Article
Automation & Control Systems
Xinyu Dao, Min Gao, Yi Wang
Summary: An optimal atom selection strategy is proposed in this paper to improve recovery performance in signal compressed recovery, which prunes possible false atoms using defined sensing information entropy. This strategy requires fewer iterations and can be applied in high sparsity or low signal-noise-ratio cases. Simulation results verify the superiority of recovery error and probability compared to existing algorithms.
Article
Computer Science, Information Systems
Wandi Liang, Zixiong Wang, Guangyu Lu, Yang Jiang
Summary: The paper introduces a novel optimized recovery algorithm, supp-BPDN, which has proven to perform well in noise-affected CS systems. Experimental results demonstrate the superiority of supp-BPDN over traditional methods.
Article
Mathematics, Applied
Ben Adcock, Claire Boyer, Simone Brugiapaglia
Summary: Improved sampling complexity bounds are presented for stable and robust sparse recovery in compressed sensing, with a unified analysis based on minimization encompassing block-structured samples and arbitrary structured sparsity. The number of measurements needed can be minimized by adapting variable density sampling to a given prior on the signal support and the coherence of the measurements, with successful application demonstrated in recovering Haar wavelet coefficients from random Fourier measurements.
INFORMATION AND INFERENCE-A JOURNAL OF THE IMA
(2021)
Article
Mathematics, Applied
Ben Adcock, Vegard Antun, Anders C. Hansen
Summary: Infinite-dimensional compressed sensing focuses on recovering analog signals from linear measurements, suitable for many real-world inverse problems. Signals have local sparsity and the sampling scheme should be designed to exploit this additional structure. Recovery guarantees can be obtained by carefully estimating the local coherence between different bases in certain imaging modalities.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
(2021)
Article
Geosciences, Multidisciplinary
Kun Zhang, Wasif Bin Mamoon, E. Schwartz, Anthony. J. J. Parolari
Summary: Monitoring water quality at high frequency is difficult and expensive. Compressed sensing (CS) can be used to reconstruct high-frequency water quality data using limited measurements, as water quality signals are often sparse in the frequency domain. In this study, the sparsity of stream flow and concentration time-series was investigated, and CS was tested for reconstruction. CS effectively reconstructed the signals with only 5%-10% of the measurements needed. The study also found that CS can be integrated with dimensionality reduction and optimization techniques for more efficient sampling schemes in environmental geosciences and engineering.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Computer Science, Information Systems
Jun Wang
Summary: In this paper, a wonderful triangle is introduced to explore the concrete metric relationship between llxll1/llxll and llxll0. Based on the analysis of the iterative soft-thresholding operator, the angle of the triangle corresponding to the side llxll. - llxll1/llxll0 is studied, demonstrating the meaningfulness of signal sparsity within a certain exact interval.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Chaofan Wang, Yuxin Zhang, Liying Sun, Jiefei Han, Lianying Chao, Lisong Yan
Summary: This paper proposes a variable step size sparsity adaptive matching pursuit (SAMPVSS) algorithm, which constructs a set of candidate atoms by calculating the correlation between the measurement matrix and the residual and selects the atom most related to the residual. The algorithm introduces an exponential function to determine the number of atoms to be selected each time and sets different step sizes based on the iteration stage. Simulation results show that the proposed algorithm has good reconstruction effects on both one-dimensional and two-dimensional signals.
Article
Computer Science, Information Systems
Zesen Gui, Qun Zhou, Hui Zhou, Zheng Liao, Ziyi Wang
Summary: In this paper, a simplified supraharmonic compressive sensing model is proposed, which reduces the computational time of compressed sensing algorithms applied to supraharmonic in online applications. The model detects the supraharmonic raw spectral array to obtain the estimated sparsity and the index of supraharmonic emissions, simplifies the sensing matrix in the iteration according to the index, and shortens the whole iteration time of compressed sensing. Simulation results show that the model can reduce computation time to less than half of the original model without sacrificing computation accuracy. The online application effect of the algorithm is also verified by experiments.
Article
Engineering, Multidisciplinary
Sudha Hanumanthu, Rajesh P. Kumar
Summary: This paper estimates the delay and Doppler parameters of multiple moving targets using Compressed Sensing (CS), with a deterministic DFT measurement matrix. It shows that CS is more effective in detecting closely spaced targets with higher resolution compared to traditional Matched Filter (MF), while also optimizing noise for reduction in storage space and high rate ADC requirement.
Article
Computer Science, Information Systems
Hui Ma, Xiaobing Yuan, Leilei Zhou, Baoqing Li, Ronghua Qin
Summary: This letter aims to improve the efficiency and reliability of joint block sparse support recovery by exploiting block features. For the block MMV problem, a Block SOMP algorithm extended from SOMP is proposed to reduce the number of iterations. The proposed algorithm is theoretically analyzed using ERC and confirmed to provide reliable recovery for higher sparsity, with special attention needed on the design of the cooperative compressed sensing matrix.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2023)
Article
Engineering, Biomedical
Jianhong Xiang, Cong Wang, Linyu Wang, Yu Zhong
Summary: This paper proposes a sparse representation method suitable for Fetal Electrocardiogram (FECG) signals based on Compressed Sensing (CS) technology. It also introduces a logistics-tent sine Bernoulli measurement matrix (LTSBM) construction algorithm for more convenient hardware applications. Furthermore, a joint block multiple orthogonal least squares (JBMOLS) algorithm is proposed to improve the reconstruction performance. Experimental results demonstrate that the FECG compressed sensing mode outperforms traditional compressed sensing technology in terms of signal reconstruction performance, reduced running time, and enhanced practicality.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Alexei Novikov, Stephen White
Summary: In this work, we introduce MISTR (Multidimensional Intersection Sparse supporT Recovery), an algorithm that utilizes the structure of multi-dimensional signals to recover the support from magnitude-only measurements with the same accuracy as the best one-dimensional algorithms. Theoretical analysis shows that MISTR can correctly recover the support of signals distributed as a Gaussian point process with high probability under certain sparsity constraints, and provides a thresholding scheme for handling noisy measurements. The algorithm's effectiveness is further demonstrated through detailed numerical experiments, showing near-linear time complexity in practice.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Computer Science, Information Systems
Lei Cai, Yuli Fu, Tao Zhu, Youjun Xiang, Huanqiang Zeng
Summary: Compressed sensing (CS) is a method to recover images from random measurements by exploiting their sparsity assumption. Recent generative model-based CS recovery methods have removed the sparsity constraint, but they are slow and constrained to the generator range. In this study, a new framework called Proximal-Gen is proposed for CS recovery. It includes a fast recovery algorithm and two sub-algorithms (NPGD and DPGD), which achieve better reconstruction performance and higher efficiency under most measurements.
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
(2022)
Article
Automation & Control Systems
Amir Moslemi
Summary: Compressive sensing is applied to reduce the number of samples required for classification in representation learning. A novel approach is presented where image pixels are treated as sensors to identify the optimal sensors in feature space. Spatial sensor locations are learned to identify discriminative information regions within images. L1-2 minimization, RRQR and SVM are used for sparse minimization, feature space extraction and discrimination vector acquiring. The proposed method is evaluated on four experiments and outperforms a state-of-art technique.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Wei He, Xinwen Liu, Linman Zhao, Ran Li, Feng Liu
Summary: This paper proposes a novel CS algorithm to simultaneously recover the magnitude and phase MR images based on the sparsity of the trigonometric function. The method improves the reconstruction of magnitude images while also enhancing the recovery of phase images, showing superiority over compared phase recovery algorithms in both simulated and in vivo images.
Article
Computer Science, Information Systems
Anelia Somekh-Baruch, Amir Leshem, Venkatesh Saligrama
IEEE TRANSACTIONS ON INFORMATION THEORY
(2018)
Article
Computer Science, Information Systems
Cem Aksoylar, George K. Atia, Venkatesh Saligrama
IEEE TRANSACTIONS ON INFORMATION THEORY
(2017)
Article
Computer Science, Artificial Intelligence
Ziming Zhang, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh Saligrama, Philip H. S. Torr
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2018)
Article
Critical Care Medicine
Kyle R. Hansen, Gloria J. DeWalt, Ali I. Mohammed, Hua-an Tseng, Moona E. Abdulkerim, Seth Bensussen, Venkatesh Saligrama, Bobak Nazer, William D. Eldred, Xue Han
JOURNAL OF NEUROTRAUMA
(2018)
Article
Statistics & Probability
Ery Arias-Castro, Bruno Pelletier, Venkatesh Saligrama
JOURNAL OF NONPARAMETRIC STATISTICS
(2018)
Article
Computer Science, Information Systems
Yuting Chen, Joseph Wang, Yannan Bai, Gregory Castanon, Venkatesh Saligrama
IEEE TRANSACTIONS ON MULTIMEDIA
(2019)
Article
Neurosciences
Howard J. Gritton, William M. Howe, Michael F. Romano, Alexandra G. DiFeliceantonio, Mark A. Kramer, Venkatesh Saligrama, Mark E. Bucklin, Dana Zemel, Xue Han
NATURE NEUROSCIENCE
(2019)
Article
Engineering, Electrical & Electronic
Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Hanxiao Wang, Venkatesh Saligrama, Stan Sclaroff, Vitaly Ablavsky
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)
(2019)
Proceedings Paper
Acoustics
Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2018)
Article
Engineering, Electrical & Electronic
Cem Aksoylar, Jing Qian, Venkatesh Saligrama
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Manjesh K. Hanawal, Csaba Szepesvari, Venkatesh Saligrama
ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 54
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Cem Aksoylar, Lorenzo Orecchia, Venkatesh Saligrama
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 70
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 70
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Tolga Bolukbasi, Kai-Wei Chang, Joseph Wang, Venkatesh Saligrama
THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
(2017)