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4D Automotive Radar Sensing for Autonomous Vehicles: A Sparsity-Oriented Approach
IEEE Journal on Selected Topics in Signal Processing, 2021We propose a high-resolution imaging radar system to enable high-fidelity four-dimensional (4D) sensing for autonomous driving, i.e., range, Doppler, azimuth, and elevation, through a joint sparsity design in frequency spectrum and array configurations ...
Shunqiao Sun, Yimin D. Zhang
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Wireless Channel Sparsity: Measurement, Analysis, and Exploitation in Estimation
IEEE wireless communications, 2021Sparse channel arises in a number of applications in wireless communications such as channel estimation and signal processing. There is growing evidence that physical wireless channels exhibit a sparse structure, and channel sparsity has been even ...
R. He +5 more
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IEEE Transactions on Cybernetics, 2020
Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and affecting subsequent processing accuracy.
Yong Chen +3 more
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Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and affecting subsequent processing accuracy.
Yong Chen +3 more
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2021
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by McCullagh and Polson (2018). In that paper, the intuitive idea that sparsity, in a statistical framework, refers to those ''phenomena that are mostly negligible or seldom appreciably large'', has, for the first time, been given a mathematical definition.
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Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by McCullagh and Polson (2018). In that paper, the intuitive idea that sparsity, in a statistical framework, refers to those ''phenomena that are mostly negligible or seldom appreciably large'', has, for the first time, been given a mathematical definition.
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Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models
IEEE Transactions on Image Processing, 2021Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC).
Zhiyuan Zha +4 more
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Toward Compact ConvNets via Structure-Sparsity Regularized Filter Pruning
IEEE Transactions on Neural Networks and Learning Systems, 2020The success of convolutional neural networks (CNNs) in computer vision applications has been accompanied by a significant increase of computation and memory costs, which prohibits their usage on resource-limited environments, such as mobile systems or ...
Shaohui Lin +4 more
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Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis
IEEE Signal Processing Letters, 2019In this letter, a sparse representation (SR) model named convolutional sparsity based morphological component analysis (CS-MCA) is introduced for pixel-level medical image fusion. Unlike the standard SR model, which is based on single image component and
Yu Liu, Xun Chen, R. Ward, Z. J. Wang
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International Symposium on Computer Architecture, 2019
Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to achieve energy efficiency for DNN inference accelerators.
Tzu-Hsien Yang +6 more
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Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to achieve energy efficiency for DNN inference accelerators.
Tzu-Hsien Yang +6 more
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Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018As a promising way for analyzing data, sparse modeling has achieved great success throughout science and engineering. It is well known that the sparsity/low-rank of a vector/matrix can be rationally measured by nonzero-entries-number ($l_0$ norm ...
Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu
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DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
European Conference on Computer Vision, 2020Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key problem.
Xuefei Ning +5 more
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