In Shannon’s Foundation of Information Theory ([27]) perhaps the most basic contributions are Source Coding Theorems (lossy and lossless) and the Channel Coding Theorem. In the most natural and simple source model DMS the source outputs a sequence X1, X2, . . . of independent, identically distributed random variables taking finitely many values. The Lossy Source Coding Theorem says that this se...

Considering the fact that sensors are energy-limited and the wireless channel conditions in wireless sensor networks, there is an urgent need for a low-complexity coding method with high compression ratio and noise-resisted features. This paper reviews the progress made in distributed joint source-channel coding which can address this issue. The main existing deployments, from the theory to pra...

Distributed source coding makes it possible to develop compression algorithms with a low−complexity encoder, while most of the signal modeling is moved to the decoder. This structure is an excellent match to the remote sensing scenario, in which the on−board processing units have limited computational capabilities. Moreover, remote sensing images do not exhibit motion, which greatly simplifies ...

In this paper we consider the problem of lossy coding of correlated vector sources with uncoded side information available at the decoder. In particular, we consider lossy coding of source x ∈ R which is correlated with source y ∈ R , known at the decoder. The general non-linear mapping between y and x capturing the correlation between the two sources can be approximated through a linear model ...

We present a novel randomized network coding approach for robust, distributed transmission and compression of information in networks, and demonstrate its advantages over routing-based