc.

Salient Features of the North Indian Ocean Associated with the Indian summer Monsoon

This study deals with the salient features of the north Indian ocean associated with the summer monsoon. The focus is given on the Arabian sea mini warm pool, which is a part of the Indian ocean. It primarily study the certain aspects of the atmosphere and ocean variability in the north Indian ocean. The attempt were made to understand various aspects of time –scale variability of major features occurring in the Indian summer monsoon. The result from the thesis can be utilized as an input for model studies for prediction of monsoon, understanding ocean dynamics, radar tracking and ranging etc.

Spectral features of laser induced plasma from YBa2Cu3O7 and GdBa2Cu3O7 highTc superconductors

Laser induced plasma emission spectra from highT c superconducting samples of YBa2Cu3O7 and GdBa2Cu3O7 obtained with 1.06µm radiation from a Q switched Nd:YAG laser beam has been analysed. The results clearly show the presence of diatomic oxides in addition to ionized species of the constituent metals in the plasma thus produced.

A study of the hydrological features of the shelf water along the west coast of India with an attempt to explain their influence upon the living resources of the region

The objective of the present study is to elucidate the hydrological conditions of the shelf waters along the southern half or the west coast of India and their relation to the sooplankton bionase and pelagic fish resources. Data from six hydrography-plankton sections worked during the 1972-75 period of cape camerin. Quilon. cochin. Kasaragod. Karwar and kotnagiri formed the basis of the present study.Stations were fixed along the transects 10 nautical miles apart. Starting with the first station at around 15 metre depth and were usually occupied 5 to 8 times in an year at an interval of about 6 weeks. Data relating to oil sardines and macherel fisheries were availed from published information relating to the period mainly of the Central Marine fisheries Research Institute. The range of different parameters namely temperature. salinity. density and dissolved oxygen at different depths and their sloping features against the coast are discussed. Three seasons. namely south-west monsoon (summer monsoon). north-east monsoon (winter monsoon) and hot weather season are designated and data of the core months of each of these seasons considered in the study.

Studies on bag models and properties of hadrons

This thesis is devoted to theoretical studies on the properties of hadrons on the basis of bag models. It contains some applications of the traditional.HIT bag model to the spectroscopy and decay of hadrons. The inadequacies of the model are brought out and a new version of the model, called the variable pressure bag model, is developed. Some of the Phenomenological applications of this model are discussed and the predictions are compared with experiment.

Application of ocean colour data to study the oceanographic and atmospheric features off the southwest coast of India with special reference to upwelling

Satellite remote sensing is being effectively used in monitoring the ocean surface and its overlying atmosphere. Technical growth in the field of satellite sensors has made satellite measurement an inevitable part of oceanographic and atmospheric research. Among the ocean observing sensors, ocean colour sensors make use of visible band of electromagnetic spectrum (shorter wavelength). The use of shorter wavelength ensures fine spatial resolution of these parameters to depict oceanographic and atmospheric characteristics of any region having significant spaio-temporal variability. Off the southwest coast of India is such an area showing very significant spatio-temporal oceanographic and atmospheric variability due to the seasonally reversing surface winds and currents. Consequently, the region is enriched with features like upwelling, sinking, eddies, fronts, etc. Among them, upwelling brings nutrient-rich waters from subsurface layers to surface layers. During this process primary production enhances, which is measured in ocean colour sensors as high values of Chl a. Vertical attenuation depth of incident solar radiation (Kd) and Aerosol Optical Depth (AOD) are another two parameters provided by ocean colour sensors. Kd is also susceptible to undergo significant seasonal variability due to the changes in the content of Chl a in the water column. Moreover, Kd is affected by sediment transport in the upper layers as the region experiences land drainage resulting from copious rainfall. The wide range of variability of wind speed and direction may also influence the aerosol source / transport and consequently AOD. The present doctoral thesis concentrates on the utility of Chl a, Kd and AODprovided by satellite ocean colour sensors to understand oceanographic and atmospheric variability off the southwest coast of India. The thesis is divided into six Chapters with further subdivisions

Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

Identifying Decisive Features for Distinctive Analysis of Writings in Malayalam

This paper presents a writer identification scheme for Malayalam documents. As the accomplishment rate of a scheme is highly dependent on the features extracted from the documents, the process of feature selection and extraction is highly relevant. The paper describes a set of novel features exclusively for Malayalam language. The features were studied in detail which resulted in a comparative study of all the features. The features are fused to form the feature vector or knowledge vector. This knowledge vector is then used in all the phases of the writer identification scheme. The scheme has been tested on a test bed of 280 writers of which 50 writers having only one page, 215 writers with at least 2 pages and 15 writers with at least 4 pages. To perform a comparative evaluation of the scheme the test is conducted using WD-LBP method also. A recognition rate of around 95% was obtained for the proposed approach

The Effect of SIFT Features as Content Descriptors in the Context of Automatic Writer Identification in Malayalam Language

The span of writer identification extends to broad domes like digital rights administration, forensic expert decisionmaking systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT).The schemes are tested on a test bed of 280 writers and performance evaluated

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.