Influence of surface and acid properties of vanadia supported on ceria promoted with rice husk silica on cyclohexanol decomposition

Cyclohexanol decomposition activity of supported vanadia catalysts is ascribed to the high surface area, total acidity and interaction between supported vanadia and the amorphous support. Among the supported catalysts, the effect of vanadia over various wt% V2O5 (2–10) loading indicates that the catalyst comprising of 6 wt% V2O5 exhibits higher acidity and decomposition activity. Structural characterization of the catalysts has been done by techniques like energy dispersive X-ray analysis, X-ray diffraction and BET surface area. Acidity of the catalysts has been measured by temperature programmed desorption using ammonia as a probe molecule and the results have been correlated with the activity of catalysts.

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

Sol-Gel Nanocrystalline Catalytic Titania Powders and Functional Coatings

In the present work Titania bulk powders and coatings were prepared by subjecting titanium isopropoxide solution to a controlled hydrolysis-condensation process. The powders were characterized using techniques such as FTIR for their chemical interactions, TG-DTA for the thermal decomposition features, XRD for the phase assemblage, BET specific surface area analysis for the textural features. The study discusses the preparation methods and the characterization techniques employed and a detailed discussion on the physico-chemical characterization of the prepared systems. The influence of dopants and leaching on the physico-chemical properties as well as their influence on photo activity is also included. The structural/functional coatings of different Titania compositions includes in this study. Coatings on pre-treated glass surfaces with the best compositions prepared showed 90 % transmittance in the visible region.

Effect of Blending Techniques on the Curing of Elastomer Blends

Compounding of styrene-butadiene copolymer/polybutadiene , natural rubber/ ethylene-propylene-diene terpolymer and natural rubber/butadiene-acrylonitrile copolymer blends was done in three different ways and their curing behaviour and the tensile properties of the es are compared.

Generation of Variability for Salt Tolerance in Rice Using Tissue Culture Techniques

The study deals with the generation of variability for salt tolerance in rice using tissue culture techniques. Rice is the staple food of more than half of the world’s population. The management of drought, salinity and acidity in soils are all energy intensive agricultural practices. The Genetic variability is the basis of crop improvement. Somaclonal and androclonal variation can be effectively used for this purpose. In the present study, eight isozymes were studied and esterase and isocitric dehydrogenase was found to have varietal specific, developmental stage specific and stress specific banding pattern in rice. Under salt stress thickness of bands and enzyme activity showed changes. Pokkali, a moderately salt tolerant variety, had a specific band 7, which was present only in this variety and showed slight changes under stress. This band was faint in tillering and flowering stage .Based on the results obtained in the present study it is suggested that esterase could possibly be used as an isozyme marker for salt tolerance in rice. Varietal differences and stage specific variations could be detected using esterase and isocitric dehydrogenase . Moreover somaclonal and androclonal variation could be effectively detected using isozyme markers.

Studies on Some Simple and Zeolite-Y Encapsulated 3d-Transition Metal Complexes of Schiff Bases Derived From 2-Aminobenzothiazole

In this regard Schiff base complexes have attracted wide attention. Furthermore, such complexes are found to play important role in analytical chemistry, organic synthesis, metallurgy, refining of metals, electroplating and photography. Many Schiff base complexes are reported in literature. Their properties depend on the nature of the metal ion as well as on the nature of the ligand. By altering the ligands it is possible to obtain desired electronic environment around the metal ion. Thus there is a continuing interest in the synthesis of simple and zeolite encapsulated Schiff base complexes of metal ions. Zeolites have a number of striking structural similarities to the protein portion of natural enzymes. Zeolite based catalysts are known for their remarkable ability of mimicking the chemistry of biological systems. In view of the importance of catalysts in all the areas of modern chemical industries, an effort has been made to synthesize some simple Schiff base complexes, heterogenize them by encapsulating within the supercages of zeoliteY cavities and to study their applications. The thesis deals with studies on the synthesis and characterization of some simple and zeoliteY encapsulated Mn(II), Fe(III), Co(II), Ni(II) and Cu(II) complexes and on the catalytic activity of these complexes on some oxidation reactions. Simple complexes were prepared from the Schiff base ligands SBT derived from 2-aminobenzothiazole and salicylaldehyde and the ligand VBT derived from 2-aminobenzothiazole and vanillin (4-hydroxy-3- methoxybenzaldehyde). ZeoliteY encapsulated Mn(II), Fe(III), Co(II), Ni(II) and Cu(II) complexes of Schiff base ligands SBT and VBT and also of 2-aminobenzothiazole were synthesized. All the prepared complexes were characterized using the physico-chemical techniques such as chemical analysis (employing AAS and CHN analyses), magnetic moment studies, conductance measurements and electronic and FTIR spectra. EPR spectra of the Cu(II) complexes were also carried out to know the probable structures and nature of Cu(II) complexes. Thermogravimetric analyses were carried out to obtain the information regarding the thermal stability of various complexes. The successful encapsulations of the complexes within the cavities of zeoliteY were ascertained by XRD, surface area and pore volume analysis. Assignments of geometries of simple and zeoliteY encapsulated complexes are given in all the cases. Both simple and zeoliteY encapsulated complexes were screened for catalytic activity towards oxidation reactions such as decomposition of hydrogen peroxide, oxidation of benzaldehyde, benzyl alcohol, 1-propanol, 2-propanol and cyclohexanol.

Comparison Study of Pattern Synthesis Techniques Using Neural Networks

In this paper, a comparison study among three neuralnetwork algorithms for the synthesis of array patterns is presented. The neural networks are used to estimate the array elements' excitations for an arbitrary pattern. The architecture of the neural networks is discussed and simulation results are presented. Two new neural networks, based on radial basis functions (RBFs) and wavelet neural networks (WNNs), are introduced. The proposed networks offer a more efficient synthesis procedure, as compared to other available techniques

Zeolite-encapsulated Co(II), Ni(II) and Cu(II) complexes as catalysts for partial oxidation of benzyl alcohol and ethylbenzene

Co(II), Ni(II) and Cu(II) complexes of dimethylglyoxime and N,N-ethylenebis(7-methylsalicylideneamine) have been synthesized in situ in Y zeolite by the reaction of ion-exchanged metal ions with the flexible ligand molecules that had diffused into the cavities. The hybrid materials obtained have been characterized by elemental analysis, SEM, XRD, surface area, pore volume, magnetic moment, FTIR, UV-Vis and EPR techniques. Analysis of data indicates the formation of complexes in the pores without affecting the zeolite framework structure, the absence of any extraneous species and the geometry of encapsulated complexes. The catalytic activities for hydrogen peroxide decomposition and oxidation of benzyl alcohol and ethylbenzene of zeolite complexes are reported. Zeolite Cu(II) complexes were found to be more active than the corresponding Co(II) and Ni(II) complexes for oxidation reactions. The catalytic properties of the complexes are influenced by their geometry and by the steric environment of the active sites. Zeolite complexes are stable enough to be reused and are suitable to be utilized as partial oxidation catalysts.

Design Techniques for Sigmadelta based ADC for Wireless Applications

Analog-to digital Converters (ADC) have an important impact on the overall performance of signal processing system. This research is to explore efficient techniques for the design of sigma-delta ADC,specially for multi-standard wireless tranceivers. In particular, the aim is to develop novel models and algorithms to address this problem and to implement software tools which are avle to assist the designer's decisions in the system-level exploration phase. To this end, this thesis presents a framework of techniques to design sigma-delta analog to digital converters.A2-2-2 reconfigurable sigma-delta modulator is proposed which can meet the design specifications of the three wireless communication standards namely GSM,WCDMA and WLAN. A sigma-delta modulator design tool is developed using the Graphical User Interface Development Environment (GUIDE) In MATLAB.Genetic Algorithm(GA) based search method is introduced to find the optimum value of the scaling coefficients and to maximize the dynamic range in a sigma-delta modulator.