Results 41 to 50 of about 1,669,292 (291)
Deep Unfolded Gridless DOA Estimation Networks Based on Atomic Norm Minimization
Remote Sensing, 2022 Deep unfolded networks have recently been regarded as an essential way to direction of arrival (DOA) estimation due to the fast convergence speed and high interpretability. However, few consider gridless DOA estimation.
Hangui Zhu +4 more
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Continuous-Time Sparse Signal Recovery
IEEE AccessThis study investigates a continuous-time method for sparse signal recovery, which is suitable for analog optical circuit implementation. The proposed method is defined by a nonlinear ordinary differential equation (ODE) derived from the gradient flow ...
Tadashi Wadayama, Ayano Nakai-Kasai
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Saliency Detection with Sparse Prototypes: An Approach Based on Multi-Dictionary Sparse Encoding
MATEC Web of Conferences, 2018 This paper proposes a bottom-up saliency detection algorithm based on multi-dictionary sparse recovery. Firstly, the SLIC algorithm is used to segment the image into superpixels in multilevel and atoms with a high background possibility are selected from
Wang Jun, Wu Zemin, Tian Chang, Hu Lei
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Jointly Iterative Adaptive Approach Based Space Time Adaptive Processing Using MIMO Radar
IEEE Access, 2018 To solve the problem of large training samples requirement of space time adaptive processing (STAP), a jointly sparse matrices recovery-based method is proposed for clutter plus noise covariance matrix estimation by exploiting the transmitting waveform ...
Weike Feng +4 more
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Sparse System Identification of Leptin Dynamics in Women With Obesity
Frontiers in Endocrinology, 2022 The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity.
Md. Rafiul Amin +5 more
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Sparse Recovery with Very Sparse Compressed Counting
CoRR, 2013 Compressed sensing (sparse signal recovery) often encounters nonnegative data (e.g., images). Recently we developed the methodology of using (dense) Compressed Counting for recovering nonnegative K-sparse signals. In this paper, we adopt very sparse Compressed Counting for nonnegative signal recovery.
Ping Li 0001 +2 more
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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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Jump-Sparse and Sparse Recovery Using Potts Functionals [PDF]
IEEE Transactions on Signal Processing, 2014 We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and provide relations to sparsity problems.
Martin Storath +2 more
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Recovery ... reporting overview; Connecticut recovery; CT recovery ... progress
, 2010 Updated quarterly; Began in 2009.; Title from home page last modified 10/29/2009 (viewed Oct. 29, 2009).; At head of title: State of Connecticut.; "On October 1, state agencies that have received Recovery Act funds began reporting to the federal ...
core +5 more sources