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As the state-of-the-art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important. Sources of data that have to be routinely dealt with today’s applications include video transmission, wireless communication, automatic fingerprint processing, massive databanks, non-weary and accurate automatic airport screening, robust night vision, just to name a few. Multidisciplinary inputs from other disciplines such as physics, computational neuroscience, cognitive science, mathematics, and biology will have a fundamental impact in the progress of imaging and vision sciences. One of the advantages of the study of biological organisms is to devise very different type of computational paradigms by implementing a neural network with a high degree of local connectivity.
This is a comprehensive and rigorous reference in the area of biologically motivated vision sensors. The study of biologically visual systems can be considered as a two way avenue. On the one hand, biological organisms can provide a source of inspiration for new computational efficient and robust vision models and on the other hand machine vision approaches can provide new insights for understanding biological visual systems. Along the different chapters, this book covers a wide range of topics from fundamental to more specialized topics, including visual analysis based on a computational level, hardware implementation, and the design of new more advanced vision sensors. The last two sections of the book provide an overview of a few representative applications and current state of the art of the research in this area. This makes it a valuable book for graduate, Master, PhD students and also researchers in the field.
Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners’ course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language.
; “> Many years ago we started programming in Python because we were working on a large computational biology project. In those days choosing Python was not nearly as common as it is today. Nonetheless things worked out well, and as our expertise grew it seemed only natural that we should run some elementary Python courses for the School of Biology at the University of Cambridge, where we were employed. The basis for those courses is what turned into the initial idea for this book. While there were many books about getting started with Python and some that were tailored to bioinformatics, we felt that there was still some room for what we wanted to put across. We began with the idea that we could write some chapters in relatively straightforward English that were aimed at biologists, who might be complete novices at programming, and have other sections that are useful to a more experienced programmer. Also, given that we didn’t consider ourselves to be typical bioinformaticians, we were thinking more broadly than just sequence-based informatics, though naturally such things would be included. We felt that although we
couldn’t anticipate all the requirements of a biological programmer there were nonetheless a number of key concepts and techniques which we could try to explain. The end result is hopefully a toolkit of ideas and examples which can be applied by biologists in a variety of situations.
This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers.
One of the leading authorities on biological farming, Zimmer is recognized for improving farming by restoring soils. Arguing that an optimally productive soil contains a balance of inorganic minerals, organic materials and living organisms, he relies less on modern improvements than on ”the things we’ve learned by improving fertility in a natural, sustainable way over many years.” This book offers invaluable scientific support for committed organic farmers as well as conventional farmers who’d like to reduce chemical inputs and use natural processes to their advantage. Advancing Biological Farming updates and expands upon Zimmer’s classic, The Biological Farmer. Technically precise yet written in friendly language, this book is for everyone who wants a future in bio-logical farming.