Search Results - "Selesnick, Ivan"

Wavelet Transform With Tunable Q-Factor by Selesnick, I. W.

“…This paper describes a discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the…”
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Resonance-based signal decomposition: A new sparsity-enabled signal analysis method by Selesnick, Ivan W.

“…Numerous signals arising from physiological and physical processes, in addition to being non-stationary, are moreover a mixture of sustained oscillations and…”
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Sparse Signal Approximation via Nonseparable Regularization by Selesnick, Ivan, Farshchian, Masoud

“…The calculation of a sparse approximate solution to a linear system of equations is often performed using either L1-norm regularization and convex optimization…”
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Sparse Signal Estimation by Maximally Sparse Convex Optimization by Selesnick, Ivan W., Bayram, Ilker

“…This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g., sparse deconvolution. This paper aims…”
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Chromatogram baseline estimation and denoising using sparsity (BEADS) by Ning, Xiaoran, Selesnick, Ivan W., Duval, Laurent

“…This paper jointly addresses the problems of chromatogram baseline correction and noise reduction. The proposed approach is based on modeling the series of…”
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Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis by Wang, Shibin, Chen, Xuefeng, Selesnick, Ivan W., Guo, Yanjie, Tong, Chaowei, Zhang, Xingwu

“…•MSST is introduced for characterizing signals with fast varying IF.•The performance of MSST is analyzed theoretically.•MSST is applied in gearbox fault…”
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Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization by Po-Yu Chen, Selesnick, Ivan W.

“…Convex optimization with sparsity-promoting convex regularization is a standard approach for estimating sparse signals in noise. In order to promote sparsity…”
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Convex 1-D Total Variation Denoising with Non-convex Regularization by Selesnick, Ivan W., Parekh, Ankit, Bayram, Ilker

“…Total variation (TV) denoising is an effective noise suppression method when the derivative of the underlying signal is known to be sparse. TV denoising is…”
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Enhanced Low-Rank Matrix Approximation by Parekh, Ankit, Selesnick, Ivan W.

“…This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with nonconvex regularization. We employ parameterized…”
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Translation-invariant shrinkage/thresholding of group sparse signals by Chen, Po-Yu, Selesnick, Ivan W.

“…This paper addresses signal denoising when large-amplitude coefficients form clusters (groups). The L1-norm and other separable sparsity models do not capture…”
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Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis by Cai, Gaigai, Selesnick, Ivan W., Wang, Shibin, Dai, Weiwei, Zhu, Zhongkui

“…Vibration signals arising from faulty gearboxes are often a mixture of the meshing component and the periodic transient component, and simultaneously…”
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Enhanced Sparsity by Non-Separable Regularization by Selesnick, Ivan W., Bayram, Ilker

“…This paper develops a convex approach for sparse one-dimensional deconvolution that improves upon L1-norm regularization, the standard convex approach. We…”
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Synthesis versus analysis priors via generalized minimax-concave penalty for sparsity-assisted machinery fault diagnosis by Wang, Shibin, Selesnick, Ivan W., Cai, Gaigai, Ding, Baoqing, Chen, Xuefeng

“…•The sparse synthesis and sparse analysis methods are proposed for sparse regularization.•The gap between synthesis and analysis priors is explored via…”
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Ridge-Aware Weighted Sparse Time-Frequency Representation by Tong, Chaowei, Wang, Shibin, Selesnick, Ivan, Yan, Ruqiang, Chen, Xuefeng

“…The ideal time-frequency (TF) representation which distributes the total energy along the instantaneous frequency (IF) of a signal is essentially sparse…”
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Repetitive transients extraction algorithm for detecting bearing faults by He, Wangpeng, Ding, Yin, Zi, Yanyang, Selesnick, Ivan W.

“…Rolling-element bearing vibrations are random cyclostationary. This paper addresses the problem of noise reduction with simultaneous components extraction in…”
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Reweighted generalized minimax-concave sparse regularization and application in machinery fault diagnosis by Cai, Gaigai, Wang, Shibin, Chen, Xuefeng, Ye, Junjie, Selesnick, Ivan W.

“…The vibration signal of faulty rotating machinery tends to be a mixture of repetitive transients, discrete frequency components and noise. How to accurately…”
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Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization by Parekh, Ankit, Selesnick, Ivan W, Rapoport, David M, Ayappa, Indu

“…This paper addresses the problem of detecting sleep spindles and K-complexes in human sleep EEG. Sleep spindles and K-complexes aid in classifying stage 2 NREM…”
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Image restoration using total variation with overlapping group sparsity by Liu, Jun, Huang, Ting-Zhu, Selesnick, Ivan W., Lv, Xiao-Guang, Chen, Po-Yu

“…Image restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its…”
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A Kalman Filter Framework for Simultaneous LTI Filtering and Total Variation Denoising by Kheirati Roonizi, Arman, Selesnick, Ivan W.

“…This paper proposes a Kalman filter framework for signal denoising that simultaneously utilizes conventional linear time-invariant (LTI) filtering and total…”
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Simultaneous Low-Pass Filtering and Total Variation Denoising by Selesnick, Ivan W., Graber, Harry L., Pfeil, Douglas S., Barbour, Randall L.

“…This paper seeks to combine linear time-invariant (LTI) filtering and sparsity-based denoising in a principled way in order to effectively filter (denoise) a…”
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