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Near optimal lossy source coding and compression-based denoising via Markov chain Monte Carlo
2008
2008 42nd Annual Conference on Information Sciences and Systems
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We also show how our approach carries over to such problems as universal Wyner-Ziv coding and compression-based denoising. ...
We propose an implementable new universal lossy source coding algorithm. ...
Our algorithm borrows two well-known tools from statistical physics and computer science, namely Markov Chain Monte Carlo (MCMC) methods, and simulated annealing [10] , [11] . ...
doi:10.1109/ciss.2008.4558567
dblp:conf/ciss/JalaliW08
fatcat:27pqhzocabeyrihutqsi3a5274
Universal MAP estimation in compressed sensing
2011
2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
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We provide initial theoretical, algorithmic, and experimental evidence based on maximum a posteriori (MAP) estimation that shows the promise of universality in CS, particularly for low-complexity sources ...
We study the compressed sensing (CS) estimation problem where an input is measured via a linear matrix multiplication under additive noise. ...
ACKNOWLEDGMENTS Preliminary conversations with Deanna Needell and Tsachy Weissman framed our thinking about universal compressed sensing. ...
doi:10.1109/allerton.2011.6120245
dblp:conf/allerton/BaronD11
fatcat:wlozif6xlreo5kdicn2t7a3xfe
Table of Contents
2020
IEEE Signal Processing Letters
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Qiao 1375 Dynamic Markov Chain Monte Carlo-Based Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . .Z. Wang, L. Liu, and K. ...
weih Hung 1035 Localization of Near-Field Sources for Exact Source-Sensor Spatial Geometry .. . . . . . . . . . . . . . . . J. He, L. Li, and T. ...
Zhu, and X. Du 1844 Optimality Verification of Tensor Completion Model via Self-Validation . . . . . . . . . . C. Liu, H. Shan, T. Ma, and B. ...
doi:10.1109/lsp.2020.3040840
fatcat:ezrfzwo6tjbkfhohq2tgec4m6y
Statistical Physics and Information Theory Perspectives on Linear Inverse Problems
[article]
2017
arXiv
pre-print
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Both SMV and MMV are linear models (LM's), and the process of estimating the underlying vector(s) x from an LM given the matrices, noisy measurements, and knowledge of the noise statistics, is called a ...
The intuition of the application of statistical physics to our problem is that statistical physics deals with large-scale problems, and we can make an analogy between an LM and a thermodynamic system. ...
Markov chain Monte Carlo Having approximated the coding length, we now describe how to optimize our objective function. ...
arXiv:1705.05070v2
fatcat:ubgidv6hqjem5cdyvhojucsoku
Table of Contents
2020
IEEE Signal Processing Letters
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Blum 16 Stochastic Gradient Population Monte Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. El-Laham and M. ...
Viola and P. Cesar 1660 Optimality Verification of Tensor Completion Model via Self-Validation . . . . . . . . . . C. Liu, H. Shan, T. Ma, and B. ...
Wang, and H. C. So 585 Localization of Near-Field Sources for Exact Source-Sensor Spatial Geometry . . . . . . . . . . . . . . . . . J. He, L. Li, and T. ...
doi:10.1109/lsp.2020.3040844
fatcat:xpovskhrvfgctk3hhufuvpyyne
Distributed correlated data gathering in wireless sensor networks via compressed sensing
2013
2013 Asilomar Conference on Signals, Systems and Computers
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The last two approaches incorporate the quantization of CS measurements and focus on lossy source coding. ...
The remaining parts deal with compressed sensing (CS) of sparse/compressible sources. ...
The multidimensional integrals in (73) and (80) are similarly evaluated via Monte Carlo integration techniques. ...
doi:10.1109/acssc.2013.6810310
dblp:conf/acssc/LeinonenCJ13
fatcat:wdpvzue3wzcklpfsevkr7uwvmm
2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53
2015
IEEE Transactions on Geoscience and Remote Sensing
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Lang, S., +, TGRS Aug. 2015 4496-4509
Improving Soil Moisture Profile Prediction With the Particle Filter-Markov
Chain Monte Carlo Method. ...
., and Moradkhani, H., Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method; TGRS Nov. 2015 6134-6147
Yan, J.,
5661-5676 Yu, J., see Yu, Y., TGRS March ...
doi:10.1109/tgrs.2015.2513444
fatcat:zuklkpk4gjdxjegoym5oagotzq
Neural Fields in Visual Computing and Beyond
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2111.11426v1 fatcat:g3pfk4fyjrd4xd32pdlq2gaizeOther Versions
Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes ...
or objects across space and time. ...
We would like to thank Sunny Li for their help in designing the website, and Alexander Rush and Hendrik Strobelt of the Mini-Conf project. ...
arXiv:2111.11426v4
fatcat:yteqzbu6gvgdzobnfzuqohix2e
2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
2019
IEEE Transactions on Geoscience and Remote Sensing
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., and Drake, V.A., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 ...
and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging ...
Ye, H., +, TGRS July 2019 4457-4469 Bayesian Inversion of Logging-While-Drilling Extra-Deep Directional Resistivity Measurements Using Parallel Tempering Markov Chain Monte Carlo Sampling. ...
doi:10.1109/tgrs.2020.2967201
fatcat:kpfxoidv5bgcfo36zfsnxe4aj4
Contents
2011
Procedia Engineering
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Baharudin . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 3891 The Monte Carlo Simulation to Magnetic Particles of Magnetorheological Fluids Liu Yongzhi, Liu Xinhua and ...
Image Denoising Fei Xiao and Yungang Zhang
. . 3998 . ...
Monte Carlo Simulation of Energy Distribution of Radiation Field Liu Yi, Chen Xiao-Bai and Wang Chun- Yan . . 3330 An Improved Association Rules Algorithm based on Frequent Item Sets Yaqiong Jiang ...
doi:10.1016/s1877-7058(11)04811-9
fatcat:adrkzzediretxma23pbeqmltvu
2020 Index IEEE Transactions on Vehicular Technology Vol. 69
2020
IEEE Transactions on Vehicular Technology
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T., R., TVT Dec. 2020 16218-16223 Hoon-Kim, T., see Kumar, G., TVT July 2020 7707-7722 Horlin, F., see Monfared, S., TVT Oct. 2020 11369-11382 Horng, S., Lu, C., and Zhou, W., An Identity-Based and ...
Yu, X., A Joint Design of Platoon Communication and Control Based on LTE-V2V; 15893-15907 Hong, C.S., see Nguyen, M.N.H., TVT May 2020 5618-5633 Hong, C.S., see Chen, D., TVT May 2020 5634-5646 Hong ...
Nguyen, N.T., +, TVT Jan. 2020 1136-1140 Heuristic Monte Carlo Algorithm for Unmanned Ground Vehicles Realtime Localization and Mapping. ...
doi:10.1109/tvt.2021.3055470
fatcat:536l4pgnufhixneoa3a3dibdma
Contrastive Topographic Models: Energy-based density models applied to the understanding of sensory coding and cortical topography
[article]
2020
arXiv
pre-print
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framework and various other types of probabilistic model such as Markov random fields and factor graphs; we also develop and discuss approximate algorithms for performing maximum likelihood learning and ...
inference in our energy based models. ...
In particular I am extremely grateful my advisors, Peter Dayan and Geoff Hinton for providing peerless guidance and inspiration. ...
arXiv:2011.03535v1
fatcat:xqev3hqpnnglzjqs7chsrc5zoe
Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview
2020
Applied Sciences
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Historical map changes include the change in boundaries of cities/states, vegetation regions, water bodies and so forth. Change detection in these regions are mainly carried out via satellite images. ...
This work highlights the methods and the suitable satellite imaging methods associated with these applications. ...
Reversible jump Markov chain Monte Carlo sampler [12] Synthetic image Extraction of rivers, channels and roads Completeness = 98.8, correctness = 94% and quality = 92.9%
Table 3 . 3 Image segmentation ...
doi:10.3390/app10124207
fatcat:zlttedt4qzht7aijl6alox43ky
2020 Index IEEE Transactions on Circuits and Systems I: Regular Papers Vol. 67
2020
IEEE Transactions on Circuits and Systems Part 1: Regular Papers
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Lin, C., +, TCSI Nov. 2020 3643-3655 Monte Carlo methods An Analytical Framework and Approximation Strategy for Efficient Implementation of Distributed Arithmetic-Based Inner-Product Architectures. ...
., +, TCSI March 2020 1045-1057 Data compression A Binary Line Buffer Circuit Featuring Lossy Data Compression at Fixed Maximum Data Rate. ...
., +, TCSI Dec. 2020 4295-4308 Enhanced Linearity in FD-SOI CMOS Body-Input Analog Circuits -Application to Voltage-Controlled Ring Oscillators and Frequency-Based ΣΔ ADCs. ...
doi:10.1109/tcsi.2021.3055003
fatcat:kbmst5td2bbvtl7vpbj3knnkri
2020 Index IEEE Transactions on Instrumentation and Measurement Vol. 69
2020
IEEE Transactions on Instrumentation and Measurement
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Meenalochani, M., and Sudha, S., Influence of Received Signal Strength on Prediction of Cluster Head and Number of Rounds; TIM June 2020 3739-3749 Hendeby, G., see Kasebzadeh, P., TIM Aug. 2020 5862 ...
Converter Using All-Digital Nested Delay-Locked Loops With 50-ps Resolution and High Throughput for LiDAR TIM Nov. 2020 9262-9271 Helsen, J., see Huchel, L., TIM July 2020 4145-4153 Hemavathi, N., ...
., +, TIM May 2020 2468-2476
Monte Carlo Analysis of Measurement Uncertainties for On-Wafer Multiline
TRL Calibration Including Dynamic Accuracy. ...
doi:10.1109/tim.2020.3042348
fatcat:a5f4fsqs45fbbetre6zwsg3dly
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