@inproceedings{Olmos2010,

title = {Tree-Structure Expectation Propagation for Decoding LDPC Codes over Binary Erasure Channels},

author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5513636},

isbn = {978-1-4244-7892-7},

year  = {2010},

date = {2010-01-01},

booktitle = {2010 IEEE International Symposium on Information Theory},

pages = {799--803},

publisher = {IEEE},

address = {Austin, TX},

abstract = {Expectation Propagation is a generalization to Belief Propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pair-wise marginal distribution constraints in some check nodes of the LDPC Tanner graph. These additional constraints allow decoding the received codeword when the BP decoder gets stuck. In this paper, we first present the new decoding algorithm, whose complexity is identical to the BP decoder, and we then prove that it is able to decode codewords with a larger fraction of erasures, as the block size tends to infinity. The proposed algorithm can be also understood as a simplification of the Maxwell decoder, but without its computational complexity. We also illustrate that the new algorithm outperforms the BP decoder for finite block-size codes.},

keywords = {belief propagation, binary erasure channels, Bipartite graph, BP decoder, Capacity planning, Channel Coding, codeword, computational complexity, Decoding, Finishing, graph theory, H infinity control, LDPC code decoding, LDPC Tanner graph, Maxwell decoder, parity check codes, Performance analysis, tree structure expectation propagation, trees (mathematics), Upper bound},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Fresia2009,

title = {Optimized Concatenated LDPC Codes for Joint Source-Channel Coding},

author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205766},

isbn = {978-1-4244-4312-3},

year  = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE International Symposium on Information Theory},

pages = {2131--2135},

publisher = {IEEE},

address = {Seoul},

abstract = {In this paper a scheme for joint source-channel coding based on low-density-parity-check (LDPC) codes is investigated. Two concatenated independent LDPC codes are used in the transmitter: one for source coding and the other for channel coding, with a joint belief propagation decoder. The asymptotic behavior is analyzed using EXtrinsic Information Transfer (EXIT) charts and this approximation is corroborated with illustrative experiments. The optimization of the degree distributions for our sparse code to maximize the information transmission rate is also considered.},

keywords = {approximation theory, asymptotic behavior analysis, Channel Coding, combined source-channel coding, Concatenated codes, Decoding, Entropy, EXIT chart, extrinsic information transfer, H infinity control, Information analysis, joint belief propagation decoder, joint source-channel coding, low-density-parity-check code, optimized concatenated independent LDPC codes, parity check codes, Redundancy, source coding, transmitter, Transmitters},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Perez-Cruz2008,

title = {Kullback-Leibler Divergence Estimation of Continuous Distributions},

author = {Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4595271},

isbn = {978-1-4244-2256-2},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 IEEE International Symposium on Information Theory},

pages = {1666--1670},

publisher = {IEEE},

address = {Toronto},

abstract = {We present a method for estimating the KL divergence between continuous densities and we prove it converges almost surely. Divergence estimation is typically solved estimating the densities first. Our main result shows this intermediate step is unnecessary and that the divergence can be either estimated using the empirical cdf or k-nearest-neighbour density estimation, which does not converge to the true measure for finite k. The convergence proof is based on describing the statistics of our estimator using waiting-times distributions, as the exponential or Erlang. We illustrate the proposed estimators and show how they compare to existing methods based on density estimation, and we also outline how our divergence estimators can be used for solving the two-sample problem.},

keywords = {Convergence, density estimation, Density measurement, Entropy, Frequency estimation, H infinity control, information theory, k-nearest-neighbour density estimation, Kullback-Leibler divergence estimation, Machine learning, Mutual information, neuroscience, Random variables, statistical distributions, waiting-times distributions},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Koch2008b,

title = {Multipath Channels of Unbounded Capacity},

author = {Tobias Koch and Amos Lapidoth},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4736611},

isbn = {978-1-4244-2481-8},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel},

pages = {640--644},

publisher = {IEEE},

address = {Eilat},

abstract = {The capacity of discrete-time, noncoherent, multipath fading channels is considered. It is shown that if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the transmit power.},

keywords = {channel capacity, discrete-time capacity, Entropy, Fading, fading channels, Frequency, H infinity control, Information rates, multipath channels, multipath fading channels, noncoherent, noncoherent capacity, path gains decay, Signal to noise ratio, statistics, Transmitters, unbounded capacity},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Santiago-Mozos2008,

title = {On the Uncertainty in Sequential Hypothesis Testing},

author = {Ricardo Santiago-Mozos and R Fernandez-Lorenzana and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4541223},

isbn = {978-1-4244-2002-5},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},

pages = {1223--1226},

publisher = {IEEE},

address = {Paris},

abstract = {We consider the problem of sequential hypothesis testing when the exact pdfs are not known but instead a set of iid samples are used to describe the hypotheses. We modify the classical test by introducing a likelihood ratio interval which accommodates the uncertainty in the pdfs. The test finishes when the whole likelihood ratio interval crosses one of the thresholds and reduces to the classical test as the number of samples to describe the hypotheses tend to infinity. We illustrate the performance of this test in a medical image application related to tuberculosis diagnosis. We show in this example how the test confidence level can be accurately determined.},

keywords = {binary hypothesis test, Biomedical imaging, Detectors, H infinity control, likelihood ratio, Medical diagnostic imaging, medical image application, medical image processing, Medical tests, patient diagnosis, Probability, Random variables, Sequential analysis, sequential hypothesis testing, sequential probability ratio test, Signal processing, Testing, tuberculosis diagnosis, Uncertainty},

pubstate = {published},

tppubtype = {inproceedings}

}