Results 31 to 40 of about 77,863 (306)

Sparse Deconvolution Using Support Vector Machines

EURASIP Journal on Advances in Signal Processing, 2008
Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them ...
Aníbal R. Figueiras-Vidal, Carlos M. Cruz, Gustavo Camps-Valls, Jordi Muñoz-Marí, Manel Martínez-Ramón, José Luis Rojo-Álvarez   +5 more
doaj   +1 more source

Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l₀ Norm and Randomized Coordinate Descent

Mathematics, 2019
Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory.
Dingfei Jin, Guang Yang, Zhenghui Li, Haode Liu   +3 more
doaj   +1 more source

Cluster-Sparse Proportionate NLMS Algorithm With the Hybrid Norm Constraint

IEEE Access, 2018
In this paper, an enhanced proportionate normalized least mean square (PNLMS) algorithm with the hybrid l2,0-norm constraint is proposed for block-sparse signal processing.
Yingsong Li, Zhengxiong Jiang, Zhan Jin, Xiao Han, Jingwei Yin   +4 more
doaj   +1 more source

Signal Processing [PDF]

, 2010
This book intends to provide highlights of the current research in signal processing area and to offer a snapshot of the recent advances in this field.

core   +1 more source

Sparse Analysis Recovery via Iterative Cosupport Detection Estimation

IEEE Access, 2021
Cosparse analysis model (CAM) provides a new signal processing paradigm for recovering cosparse signals with respect to a given analysis operator from the undersampled linear measurements in the context of emerging theory of compressed sensing (CS).
Heping Song, Xiaolong Ren, Yuping Lai, Hongying Meng   +3 more
doaj   +1 more source

Convolutional compressed sensing using deterministic sequences [PDF]

, 2012
This is the author's accepted manuscript (with working title "Semi-universal convolutional compressed sensing using (nearly) perfect sequences"). The final published article is available from the link below. Copyright @ 2012 IEEE.
Cong Ling, Li, K, Gan, L, Kezhi Li, Ling, C   +4 more
core   +1 more source

A Review of Radar Signal Processing Based on Sparse Recovery

Leida xuebao
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing.
Yinghui QUAN, Yaojun WU, Lining DUAN, Gang XU, Min XUE, Zhixing LIU, Mengdao XING   +6 more
doaj   +1 more source

Imaging Method for Co-prime-sampling Space-borne SAR Based on 2D Sparse-signal Reconstruction

Leida xuebao, 2020
Co-prime-sampling space-borne Synthetic Aperture Radar (SAR) replaces the traditional uniform sampling by performing co-prime sampling in azimuth, which effectively alleviates the conflict between spatial resolution and effective swath width, while also
ZHAO Wanwan, WANG Pengbo, MEN Zhirong, LI Chunsheng   +3 more
doaj   +1 more source

2-D Joint Sparse Reconstruction and Micro-Motion Parameter Estimation for Ballistic Target Based on Compressive Sensing

Sensors, 2020
The sparse frequency band (SFB) signal presents a serious challenge to traditional wideband micro-motion curve extraction algorithms. This paper proposes a novel two-dimension (2-D) joint sparse reconstruction and micro-motion parameter estimation (2D ...
Jiaqi Wei, Shuai Shao, Lei Zhang, Hongwei Liu   +3 more
doaj   +1 more source

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, Shunming Li, Xianglian Li, Jiantao Lu   +3 more
doaj   +1 more source