Results 31 to 40 of about 799,916 (290)

Signal Reconstruction from Noisy Multichannel Samples

SSRN Electronic Journal, 2022
31 pages, 6 ...
Dong Cheng, Xiaoxiao Hu, Kit Ian Kou
openaire   +2 more sources

A Modified Spectral Gradient Projection Method for Solving Non-Linear Monotone Equations With Convex Constraints and Its Application

IEEE Access, 2020
In this paper, we propose a derivative free algorithm for solving non-linear monotone equations with convex constraints. The proposed algorithm combines the method of spectral gradient and the projection method.
Li Zheng, Lei Yang, Yong Liang
doaj   +1 more source

A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory

Energies, 2022
This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory.
Xiao Yang, Fengrong Bi, Yabing Jing, Xin Li, Guichang Zhang   +4 more
doaj   +1 more source

A Max-Product EM Algorithm for Reconstructing Markov-tree Sparse Signals from Compressive Samples [PDF]

, 2013
We propose a Bayesian expectation-maximization (EM) algorithm for reconstructing Markov-tree sparse signals via belief propagation. The measurements follow an underdetermined linear model where the regression-coefficient vector is the sum of an unknown ...
Dogandžić, Aleksandar, Dogandžić, Aleksandar, Song, Zhao   +2 more
core   +4 more sources

SNR enhancement through phase dependent signal reconstruction algorithms for phase separated interferometric signals [PDF]

, 2007
We report several signal reconstruction algorithms for processing phase separated homodyne interferometric signals. Methods that take advantage of the phase of the signal are experimentally shown to achieve a signal-to-noise ratio (SNR) improvement of up
McDowell, Emily J., Sarunic, Marinko V., Yang, Changhuei, Yaqoob, Zahid   +3 more
core   +1 more source

Exact CS Reconstruction Condition of Undersampled Spectrum-Sparse Signals

Journal of Applied Mathematics, 2013
Compressive sensing (CS) reconstruction of a spectrum-sparse signal from undersampled data is, in fact, an ill-posed problem. In this paper, we mathematically prove that, in certain cases, the exact CS reconstruction of a spectrum-sparse signal from ...
Ying Luo, Qun Zhang, Guozheng Wang, Youqing Bai   +3 more
doaj   +1 more source

Super‐resolution forward‐looking imaging method for manoeuvering platform with optimised dictionary and extended sparsity adaptive matching pursuit

IET Radar, Sonar & Navigation, 2022
In forward‐looking imaging, left‐right Doppler is ambiguous and cross‐range resolution is limited by real aperture, which result in traditional synthetic aperture radar imaging methods no longer applicable.
Gang Zhang, Yi Liang, Shuxuan Chen, Mengdao Xing, Zhenfang Li   +4 more
doaj   +1 more source

Kernel-Based Reconstruction of Graph Signals [PDF]

IEEE Transactions on Signal Processing, 2017
Submitted May ...
Romero, Daniel, Ma, Meng, Giannakis, Georgios B.   +2 more
openaire   +2 more sources

Generalized Cardinal Polishing Splines Signal Reconstruction

Mathematics
Sampling and reconstruction are indispensable processes in signal processing, and appropriate foundations are crucial for spline reconstruction models.
Fangli Sun, Zhanchuan Cai
doaj   +1 more source

Analysis of physiological signals using state space correlation entropy

Healthcare Technology Letters, 2016
In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series.
Rajesh Kumar Tripathy, Suman Deb, Suman Deb, Samarendra Dandapat, Samarendra Dandapat   +4 more
doaj   +1 more source