CLUSTER:IEEE International Conference on Cluster Computing。 Explanation:IEEE集群计算国际会议。 Publisher:IEEE。 SIT: https://dblp.uni-trier.de/db/conf/cluster/

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In a distributed information application an encoder compresses an arbitrary vector while a similar reference vector is available to the decoder as side information. For the Hamming-distance similarity measure, and when guaranteed perfect reconstruction is required, we present two contributions to the solution of this problem. One result shows that when a set of potential reference vectors is available to the encoder, lower compression rates can be achieved when the set satisfies a certain clustering property. Another result reduces the best known decoding complexity from exponential in the vector length $n$ to $O(n^{1.5})$ by generalized concatenation of inner coset codes and outer error-correcting codes. One potential application of the results is the compression of DNA sequences, where similar (but not identical) reference vectors are shared among senders and receivers.

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In a distributed information application an encoder compresses an arbitrary vector while a similar reference vector is available to the decoder as side information. For the Hamming-distance similarity measure, and when guaranteed perfect reconstruction is required, we present two contributions to the solution of this problem. One result shows that when a set of potential reference vectors is available to the encoder, lower compression rates can be achieved when the set satisfies a certain clustering property. Another result reduces the best known decoding complexity from exponential in the vector length $n$ to $O(n^{1.5})$ by generalized concatenation of inner coset codes and outer error-correcting codes. One potential application of the results is the compression of DNA sequences, where similar (but not identical) reference vectors are shared among senders and receivers.

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