S. Sitharama Iyengar

Helping to advance a valuable paradigm shift in the next generation and processing of knowledge, this seminal work provides a comprehensive model for constructing a contextually based processing system that can support advanced semantic web and cloud computing capabilities at a global scale. It explores the components of the system, the interactions of the components, key mathematical foundations behind the model, and new concepts necessary for operating the system. The book also describes numerous cutting-edge applications and research directions for contextual processing and offers novel methods for addressing the producer-consumer problem.

Helping to advance a valuable paradigm shift in the next generation and processing of knowledge, this seminal work provides a comprehensive model for constructing a contextually based processing system that can support advanced semantic web and cloud computing capabilities at a global scale. It explores the components of the system, the interactions of the components, key mathematical foundations behind the model, and new concepts necessary for operating the system. The book also describes numerous cutting-edge applications and research directions for contextual processing and offers novel methods for addressing the producer-consumer problem.

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.

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.

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.