Results 61 to 70 of about 522,381 (263)
Weak Signal Detection Based on Combination of Sparse Representation and Singular Value Decomposition
Applied Sciences, 2022 Due to the inevitable acquisition system noise and strong background noise, it is often difficult to detect the features of weak signals. To solve this problem, sparse representation can effectively extract useful information according to the sparse ...
Huijie Ma +3 more
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Nonparametric Sparse Representation
, 2012 This paper suggests a nonparametric scheme to find the sparse solution of the underdetermined system of linear equations in the presence of unknown impulsive or non-Gaussian noise. This approach is robust against any variations of the noise model and its parameters.
Mayiami, Mahmoud Ramezani, Seyfe, Babak
openaire +2 more sources
Refractory Status Epilepticus Treated With Bilateral Pulvinar Deep Brain Stimulation—A Case Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT New‐onset refractory status epilepticus (NORSE) arises without an identifiable cause or prior epilepsy history, with a 16%–27% mortality rate and significant long‐term neurological sequelae. Neuromodulation such as deep brain stimulation (DBS) targeting the anterior and centromedian thalamic nuclei has shown promise when the traditional ...
Mengxuan Tang +16 more
wiley +1 more source
Parametric Sparse Representation and Its Applications to Radar Sensing
Leida xuebao, 2016 Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally ...
Li Gang, Xia Xiang-Gen
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Information-theoretically Optimal Sparse PCA
, 2014 Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of a data matrix with additional sparsity constraints on the obtained representation. We consider two probabilistic formulations
Deshpande, Yash, Montanari, Andrea
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Spiralet Sparse Representation
, 2014 10 pages, Working Paper Number: WP-RFM-14 ...
Moghaddam, Reza Farrahi +1 more
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Glymphatic Dysfunction Reflects Post‐Concussion Symptoms: Changes Within 1 Month and After 3 Months
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Mild traumatic brain injury (mTBI) may alter glymphatic function; however, its progression and variability remain obscure. This study examined glymphatic function following mTBI within 1 month and after 3 months post‐injury to determine whether variations in glymphatic function are associated with post‐traumatic symptom severity ...
Eunkyung Kim +3 more
wiley +1 more source
, 2015 Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction ...
Nasrabadi, Nasser M. +2 more
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Patterns of Postictal Abnormalities in Relation to Status Epilepticus in Adults
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Abnormalities on peri‐ictal diffusion‐weighted magnetic resonance imaging (DWI‐PMAs) are well‐established for patients with status epilepticus (SE), but knowledge on patterns of DWI‐PMAs and their prognostic impact is sparse. Methods This systematic review and individual participant data meta‐analysis included observational studies ...
Andrea Enerstad Bolle +11 more
wiley +1 more source
Analysis for sparse channel representation based on dictionary learning in massive MIMO systems
IET CommunicationsThe accuracy analysis of dictionary sparse representation for channels in massive MIMO systems is a relatively unexplored field. Existing research has primarily focused on investigating the accuracy of dictionary sparse representation using simulation in
Qing‐Yang Guan
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