Visual Communication: An Information Theory Approach presents an entirely new look at the assessment and optimization of visual communication channels, such as are employed for telephotography and television. The electro-optical design of image gathering and display devices, and the digital processing for image coding and restoration, have remained independent disciplines which follow distinctly separate traditions; yet the performance of visual communication channels cannot be optimized just by cascading image-gathering devices, image-coding processors, and image-restoration algorithms as the three obligatory, but independent, elements of a modern system. Instead, to produce the best possible picture at the lowest data rate', it is necessary to jointly optimize image gathering, coding, and restoration. Although the mathematical development in Visual Communication: An Information Theory Approach is firmly rooted in familiar concepts of communication theory, it leads to formulations that are significantly different from those that are found in the traditional literature on either rate distortion theory or digital image processing. For example, the Wiener filter, which is perhaps the most common image restoration algorithm in the traditional digital image processing literature, fails to fully account for the constraints of image gathering and display. As demonstrated in the book, digitally restored images improve in sharpness and clarity when these constraints are properly accounted for. Visual Communication: An Information Theory Approach is unique in its extension of modern communication theory to the end-to-end assessment of visual communication. from scene to observer. As such, itties together the traditional textbook literature on electro-optical design and digital image processing. This book serves as an invaluable reference for image processing and electro-optical system design professionals and may be used as a text for advanced courses on the subject.

This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering, biomedical engineering, and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work. Features Presents an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods Covers various theoretical considerations of adaptive filtering techniques used for DNA analysis, introduction to adaptive filtering, properties of adaptive algorithms, need for development of adaptive exon predictors (AEPs), and structure of AEP used for DNA analysis Extends the approach of LMS algorithm and its sign-based realizations with normalization factor for DNA analysis Presents the normalized logarithmic-based realizations of LMLS and LLAD adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants Provides an overview of the goals achieved and highlights the primary achievements using all proposed techniques Dr. Md. Zia Ur Rahman is a professor in the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His current research interests include adaptive signal processing, biomedical signal processing, genetic engineering, medical imaging, array signal processing, medical telemetry, and nanophotonics. Dr. Srinivasareddy Putluri is currently a Software Engineer at Tata Consultancy Services Ltd., Hyderabad. He received his Ph.D. degree (Genomic Signal Processing using Adaptive Signal Processing algorithms) from the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His research interests include genomic signal processing and adaptive signal processing. He has published 15 research papers in various journals and proceedings. He is currently a reviewer of publishers like the IEEE Access and IGI.

This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering, biomedical engineering, and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work. Features Presents an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods Covers various theoretical considerations of adaptive filtering techniques used for DNA analysis, introduction to adaptive filtering, properties of adaptive algorithms, need for development of adaptive exon predictors (AEPs), and structure of AEP used for DNA analysis Extends the approach of LMS algorithm and its sign-based realizations with normalization factor for DNA analysis Presents the normalized logarithmic-based realizations of LMLS and LLAD adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants Provides an overview of the goals achieved and highlights the primary achievements using all proposed techniques Dr. Md. Zia Ur Rahman is a professor in the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His current research interests include adaptive signal processing, biomedical signal processing, genetic engineering, medical imaging, array signal processing, medical telemetry, and nanophotonics. Dr. Srinivasareddy Putluri is currently a Software Engineer at Tata Consultancy Services Ltd., Hyderabad. He received his Ph.D. degree (Genomic Signal Processing using Adaptive Signal Processing algorithms) from the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His research interests include genomic signal processing and adaptive signal processing. He has published 15 research papers in various journals and proceedings. He is currently a reviewer of publishers like the IEEE Access and IGI.

This book describes various types of image patterns for image retrieval. All these patterns are texture dependent. Few image patterns such as Improved directional local extrema patterns, Local Quantized Extrema Patterns, Local Color Oppugnant Quantized Extrema Patterns and Local Mesh quantized extrema patterns are presented. Inter-relationships among the pixels of an image are used for feature extraction. In contrast to the existing patterns these patterns focus on local neighborhood of pixels to creates the feature vector. Evaluation metrics such as precision and recall are calculated after testing with standard databases i.e., Corel-1k, Corel-5k and MIT VisTex database. This book serves as a practical guide for students and researchers. -The text introduces two models of Directional local extrema patterns viz., Integration of color and directional local extrema patterns Integration of Gabor features and directional local extrema patterns. -Provides a framework to extract the features using quantization method -Discusses the local quantized extrema collected from two oppugnant color planes -Illustrates the mesh structure with the pixels at alternate positions.

not a coincidence, but is the result of a carefully planned time of landing (sun elevation) and lander orientation (sun azimuth). * The picture was started 25 seconds after touchdown and took 15 seconds to acquire. The alternating bright and dark vertical striations at the left side of the image and the fine particles deposited on the footpad at the right side were caused by a turbulent cloud of dust raised by the lander's retrorockets. t *F. O. Huck and S. D. Wall, "Image quality prediction: An aid to the Viking Lander imaging investigation on Mars. " Appl. Opt. 15, 1748-1766 (1976). tT. A. Mutch, A. B. Binder, F. O. Huck, E. C. Levinthal, S. Liebes, Jr., E. C. Morris, W. R. Patterson, J. B. Pollack, C. Sagan and G. R. Taylor, "The Surface of Mars: The view from the Viking 1 Lander. " Science 193, 791-801 (1976). VISUAL COMMUNICATION An Information Theory Approach Chapter 1 Introduction 1. 1 OBJECTIVE l The fundamental problem of communication, as Shannon stated it, is that of reproducing at one point either exactly or approximately a message selected at another point. In the classical model of communication (Fig. 1. 1), the infor mation source selects a desired message from a set of possible messages which the transmitter changes into the signal that is actually sent over the commu nication channel to the receiver. The receiver changes this signal back into a message, and hands this message to the destination."

Adaptive Filtering Techniques for Remote Health Care Monitoring Systems aims to present a full picture of the state-of-the-art research and development of adaptive signal processing applications in various real-time applications. This book covers some important applications like MIMO, artifact removal, speech enhancement, beam forming, brain computer interface, genomic analysis, biomedical signal processing, healthcare technology, inter symbol interference cancellation, and others.It is certainly not the authors ambition to cover everything concerning adaptive filtering principles and applications. Rather, this edited book features the latest methodological, technical and practical progress on promoting the successful use of adaptive filtering principles and applications, which are more useful in the current day scenario. This book consists of ten chapters contributed by prominent researchers from throughout the world.The intended audience of this book will mainly consist of researchers, research students and practitioners in adaptive filtering and applications. This book is also of interest to researchers and industrial practitioners in areas such as algorithm developers, biomedical engineering, biomedical instrumentation, VLSI circuits design, and embedded systems. This edited book will present research outcomes on theoretical and technical issues related to real-time applications.The authors would like to convey their appreciation to all the contributors, including the accepted chapters authors, and many other participants who submitted their chapters that cannot be included in the book due to space limitation.

This book titled Adaptive Filtering: Principles, Concepts and Applications covers principles, concepts and applications of adaptive filtering. The development of adaptive filtering started in 1976 and widely developed over different application areas. It is certainly not our ambition to cover everything of adaptive filtering principles and applications. Rather, this edited book features the latest methodological, technical and practical progress on promoting the successful use of adaptive filtering principles and applications, which are more useful in the current day scenario. The book contains ten chapters contributed by the experts in the area of adaptive filtering throughout the world. The various applications addressed are MIMO receivers, adaptive exon prediction for DNA analysis, beam steering for smart antennas for mobile applications, telecardiology systems, physiological signal analysis, brain computer interface applications, speech signal conditioning, filtering thoracic electrical bio-impedance, and inter symbol interference cancellation in wireless communication systems. The intended audience of this book will mainly consist of researchers, research students and practitioners in adaptive filtering and applications. The book is also of interest to researchers and industrial practitioners in areas such as algorithm developers, biomedical engineering, biomedical instrumentation, VLSI circuits design, and embedded systems. This edited book will present research outcomes on theoretical and technical issues related to real time applications.

The present research was conducted to investigate the impacts of oxytocin injection on lactating buffaloes. One group of buffaloes was injected with 30 IU of oxytocin at each milking, while second group was kept as control without any treatment. A significant higher level (P 0.01) of glucose, total cholesterol, LDL-C, triglycerides, total proteins and CRP was found in oxytocin injected lactating buffaloes. The serum total oxidant status and total antioxidant status were found to be significantly higher and lower respectively in the oxytocin injected lactating buffaloes. The overall mean serum PON1 and arylesterase concentration was significantly lower while malondialdehyde, total homocysteine and ceruloplasmin was increased significantly in the oxytocin injected lactating buffaloes. The liver enzymes like serum AST and ALT concentration did increase significantly in the oxytocin treated group. Serum T3, estradiol and progesterone was significantly (P 0.05) high in the oxytocin injected lactating buffaloes. Long use of oxytocin did effect the serum composition and animal health.

Lactating Nili-Ravi Buffaloes were injected rbST (500 mg/16 day) to observe effect on milk production, composition, physiological profile, minerals of serum, milk and mammary health biomarkers. Respiration rate was significantly high after day 1 of first injection and low during days 24, 28 and 32 of second injection irrespective of their groups. Overall mean ESR, neutrophils percent decreased and lymphocytes percent increased significantly in bST treated as compared to control buffaloes. The overall increase in milk production was 9.31% from bST treated buffaloes. Overall mean milk lactose increased significantly and milk fat was 1.03% higher (P 0.05) in bST treated buffaloes. Milk calcium, magnesium, sodium, chloride and phosphorus increased significantly. Serum calcium and sodium did increase (P 0.05) while magnesium, potassium, chloride, and phosphorus did decrease (P 0.05) in bST injected buffaloes. A significant increase in milk plasminogen and a decrease in milk plasmin was observed in bST treated buffaloes. Serum ALT, paraoxonase, MDA were significantly high and alkaline phosphatase and the AOS decreased significantly in bST injected buffaloes.