Results 41 to 50 of about 2,367,946 (319)
Neural Decoder for Topological Codes
Physical Review Letters, 2017 We present an algorithm for error correction in topological codes that exploits modern machine learning techniques. Our decoder is constructed from a stochastic neural network called a Boltzmann machine, of the type extensively used in deep learning.
Torlai, Giacomo, Melko, Roger G.
openaire +4 more sources
Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation [PDF]
, 2009 The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported.
Ghassemlooy, Zabih +2 more
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Decoding neural responses to motion-in-depth using EEG [PDF]
, 2020 MH was supported by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement no. 641805. AW was supported by United Kingdom Biotechnology and Biological Science Research Council (BBSRC) grant ...
Himmelberg, Marc M +9 more
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Neural speech decoding with magnetoencephalography [PDF]
, 2022 Severe brain damage or amyotrophic lateral sclerosis (ALS) may lead the patients to a locked-in state where the patients are motorically paralyzed otherwise being cognitively normal. The brain might be the only source of communication for these patients.
Dash, Debadatta
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Graph-Based Codes and Iterative Decoding [PDF]
, 2003 The field of error correcting codes was revolutionized by the introduction of turbo codes in 1993. These codes demonstrated dramatic performance improvements over any previously known codes, with significantly lower complexity.
Khandekar, Aamod Dinkar
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PLoS ONE, 2022 Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo.
Charles R Heller, Stephen V David
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Neural decoding of semantic concepts: a systematic literature review
Journal of Neural Engineering, 2022 Objective. Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world.
M. Rybář, I. Daly
semanticscholar +1 more source
An Empirical Study of Encoders and Decoders in Graph-Based Dependency Parsing
IEEE Access, 2020 Graph-based dependency parsing consists of two steps: first, an encoder produces a feature representation for each parsing substructure of the input sentence, which is then used to compute a score for the substructure; and second, a decoder finds the ...
Ge Wang, Ziyuan Hu, Zechuan Hu, Kewei Tu
doaj +1 more source
Deep Neural Network Based Reconciliation for CV-QKD
Photonics, 2022 High-speed reconciliation is indispensable for supporting the continuous-variable quantum key distribution (CV-QKD) system to generate the secure key in real-time. However, the error correction process’s high complexity and low processing speed limit the
Jun Xie +3 more
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Neural network approaches to point lattice decoding [PDF]
, 2022 International audienceWe characterize the complexity of the lattice decoding problem from a neural network perspective. The notion of Voronoi-reduced basis is introduced to restrict the space of solutions to a binary set. On the one hand, this problem is
Ciblat, Philippe +3 more
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