In this paper, we consider the problem of decoding network coded correlated data when the decoder does not receive sufficient information for exact decoding. We propose an iterative decoding algorithm based on belief propagation that efficiently exploits t ...
Many recent algorithms for sparse signal recovery can be interpreted as maximum-a-posteriori (MAP) estimators relying on some specific priors. From this Bayesian perspective, state-of-the-art methods based on discrete-gradient regularizers, such as total-v ...
Estimation of a vector from quantized linear measurements is a common problem for which simple linear techniques are suboptimal—sometimes greatly so. This paper develops message-passing de-quantization (MPDQ) algorithms for minimum mean-squared error estim ...
A partially parallel low density parity check (LDPC) decoder compliant with the IEEE 802.3an standard for 10GBASE-T Ethernet is presented. The design is optimized for minimum silicon area and is based on the layered offset-min-sum algorithm which speeds up ...
Until recently, most data integration techniques involved central components, e.g., global schemas, to enable transparent access to heterogeneous databases. Today, however, with the democratization of tools facilitating knowledge elicitation in machine-pro ...
In this thesis, a new class of codes on graphs based on chaotic dynamical systems are proposed. In particular, trellis coded modulation and iteratively decodable codes on graphs are studied. The codes are designed by controlling symbolic dynamics of chaoti ...
In this paper a class of discretized piecewise linear chaotic maps of a very high dimensions are used for communication over a noisy channel. An information payload that is sent over a channel is controlled in the transmitter and is related to the symbolic ...
