Results 61 to 70 of about 10,846,082 (297)
Sparse signal recovery in Hilbert spaces [PDF]
2012 IEEE International Symposium on Information Theory Proceedings, 2012 This paper reports an effort to consolidate numerous coherence-based sparse signal recovery results available in the literature. We present a single theory that applies to general Hilbert spaces with the sparsity of a signal defined as the number of (possibly infinite-dimensional) subspaces participating in the signal's representation.
Graeme Pope, Helmut Bölcskei
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A Missing Link: Nutritional Healing in Recovery [PDF]
, 2018 The journey of students in a Collegiate Recovery Program (CRP) begins with early recovery and continues towards a well-developed mental, spiritual, emotional, and physical state. Much emphasis is placed on mental, emotional, and spiritual healing through
Henley, Emmy L
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Unlimited dynamic range signal recovery for folded graph signals
, 2022 Recovery of a graph signal from samples has many important applications in signal processing over networks and graph-structured data. To capture very high or even unlimited dynamic range signals, modulo sampling has been investigated.
Ji, Feng +5 more
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Constraint removal for sparse signal recovery [PDF]
Signal Processing, 2012 This paper presents a new iterative algorithm called constraint removal (CR) for the recovery of a sparse signal x from an incomplete number of linear measurements y such that ym× 1= Am× nxn× 1 and m<n. It is empirically demonstrated that the CR algorithm has a recovery performance which is between basis pursuit linear programming (BP-LP) and ...
Şahin, Ahmet, Özen, Serdar
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Recovery From Alcohol and Other Drug Problems in the U.S. Population: Prevalence, Pathways, and Predictors [PDF]
, 2018 The concept of "recovery" has become the organizing paradigm in many middle and high income countries globally with a goal of orienting services in cost effective ways to help sufferers achieve and maintain long term remission. Despite this focus, little
Hoeppner, Bettina +5 more
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Recovery of compressible signals in unions of subspaces [PDF]
2009 43rd Annual Conference on Information Sciences and Systems, 2009 Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals; instead of taking periodic samples, we measure inner products with M ≪ N random vectors and then recover the signal via a sparsity-seeking optimization or greedy algorithm.
Marco F. Duarte +3 more
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Eating Disorder Recovery: Fostering an Inclusive Culture [PDF]
, 2018 Eating disorder (ED) recovery is often difficult to define, which may leave those offering recovery support feeling lost and discouraged when determining ED services in their Collegiate Recovery Program (CRP).
Monsour, Diana L, Henley, Emmy L
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MicromachinesA two-channel, time–wavelength interleaved photonic analog-to-digital converter (PADC) system with a sampling rate of 10.4 GSa/s was established, and a concise method for measuring and data correcting the channel sampling timing walk-off of PADCs for ...
Junli Qi +11 more
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Learning-based accelerated sparse signal recovery algorithms
ICT Express, 2021 In this paper, we propose an accelerated sparse recovery algorithm based on inexact alternating direction of multipliers. We formulate a sparse recovery problem with a concave regularizer and solve it with the relaxed and accelerated alternating method ...
Dohyun Kim, Daeyoung Park
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Phase Retrieval With Bregman Divergences and Application to Audio Signal Recovery [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2020 Phase retrieval (PR) aims to recover a signal from the magnitudes of a set of inner products. This problem arises in many audio signal processing applications which operate on a short-time Fourier transform magnitude or power spectrogram, and discard the
Pierre-Hugo Vial +3 more
semanticscholar +1 more source