Results 61 to 70 of about 794 (157)
IEEE Access, 2020 Air pollution is harmful to human health and restricts economic development, so predicting when and where air pollution will occur is a challenging and important issue, especially in fields of urban planning, factory production and human activities.
Lei Zhang, Dong Li, Quansheng Guo
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The Restricted Isometry Property For Random Convolutions
, 2013 Publication in the conference proceedings of SampTA, Bremen, Germany ...
Krahmer, Felix +2 more
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IEEE Access, 2018 The existence of missing data severely affects the establishment of correct data mining model from the raw data. Unfortunately, most of the existing missing data prediction approaches are inefficient to predict missing data from multivariable time series
Yan Guo +3 more
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Noise Folding in Completely Perturbed Compressed Sensing
Journal of Applied Mathematics, 2016 This paper first presents a new generally perturbed compressed sensing (CS) model y=(A+E)(x+u)+e, which incorporated a general nonzero perturbation E into sensing matrix A and a noise u into signal x simultaneously based on the standard CS model y=Ax+e ...
Limin Zhou, Xinxin Niu, Jing Yuan
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RIPML: A Restricted Isometry Property based Approach to Multilabel Learning
CoRR, 2017 6 ...
Akshay Soni, Yashar Mehdad
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A Sparsity Adaptive Algorithm for Wideband Compressive Spectrum Sensing
Dianxin kexue, 2014 Traditional spectrum sensing based on compressed sensing assumes that the sparsity is known, in fact,it is unknown and time-varying. To solve the problem, a sparsity adaptive algorithm for wideband spectrum sensing was proposed.
Zhijin Zhao, Junwei Hu
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An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks
Sensors, 2016 To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed.
Donghao Wang +3 more
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Sparse Signal Recovery via Rescaled Matching Pursuit
AxiomsWe propose the Rescaled Matching Pursuit (RMP) algorithm to recover sparse signals in high-dimensional Euclidean spaces. The RMP algorithm has less computational complexity than other greedy-type algorithms, such as Orthogonal Matching Pursuit (OMP).
Wan Li, Peixin Ye
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New Sufficient Conditions of Signal Recovery With Tight Frames via
IEEE Access, 2018 This paper discusses the recovery of signals that are nearly sparse with respect to a tight frame D by means of the l1-analysis approach. We establish several new sufficient conditions regarding the D-restricted isometry property to ensure stable ...
Jianwen Huang +3 more
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Incoherent dictionaries and the statistical restricted isometry property
CoRR, 2008 In this article we present a statistical version of the Candes-Tao restricted isometry property (SRIP for short) which holds in general for any incoherent dictionary which is a disjoint union of orthonormal bases. In addition, under appropriate normalization, the eigenvalues of the associated Gram matrix fluctuate around 1 according to the Wigner ...
Shamgar Gurevich, Ronny Hadani
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