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

Microwave studies of a poly vinyl acetate (PVA)-based phantom for applications in medical imaging

A phantom that exhibits complex dielectric properties similar to low-water-content biological tissues over the electromagnetic spectrum of 2000–3000 MHz has been synthesized from carbon black, graphite powder, and poly vinyl acetate (PVA)-based adhesive. The material overcomes various problems that are inherent in conventional phantoms such as decomposition and deterioration due to the invasion of bacteria or mold. The absorption coefficients of the material for various concentrations of carbon and graphite are studied. A combination of 50% poly-vinyl-acetate-based adhesive, 20% carbon, and 30% graphite exhibits a high absorption coefficient, which suggests another application of the material as a good microwave absorber for the interior lining of tomographic chamber in microwave imaging. The cavity-perturbation technique is adopted to study the dielectric properties of the material.

Dielectric studies of polyvinyl-acetate-based phantom for applications in microwave medical imaging

Phantoms that exhibit complex dielectric properties similar to low water content biological tissues over the electromagnetic spectrum of 2–3 GHz have been synthesized from carbon black powder, graphite powder and polyvinyl-acetate-based adhesive. The materials overcome various problems that are inherent in conventional phantoms such as decomposition and deterioration due to the invasion of bacteria or mold. The absorption coefficients of the materials for various compositions of carbon black and graphite powder are studied. A combination of 50% polyvinylacetate- based adhesive, 20% carbon black powder and 30% graphite powder exhibits high absorption coefficient, which suggests another application of the material as good microwave absorber for interior lining of tomographic chamber in microwave imaging. Cavity perturbation technique is adopted to study the dielectric properties of the material.

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

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Enhancement of Calcifications in Mammograms using Volterra Series based Quadratic Filter

The paper summarizes the design and implementation of a quadratic edge detection filter, based on Volterra series, for enhancing calcifications in mammograms. The proposed filter can account for much of the polynomial nonlinearities inherent in the input mammogram image and can replace the conventional edge detectors like Laplacian, gaussian etc. The filter gives rise to improved visualization and early detection of microcalcifications, which if left undetected, can lead to breast cancer. The performance of the filter is analyzed and found superior to conventional spatial edge detectors

Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.

Investigations on TiO2 Based Nanocomposites as Potential Photonic Materials

This Study overviews the basics of TiO2with respect to its structure, properties and applications. A brief account of its structural, electronic and optical properties is provided. Various emerging technological applications utilising TiO2 is also discussed.Till now, exceptionally large number of fundamental studies and application-oriented research and developments has been carried out by many researchers worldwide in TiO2 with its low-dimensional nanomaterial form due to its various novel properties. These nanostructured materials have shown many favourable properties for potential applications, including pollutant photocatalytic decomposition, photovoltaic cells, sensors and so on. This thesis aims to make an in-depth investigation on different linear and nonlinear optical and structural characteristics of different phases of TiO2. Correspondingly, extensive challenges to synthesise different high quality TiO2 nanostructure derivatives such as nanotubes, nanospheres, nanoflowers etc. are continuing. Here, different nanostructures of anatase TiO2 were synthesised and analysed. Morphologically different nanostructures were found to have different impact on their physical and electronic properties such as varied surface area, dissimilar quantum confinement and hence diverged suitability for different applications. In view of the advantages of TiO2, it can act as an excellent matrix for nanoparticle composite films. These composite films may lead to several advantageous functional optical characteristics. Detailed investigations of these kinds of nanocomposites were also performed, only to find that these nanocomposites showed higher adeptness than their parent material. Fine tuning of these parameters helps researchers to achieve high proficiency in their respective applications. These innumerable opportunities aims to encompass the new progress in studies related to TiO2 for an efficient utilization in photo-catalytic or photo-voltaic applications under visible light, accentuate the future trends of TiO2-research in the environment as well as energy related fields serving promising applications benefitting the mankind. The last section of the thesis discusses the applicability of analysed nanomaterials for dye sensitised solar cells followed by future suggestions.