Results 121 to 130 of about 2,602 (222)

Unifying Performance Metric of Viterbi Decoders

, 2007
Convolutional codes and Viterbi decoders were extensively used in error control systems. The survivor memory management (SMM) unit of Viterbi decoder is extremely important in determining the throughput, hardware area and coding gain performance of the ...
簡用典, Jian, Yong-Dian
core  

Advanced receiver design for AF-FD cooperative schemes. [PDF]

Sci Rep
Al-Hattab M, Mostafa H, Marey M.
europepmc   +1 more source

Simulation results for the Viterbi decoding algorithm

, 1972
Concepts involved in determining the performance of coded digital communications systems are introduced. The basic concepts of convolutional encoding and decoding are summarized, and hardware implementations of sequential and maximum likelihood decoders ...
Batson, B. H., Taqvi, S. Z. H., Moorehead, R. W.   +2 more
core  

ReBaCCA-ss: Relevance-Balanced Continuum Correlation Analysis With Smoothing and Surrogating for Quantifying Similarity Between Population Spiking Activities. [PDF]

Neural Comput
Zhang X, Xu C, Lu Z, Wang H, Song D.
europepmc   +1 more source

A Novel PTS Technique with Side Information Blind Detector Based on Minimized Error Accumulation for PAPR Reduction in Coded Underwater Acoustic OFDM Systems. [PDF]

Sensors (Basel)
Xing S, Wei B, Yu Y, Bai Y, Yin J.
europepmc   +1 more source

Turbo-Coded Parallel Modem Techniques for Personal Communications

, 1997
In this paper we study the performance of the wideband Orthogonal Frequency Division Multiplexing (OFDM) system in conjunction with channel coding based on turbo codes over a range of wideband Rayleigh fading channels.
Woodard, J P, Keller, T, Hanzo, L
core  

Probabilistic Cognitive State Modeling (PCSM): Decoding dynamic brain states to derive emergent cognitive processing properties from task fMRI. [PDF]

Neuroimage
Winters DE.
europepmc   +1 more source

Unsupervised learning of stationary and switching dynamical system models from Poisson observations. [PDF]

J Neural Eng, 2023
Song CY, Shanechi MM.
europepmc   +1 more source

Progress, challenges and future of linguistic neural decoding with deep learning. [PDF]

Commun Biol
Wang Y, Liu H, Wang Y, Xuan C, Hou Y, Feng S, Liu H, Liao Y, Wang Y.   +8 more
europepmc   +1 more source

Nanopore-Aware Embedded Detection for Mobile DNA Sequencing: A Viterbi-HMM Design Versus Deep Learning Approaches. [PDF]

Biosensors (Basel)
Hammad K, Wu Z, Ghafar-Zadeh E, Magierowski S.   +3 more
europepmc   +1 more source