Neural prosthetic systems for motor control and communication have produced striking results in recent studies with non-human primates and human volunteers. We describe a new approach in our ongoing work toward develo...
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ISBN:
(纸本)9781424447138
Neural prosthetic systems for motor control and communication have produced striking results in recent studies with non-human primates and human volunteers. We describe a new approach in our ongoing work toward developing an intracortical neural prosthesis for speech restoration with a 26 year old human volunteer with tetraplegia (including loss of vocal and facial muscle control). We propose to use hidden Markov models (HMMs) to decode neural firing activity in speech motor cortex. We show how classical and recent approaches to automatic speech recognition (ASR) apply directly to the decoding stage of a neural prosthesis. We outline a series of experiments in collecting cortical neural firing data from our human volunteer, and discuss important challenges and considerations in implementing an HMM framework for a neural speech prosthesis.
Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-cla...
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Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach.
Belief propagation, via a novel reinterpretation of the Bethe free energy's pseudo-dual, is shown to be related to a novel relaxation of maximum likelihood detection via a constrained optimization. The conventiona...
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Belief propagation, via a novel reinterpretation of the Bethe free energy's pseudo-dual, is shown to be related to a novel relaxation of maximum likelihood detection via a constrained optimization. The conventional maximum likelihood detection falls out for a zero constraint, and belief propagation's fixed points are obtained for other constraint values.
We present a new Reed-Solomon decoding algorithm, which embodies several refinements of an earlier algorithm. Some portions of this new decoding algorithm operate on symbols of length Igq bits;other portions operate o...
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We present a new Reed-Solomon decoding algorithm, which embodies several refinements of an earlier algorithm. Some portions of this new decoding algorithm operate on symbols of length Igq bits;other portions operate on somewhat longer symbols. In the worst case, the total number of calculations required by the new decoding algorithm is proportional to nr, where n is the code's block length and r is its redundancy. This worst case workload is very similar to prior algorithms. But in many applications, average-case workload and error-correcting performance are both much better. The input to the new algorithm consists of n received symbols from GF(q), and rr nonnegative real numbers, each of which is the reliability of the corresponding received symbol. Any conceivable errata pattern has a ''score'' equal to the sum of the reliabilities of its locations with nonzero errata values. A max-likelihood decoder would find the minimum score over all possible errata patterns. Our new decoding algorithm finds the minimum score only over a subset of these possible errata patterns. The errata within any candidate errata pattern may be partitioned into ''errors'' and ''erasures,'' depending on whether the corresponding reliabilities are above or below an ''erasure threshold.'' Different candidate errata patterns may have different thresholds, each chosen to minimize its corresponding ERRATA COUNT, which is defined as 2 . (number of errors) c (number of erasures). The new algorithm finds an errata pattern with minimum score among all errata patterns for which ERRATA COUNT less than or equal to r + 1 where r is the redundancy of the RS code. This is one check symbol better than conventional RS decoding algorithms. Conventional algorithms also require that the erasure threshold be set a priori;the new algorithm obtains the best answer over all possible settings of the erasure threshold. Conventional cyclic RS codes have length n = q - 1, and their locations correspond to the nonzero
Sparse-graph codes appropriate for use in quantum error-correction are presented. Quantum error-correcting codes based on sparse graphs are of interest for three reasons. First, the best codes currently known for clas...
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Sparse-graph codes appropriate for use in quantum error-correction are presented. Quantum error-correcting codes based on sparse graphs are of interest for three reasons. First, the best codes currently known for classical channels are based on sparse graphs. Second, sparse-graph codes keep the number of quantum interactions associated with the quantum error-correction process small: a constant number per quantum bit, independent of the block length. Third, sparse-graph codes often offer great flexibility with respect to block length and rate. We believe some of the codes we present are unsurpassed by previously published quantum error-correcting codes.
In this correspondence, the reliability-based decoding approach using the reprocessing of the most reliable information set only is extended into the iterative reprocessing of several information sets. At the end of e...
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In this correspondence, the reliability-based decoding approach using the reprocessing of the most reliable information set only is extended into the iterative reprocessing of several information sets. At the end of each information set reprocessing, some information bits are delivered by the decoder. Consequently, information sets with decreasing cardinality values are considered at each iteration. A tight upper bound on the error performance achieved by this new method is derived. Compared to previously proposed competitive approaches, this new method reduces the number of candidate codewords needed to achieve practically optimum decoding. Importantly, it also preserves the very simple structured implementation of the order statistic decoding.
In this paper, a new reliability-based soft-decision decoding algorithm is presented. This algorithm repeatedly uses biased reliability values to construct the most-reliable-basis (MRB). As a result, this new method m...
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In this paper, a new reliability-based soft-decision decoding algorithm is presented. This algorithm repeatedly uses biased reliability values to construct the most-reliable-basis (MRB). As a result, this new method makes use of multiple information sets in a stochastic way. Compared to previously proposed competitive approaches, this new method produces a more efficient MRB reprocessing type algorithm to achieve near maximum-likelihood decoding (MLD) performance with a proper choice of the bias value. It can be combined with any MRB reprocessing type algorithm and in each case, a tight performance analysis can be derived.
The authors report the empirical performance of Gallager's low density parity check codes on Gaussian channels. They show that performance substantially better than that of standard convolutional and concatenated ...
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The authors report the empirical performance of Gallager's low density parity check codes on Gaussian channels. They show that performance substantially better than that of standard convolutional and concatenated codes can be achieved;indeed the performance is almost as close to the Shannon limit as that of turbo codes.
In this paper, we consider to develop a recovery algorithm of a sparse signal for a compressed sensing (CS) framework over finite fields. A basic framework of CS for discrete signals rather than continuous signals is ...
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In this paper, we consider to develop a recovery algorithm of a sparse signal for a compressed sensing (CS) framework over finite fields. A basic framework of CS for discrete signals rather than continuous signals is established from the linear measurement step to the reconstruction. With predetermined priori distribution of a sparse signal, we reconstruct it by using a message passing algorithm, and evaluate the performance obtained from simulation. We compare our simulation results with the theoretic bounds obtained from probability analysis.
Binary Low Density Parity Check (LDPC) codes have been shown to have near Shannon limit performance when decoded using a probabilistic decoding algorithm. The analogous codes defined over finite fields GF(q) of order ...
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ISBN:
(纸本)0780344081
Binary Low Density Parity Check (LDPC) codes have been shown to have near Shannon limit performance when decoded using a probabilistic decoding algorithm. The analogous codes defined over finite fields GF(q) of order q > 2 show significantly improved performance. We present the results of Monte Carlo simulations of the decoding of infinite LDPC Codes which can be used to obtain good constructions for finite Codes. We also present empirical results for the Gaussian channel including a rate 1/4 code with bit error probability of 10(-4) at E-b/N-o = -0.05dB.
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