Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Studies on Catalysis by Nano Titania Modified with Metals and Nonmetals

Semiconductor photocatalysis has received much attention during last three decades as a promising solution for both energy generation and environmental problems. Heterogeneous photocatalytic oxidation allows the degradation of organic compounds into carbon dioxide and water in the presence of a semiconductor catalyst and UV light source. The •OH radicals formed during the photocatalytic processes are powerful oxidizing agents and can mineralise a number of organic contaminants. Titanium dioxide (TiO2), due to its chemical stability, non-toxicity and low cost represents one of the most efficient photocatalyst. However, only the ultraviolet fraction of the solar radiation is active in the photoexcitation processes using pure TiO2 and although, TiO2 can treat a wide range of organic pollutants the effectiveness of the process for pollution abatement is still low. A more effective and efficient catalyst therefore must be formulated. Doping of TiO2 was considered with the aim of improving photocatalytic properties. In this study TiO2 catalyst was prepared using the sol-gel method. Metal and nonmetal doped TiO2 catalysts were prepared. The photoactivity of the catalyst was evaluated by the photodegradation of different dyes and pesticides in aqueous solution. High photocatalytic degradation of all the pollutants was observed with doped TiO2. Structural and optical properties of the catalysts were characterized using XRD, BET surface area, UV-Vis. DRS, CHNS analysis, SEM, EDX, TEM, XPS, FTIR and TG. All the catalysts showed the anatase phase. The presence of dopants shifts the absorption of TiO2 into the visible region indicating the possibility of using visible light for photocatalytic processes.

Modified Block Adaptive Predictive Coder For Speech Processing

This thesis investigated the potential use of Linear Predictive Coding in speech communication applications. A Modified Block Adaptive Predictive Coder is developed, which reduces the computational burden and complexity without sacrificing the speech quality, as compared to the conventional adaptive predictive coding (APC) system. For this, changes in the evaluation methods have been evolved. This method is as different from the usual APC system in that the difference between the true and the predicted value is not transmitted. This allows the replacement of the high order predictor in the transmitter section of a predictive coding system, by a simple delay unit, which makes the transmitter quite simple. Also, the block length used in the processing of the speech signal is adjusted relative to the pitch period of the signal being processed rather than choosing a constant length as hitherto done by other researchers. The efficiency of the newly proposed coder has been supported with results of computer simulation using real speech data. Three methods for voiced/unvoiced/silent/transition classification have been presented. The first one is based on energy, zerocrossing rate and the periodicity of the waveform. The second method uses normalised correlation coefficient as the main parameter, while the third method utilizes a pitch-dependent correlation factor. The third algorithm which gives the minimum error probability has been chosen in a later chapter to design the modified coder The thesis also presents a comparazive study beh-cm the autocorrelation and the covariance methods used in the evaluaiicn of the predictor parameters. It has been proved that the azztocorrelation method is superior to the covariance method with respect to the filter stabf-it)‘ and also in an SNR sense, though the increase in gain is only small. The Modified Block Adaptive Coder applies a switching from pitch precitzion to spectrum prediction when the speech segment changes from a voiced or transition region to an unvoiced region. The experiments cont;-:ted in coding, transmission and simulation, used speech samples from .\£=_‘ajr2_1a:r1 and English phrases. Proposal for a speaker reecgnifion syste: and a phoneme identification system has also been outlized towards the end of the thesis.

“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.

“Catalysis by Nanocrystalline Ceria Modified with Transition Metals”

The objective of the present work is to improve the textural and structural properties of cerium oxide by the incorporation of transition metals as well as sulphate ions. We have incorporated tungsten, molybdenum and chromium oxide into pure as well as sulphated cerium oxide and the catalytic systems thus prepared were characterised using various techniques. lndustrially important reactions such as acetalization and deacetalization, oxidative dehydrogenation of ethylbenzene, MTBE synthesis and Beckmann rearrangement of cinnamaldoxime and salicylaldoxime have been selected for the measurement of the catalytic activity of the systems. The work is presented in eight chapters

Chemically Modified Glassy Carbon Electrode as Sensors for Various Pharmaceuticals

Voltammetric methods are applicable for the determination of a wide variety of both organic and inorganic species. Its features are compact equipment, simple sample preparation, short analysis time, high accuracy and sensitivity. Voltammetry is especially suitable for laboratories in which only a few parameters have to be monitored with a moderate sample throughput. Of various electrode materials, glassy carbon electrode is particularly useful because of its high electrical conductivity, impermeability to gases, high chemical resistance, reasonable mechanical and dimensional stability and widest potential range of all carbonaceous electrodes. Electrode modification is a vigorous research area by which the electrochemical determination of various analyte species is facilitated. The scope of pharmaceutical analysis includes the analytical investigation of pure drug, drug formulations, impurities and degradation products of drugs, biological samples containing the drugs and their metabolites with the aim of obtaining data that can contribute to the maximal efficacy and maximal safety of drug therapy. This thesis presents the modification of glassy carbon electrode using metalloporphyrin and dyes and subsequently using these modified electrodes for the determination of various pharmaceuticals. The thesis consists of 9 chapters.

‘‘catalysis by nano sulfated titania modified by transition metals"

in the present study, we have prepared and evaluated the physical and chemical properties and catalytic activities of transition metal loaded sulfated titania via the sol-gel route. Sol-gel method is widely used for preparing porous materials having controlled properties and leads to the formation of oxide particles in nano range, which are spherical or interconnected to each other. Characterization using various physico-chemical techniques and a detailed study of acidic properties are also carried out. Some reactions of industrial importance such as Friedel-Crafts reaction, fen-butylation of phenol,Beckmann rearrangement of cyclohexanone oxime, nitration of phenol and photochemical degradation of methylene blue have been selected for catalytic activity study in the present venture. The work is organized into eight chapters

Use of Novel Algorithms MAJE4 and MACJER-320 for Achieving Confidentiality and Message Authentication in SSL & TLS.

Extensive use of the Internet coupled with the marvelous growth in e-commerce and m-commerce has created a huge demand for information security. The Secure Socket Layer (SSL) protocol is the most widely used security protocol in the Internet which meets this demand. It provides protection against eaves droppings, tampering and forgery. The cryptographic algorithms RC4 and HMAC have been in use for achieving security services like confidentiality and authentication in the SSL. But recent attacks against RC4 and HMAC have raised questions in the confidence on these algorithms. Hence two novel cryptographic algorithms MAJE4 and MACJER-320 have been proposed as substitutes for them. The focus of this work is to demonstrate the performance of these new algorithms and suggest them as dependable alternatives to satisfy the need of security services in SSL. The performance evaluation has been done by using practical implementation method.

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching.

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

SPATIAL CLUSTERING ALGORITHMS-AN OVERVIEW.

An Overview of known spatial clustering algorithms The space of interest can be the two-dimensional abstraction of the surface of the earth or a man-made space like the layout of a VLSI design, a volume containing a model of the human brain, or another 3d-space representing the arrangement of chains of protein molecules. The data consists of geometric information and can be either discrete or continuous. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, spatial data mining algorithms are required for spatial characterization and spatial trend analysis. Spatial data mining or knowledge discovery in spatial databases differs from regular data mining in analogous with the differences between non-spatial data and spatial data. The attributes of a spatial object stored in a database may be affected by the attributes of the spatial neighbors of that object. In addition, spatial location, and implicit information about the location of an object, may be exactly the information that can be extracted through spatial data mining

Synthesis, Characterization and Catalytic Activity of Nanocrystalline Ceria Modified With Zirconia

The use of catalysts in chemical and refining processes has increased rapidly since 1945, when oil began to replace coal as the most important industrial raw material. Catalysis has a major impact on the quality of human life as well as economic development. The demand for catalysts is still increasing since catalysis is looked up as a solution to eliminate or replace polluting processes. Metal oxides represent one of the most important and widely employed classes of solid catalysts. Much effort has been spent in the preparation, characterization and application of metal oxides. Recently, great interest has been devoted to the cerium dioxide (CeO2) containing materials due to their broad range of applications in various fields, ranging from catalysis to ceramics, fuel cell technologies, gas sensors, solid state electrolytes, ceramic biomaterials, etc., in addition to the classical application of CeO2 as an additive in the so-called three way catalysts (TWC) for automotive exhaust treatment. Moreover, it can promote water gas shift and steam reforming reactions, favours catalytic activity at the interfacial metal-support sites. The solid solutions of ceria with Group IV transitional-metals deserve particular attention for their applicability in various technologically important catalytic processes. Mesoporous CeO2−ZrO2 solid solutions have been reported to be employed in various reactions which include CO oxidation, soot oxidation, water-gas shift reaction, and so on. Inspired by the unique and promising characteristics of ceria based mixed oxides and solid solutions for various applications, we have selected ceria-zirconia oxides for our studies. The focus of the work is the synthesis and investigation of the structural and catalytic properties of modified and pure ceria-zirconia mixed oxide.

A Data Mining Approach for the Identification of Adverse Drug Events (ADE) resulting from Drug-Drug Interactions (DDI) to improve pharmacovigilance

In the current study, epidemiology study is done by means of literature survey in groups identified to be at higher potential for DDIs as well as in other cases to explore patterns of DDIs and the factors affecting them. The structure of the FDA Adverse Event Reporting System (FAERS) database is studied and analyzed in detail to identify issues and challenges in data mining the drug-drug interactions. The necessary pre-processing algorithms are developed based on the analysis and the Apriori algorithm is modified to suit the process. Finally, the modules are integrated into a tool to identify DDIs. The results are compared using standard drug interaction database for validation. 31% of the associations obtained were identified to be new and the match with existing interactions was 69%. This match clearly indicates the validity of the methodology and its applicability to similar databases. Formulation of the results using the generic names expanded the relevance of the results to a global scale. The global applicability helps the health care professionals worldwide to observe caution during various stages of drug administration thus considerably enhancing pharmacovigilance

Planar UWB Antenna with Modified Slotted Ground Plane

A compact coplanar waveguide-fed (CPW) monopole antenna for ultra-wideband wireless communication is presented. The proposed antenna comprises of a CPW-fed beveled rectangular patch with a modified slotted ground. The overall size of the antenna is 30 mm 27 mm 1.6 mm. The lower edge of the band is attained by properly decoupling the resonant frequencies due to the extended ground plane and the beveled rectangular patch of the antenna. The upper edge of the radiating band is enhanced by beveling the ground plane corners near the feed point. Experimental results show that the designed antenna operates in the 2.7–12 GHz band, for S11 10 dB with a gain of 2.7–5 dBi. Both the frequency domain and time domain characteristics of the antenna are investigated using antenna transfer function. It is observed that the antenna exhibits identical radiation patterns and reasonable transient characteristics over the entire operating band

A Compact Modified Ground CPW Fed Antenna for UWB Applications

A Coplanar waveguide fed compact planar monopole antenna with a modified ground plane is presented. Measured and simulated results reveal that the antenna operates in the Ultra Wide Band with almost constant group delay throughout the band. Developed design equations of the antenna are validated for different substrates. Time domain performance of the antenna is also discussed in order to assess its suitability for impulse radio applications