Results 31 to 40 of about 1,528,226 (285)
Reduced-complexity syndrome-based TTCM decoding [PDF]
, 2013 The iterative decoder of Turbo Trellis Coded Modulation (TTCM) exchanges extrinsic information between the constituent TCM decoders, which imposes a high computational complexity at the receiver. Therefore we conceive the syndrome-based block decoding of
Ng, Soon Xin +2 more
core +1 more source
Polar codes decoding algorithm based on convolutional neural network
Dianxin kexue, 2020 In order to solve the problem that the existing Polar code decoding algorithm based on neural network can only decode short codewords (codewords length N≤64),a new decoding algorithm using convolution neural network for long codewords (N≥512) was put ...
Rui GUO, Fanchun RAN
doaj +2 more sources
Neural decoding based on probabilistic neural network [PDF]
Journal of Zhejiang University SCIENCE B, 2010 Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices, such as robot arms, computer cursors, and paralyzed muscles.
Yi, Yu +6 more
openaire +2 more sources
Neural Decoding with Hierarchical Generative Models [PDF]
Neural Computation, 2010 Recent research has shown that reconstruction of perceived images based on hemodynamic response as measured with functional magnetic resonance imaging (fMRI) is starting to become feasible. In this letter, we explore reconstruction based on a learned hierarchy of features by employing a hierarchical generative model that consists of conditional ...
Marcel van Gerven +2 more
openaire +5 more sources
Shared Graph Neural Network for Channel Decoding
Applied Sciences, 2023 With the application of graph neural network (GNN) in the communication physical layer, GNN-based channel decoding algorithms have become a research hotspot.
Qingle Wu +5 more
doaj +1 more source
Neural offset min-sum decoding [PDF]
2017 IEEE International Symposium on Information Theory (ISIT), 2017 Published as a conference paper at the 2017 International Symposium on Information Theory (ISIT)
Loren Lugosch, Warren J. Gross
openaire +3 more sources
Hypernetwork Based Model-Driven Channel Neural Decoding
IEEE AccessChannel decoding algorithms based on model-driven deep learning, also known as channel neural decoding algorithms, have received a lot of attention in recent years.
Yuanhui Liang +4 more
doaj +1 more source
Spikebench: An open benchmark for spike train time-series classification.
PLoS Computational Biology, 2023 Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially
Ivan Lazarevich +3 more
doaj +1 more source
Kernel Temporal Differences for Neural Decoding [PDF]
Computational Intelligence and Neuroscience, 2015 We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning ...
Jihye Bae +5 more
openaire +3 more sources
De Bruijn cycles for neural decoding [PDF]
Journal of Vision, 2011 Stimulus counterbalance is critical for studies of neural habituation, bias, anticipation, and (more generally) the effect of stimulus history and context. We introduce de Bruijn cycles, a class of combinatorial objects, as the ideal source of pseudo-random stimulus sequences with arbitrary levels of counterbalance. Neuro-vascular imaging studies (such
Geoffrey Karl Aguirre +2 more
openaire +3 more sources