Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Miloš Doroslovački"'
Autor:
Hongyang Deng, Miloš Doroslovački
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2007 (2007)
The μ-law proportionate normalized least mean square (MPNLMS) algorithm has been proposed recently to solve the slow convergence problem of the proportionate normalized least mean square (PNLMS) algorithm after its initial fast converging peri
Externí odkaz:
https://doaj.org/article/9f3974112d774829a305760379059773
Autor:
Adnan Hanif, Miloš Doroslovački
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::48fb05435c7bffdb00321abfa1245ffe
https://doi.org/10.1109/jstsp.2023.3280343
https://doi.org/10.1109/jstsp.2023.3280343
Autor:
Adnan Hanif, Miloš Doroslovački
Publikováno v:
2022 56th Asilomar Conference on Signals, Systems, and Computers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::645239ad62715a2ee80989359ee3ff76
https://doi.org/10.1109/ieeeconf56349.2022.10052100
https://doi.org/10.1109/ieeeconf56349.2022.10052100
Autor:
Kevin Wagner, Milos Doroslovacki
The topic of this book is proportionate-type normalized least mean squares (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response a
Publikováno v:
IEEE Access, Vol 10, Pp 11809-11816 (2022)
Decoding communication signals in a non-cooperative environment has always been a challenging task. Even after the estimation of various transmission-related parameters, the unknown received signal still cannot be decoded without the correct classifi
Externí odkaz:
https://doaj.org/article/b7fc1fe88b764e15891dd8fed41f6f48
Publikováno v:
IEEE Access, Vol 8, Pp 111626-111635 (2020)
A loss function has two crucial roles in training a conventional discriminant deep neural network (DNN): (i) it measures the goodness of classification and (ii) generates the gradients that drive the training of the network. In this paper, we approxi
Externí odkaz:
https://doaj.org/article/156340beb6de4632b54d56a97050df34
Autor:
Hongyang Deng1 hongyangdeng@yahoo.com, Miloš Doroslovački2 dorosiov@gwu.edu
Publikováno v:
IEEE Transactions on Signal Processing. May2006, Vol. 54 Issue 5, p1794-1803. 10p. 2 Charts, 14 Graphs.
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2008 (2008)
We propose a new low complexity, low delay, and fast converging frequency-domain adaptive algorithm for network echo cancellation in VoIP exploiting MMax and sparse partial (SP) tap-selection criteria in the frequency domain. We incorporate these tap
Externí odkaz:
https://doaj.org/article/168b06731f1e4bb29e32475728f87a5f
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