Results 41 to 50 of about 3,600,645 (297)
Structured sampling and recovery of iEEG signals [PDF]
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding, and potentially treating, mental diseases such as epilepsy and depression. Compressive sensing (CS) is emerging as a promising approach to directly acquire compressed signals, allowing to reduce the power consumption
Luca Baldassarre +4 more
openaire +2 more sources
Students Voicing Collegiate Recovery [PDF]
, 2018 Young adults increasingly enter college with substance use addiction. Some may achieve recovery before setting their foot on a college campus whereas others during their college years.
Cravalho, Danielle, Cheney, Ann M
core +1 more source
Lipschitz Learning for Signal Recovery
CoRR, 2019 We consider the recovery of signals from their observations, which are samples of a transform of the signals rather than the signals themselves, by using machine learning (ML). We will develop a theoretical framework to characterize the signals that can be robustly recovered from their observations by an ML algorithm, and establish a Lipschitz ...
Hong Jiang 0002 +2 more
openaire +2 more sources
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
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
openaire +3 more sources
Recovery capital pathways : modelling the components of recovery wellbeing. [PDF]
, 2017 In recent years, there has been recognition that recovery is a journey that involves the growth of recovery capital. Thus, recovery capital has become a commonly used term in addiction treatment and research yet its operationalization and measurement has
David Best +7 more
core +1 more source
Number of measurements in sparse signal recovery [PDF]
2009 IEEE International Symposium on Information Theory, 2009 6 pages, 1 figure.
Paul Tune +2 more
openaire +3 more sources
Stable signal recovery in compressed sensing with a structured matrix perturbation [PDF]
, 2012 The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing matrix be known a priori. Such an ideal assumption may not be met in practical applications where various errors and fluctuations exist in the sensing ...
Zai Yang +5 more
core +1 more source
Structure-Blind Signal Recovery
, 2016 We consider the problem of recovering a signal observed in Gaussian noise. If the set of signals is convex and compact, and can be specified beforehand, one can use classical linear estimators that achieve a risk within a constant factor of the minimax risk.
Dmitry Ostrovsky +3 more
openaire +3 more sources
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
openaire +2 more sources