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When correlated sources are to be communicated over a network to more than one sink, joint source-network coding is, in general, required for information theoretically optimal transmission. Whereas on the encoder side simple randomized schemes based on linear codes suffice, the decoder is required to perform joint source-network decoding which is computationally expensive. Focusing on maximum a-posteriori...
Focusing on the design of scalar quantizers for secure communication in a wiretap environment we propose a low-complexity encoding method that minimizes the end-to-end distortion between legitimate terminals while maximizing the distortion at the eavesdropping receiver. The key idea is to generate confusion by means of randomized index assignments. A simple iterative optimization scheme is shown to...
We consider the design of index assignments for the distributed source coding problem in large-scale sensor networks. Using basic tools from number theory, specifically Diophantine analysis, we provide a framework for constructing cyclic index assignments that have very low complexity yet perform very close to fundamental bounds provided by rate-distortion theory.
Motivated by the design of low-complexity distributed quantizers and iterative decoding algorithms that leverage the correlation in the data picked up by a large-scale sensor network, we address the problem of finding correlation preserving clusters. To construct a factor graph describing the statistical dependencies between sensor measurements, we develop a hierarchical clustering algorithm that...
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