Statistical signal extraction using stable processes

The standard models for statistical signal extraction assume that the signal and noise are generated by linear Gaussian processes. The optimum filter weights for those models are derived using the method of minimum mean square error. In the present work we study the properties of signal extraction models under the assumption that signal/noise are generated by symmetric stable processes. The optimum filter is obtained by the method of minimum dispersion. The performance of the new filter is compared with their Gaussian counterparts by simulation.

Aqueous Sol-gel Process for Nanocrystalline Photocatalytic Titania, Transparent Functional Coatings and Ceramic Membrane

The present thesis develops from the point of view of titania sol-gel chemistry and an attempt is made to address the modification of the process for better photoactive titania by selective doping and also demonstration of utilization of the process for the preparation of supported membranes and self cleaning films.A general introduction to nanomaterials, nanocrystalline titania and sol-gel chemistry are presented in the first chapter. A brief and updated literature review on sol-gel titania, with special emphasis on catalytic and photocatalytic properties and anatase to rutile transformation are covered. Based on critical assessment of the reported information the present research problem has been defined.The second chapter describes a new aqueous sol-gel method for the preparation of nanocrystalline titania using titanyl sulphate as precursor. This approach is novel since no earlier work has been reported in the same lines proposed here. The sol-gel process has been followed at each step using particle size, zeta potential measurements on the sol and thermal analysis of the resultant gel. The prepared powders were then characterized using X-ray diffraction, FTIR, BET surface area analysis and transmission electron microscopy.The third chapter presents a detailed discussion on the physico-chemical characterization of the aqueous sol-gel derived doped titania. The effect of dopants such as tantalum, gadolinium and ytterbium on the anatase to rutile phase transformation, surface area as well as their influence on photoactivity is also included. The fourth chapter demonstrates application of the aqueous sol-gel method in developing titania coatings on porous alumina substrates for controlling the poresize for use as membrane elements in ultrafiltration. Thin coatings having ~50 nm thickness and transparency of ~90% developed on glass surface were tested successfully for self cleaning applications.

Synthesis of Bio-Compatible SPION–based Aqueous Ferrofluids and Evaluation of RadioFrequency Power Loss for Magnetic Hyperthermia

Bio-compatible magnetic fluids having high saturation magnetization find immense applications in various biomedical fields. Aqueous ferrofluids of superparamagnetic iron oxide nanoparticles with narrow size distribution, high shelf life and good stability is realized by controlled chemical co-precipitation process. The crystal structure is verified by X-ray diffraction technique. Particle sizes are evaluated by employing Transmission electron microscopy. Room temperature and low-temperature magnetic measurements were carried out with Superconducting Quantum Interference Device. The fluid exhibits good magnetic response even at very high dilution (6.28 mg/cc). This is an advantage for biomedical applications, since only a small amount of iron is to be metabolised by body organs. Magnetic field induced transmission measurements carried out at photon energy of diode laser (670 nm) exhibited excellent linear dichroism. Based on the structural and magnetic measurements, the power loss for the magnetic nanoparticles under study is evaluated over a range of radiofrequencies.

Optimization of Process Parameters for the Extraction of Gelatin from the Skin of Freshwater Fish and the Evaluation of Physical anal Chemical Characteristics

Faculty of Marine Sciences,Cochin University of Science and Technology

“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

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

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Anti–white spot syndrome virus activity of Ceriops tagal aqueous extract in giant tiger shrimp Penaeus monodon

White spot syndrome virus (WSSV), the most contagious pathogen of cultured shrimp, causes mass mortality, leading to huge economic loss to the shrimp industry. The lack of effective therapeutic or prophylactic measures has aggravated the situation, necessitating the development of antiviral agents. With this objective, the antiviral activity in the aqueous extract of a mangrove plant Ceriops tagal in Penaeus monodon was evaluated. The Ceriops tagal aqueous extract (CTAE) was non-toxic to shrimps at 50 mg/ml when injected intramuscularly at a dosage of 10 lL/animal (0.5 mg/animal) and showed a protective effect against WSSV at 30 mg/ml when mixed with WSSV suspension at a 1:1 ratio. When the extract was administered along with the diet and the animals were challenged orally, there was a dose-dependent increase in survival, culminating in 100 % survival at a concentration of 500 mg/kg body weight/day. Neither hypertrophied nuclei nor the viral envelope protein VP28 could be demonstrated in surviving shrimps using histology and indirect immunofluorescence histochemistry (IIFH), respectively. To elucidate the mode of action, the temporal expression of WSSV genes and shrimp immune genes, including antimicrobial peptides, was attempted. None of the viral genes were found to be expressed in shrimps that were fed with the extract and challenged or in those that were administered CTAE-exposed WSSV. The overall results suggest that the aqueous extract from C. tagal can protect P. monodon from white spot syndrome virus infection.

Potential application of β-1, 3 glucanase from an environmental isolate of Pseudomonas aeruginosa MCCB 123 in fungal DNA extraction

Pseudomonas aeruginosa MCCB 123 was grown in a synthetic medium for β-1,3 glucanase production. From the culture filtrate, β-1,3 glucanase was purified with a molecular mass of 45 kDa. The enzyme was a metallozyme as its β-1,3 glucanase activity got inhibited by the metal chelator EDTA. Optimum pH and temperature for β-1,3 glucanase activity on laminarin was found to be 7 and 50 °C respectively. The MCCB 123 β-1,3 glucanase was found to have good lytic action on a wide range of fungal isolates, and hence its application in fungal DNA extraction was evaluated. β-1,3 glucanase purified from the culture supernatant of P. aeruginosa MCCB 123 could be used for the extraction of fungal DNA without the addition of any other reagents generally used. Optimum pH and temperature of enzyme for fungal DNA extraction was found to be 7 and 65 °C respectively. This is the first report on β-1,3 glucanase employed in fungal DNA extraction

Ontology based Framework for News Extraction in Visual Media

Anticipating the increase in video information in future, archiving of news is an important activity in the visual media industry. When the volume of archives increases, it will be difficult for journalists to find the appropriate content using current search tools. This paper provides the details of the study we conducted about the news extraction systems used in different news channels in Kerala. Semantic web technologies can be used effectively since news archiving share many of the characteristics and problems of WWW. Since visual news archives of different media resources follow different metadata standards, interoperability between the resources is also an issue. World Wide Web Consortium has proposed a draft for an ontology framework for media resource which addresses the intercompatiblity issues. In this paper, the w3c proposed framework and its drawbacks is also discussed

Automatic Optic Disc Boundary Extraction from Color Fundus Images

Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases

Theoretical study of the physicochemical properties of the light transactinides

A theoretical study of the physicochemical properties of elements 104, 105, and 106 and their compounds in the gas phase and aqueous solutions has been undertaken using relativistic atomic and molecular codes. Trends in properties such as bonding, ionization potentials, electron affinities, energies of electronic transitions, stabilities of oxidation states etc. have been defined within the corresponding chemical groups and within the transactinides. These trends are shown to be determined by increasing relativistic effects within the groups. The behaviour of some gas phase compounds and complexes in solutions is predicted for the gas chromatography and solvent extraction experiments. Redox potentials in aqueous solutions of these elements are estimated.

Redox reactions of group 5 elements, including element 105, in aqueous solutions

Standard redox potentials E^0(M^z+x/M^z+) in acidic solutions for group 5 elements including element 105 (Ha) and the actinide, Pa, have been estimated on the basis of the ionization potentials calculated via the multiconfiguration Dirac-Fock method. Stability of the pentavalent state was shown to increase along the group from V to Ha, while that of the tetra- and trivalent states decreases in this direction. Our estimates have shown no extra stability of the trivalent state of hahnium. Element 105 should form mixed-valence complexes by analogy with Nb due to the similar values of their potentials E^0(M^3+/M^2+). The stability of the maximumoxidation state of the elements decreases in the direction 103 > 104 > 105.