Development of Time - Frequency Techniques for Sonar Applications

Sonar signal processing comprises of a large number of signal processing algorithms for implementing functions such as Target Detection, Localisation, Classification, Tracking and Parameter estimation. Current implementations of these functions rely on conventional techniques largely based on Fourier Techniques, primarily meant for stationary signals. Interestingly enough, the signals received by the sonar sensors are often non-stationary and hence processing methods capable of handling the non-stationarity will definitely fare better than Fourier transform based methods.Time-frequency methods(TFMs) are known as one of the best DSP tools for nonstationary signal processing, with which one can analyze signals in time and frequency domains simultaneously. But, other than STFT, TFMs have been largely limited to academic research because of the complexity of the algorithms and the limitations of computing power. With the availability of fast processors, many applications of TFMs have been reported in the fields of speech and image processing and biomedical applications, but not many in sonar processing. A structured effort, to fill these lacunae by exploring the potential of TFMs in sonar applications, is the net outcome of this thesis. To this end, four TFMs have been explored in detail viz. Wavelet Transform, Fractional Fourier Transfonn, Wigner Ville Distribution and Ambiguity Function and their potential in implementing five major sonar functions has been demonstrated with very promising results. What has been conclusively brought out in this thesis, is that there is no "one best TFM" for all applications, but there is "one best TFM" for each application. Accordingly, the TFM has to be adapted and tailored in many ways in order to develop specific algorithms for each of the applications.

Studies on Some Aspects of Landings,Utilization and Export of Commercially Important Cephalopods

In the present study two aspects of cephalopod fishery such as (i) landings and (ii) utilization including export of cephalopods from Indian are dealt with. Under the topic landings, craft, both traditional and mechanized and fishing techniques employed for capturing cephalopods are discussed. General trend in cephalopod landing in India and particularly south west coast, species composition, description and geographic distribution of important species of cuttle fishes, squids and octopus and the distribution and abundance of cephalopods along the continental shelf of the south west coast are also discussed. Some biological aspects like population parameters, maturation and spawning, food and feeding of the two representive species of cuttle fishes and squid viz, Sepia pharaonis and Loligo duvauceli are studied.Under utilization, general handling and processing of various frozen cephalopods products for exports and commercial operation in a typical plant are studied. The export of cephalopod products since its commencement is studied in detail and market strategy for enhancing the export are suggested.

Carbon/Nitrogen Ratio Optimization and Periphyton Development on the Production and Sustainability of Penaeus Monodon(Fabricius)in Extensive Culture System

Controlling the inorganic nitrogen by manipulating carbon / nitrogen ratio is a method gaining importance in aquaculture systems. Nitrogen control is induced by feeding bacteria with carbohydrates and through the subsequent uptake of nitrogen from the water for the synthesis of microbial proteins. The relationship between addition of carbohydrates, reduction of ammonium and the production of microbial protein depends on the microbial conversion coefficient. The carbon / nitrogen ratio in the microbial biomass is related to the carbon contents of the added material. The addition of carbonaceous substrate was found to reduce inorganic nitrogen in shrimp culture ponds and the resultant microbial proteins are taken up by shrimps. Thus, part of the feed protein is replaced and feeding costs are reduced in culture systems.The use of various locally available substrates for periphyton based aquaculture practices increases production and profitability .However, these techniques for extensive shrimp farming have not so far been evaluated. Moreover, an evaluation of artificial substrates together with carbohydrate source based farming system in reducing inorganic nitrogen production in culture systems has not yet been carried-out. Furthermore, variations in water and soil quality, periphyton production and shrimp production of the whole system have also not been determined so-far.This thesis starts with a general introduction , a brief review of the most relevant literature, results of various experiments and concludes with a summary (Chapter — 9). The chapters are organised conforming to the objectives of the present study. The major objectives of this thesis are, to improve the sustainability of shrimp farming by carbohydrate addition and periphyton substrate based shrimp production and to improve the nutrient utilisation in aquaculture systems.

Investigations of Optical Interaction Processes in Certain Photonic Materials Using Z-scan and Thermal Lens Techniques

International School of Photonics, Cochin University of Science and Technology

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.

Techniques for Enhancing Wind Energy Generation - A CFD Based Multibody Dynamics Approach in Horizontal Axis Wind Turbines

Wind energy has emerged as a major sustainable source of energy.The efficiency of wind power generation by wind mills has improved a lot during the last three decades.There is still further scope for maximising the conversion of wind energy into mechanical energy.In this context,the wind turbine rotor dynamics has great significance.The present work aims at a comprehensive study of the Horizontal Axis Wind Turbine (HAWT) aerodynamics by numerically solving the fluid dynamic equations with the help of a finite-volume Navier-Stokes CFD solver.As a more general goal,the study aims at providing the capabilities of modern numerical techniques for the complex fluid dynamic problems of HAWT.The main purpose is hence to maximize the physics of power extraction by wind turbines.This research demonstrates the potential of an incompressible Navier-Stokes CFD method for the aerodynamic power performance analysis of horizontal axis wind turbine.The National Renewable Energy Laboratory USA-NREL (Technical Report NREL/Cp-500-28589) had carried out an experimental work aimed at the real time performance prediction of horizontal axis wind turbine.In addition to a comparison between the results reported by NREL made and CFD simulations,comparisons are made for the local flow angle at several stations ahead of the wind turbine blades.The comparison has shown that fairly good predictions can be made for pressure distribution and torque.Subsequently, the wind-field effects on the blade aerodynamics,as well as the blade/tower interaction,were investigated.The selected case corresponded to a 12.5 m/s up-wind HAWT at zero degree of yaw angle and a rotational speed of 25 rpm.The results obtained suggest that the present can cope well with the flows encountered around wind turbines.The areodynamic performance of the turbine and the flow details near and off the turbine blades and tower can be analysed using theses results.The aerodynamic performance of airfoils differs from one another.The performance mainly depends on co-efficient of performnace,co-efficient of lift,co-efficient of drag, velocity of fluid and angle of attack.This study shows that the velocity is not constant for all angles of attack of different airfoils.The performance parameters are calculated analytically and are compared with the standardized performance tests.For different angles of ,the velocity stall is determined for the better performance of a system with respect to velocity.The research addresses the effect of surface roughness factor on the blade surface at various sections.The numerical results were found to be in agreement with the experimental data.A relative advantage of the theoretical aerofoil design method is that it allows many different concepts to be explored economically.Such efforts are generally impractical in wind tunnels because of time and money constraints.Thus, the need for a theoretical aerofoil design method is threefold:first for the design of aerofoil that fall outside the range of applicability of existing calalogs:second,for the design of aerofoil that more exactly match the requirements of the intended application:and third,for the economic exploration of many aerofoil concepts.From the results obtained for the different aerofoils,the velocity is not constant for all angles of attack.The results obtained for the aerofoil mainly depend on angle of attack and velocity.The vortex generator technique was meticulously studies with the formulation of the specification for the right angle shaped vortex generators-VG.The results were validated in accordance with the primary analysis phase.The results were found to be in good agreement with the power curve.The introduction of correct size VGs at appropriate locations over the blades of the selected HAWT was found to increase the power generation by about 4%

Studies on food and feeding of marine demersal finfishes with special reference to trophic interactions

Food and feeding habits of fourteen demersal finfishes exploited off the Karnataka coast were studied to investigate trophic interactions within the marine food web. Index of Relative Importance (lRI),Ontogenetic, seasonal (pre-monsoon, monsoon and post-monsoon) variation in feeding and prey-predator relationship studies were conducted.The results of prey-predator trophic interaction studies identified four major trophic guilds based on the predators feeding similarity.Trophic guild I is 'copepod and detritus fceders'with an average group similarity of 61.4%. The second trophic guild, 'prawn and crab feeders'with an average similarity of 52.7%. 'Acetes feeders', the largest trophic guild with an average group similarity of 62.5%, composed of six demersal finfish species.The guild 'piscivores' is constituted by C. limba/us and P. arsius with an average similarity of 45%.For each predator, ontogenetic diet shift is common and is characterized by prey of low to high trophic level.Strong selection of certain prey types was observed in some predators while most of them avoided abundant prey.In addition to Acetes spp, strong predation impact was observed for penaeid prawns, epibenthic crabs and detritus.This information on trophic guilds and prey-predator interactions can be used to construct trophic model on the benthic ecosystem off Karnataka and to investigate fishery induced changes as well as predation impact of different animals on commercially important demersals

Fractal based techniques for classification of mammograms and identification of microcalcifications

After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of • A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant) • A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram. The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Computer vision techniques in seafood quality control

The application of computer vision based quality control has been slowly but steadily gaining importance mainly due to its speed in achieving results and also greatly due to its non- destnictive nature of testing. Besides, in food applications it also does not contribute to contamination. However, computer vision applications in quality control needs the application of an appropriate software for image analysis. Eventhough computer vision based quality control has several advantages, its application has limitations as to the type of work to be done, particularly so in the food industries. Selective applications, however, can be highly advantageous and very accurate.Computer vision based image analysis could be used in morphometric measurements of fish with the same accuracy as the existing conventional method. The method is non-destructive and non-contaminating thus providing anadvantage in seafood processing.The images could be stored in archives and retrieved at anytime to carry out morphometric studies for biologists.Computer vision and subsequent image analysis could be used in measurements of various food products to assess uniformity of size. One product namely cutlet and product ingredients namely coating materials such as bread crumbs and rava were selected for the study. Computer vision based image analysis was used in the measurements of length, width and area of cutlets. Also the width of coating materials like bread crumbs was measured.Computer imaging and subsequent image analysis can be very effectively used in quality evaluations of product ingredients in food processing. Measurement of width of coating materials could establish uniformity of particles or the lack of it. The application of image analysis in bacteriological work was also done

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.

“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.

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.

Effect of Feeding Rate in Polymer Extrusion ”

Starve feeding of single screw extruder was described as an important means of improving the performance characteristics of the extruder. In addition to such improvement with versatility, the starve feeding technique also may affect the mechanical properties of the extrudate since the heat transfer an(l mixing characteristics in the starve fed and Hood fed extruders are not the same. Since the material is more loosely packed in the channels of the starve fed extruder, there may be greater bed mobility and uniformity. Further, the. thermal an(l shear induced degradation are also less since possibilities of developing local high temperatures are less compared to a densely compacted extruder bed. This study has been undertaken mainly to explore the effect of feeding rate on the mechanical properties of rubber and plastic extrudates since the effect of feeding rate has not been analysed from this angle so far.