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- W1979374503 abstract "We consider the distributed source coding problem in which correlated data picked up by scattered sensors has to be encoded separately and transmitted to a common receiver, subject to a rate-distortion constraint. Although near-to-optimal solutions based on Turbo and LDPC codes exist for this problem, in most cases the proposed techniques do not scale to networks of hundreds of sensors. We present a scalable solution based on the following key elements: (a) distortion-optimized index assignments for low-complexity distributed quantization, (b) source-optimized hierarchical clustering based on the Kullback-Leibler distance and (c) sum-product decoding on specific factor graphs exploiting the correlation of the data." @default.
- W1979374503 created "2016-06-24" @default.
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- W1979374503 date "2009-05-01" @default.
- W1979374503 modified "2023-09-24" @default.
- W1979374503 title "Low-complexity coding and source-optimized clustering for large-scale sensor networks" @default.
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- W1979374503 doi "https://doi.org/10.1145/1525856.1525862" @default.
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