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MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance [article]

Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad
2019 arXiv   pre-print
In this work, we suggest enhancing the performance of sparse coding algorithms by a deliberate and controlled contamination of the input with random noise, a phenomenon known as stochastic resonance.  ...  Sparse coding refers to the pursuit of the sparsest representation of a signal in a typically overcomplete dictionary.  ...  MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M.  ... 
arXiv:1806.10171v5 fatcat:t7ubtax32jfhnkdbblg7jnvxba

A weak fault diagnosis method for rotating machinery based on compressed sensing and stochastic resonance

Peiming Shi, Xiaojie Ma, Dongying Han
2019 Journal of Vibroengineering  
First, the machine fault vibration signals are pretreated by stochastic resonance.  ...  Vibration signals used for rotating machinery fault diagnosis often constitute large amount of data. It is a big challenge to extract faults feature information from these data.  ...  In the method, we use stochastic resonance (SR) to pre-processing the vibration signal.  ... 
doi:10.21595/jve.2018.20140 fatcat:k6ydz652uzb53g4n6tvikfmvwq

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Hongjiang Cui, Ying Guan, Wu Deng
2021 Applied Sciences  
In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale  ...  Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain  ...  Multi-component fault diagnosis of wheelset-bearing using shift-invariant impulsive dictionary matching pursuit and sparrow search algorithm. Measurement 2021, 178, 109375. [CrossRef] 51.  ... 
doi:10.3390/app112311480 fatcat:gvxhrppz65bjjd6paulst7dgxy

Elements of a stochastic 3D prediction engine in larval zebrafish prey capture [article]

Andrew D Bolton, Martin Haesemeyer, Josua Jordi, Ulrich Schaechtle, Feras A Saad, Vikash K Mansinghka, Joshua B Tenenbaum, Florian Engert
2019 bioRxiv   pre-print
Further, we demonstrate that fish use a graded stochasticity algorithm where the variance around the mean result of each swim scales with distance from the target.  ...  In sum, our quantitative and probabilistic modeling shows that zebrafish are equipped with a stochastic recursive algorithm that embodies an implicit predictive model of the world.  ...  Further, we demonstrate that fish use a graded stochasticity algorithm where the variance around the mean result of each swim scales with distance from the target.  ... 
doi:10.1101/755777 fatcat:vetulsago5efhgulcs3tyh2m5u

Elements of a stochastic 3D prediction engine in larval zebrafish prey capture

Andrew D Bolton, Martin Haesemeyer, Josua Jordi, Ulrich Schaechtle, Feras A Saad, Vikash K Mansinghka, Joshua B Tenenbaum, Florian Engert
2019 eLife  
In sum, our study reveals that zebrafish are equipped with a recursive prey capture algorithm, built up from simple stochastic rules, that embodies an implicit predictive model of the world.  ...  Using a novel naturalistic 3D setup, we show that the zebrafish combines position and velocity perception to construct a future positional estimate of its prey, indicating an ability to project trajectories  ...  Graded stochasticity in sensorimotor transformations improves hunting performance.  ... 
doi:10.7554/elife.51975 pmid:31769753 pmcid:PMC6930116 fatcat:qxj5rd7kkvhzzmksofrydo5xsm

A team of pursuit learning automata for solving deterministic optimization problems

Anis Yazidi, Nourredine Bouhmala, Morten Goodwin
2020 Applied intelligence (Boston)  
We provide some experimental results that show how our Pursuit-LA scheme can be used to solve the Maximum Satisfiability (Max-SAT) problem.  ...  Although many LA algorithms have been devised in the literature, those LA schemes are not able to solve deterministic optimization problems as they suppose that the environment is stochastic.  ...  The Pursuit-LA algorithm is a stochastic algorithm. It has been compared in Table 2 with 3 stochastic algorithms RoTS, IRoTS and AdaptNovelty+ using the software UBCSAT [80] .  ... 
doi:10.1007/s10489-020-01657-9 fatcat:gb6yvu2nj5dw7on453brkeuspq

Support vector machines for improved multiaspect target recognition using the Fisher kernel scores of hidden Markov models

Krishnapuram, Carin
2002 IEEE International Conference on Acoustics Speech and Signal Processing  
The use of prior knowledge concerning sensor motion is employed in modeling the sequential data, improving classification performance.  ...  Improved discrimination results are presented for measured acoustic scattering data.  ...  Recently, Jaakola and Haussler have proposed a general technique for improving the classification performance of generative stochastic models like HMMs using the support vector machine (SVM) classifier  ... 
doi:10.1109/icassp.2002.1005315 fatcat:2iuvmf7cbnbg7pjk3xjenceree

Support Vector Machines for improved multiaspect target recognition using the fisher kernel scores of Hidden Markov Models

Balaji Krishnapuram, Lawrence Carin
2002 IEEE International Conference on Acoustics Speech and Signal Processing  
The use of prior knowledge concerning sensor motion is employed in modeling the sequential data, improving classification performance.  ...  Improved discrimination results are presented for measured acoustic scattering data.  ...  Recently, Jaakola and Haussler have proposed a general technique for improving the classification performance of generative stochastic models like HMMs using the support vector machine (SVM) classifier  ... 
doi:10.1109/icassp.2002.5745277 dblp:conf/icassp/KrishnapuramC02 fatcat:jlleyriebrddbitkfgenzvuf7a

Table of contents

2019 IEEE Transactions on Circuits and Systems - II - Express Briefs  
Zhu 1272 Spectral Weighting Orthogonal Matching Pursuit Algorithm for Enhanced Out-of-Band Digital Predistortion Linearization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Liu 1217 Analysis of Resonance and Anti-Resonance Frequencies in a Wireless Power Transfer System: Analytical Model and Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tcsii.2019.2919084 fatcat:5yi7eklzvjcmtj4abif47tz54i

Sparse signal recovery from compressed measurements using hybrid particle swarm optimization

Hassaan Haider, Jawad Ali Shah, Shahid Ikram, Idris Abd Latif
2017 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)  
This workpresents a novel technique of recovering a K-sparse signal from compressive measurements using hybrid Differential Evolution (DE).  ...  In the proposed algorithm, the slow convergence of conventional DE is accelerated with the help of Separable Surrogate Functionals (SSF) algorithm.  ...  DE is a general-purpose stochastic optimization approach that has simple structure and uses mutation as a primary search mechanism [10] .  ... 
doi:10.1109/icsipa.2017.8120649 dblp:conf/icsipa/HaiderSIL17 fatcat:j6ikrpswnfgmrkabb522ggeqlm

Dynamic Iterative Pursuit

Dave Zachariah, Saikat Chatterjee, Magnus Jansson
2012 IEEE Transactions on Signal Processing  
improve.  ...  For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over time/space.  ...  We then develop a predictive orthogonal matching pursuit algorithm that can incorporate prior information in a stochastic framework, using the signal to prediction error ratio as a statistic.  ... 
doi:10.1109/tsp.2012.2203813 fatcat:fvhztxjnqrcu3cm3ombjvmseku

Table of contents

2018 IEEE Transactions on Smart Grid  
Xia 4828 A New Approach for Fault Classification in Microgrids Using Optimal Wavelet Functions Matching Pursuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Majumdar 4669 Quickest Localization of Anomalies in Power Grids: A Stochastic Graphical Framework . . . . . . . . . . . . . . J. Heydari and A.  ... 
doi:10.1109/tsg.2018.2861278 fatcat:nzuxowcdlzclbhlm6m3qipbuzi

A Review on Techniques for the Extraction of Transients in Musical Signals [chapter]

Laurent Daudet
2006 Lecture Notes in Computer Science  
The absence of a unique definition of what a "transient" means for signals that are by essence non-stationary implies that a lot of methods can be used and sometimes lead to significantly different results  ...  Besides a reduced computational complexity, selecting molecules improves significantly the TSS separation over the original matching pursuit: first, it prevents isolated large atoms to be tagged as significant  ...  It also contains some slowly-varying stochastic residual.  ... 
doi:10.1007/11751069_20 fatcat:jek6yyvduzf77psswbdsqyfguy

Segmentation of thalamus from MR images via task-driven dictionary learning

Luoluo Liu, Jeffrey Glaister, Xiaoxia Sun, Aaron Carass, Trac D. Tran, Jerry L. Prince, Martin A. Styner, Elsa D. Angelini
2016 Medical Imaging 2016: Image Processing  
The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms.  ...  Automatic thalamus segmentation is useful to track changes in thalamic volume over time.  ...  Due to the size of the training data, we take S to be a random subset of voxels from the training data and the stochastic gradient descent algorithm is used to optimize (2).  ... 
doi:10.1117/12.2214206 pmid:27601772 pmcid:PMC5010870 dblp:conf/miip/LiuGSCTP16 fatcat:dihcj7ggxndm5ckkqqthvvjupm

Stochastic Deconvolution

James Gregson, Felix Heide, Matthias B. Hullin, Mushfiqur Rouf, Wolfgang Heidrich
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks.  ...  Stochastic Deconvolution is straightforward to implement, produces state-of-the-art results and directly leads to a natural boundary condition for image boundaries and saturated pixels.  ...  Deblurred result using Stochastic Deconvolution algorithm (right).  ... 
doi:10.1109/cvpr.2013.139 dblp:conf/cvpr/GregsonHHRH13 fatcat:bclnjedekba6lpvetymptkhqpe
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