Results 11 to 20 of about 10,846,082 (297)
Adaptive algorithm for sparse signal recovery [PDF]
Digital Signal Processing, 2019 Spike and slab priors play a key role in inducing sparsity for sparse signal recovery. The use of such priors results in hard non-convex and mixed integer programming problems. Most of the existing algorithms to solve the optimization problems involve either simplifying assumptions, relaxations or high computational expenses.
Fekadu L. Bayisa +3 more
core +4 more sources
Mathematics, 2022 This article considers a parallel monotone hybrid algorithm for a finite family of G-nonexpansive mapping in Hilbert spaces endowed with graphs and suggests iterative schemes for finding a common fixed point by the two different hybrid projection methods.
Suthep Suantai +3 more
doaj +1 more source
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples [PDF]
arXiv.org, 2008 Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples.
D. Needell, J. Tropp
semanticscholar +1 more source
Dual-Mic Speech Enhancement Based on TF-GSC with Leakage Suppression and Signal Recovery
Applied Sciences, 2021 The transfer function-generalized sidelobe canceller (TF-GSC) is one of the most popular structures for the adaptive beamformer used in multi-channel speech enhancement. Although the TF-GSC has shown decent performance, a certain amount of steering error
Hansol Kim, Jong Won Shin
doaj +1 more source
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? [PDF]
IEEE Transactions on Information Theory, 2004 Suppose we are given a vector f in a class FsubeRopfN , e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to recover f to within precision epsi in the Euclidean (lscr2) metric?
E. Candès, T. Tao
semanticscholar +1 more source
Block-Sparse Signal Recovery via General Total Variation Regularized Sparse Bayesian Learning
IEEE Transactions on Signal Processing, 2022 One of the main challenges in block-sparse signal recovery, as encountered in, e.g., multi-antenna mmWave channel models, is block-patterned estimation without knowledge of block sizes and boundaries.
Aditya Sant, Markus Leinonen, B. Rao
semanticscholar +1 more source
Greedy signal recovery review [PDF]
2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008 The two major approaches to sparse recovery are L1-minimization and greedy methods. Recently, Needell and Vershynin developed Regularized Orthogonal Matching Pursuit (ROMP) that has bridged the gap between these two approaches. ROMP is the first stable greedy algorithm providing uniform guarantees.
Deanna Needell +2 more
openaire +6 more sources
Approximation of fixed points for Garcia-Falset mappings in a uniformly convex Banach space
Results in Nonlinear Analysis, 2021 The aim of this research is to introduce a novel iterative technique termed CC-iteration for identifying the fixed points of Garcia-Falset mappings.
Tanapat Chalarux, Khuanchanok Chaichana
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Experimental Characterization of an Embossed Capacitive Micromachined Ultrasonic Transducer Cell
Micromachines, 2020 Capacitive Micromachined Ultrasonic Transducer (CMUT) is a promising ultrasonic transducer in medical diagnosis and therapeutic applications that demand a high output pressure.
Yuanyu Yu +8 more
doaj +1 more source
Analytical Blind Beamforming for a Multi-Antenna UAV Base-Station Receiver in Millimeter-Wave Bands
Sensors, 2021 Over the last decade, unmanned aerial vehicles (UAVs) with antenna arrays have usually been employed for the enhancement of wireless communication in millimeter-wave bands.
Pingchuan Liu, Kuangang Fan, Yuhang Chen
doaj +1 more source