Results 31 to 40 of about 10,846,082 (297)
Message Passing-Based Structured Sparse Signal Recovery for Estimation of OTFS Channels With Fractional Doppler Shifts [PDF]
IEEE Transactions on Wireless Communications, 2020 The orthogonal time frequency space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To enable efficient OTFS detection in the delay-Doppler (DD) domain, the DD domain channels need to be acquired ...
Fei Liu +4 more
semanticscholar +1 more source
Results in Nonlinear Analysis, 2021 In this paper, we introduce an inertial parallel CQ subgradient extragradient method for finding a common solutions of variational inequality problems.
Ponkamon Kitisak, Watcharaporn Cholamjiak, Damrongsak Yambangwai
doaj +1 more source
Recovery-informed Theory: Situating the Subjective in the Science of Substance Use Disorder Recovery [PDF]
, 2019 As recovery from substance use disorder becomes more than a mere quantifiable outcome, there exists a need to discuss and propose the underlying theoretical constructs that ultimately describe and identify the science of recovery.
Brown, Austin M, Ashford, Robert D
core +1 more source
Blind source separation algorithm for complex signals in noise
Dianzi Jishu Yingyong, 2022 Complex signal analysis is one of the common problems in signal processing technology. In blind signal separation technology, especially convolution mixing problem or frequency domain analysis, it is necessary to establish the corresponding complex value
Feng Pingxing, Zhang Hongbo, Li Wenxiang
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Sparse Signal Recovery From Phaseless Measurements via Hard Thresholding Pursuit [PDF]
Applied and Computational Harmonic Analysis, 2020 In this paper, we consider the sparse phase retrival problem, recovering an $s$-sparse signal $\bm{x}^{\natural}\in\mathbb{R}^n$ from $m$ phaseless samples $y_i=|\langle\bm{x}^{\natural},\bm{a}_i\rangle|$ for $i=1,\ldots,m$.
Jian-Feng Cai +3 more
semanticscholar +1 more source
On the Optimal Recovery of Graph Signals
2023 International Conference on Sampling Theory and Applications (SampTA), 2023 Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning. We consider this task from the perspective of optimal recovery, a mathematical framework for learning a function from observational data that adopts a worst-case perspective tied to model assumptions on the function to be learned. Earlier
Simon Foucart, Chunyang Liao, Nate Veldt
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Sketching and Stacking With Random Fork Based Exact Signal Recovery Under Sample Corruption
IEEE Access, 2020 In this paper, we propose a new technique for exact recovery of missing data due to impulsive noise in time-domain sampled acoustic waves, named as sketching and stacking with random fork (SSRF).
Joo Hyun Park +3 more
doaj +1 more source
IEEE Open Journal of Antennas and Propagation, 2021 We propose an innovative approach to the phase retrieval of 2-D discrete complex signals. The solution strategy profitably exploits some fundamental results available for the phase retrieval of one-dimensional discrete signals by following a simple yet ...
G. M. Battaglia +4 more
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Statistical Graph Signal Recovery Using Variational Bayes [PDF]
IEEE Transactions on Circuits and Systems - II - Express Briefs, 2020 This brief investigates the problem of Graph Signal Recovery (GSR) when the topology of the graph is not known in advance. In this brief, the elements of the weighted adjacency matrix is statistically related to normal distribution and the graph signal ...
Razieh Torkamani, Hadi Zayyani
semanticscholar +1 more source
Dualization of Signal Recovery Problems [PDF]
Set-Valued and Variational Analysis, 2010 In convex optimization, duality theory can sometimes lead to simpler solution methods than those resulting from direct primal analysis. In this paper, this principle is applied to a class of composite variational problems arising in particular in signal recovery.
Combettes, Patrick L. +2 more
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