Measurement of thermal diffusivity of some halogeno benzimidazole complexes of cobalt(II), copper (II) and copper(I) using laser induced photoacoustic effect

Cochin University of Science and Technology

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.

Amorphous Selenium films using CBD: A Novel Selenisation technique for preparing lndium Selenide and Copper lndium Selenide Thin Films

In the present work, we describe our efforts to develop device quality CuInSe2, films through low cost, simple and eco-friendly hybrid techniques. The most important point to be highlighted here is that the method fully avoids the use of poisonous gases such as H2Se/Se vapour. Instead, selenisation is achieved through solid state reaction between amorphous selenium and polycrystalline metal layers resulting in both binary and ternary selenides. Thin films of amorphous selenium (a-Se) used for this is deposited using Chemical Bath Deposition (CBD). CulnSe2 films are prepared through the selenisation process. Another PV material, indium selenide (In2Se3) thin films are also prepared using this process.

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

Toxicological effects of Copper and Mercury on the fish Macrones gulio (Hamilton-Buchanan)

Asha M. R This thesis Entitled Toxicological effects of copper and mercury on the fish macerones gulio (hamiloton – buchanan).Chapter 1. In this chapter, a broad outline of heavy metal uptake, requirement of a suitable bio — monitoring organism, criteria for a standard test fish, and suitability of Macrones gulig for the toxicological study are given. Chapter 2. This chapter deals with the lethal toxicity bioassays to find the 96 hr LC 50 of copper and mercury for the fish Macrones gglig. The experimental results indicated that of the two metals tested, copper was more toxic than mercury.Chapter 3. The effect of copper and mercury on the haemoglobin, haematocrit, erythrocyte count, MCV, MCH and MCHC was studied.Chapter 4. The glycogen and protein contents of liver and muscle after exposure to copper and mercury were studied. There was a significant decrease of glycogen in the liver and muscle of metal treated fishes.Chapter 5. The histopathological changes of the tissues like liver, kidney and gill after exposure to copper and mercury were studied.

Physiological effects of copper (II) on sunetta scripta L.

In India industrial pollution has become a subject of increasing concern.Incidents of industrial pollution have been reported from many parts of the country. Cochin, the collection site of the present study, being the industrial capital of Kerela is also a harbour, is vulnerable to pollution by trace metal contaminants. In the recent times, pollutants of greatest concern in the aquatic environment are those which are persistent such as toxic heavy metals and the chlorinated hydrocarbons which include insecticides and pesticides.The animals collected from the clam bed situated on the northern side af Cochin bermouth are subject to wide fluctuations in salinity both seasonal and tidal. also; salinity is considered as an important parameter influencing the.-physiological functioning of an organism. Hence, the salinity tolerance of the animal is worked out. Considering the potential vulnerability of Cochin backwaters to heavy metal pollution, the impact of heavy metal copper (II) on the bivalve Sunetta sripta was conceived. Static bioassays were conducted for the determination of the sublethal concentrations of the metal as a preliminary step towards the toxicity studies. Oxygen consumption and filtration rate which are considered as reliable sublethal toxicity indices were employed for investigating the toxic effects of the metal. Bioaccumulation, a physiological phenomenon which can be of importance from the public health point of view, and also in the assessment of environmental quality is also dealt with.

The ligating versatility of halides and pseudohalides in copper(II) complexes- monomers, dimers and chains”

The work embodied in this thesis was carried out by the author in the Department of Applied Chemistry, CUSAT, Kochi, during the period 2008-2013. The thesis brings to light, our attempts to evaluate the coordination behavior of some compounds of interest. The biological activities of semicarbazones and their metal complexes have been an active area of research during the past years because of their significant role in naturally occurring biological systems. Tridentate NNO and ONO semicarbazone systems formed from heterocyclic and aromatic carbonyl compounds and their transition metal complexes are well-authenticated compounds in this field and their synthesis, crystal structures and spectral studies are well desirable. Hence, we decided to develop a research program aimed at the syntheses, crystal structures and spectral studies of copper complexes with halides and pseudohalides. In addition to single crystal X-ray diffraction studies, various physico-chemical methods of analysis were also used for the characterization of the complexes

“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.

"Effects of some heavy metals copper. zinc and lead on certain tissues of £153 Qarsia (Hamilton and Buchanan) in different environments

The research investigations on pollution, particularly in coastal/ estuarine environments are recent ones and started only in 1970s. Hence the informations available are fragmentary and scattered. They throw some light only on either the concentration of heavy metals in water or in sediment or in organisms. No concerted efforts have been made to consolidate and correlate the results between the environment and biota. Literature on the level of concentration of heavy metals in different tissues of organisms with regard to their availability in the living media, their ratio, their inter—relationship, tolerance limit of organisms, etc. are very few or rather nil. in view of the importance enumerated above, the candidate has selected the topic "Effects of some heavy metals copper, zinc and lead on certain tissues of E E (Hamilton and Buchanan) in different environments" for detailed studies and to understand systematically (i) the source of effluents and wastes, (ii) the concentration of heavy metals copper, zinc and lead in water, in sediments and in tissues of the test animal, (iii) their effects, (iv) capacity of tolerance and accumulation in different tissues of the animal, and (V) the "Bioaccumulation Factor", etc.

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.

Copper(II) complexes derived from di-2-pyridyl ketone-N4-phenyl-3-semicarbazone: Synthesis and spectral studies

Five copper(II) complexes [CuLCl]2·CuCl2·4H2O (1), [CuLOAc] (2), [CuLNO3]2 (3), [CuLN3] (4) and [CuLNCS]·3/2H2O (5) of di-2-pyridyl ketone-N4-phenyl-3-semicarbazone (HL) were synthesized and characterized by elemental analyses and electronic, infrared and EPR spectral techniques. In all these complexes the semicarbazone undergoes deprotonation and coordinates through enolate oxygen, azomethine and pyridyl nitrogen atoms. All the complexes are EPR active due to the presence of an unpaired electron. EPR spectra of all the complexes in DMF at 77K suggest axial symmetry and the presence of half field signals for the complexes 1 and 3 indicates dimeric structures

Formation of an unusual copper(II) complex from the degradation of a novel tricopper(II) carbohydrazone complex

An unusual copper(II) complex [Cu(L1a)2Cl2] CH3OH H2O H3O+Cl (1a) was isolated from a solution of a novel tricopper(II) complex [Cu3(HL1)Cl2]Cl3 2H2O (1) in methanol, where L1a is 3-(2-pyridyl)triazolo [1,5-a]-pyridine, and characterized with single crystal X-ray diffraction study. The tricopper(II) complex of potential ligand 1,5-bis(di-2-pyridyl ketone) carbohydrazone (H2L1) was synthesized and physicochemically characterized, while the formation of the complex 1a was followed by time-dependant monitoring of the UV–visible spectra, which reveals degradation of ligand backbone as intensity loss of bands corresponding to O?Cu(II) charge transfer