In this letter, we propose a new ordered decoding scheme for a product code for mobile data communications. The ordered decoding scheme determines the order of decoding for both row and column component codewords according to the probability of decoding the component codeword correctly. Component codewords are decoded independently. To randomize burst errors in both row and column codewords, a ...

In this paper, delay-constrained data transmission is considered over error-prone networks. Network coding is deployed for efficient information exchange, and an approximate decoding approach is deployed to overcome potential all-or-nothing problems. Our focus is on determining the cluster size and its impact on approximate decoding performance. Decoding performance is quantified, and we show t...

This paper considers a framework where data from correlated sources are transmitted with the help of network coding in ad hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder whe...

Algebraic space–time coding allows for reliable data exchange across fading multiple-input multiple-output channels. A powerful technique for decoding space–time codes is Maximum-Likelihood (ML) decoding, but well-performing and widely-used codes such as the Golden code often suffer from high ML-decoding complexity. In this article, a recursive algorithm for decoding general algebraic space–tim...

We examine the ability of combining symbolic and subsymbolic approaches b y means of recursively encoding and decoding structured data. We show that encoding of symbolic data is possible in this w ay { hence neural netw orks seem well suited for control or classi cation in symbolic approaches { whereas decoding requires an increasing complexit y of the decoding function { hence netw orks with t...

Sequential decoding can achieve a very low computational complexity and short decoding delay when the signalto-noise ratio (SNR) is relatively high. In this article, a low-complexity high-throughput decoding architecture based on a sequential decoding algorithm is proposed for convolutional codes. Parallel Fano decoders are scheduled to the codewords in parallel input buffers according to buffe...

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we des...