Item type Location Call Number Status Date Due
E-Book E-Book AUM Main Library 006.6 (Browse Shelf) Not for loan

Chap. 1 Introduction -- Chap. 2 Wireless Multimedia Sensor Network Technology -- Chap. 3 Hardware Technology and Programming Languages for Reconfigurable Devices -- Chap. 4 FPGA Wireless Multimedia Sensor Node Hardware Platforms -- Chap. 5 Single-View Information Reduction Techniques for WMSN Using Event Detection -- Chap. 6 Single-View Information Reduction Techniques for WMSN Using Event Compression -- Chap. 7 Multiple-View Information Reduction Techniques for WMSN Using Image Stitching -- Appendix -- References.

Traditional wireless sensor networks (WSNs) capture scalar data such as temperature, vibration, pressure, or humidity. Motivated by the success of WSNs and also with the emergence of new technology in the form of low-cost image sensors, researchers have proposed combining image and audio sensors with WSNs to form wireless multimedia sensor networks (WMSNs). This introduces practical and research challenges, because multimedia sensors, particularly image sensors, generate huge amounts of data to be processed and distributed within the network, while sensor nodes have restricted battery power and hardware resources.   This book describes how reconfigurable hardware technologies such as field-programmable gate arrays (FPGAs) offer cost-effective, flexible platforms for implementing WMSNs, with a main focus on developing efficient algorithms and architectures for information reduction, including event detection, event compression, and multicamera processing for hardware implementations. The authors include a comprehensive review of wireless multimedia sensor networks, a complete specification of a very low-complexity, low-memory FPGA WMSN node processor, and several case studies that illustrate information reduction algorithms for visual event compression, detection, and fusion.   The book will be of interest to academic researchers, R&D engineers, and computer science and engineering graduate students engaged with signal and video processing, computer vision, embedded systems, and sensor networks.

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