Jeng-Shyang Pan

The aim of this book is to provide some useful methods to improve the spectrum sensing performance in a systematic way, and point out an effective method for the application of cognitive radio technology in wireless communications. The book gives a a state-of-the-art survey and proposes some new cooperative spectrum sensing (CSS) methods attempting to achieve better performance. For each CSS, the main idea and corresponding algorithm design are elaborated in detail. This book covers the fundamental concepts and the core technologies of CSS, especially its latest developments. Each chapter is presented in a self-sufficient and independent way so that the reader can select the chapters interesting to them. The methodologies are described in detail so that the readers can repeat the corresponding experiments easily. It will be a useful book for researchers helping them to understand the classifications of CSS, inspiring new ideas about the novel CSS technology for CR, and learning newideas from the current status of CSS. For engineers, it will be a good guidebook to develop practical applications for CSS.

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.

The aim of this book is to provide some useful methods to improve the spectrum sensing performance in a systematic way, and point out an effective method for the application of cognitive radio technology in wireless communications. The book gives a a state-of-the-art survey and proposes some new cooperative spectrum sensing (CSS) methods attempting to achieve better performance. For each CSS, the main idea and corresponding algorithm design are elaborated in detail. This book covers the fundamental concepts and the core technologies of CSS, especially its latest developments. Each chapter is presented in a self-sufficient and independent way so that the reader can select the chapters interesting to them. The methodologies are described in detail so that the readers can repeat the corresponding experiments easily. It will be a useful book for researchers helping them to understand the classifications of CSS, inspiring new ideas about the novel CSS technology for CR, and learning newideas from the current status of CSS. For engineers, it will be a good guidebook to develop practical applications for CSS.

This book examines distributed video coding (DVC) and multiple description coding (MDC), two novel techniques designed to address the problems of conventional image and video compression coding. Covering all fundamental concepts and core technologies, the chapters can also be read as independent and self-sufficient, describing each methodology in sufficient detail to enable readers to repeat the corresponding experiments easily. Topics and features: provides a broad overview of DVC and MDC, from the basic principles to the latest research; covers sub-sampling based MDC, quantization based MDC, transform based MDC, and FEC based MDC; discusses Sleplian-Wolf coding based on Turbo and LDPC respectively, and comparing relative performance; includes original algorithms of MDC and DVC; presents the basic frameworks and experimental results, to help readers improve the efficiency of MDC and DVC; introduces the classical DVC system for mobile communications, providing the developmental environment in detail.

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.

This book examines distributed video coding (DVC) and multiple description coding (MDC), two novel techniques designed to address the problems of conventional image and video compression coding. Covering all fundamental concepts and core technologies, the chapters can also be read as independent and self-sufficient, describing each methodology in sufficient detail to enable readers to repeat the corresponding experiments easily. Topics and features: provides a broad overview of DVC and MDC, from the basic principles to the latest research; covers sub-sampling based MDC, quantization based MDC, transform based MDC, and FEC based MDC; discusses Sleplian-Wolf coding based on Turbo and LDPC respectively, and comparing relative performance; includes original algorithms of MDC and DVC; presents the basic frameworks and experimental results, to help readers improve the efficiency of MDC and DVC; introduces the classical DVC system for mobile communications, providing the developmental environment in detail.