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Soft cover. Zustand: New. Contents: Preface to the second edition. Foreword. Acknowledgements. 1. From cell biology to biotechnology/D. Balasubramanian. 2. Interplay of macromolecules in a living cell/A. Pena. 3. Structural and functional dynamics of the cell/G.E. Herve. 4. Gene structure and expression/A.C. Robinson, L.L. Kisselev. 5. Gene technology/Kunthala Jayaraman, M. Sritharan. 6. Protein engineering and design/V. Pattabhi and N. Gautham. 7. Enzyme technology/D. Thomas and J.M. Laval. 8. Bioprocess technology: exploitation of microorganisms for the production of chemicals/J. Green, M. El-Mansi. 9. Bioprocess technology: exploitation of animal cells/A. Fiechter. 10. Immunotechnology/G.P. Talwar, R. Raghupathy, S.K. Gupta and V. Bal. 11. Biotechnology as a new frontier in health/M.G. Deo and R. Mulherkar. 12. Plant biotechnology/K. Dharmalingam and K. Veluthambi. 13. Biotechnology in livestock production/K.C. Reed and G.A. Smith. 14. Biotechnology in food and beverage production. 15. Environmental biotechnology. 16. Bio-informatics and pattern recognition in DNA and protein sequences/G.I. Bell and M. El-Mansi. 17. Marine biotechnology/R.R. Colwell and R.T. Hill. 18. Impact of biotechnology on the sustainability of the environmental/F.W.G. Baker. 19. Biotechnology, international competition, and economic, ethical and social implications in developing countries/D.R.J. Macer. Contributors and editors. Glossary. Index. "This Textbook is the outcome of a COSTED--IBN project on curriculum development in biotechnology for undergraduate study, especially for students in developing countries. It is designed to provide a strong base in this emerging, interdisciplinary area which holds great promise for economic development. The book presents an overview of biotechnology, which is concerned with the applications of living cells or their components to the benefit of humankind. The early chapters review the structure and function of biological molecules and living cells, and the way in which cellular structure and function is controlled by the genetic makeup of the cell. The diverse technologies associated with the application of living systems in select areas--such as health care, agriculture, animal systems, food and beverage production, environment maintenance, bioprocess technologies--are then described. Finally, the impact of biotechnology on the environment, and ethical and social implications of this technology, are discussed. The text is interspersed with assignments and self assessment exercises designed to reinforce key concepts. The end-of-chapter reading lists and glossary of technical terms further enhance the the usefulness of the book.".…

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Taschenbuch. Zustand: Neu. Image Understanding using Sparse Representations | Jayaraman J. Thiagarajan (u. a.) | Taschenbuch | Synthesis Lectures on Image, Video, and Multimedia Processing | xi | Englisch | 2014 | Springer | EAN 9783031011221 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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Hardcover. Zustand: Fine. Zustand des Schutzumschlags: Very Good. 520 pp., illustrations, bibliography, index. This monograph provides a bird's-eye view of the complex technology of Unmanned Aerial Vehicles (UAVs). It is targeted towards a wider audience that includes, beginners who need an introduction to the subject; practicing specialist engineers who need to learn about other disciplines; and programme managers, bureaucrats, planners and policy makers who would like to get a holistic picture of the UAV technology. It provides an approach to the system engineering methodology for the initial sizing of an UAV. A very informative chapter on the lesson learnt provides practicing engineers a glimpse of real life problems. The book discusses several issues relating to the operation of UAVs. An exhaustive compilation of references is provided with every chapter to explore the depth when desired.…

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations. 120 pp. Englisch.…

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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual co.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch.…