9781155729213 - wireless sensor network: distributed source coding, zigbee, sensor web, dash7, zigbee specification, list of wireless sensor nodes, network coding, ... with collision avoidance for wireless, tinyos (3 Ergebnisse)
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Source: Wikipedia. Pages: 49. Chapters: Distributed source coding, ZigBee, Sensor web, DASH7, ZigBee specification, List of wireless sensor nodes, Network coding, Z-Wave, Location estimation in sensor networks, ANT, Multiple Access wi…th Collision Avoidance for Wireless, TinyOS, Smartdust, Topology control, Dynamic Source Routing, Ad hoc On-Demand Distance Vector Routing, Dust Networks, Daintree Networks, Contiki, PowWow, Digi International, Visual sensor network, WirelessHART, Sun SPOT, Virtual Sensor Networks, International Conference on Information Processing in Sensor Networks, ERIKA Enterprise, Sensor grid, Key distribution in wireless sensor networks, TakaTuka, Isa100.11a, OCARI, Conference on Embedded Networked Sensor Systems, OSIAN, NesC, Wide Area Tracking System, ANT+, Wireless Identification and Sensing Platform, Low Energy Adaptive Clustering Hierarchy, Modulo-N code, Nano-RK, TSMP, European Conference on Wireless Sensor Networks, DISCUS, Secure Data Aggregation in WSN, Cooperative Positioning for Vehicular Networks, LiteOS, NeuRFon, Hogthrob. Excerpt: Distributed source coding (DSC) is an important problem in information theory and communication. DSC problems regard the compression of multiple correlated information sources that do not communicate with each other. By modeling the correlation between multiple sources at the decoder side together with channel codes, DSC is able to shift the computational complexity from encoder side to decoder side, therefore provide appropriate frameworks for applications with complexity-constrained sender, such as sensor networks and video/multimedia compression (see distributed video coding). One of the main properties of distributed source coding is that the computational burden in encoders is shifted to the joint decoder. In 1973, David Slepian and Jack Keil Wolf proposed the information theoretical lossless compression bound on distributed compression of two statistically dependent i.i.d. sources X and Y . After that, this bound was extended to cases with more than two sources by Thomas M. Cover in 1975 , while the theoretical results on lossy compression case are presented by Aaron D. Wyner and Jacob Ziv in 1976 . Although the theorems on DSC were proposed on 1970s, it was after about 30 years that attempts were started for practical techniques, based on the idea that DSC is closely related to channel coding proposed in 1974 by Aaron D. Wyner . The asymmetric DSC problem was addressed by S. S. Pradhan and K. Ramchandran in 1999, which focused on statistically dependent binary and Guassian sources and used scalar and trellis coset constructions to solve the problem . They further extended the work into symmetric DSC case lately . Syndrome decoding technology was first used in distributed source coding by the DISCUS system of SS Pradhan and K Ramachandran (Distributed Source Coding Using Syndromes). They compress binary block data from one source into syndromes and transmit data from the other source uncompressed as side information. This kind of DSC scheme achieves asymmetric 50 pp. Englisch.
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Source: Wikipedia. Pages: 49. Chapters: Distributed source coding, ZigBee, Sensor web, DASH7, ZigBee specification, List of wireless sensor nodes, Network coding, Z-Wave, Location estimation in sensor networks, ANT, Multiple Access with C…ollision Avoidance for Wireless, TinyOS, Smartdust, Topology control, Dynamic Source Routing, Ad hoc On-Demand Distance Vector Routing, Dust Networks, Daintree Networks, Contiki, PowWow, Digi International, Visual sensor network, WirelessHART, Sun SPOT, Virtual Sensor Networks, International Conference on Information Processing in Sensor Networks, ERIKA Enterprise, Sensor grid, Key distribution in wireless sensor networks, TakaTuka, Isa100.11a, OCARI, Conference on Embedded Networked Sensor Systems, OSIAN, NesC, Wide Area Tracking System, ANT+, Wireless Identification and Sensing Platform, Low Energy Adaptive Clustering Hierarchy, Modulo-N code, Nano-RK, TSMP, European Conference on Wireless Sensor Networks, DISCUS, Secure Data Aggregation in WSN, Cooperative Positioning for Vehicular Networks, LiteOS, NeuRFon, Hogthrob. Excerpt: Distributed source coding (DSC) is an important problem in information theory and communication. DSC problems regard the compression of multiple correlated information sources that do not communicate with each other. By modeling the correlation between multiple sources at the decoder side together with channel codes, DSC is able to shift the computational complexity from encoder side to decoder side, therefore provide appropriate frameworks for applications with complexity-constrained sender, such as sensor networks and video/multimedia compression (see distributed video coding). One of the main properties of distributed source coding is that the computational burden in encoders is shifted to the joint decoder. In 1973, David Slepian and Jack Keil Wolf proposed the information theoretical lossless compression bound on distributed compression of two statistically dependent i.i.d. sources X and Y . After that, this bound was extended to cases with more than two sources by Thomas M. Cover in 1975 , while the theoretical results on lossy compression case are presented by Aaron D. Wyner and Jacob Ziv in 1976 . Although the theorems on DSC were proposed on 1970s, it was after about 30 years that attempts were started for practical techniques, based on the idea that DSC is closely related to channel coding proposed in 1974 by Aaron D. Wyner . The asymmetric DSC problem was addressed by S. S. Pradhan and K. Ramchandran in 1999, which focused on statistically dependent binary and Guassian sources and used scalar and trellis coset constructions to solve the problem . They further extended the work into symmetric DSC case lately . Syndrome decoding technology was first used in distributed source coding by the DISCUS system of SS Pradhan and K Ramachandran (Distributed Source Coding Using Syndromes). They compress binary block data from one source into syndromes and transmit data from the other source uncompressed as side information. This kind of DSC scheme achieves asymmetricBooks on Demand GmbH, Überseering 33, 22297 Hamburg 50 pp. Englisch.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Source: Wikipedia. Pages: 49. Chapters: Distributed source coding, ZigBee, Sensor web, DASH7, ZigBee specification, List of wireless sensor nodes, Network coding, Z-Wave, Location estimation in sensor networks, ANT, Multiple Access with Co…llision Avoidance for Wireless, TinyOS, Smartdust, Topology control, Dynamic Source Routing, Ad hoc On-Demand Distance Vector Routing, Dust Networks, Daintree Networks, Contiki, PowWow, Digi International, Visual sensor network, WirelessHART, Sun SPOT, Virtual Sensor Networks, International Conference on Information Processing in Sensor Networks, ERIKA Enterprise, Sensor grid, Key distribution in wireless sensor networks, TakaTuka, Isa100.11a, OCARI, Conference on Embedded Networked Sensor Systems, OSIAN, NesC, Wide Area Tracking System, ANT+, Wireless Identification and Sensing Platform, Low Energy Adaptive Clustering Hierarchy, Modulo-N code, Nano-RK, TSMP, European Conference on Wireless Sensor Networks, DISCUS, Secure Data Aggregation in WSN, Cooperative Positioning for Vehicular Networks, LiteOS, NeuRFon, Hogthrob. Excerpt: Distributed source coding (DSC) is an important problem in information theory and communication. DSC problems regard the compression of multiple correlated information sources that do not communicate with each other. By modeling the correlation between multiple sources at the decoder side together with channel codes, DSC is able to shift the computational complexity from encoder side to decoder side, therefore provide appropriate frameworks for applications with complexity-constrained sender, such as sensor networks and video/multimedia compression (see distributed video coding). One of the main properties of distributed source coding is that the computational burden in encoders is shifted to the joint decoder. In 1973, David Slepian and Jack Keil Wolf proposed the information theoretical lossless compression bound on distributed compression of two statistically dependent i.i.d. sources X and Y . After that, this bound was extended to cases with more than two sources by Thomas M. Cover in 1975 , while the theoretical results on lossy compression case are presented by Aaron D. Wyner and Jacob Ziv in 1976 . Although the theorems on DSC were proposed on 1970s, it was after about 30 years that attempts were started for practical techniques, based on the idea that DSC is closely related to channel coding proposed in 1974 by Aaron D. Wyner . The asymmetric DSC problem was addressed by S. S. Pradhan and K. Ramchandran in 1999, which focused on statistically dependent binary and Guassian sources and used scalar and trellis coset constructions to solve the problem . They further extended the work into symmetric DSC case lately . Syndrome decoding technology was first used in distributed source coding by the DISCUS system of SS Pradhan and K Ramachandran (Distributed Source Coding Using Syndromes). They compress binary block data from one source into syndromes and transmit data from the other source uncompressed as side information. This kind of DSC scheme achieves asymmetric.
