Multichannel Remote Data Acquisition and Analysing Systems For Fisheries / Environmental Investigations

Remote Data acquisition and analysing systems developed for fisheries and related environmental studies have been reported. It consists of three units. The first one namely multichannel remote data acquisition system is installed at the remote place powered by a rechargeable battery. It acquires and stores the 16 channel environmental data on a battery backed up RAM. The second unit called the Field data analyser is used for insitue display and analysis of the data stored in the backed up RAM. The third unit namely Laboratory data analyser is an IBM compatible PC based unit for detailed analysis and interpretation of the data after bringing the RAM unit to the laboratory. The data collected using the system has been analysed and presented in the form of a graph. The system timer operated at negligibly low current, switches on the power to the entire remote operated system at prefixed time interval of 2 hours.Data storage at remote site on low power battery backedupRAM and retrieval and analysis of data using PC are the special i ty of the system. The remote operated system takes about 7 seconds including the 5 second stabilization time to acquire and store data and is very ideal for remote operation on rechargeable bat tery. The system can store 16 channel data scanned at 2 hour interval for 10 days on 2K backed up RAM with memory expansion facility for 8K RAM.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Studies on the Effect of Randomness on the Synchronization of Coupled Systems and on the Dynamics of Intermittently driven systems

Nonlinear dynamics has emerged into a prominent area of research in the past few Decades.Turbulence, Pattern formation,Multistability etc are some of the important areas of research in nonlinear dynamics apart from the study of chaos.Chaos refers to the complex evolution of a deterministic system, which is highly sensitive to initial conditions. The study of chaos theory started in the modern sense with the investigations of Edward Lorentz in mid 60's. Later developments in this subject provided systematic development of chaos theory as a science of deterministic but complex and unpredictable dynamical systems. This thesis deals with the effect of random fluctuations with its associated characteristic timescales on chaos and synchronization. Here we introduce the concept of noise, and two familiar types of noise are discussed. The classifications and representation of white and colored noise are introduced. Based on this we introduce the concept of randomness that we deal with as a variant of the familiar concept of noise. The dynamical systems introduced are the Rossler system, directly modulated semiconductor lasers and the Harmonic oscillator. The directly modulated semiconductor laser being not a much familiar dynamical system, we have included a detailed introduction to its relevance in Chaotic encryption based cryptography in communication. We show that the effect of a fluctuating parameter mismatch on synchronization is to destroy the synchronization. Further we show that the relation between synchronization error and timescales can be found empirically but there are also cases where this is not possible. Studies show that under the variation of the parameters, the system becomes chaotic, which appears to be the period doubling route to chaos.

Buyer information and brand Choice behavior in markets with asymmetries

This thesis Entitled Buyer information and brand choice behaviour in markets with asymmetries.The period of transition set in by globalization and liberalization has ensued a onsiderable degree of homogeneity with western societies with respect to quantity and quality of goods and services.The study is aimed at finding out how the buyers adapt to the prevalent complex and dynamic market configuration by taking an archetypical situation of information gathering and brand- choice decision of select household consumer durables.The study was based on a set of 301 sample respondents who were either first time purchasers or repeat purchasers for household use, of the items under study in the sample area comprising of rural, urban and semi-urban areas. Data were collected using interview schedule and analysis of the same was done with standard statistical computer programs.Buyer confidence as perceived by buyers with respect to information acquisition and brand-choice represents the felt competence to effectively function in the market.In general, lower levels of education, income and occupation showed lower levels of search. The oldest were also low searchers. The repeat purchasers of the product searched less than the first purchasers. The most important source of information was word of mouth or information from others followed by television advertisements. The least important source of information was billboards, displays and similar forms of advertisements.The second factor is characterized by items representing ‘social attributes’ like, use by many others, use by peers, recommendation by significant others and reputation of the brand. The third factor represents ‘susceptibility to incentives and promotions’.

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

“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.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

Collection and Assets Management: One University Library’s Journey to the Future

Academic libraries worldwide have witnessed a number of trends and paradigm shifts over the last decade. It is vital for university libraries to develop a collection of high standards to satisfy academics and researchers for supporting the vision and mission of a university. The area of collection development and management is the most important part of any library. This paper reports on the problems and prospects of collection and asset management of the University Library of Cochin University of Science and Technology (CUSAT). The insight for the paper comes from the authors’ first-hand experience supported by literature review. Detailed information regarding the purchase of books, serials, policies regarding the acquisition, and changing trends and problems were collected from the official records with the help of a structured data sheet. The study discovers the current trends in collection and asset management in CUSAT and point out the changes likely to be adopted in future.

Integron study, molecular typing and characterization of Salmonella serotypes isolated from seafood and poultry

The primary habitat of Salmonella is the gastrointestinal tract of animals and they are discharged into the water bodies through the feces. Aquatic animals act as asymptomatic reservoirs of a wide range of Salmonella serotypes. The inevitable delay in the detection of Salmonella contamination and the low sensitivity of the conventional methods is a serious issue in the seafood industry. Due to the indiscriminate use, the antibiotics are finally accumulated in the aquatic environment which provides the required antibiotic stress for the emergence of more and more antibiotic resistant phenotypes ofSalmonella. Several genetic determinants like integrons, genomic islands etc. play their role in acquisition and reshuffling of antibiotic resistance genes. A large number of virulence determinants are required for Salmonella pathogenicity. The virulence potential of Salmonella is determined, to some extent, by the presence of phages or phage mediated genes in the bacterial genome. There is much intra-serotype polymorphism in Salmonella and epidemiological studies rely on genetic resemblance of the isolated strains. Proper identification of the strain employing the traditional and molecular techniques is a prerequisite for accurate epidemiological studies (Soto et al., 2000). In this context, a study was undertaken to determine the prevalence of different Salmonella serotypes in seafood and to characterize them