Transition metal complexes of some N4-phenylsemicarbazones: crystal structures and spectral studies

The unusual coordination modes of semicarbazones when bound to metals, the wide applications and structural diversity of metal complexes of semicarbazones provoked us to synthesize and characterize the tridentate ONO and NNO-donor semicarbazones and their transition metal complexes. This work is focused on the studies on complexes of three N4-phenylsemicarbazones synthesized by changing the carbonyl compounds. This work is concerned with the studies of two new semicarbazones, 2- formylpyridine-N4-phenylsemicarbazone (HL1) and 3-ethoxysalicylaldehyde- N4-phenylsemicarbazone (H2L2) and a reported semicarbazone 2-benzoylpyridine-N4-phenylsemicarbazone (HL3) [29]. The compositions of these semicarbazones were determined by the CHN analyses and IR, UV and NMR spectral studies were used for the characterization of these compounds. The molecular structure of 3-ethoxysalicylaldehyde-N4-phenylsemicarbazone (H2L2) was obtained by single crystal X-ray diffraction studies. Also, we have synthesized Cu(II), Cd(II), Zn(II) and Ni(II) complexes of these three semicarbazones. The complexes were characterized by various spectroscopic techniques, magnetic and conductivity studies. We could isolate single crystals of some complexes of all metals suitable for X-ray diffraction studies. This thesis is divided into six chapters.

Transition metal complexes derived from ketone based N(4)-substituted thiosemicarbazones: Crystal structures and spectral studies

Thiosemicarbazones have recently attracted considerable attention due to their ability to form tridentate chelates with transition metal ions through either two nitrogen and sulfur atoms, N–N–S or oxygen, nitrogen and sulfur atoms, O–N–S. Considerable interest in thiosemicarbazones and their transition metal complexes has also grown in the areas of biology and chemistry due to biological activities such as antitumoral, fungicidal, bactericidal, antiviral and nonlinear optical properties. They have been used for metal analyses, for device applications related to telecommunications, optical computing, storage and information processing.The versatile applications of metal complexes of thiosemicarbazones in various fields prompted us to synthesize the tridentate NNS-donor thiosemicarbazones and their metal complexes. As a part of our studies on transition metal complexes with these ligands, the researcher undertook the current work with the following objectives. 1. To synthesize and physico-chemically characterize the following thiosemicarbazone ligands: a. Di-2-pyridyl ketone-N(4)-methyl thiosemicarbazone (HDpyMeTsc) b. Di-2-pyridyl ketone-N(4)-ethyl thiosemicarbazone (HDpyETsc) 2. To synthesize oxovanadium(IV), manganese(II), nickel(II), copper(II), zinc(II) and cadmium(II) complexes using the synthesized thiosemicarbazones as principal ligands and some anionic coligands. 3. To study the coordination modes of the ligands in metal complexes by using different physicochemical methods like partial elemental analysis, thermogravimetry and by different spectroscopic techniques. 4. To establish the structure of compounds by single crystal XRD studies

Studies on molecular structure and properties using microwave and infrared spectral data

The major objective of the thesis is essentially to evolve and apply certain computational procedures to evaluate the structure and properties of some simple polyatomic molecules making use of spectroscopic data available from the literature. It must be said that though there is dwindling interest in recent times in such analyses, there exists tremendous scope and utility for attempting such calculations as the precision and reliability of'experimental techniques in spectroscopy have increased vastly due to enormous sophistication of the instruments used for these measurements. In the present thesis an attempt is made to extract maximum amount of information regarding the geometrical structure and interatmic forces of simple molecules from the experimental data on microwave and infrared spectra of these molecules

Studies in molecular mechanics using spectroscopic data exact force field and bond in homogeneity

This thesis deals with some studies in molecular mechanic using spectroscopic data. It includes an improvement in the parameter technique for the evaluation of exact force fields, the introduction of a new and simple algebraic method for the force field calculation and a study of asymmetric variation of bonding forces along a bond.

Detection of sandal spike phytoplasma using immunological and molecular techniques

Spike disease in sandal is generally diagnosed by the manifestation of external symptoms. Attempts have been made to detect the diseased plants by determining the length/breadth ratio of leaves (lyengar, 1961) and histochemical tests using Mann's stain (Parthasarathi et al., 1966), Dienes' stain (Ananthapadmanabha et a/., 1973) aniline blue and Hoechst 33258 (Ghosh et a/., 1985, Rangaswamy, 1995). But most of these techniques are insensitive, indirect detection methods leading to misinterpretation of results. Moreover, to identify disease resistant sandal trees, highly sensitive techniques are needed to detect the presence of the pathogen. In sandal forests, several host plants of sandal like Zizyphus oenop/ea (Fig. 1.3) also exhibit the yellows type disease symptoms. Immunological and molecular assays have to be developed to confirm the presence of sandal spike phytoplasma in such hosts. The major objectives of the present work includes:In situ detection of sandal spike phytoplasma by epifluorescence microscopy and scanning electron microscopy.,Purification of sandal spike phytoplasma and production of polyclonal antibodies.,Amino acid and total protein estimation of sandal spike phytoplasma.,Immunological detection of sandal spike phytoplasma., Molecular detection of sandal spike phytoplasma.,Screening for phytoplasma in host plants of spike disease affected sandal using immunological and molecular techniques.

Optical and thermal characterization of dye intercalated montmorillonites and rare earth doped materials

The advent of high optical quality transparent nano—structured glasses, the so-called transparent glass ceramics or vitroceramics disclosed the possibility of producing nano-sized photonic devices based on rare-earth doped up—converters. Transparent glass ceramics have been investigated as hosts for lanthanide ions envisioning the production of materials that are easy to shape and with high performance for photonic applications. Rare earth doped glasses have been extensively studied due to their potential applications in optical devices such as solid state lasers and optical fibers. Various photothermal and optical techniques have been successfully applied for the thermal and optical characterization of these rare earth doped materials. In the present thesis, the effective thermal parameters like thermal diffusivity and thermal effusivity of complex materials for various applications have been investigated using photothermal methods along with their optical characterization utilising the common optical absorption as well as fluorescence spectroscopic techniques. These sensitive optical procedures are also essential for exploiting these materials for further photonic applications.

NIR Spectroscopic and Laser Induced Fluorescence studies of some organic molecules

Vibrational overtone spectroscopy of molecules containing X-H oscillators (X = C, N, O...) has become an effective tool for the study of molecular structure, dynamics, inter and intramolecular interactions, conformational aspects and substituent effects in aliphatic and aromatic compounds. In the present work, the author studied the NIR overtone spectra of some liquid phase organic compounds. The analysis of the CH, NH and OH overtones yielded important structural information about these systems. In an attempt to get information on electronic energy levels, we studied the pulsed Nd:YAG laser induced fluorescence spectra of certain organic compounds. The pulsed laser Raman spectra of some organic compounds are also studied. The novel high resolution technique of near infrared tunable diode laser absorption spectroscopy (TDLAS) is used to record the rotational structure of the second OH overtone spectrum of 2-propanol. The spectral features corresponding to the different molecular conformations could be identified from the high resolution spectrum. The whole work described in this thesis is divided into five chapters.

“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.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Photometric and fundamental plane studies of galaxies

Determining the morphological parameters that describe galaxies has always been a challenging task. The studies on the correlations between different photometric as well as spectroscopic parameters of the galaxies help in understanding their structure, properties of the stars and gas which constitute the galaxy, the various physical and chemical processes which determine the properties, and galaxy formation and evolution. In the last few decades, the advent of Charge Coupled Devices (CCDs) and near infrared arrays ha\·e provided quick and reliable digitized data acquisition, in the optical and near infrared bands. This has provided an avalanche of data, which can be processed using sophisticated image analysis techniques to obtain information about the morphology of galaxies. The photometric analysis performed in this thesis involve the extraction of structural parameters of early type gala.xies imaged in the near infrared K (2.2ttm) band, obtaining correlations between these, parameters and using them to constrain the large scale properties of galaxi,~s.

Cooperative caching techniques for mobile ad hoc networks

Data caching can remarkably improve the efficiency of information access in a wireless ad hoc network by reducing the access latency and bandwidth usage. The cache placement problem minimizes total data access cost in ad hoc networks with multiple data items. The ad hoc networks are multi hop networks without a central base station and are resource constrained in terms of channel bandwidth and battery power. By data caching the communication cost can be reduced in terms of bandwidth as well as battery energy. As the network node has limited memory the problem of cache placement is a vital issue. This paper attempts to study the existing cooperative caching techniques and their suitability in mobile ad hoc networks.

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.

Vibrational spectroscopic studies and computational study of quinoline-2-carbaldehyde benzoyl hydrazone

FT-IR spectrum of quinoline-2-carbaldehyde benzoyl hydrazone (HQb H2O) was recorded and analyzed. The synthesis and crystal structure data are also described. The vibrational wavenumbers were examined theoretically using the Gaussian03 package of programs using HF/6-31G(d) and B3LYP/6-31G(d) levels of theory. The data obtained from vibrational wavenumber calculations are used to assign vibrational bands obtained in infrared spectroscopy of the studied molecule. The first hyperpolarizability, infrared intensities and Raman activities are reported. The calculated first hyperpolarizability is comparable with the reported values of similar derivatives and is an attractive object for future studies of non-linear optics. The geometrical parameters of the title compound obtained from XRD studies are in agreement with the calculated values. The changes in the CAN bond lengths suggest an extended p-electron delocalization over quinoline and hydrazone moieties which is responsible for the non-linearity of the molecule