This book provides the basics needed to develop sensor network software and supplements it with many case studies covering network applications. It also examines how to develop onboard applications on individual sensors, how to interconnect these sensors, and how to form networks of sensors, although the major aim of this book is to provide foundational principles of developing sensor networking software and critically examine sensor network applications.

Fundamentals of Sensor Network Programming: Applications and Technology

Engineering & Transportation

Balakrishnan, Narayanaswamy

Phoha, Vir V.

Iyengar, S. Sitharama

Parameshwaran, Nandan

Okoye, Chuka D.

Engineering

Wiley-IEEE Press

Wavelet analysis is among the newest additions to the arsenals of mathematicians, scientists, and engineers, and offers common solutions to diverse problems. However, students and professionals in some areas of engineering and science, intimidated by the mathematical background necessary to explore this subject, have been unable to use this powerful tool. The first book on the topic for readers with minimal mathematical backgrounds, Wavelet Analysis with Applications to Image Processing provides a thorough introduction to wavelets with applications in image processing. Unlike most other works on this subject, which are often collections of papers or research advances, this book offers students and researchers without an extensive math background a step-by-step introduction to the power of wavelet transforms and applications to image processing. The first four chapters introduce the basic topics of analysis that are vital to understanding the mathematics of wavelet transforms. Subsequent chapters build on the information presented earlier to cover the major themes of wavelet analysis and its applications to image processing. This is an ideal introduction to the subject for students, and a valuable reference guide for professionals working in image processing.

Wavelet Analysis with Applications to Image Processing

Computers & Technology

Prasad, Lakshman

Computer Science

CRC-Press

:Computer modeling of biological systems, a process of building new sets of conceptualizations, theories, and implementations is cov-ered in depth. The emphasis is on how computer modeling encom-passes three distinct areas of science (computer science, biology, and medicine) for building a framework for investigating the behavior of a complex biological system. Simulation and modeling are dis-cussed because these two factors provide the methodology for the design, development, experimentation, analysis, and evaluation of a experiment under study. Because of their high degree of modular-ity and collective complexity, computer systems such as analog, digital, or hybrid are reviewed. This presentation of computer mod-eling of biological systems delves into the study of data represen-tation (or data structures) and their transformation into an empirical model via a software program.

Computer Modeling of Complex Biological Systems

Science & Math

Biological Sciences

Structuring Biological Systems focuses on the important components of biological systems in order to develop genetic algorithms for modeling purposes. The book considers the characteristics of biological systems from the artificial intelligence point of view, examines modeling examples of complex biological systems (such as molecular level modeling, a model of renal hemodynamics, and cognitive aspects of modeling), describes the entropy-based probability distribution for modeling of environmental and biological systems, and presents a detailed analysis of modeling cancer phenomena. Structuring Biologic Systems will benefit students and researchers interested in an interdisciplinary approach to complex problems of biological systems, as well as biologists, chemists, engineers, research physicians, and computer scientists.

Structuring Biological Systems: A Computer Modeling Approach (Neurocomputing for Modeling Complex Biological Systems Series)

Introduction to Contextual Processing: Theory and Applications

Develops a Comprehensive, Global Model for Contextually Based Processing SystemsA new perspective on global information systems operation Helping to advance a valuable paradigm shift in the next generation and processing of knowledge, Introduction to Contextual Processing: Theory and Applications provides a comprehensive model for constructing a contextually based processing system. It explores the components of this system, the interactions of the components, key mathematical foundations behind the model, and new concepts necessary for operating the system.  After defining the key dimensions of a model for contextual processing, the book discusses how data is used to develop a semantic model for contexts as well as language-driven context-specific processing actions. It then applies rigorous mathematical methods to contexts, examines basic sensor data fusion theory and applies it to the contextual fusion of information, and describes the means to distribute contextual information. The authors also illustrate a new type of data repository model to manage contextual data, before concluding with the requirements of contextual security in a global environment.  This seminal work presents an integrated framework for the design and operation of the next generation of IT processing. It guides the way for developing advanced IT systems and offers new models and concepts that can support advanced semantic web and cloud computing capabilities at a global scale.

Vert, Gregory

Games & Strategy Guides

Chapman and Hall/CRC

Traditionally, neural networks and wavelet theory have been two separate disciplines, taught separately and practiced separately. In recent years the offspring of wavelet theory and neural networks-wavelet networks-have emerged and grown vigorously both in research and applications. Yet the material needed to learn or teach wavelet networks has remained scattered in various research monographs. Foundations of Wavelet Networks and Applications unites these two fields in a comprehensive, integrated presentation of wavelets and neural networks. It begins by building a foundation, including the necessary mathematics. A transitional chapter on recurrent learning then leads to an in-depth look at wavelet networks in practice, examining important applications that include using wavelets as stock market trading advisors, as classifiers in electroencephalographic drug detection, and as predictors of chaotic time series. The final chapter explores concept learning and approximation by wavelet networks. The potential of wavelet networks in engineering, economics, and social science applications is rich and still growing. Foundations of Wavelet Networks and Applications prepares and inspires its readers not only to help ensure that potential is achieved, but also to open new frontiers in research and applications.

Foundations of Wavelet Networks and Applications

Phoha, V.V.

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