We present several novel constructions of codes which share the common thread of using expander (or expander-like) graphs as a component. The expanders enable the design of efficient decoding algorithms that correct a large number of errors through various forms of “voting ” procedures. We consider

is presented that efficiently decodes highly spontaneous speech within this new representational framework.

We consider the following “efficiently decodable ” nonadaptive group testing problem. There is an unknown string x ∈ {0, 1} n with at most d ones in it. We are allowed to test any subset S ⊆ [n] of the indices. The answer to the test tells whether xi = 0 for all i ∈ S or not. The objective

employed by the cooperating radios, including fixed relaying schemes such as amplify-and-forward and decode-and-forward, selection relaying schemes that adapt based upon channel measurements between the cooperating terminals, and incremental relaying schemes that adapt based upon limited feedback from

suppressor are examined, and an efficient recursive implementation is derived. Simulation results demonstrate that the proposed low-complexity iterative receiver structure for interference suppression and decoding offers significant performance gain over the traditional noniterative receiver structure

: A new data structure is investigated, which allows fast decoding of texts encoded by canonical Huffman codes. The storage requirements are much lower than for conventional Huffman trees, O(log 2 n) for trees of depth O(log n), and decoding is faster, because a part of the bit

Recently a class of generalised Reed-Muller codes has been suggested for use in power-controlled OFDM modulation. A number of approaches to decoding these codes have already been developed. Here we present low complexity alternatives which are inspired by the classical Reed decoding algorithm

We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder

We address the problem of adaptive compression of natural language text, focusing on the case where low bandwidth is available and the receiver has little processing power, as in mobile applications. Our technique achieves compression ratios around 32 % and requires very little effort from the receiver. This tradeoff, not previously achieved with alternative techniques, is obtained by breaking the usual symmetry between sender and receiver present in statistical adaptive compression. Moreover, we show that our technique can be adapted to avoid decompression at all in cases where the receiver only wants to detect the presence of some keywords in the document, which is useful in scenarios such as selective dissemination of information, news clipping, alert systems, text categorization, and clustering. We show that, thanks to the same asymmetry, the receiver can search the compressed text much faster than the plain text. This was previously achieved only in semistatic compression scenarios. 1.

Known strategies for sending bits at the capacity rate over a general channel with classical input and quantum output (a cq channel) require the decoder to implement impractically complicated collective measurements. Here, we show that a fully collective strategy is not necessary in order