Results 11 to 20 of about 1,096,634 (365)
Optimum non-iterative turbo-decoding [PDF]
Proceedings of 8th International Symposium on Personal, Indoor and Mobile Radio Communications - PIMRC '97, 2002 By observing the structure of the decoder's trellis a new, non-iterative turbo-decoder based on a super-trellis structure is proposed, which exhibits the same decoding complexity as a conventional convolutional decoder possessing an identical number of trellis states.
Breiling, M, Hanzo, L
openaire +4 more sources
Iterative LDPC decoding using neighborhood reliabilities [PDF]
arXiv, 2007 In this paper we study the impact of the processing order of nodes of a bipartite graph, on the performance of an iterative message-passing decoding. To this end, we introduce the concept of neighborhood reliabilities of graph's nodes. Nodes reliabilities are calculated at each iteration and then are used to obtain a processing order within a serial or
Valentin Savin
arxiv +3 more sources
Iterative decoding beyond belief propagation
2010 Information Theory and Applications Workshop (ITA), 2010 At the heart of modern coding theory lies the fact that low-density parity-check (LDPC) codes can be efficiently decoded by belief propagation (BP). The BP is an inference algorithm which operates on a graphical model of a code, and lends itself to low ...
S. Planjery +4 more
semanticscholar +5 more sources
Iterative Reed–Muller Decoding [PDF]
2021 11th International Symposium on Topics in Coding (ISTC), 2021 Reed-Muller (RM) codes are known for their good maximum likelihood (ML) performance in the short block-length regime. Despite being one of the oldest classes of channel codes, finding a low complexity soft-input decoding scheme is still an open problem. In this work, we present a belief propagation (BP) decoding architecture for RM codes based on their
Sebastian Cammerer +4 more
openaire +3 more sources
Learned Neural Iterative Decoding for Lossy Image Compression Systems [PDF]
Data Compression Conference, 2018 For lossy image compression systems, we develop an algorithm, iterative refinement, to improve the decoder's reconstruction compared to standard decoding techniques.
Alexander Ororbia +6 more
semanticscholar +1 more source
Convolutional codes for iterative decoding [PDF]
2008 IEEE 10th International Symposium on Spread Spectrum Techniques and Applications, 2008 This paper gives an overview of three different classes of convolutional codes that are suitable for iterative decoding. They are characterized by the type of component codes that are used to construct the overall codes, which can be trivial parity-check constraints, block component codes, or convolutional component codes.
Lentmaier, Michael +3 more
openaire +4 more sources
PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition [PDF]
ACM Multimedia, 2021 Nowadays, scene text recognition has attracted more and more attention due to its various applications. Most state-of-the-art methods adopt an encoder-decoder framework with attention mechanism, which generates text autoregressively from left to right ...
Zhi Qiao +7 more
semanticscholar +1 more source
An Iterative BP-CNN Architecture for Channel Decoding [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2017 Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation – convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise.
Fei Liang, Cong Shen, Feng Wu
semanticscholar +1 more source
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery [PDF]
Image, Video, and Multidimensional Signal Processing Workshop, 2018 In “extreme” computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging.
Il Yong Chun, J. Fessler
semanticscholar +1 more source
Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source