Applications of the Orthogonal Matching Pursuit/ Nonlinear Least Squares Algorithm to Compressive Sensing Recovery

On the Dissimilarity of Orthogonal Least Squares and Orthogonal Matching Pursuit Compressive Sensing Reconstruction

Smart Innovation, Systems and Technologies - Advanced Computing, Networking and Informatics- Volume 1 ◽

10.1007/978-3-319-07353-8_6 ◽

2014 ◽

pp. 41-46

Author(s):

Arvinder Kaur ◽

Sumit Budhiraja

Keyword(s):

Compressive Sensing ◽

Least Squares ◽

Matching Pursuit ◽

Orthogonal Matching Pursuit ◽

Orthogonal Least Squares

Orthogonal Matching Pursuit for Least Squares Temporal Difference with Gradient Correction

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327001 ◽

2020 ◽

Author(s):

Dazi Li ◽

Chao Ma ◽

Jianqing Zhang ◽

Xin Ma ◽

Qibing Jin

Keyword(s):

Least Squares ◽

Matching Pursuit ◽

Orthogonal Matching Pursuit ◽

Temporal Difference ◽

Gradient Correction

Effects of signal to noise and parametric limitations on fitting biexponential magnetic resonance (MR) inversion-recovery curves using a constrained nonlinear least-squares algorithm

Proceedings of the IEEE 24th Annual Northeast Bioengineering Conference (Cat. No.98CH36210) ◽

10.1109/nebc.1998.664869 ◽

2002 ◽

Author(s):

M.D. Silva ◽

S.S. Han ◽

C.H. Sotak

Keyword(s):

Magnetic Resonance ◽

Least Squares ◽

Nonlinear Least Squares ◽

Inversion Recovery ◽

Signal To Noise ◽

Recovery Curves ◽

Least Squares Algorithm

Cryptography with compressive sensing Orthogonal Matching Pursuit method

2015 23nd Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2015.7129797 ◽

2015 ◽

Author(s):

Ertan Atar ◽

Okan K. Ersoy ◽

Lale Ozyilmaz

Keyword(s):

Compressive Sensing ◽

Matching Pursuit ◽

Orthogonal Matching Pursuit

Image Steganography Compressive Sensing Orthogonal Matching Pursuit

10.1109/icoict52021.2021.9527473 ◽

2021 ◽

Author(s):

Irma Safitri ◽

Ratri Dwi Atmaja ◽

Vikra Akbar

Keyword(s):

Compressive Sensing ◽

Matching Pursuit ◽

Orthogonal Matching Pursuit ◽

Image Steganography

The reliability of nonlinear least-squares algorithm for data analysis of neural response activity during sinusoidal rotational stimulation in semicircular canal neurons

PLoS ONE ◽

10.1371/journal.pone.0190596 ◽

2018 ◽

Vol 13 (1) ◽

pp. e0190596 ◽

Cited By ~ 3

Author(s):

Pengyu Ren ◽

Bowen Li ◽

Shiyao Dong ◽

Lin Chen ◽

Yuelin Zhang

Keyword(s):

Data Analysis ◽

Least Squares ◽

Semicircular Canal ◽

Nonlinear Least Squares ◽

Neural Response ◽

Least Squares Algorithm ◽

Rotational Stimulation

Performance Comparison of Orthogonal Matching Pursuit and Novel Incremental Gaussian Elimination OMP Reconstruction Algorithms for Compressive Sensing

10.1109/comcas52219.2021.9629027 ◽

2021 ◽

Author(s):

V H Prasad Reddy ◽

Puli Kishore Kumar

Keyword(s):

Compressive Sensing ◽

Matching Pursuit ◽

Gaussian Elimination ◽

Performance Comparison ◽

Orthogonal Matching Pursuit ◽

Reconstruction Algorithms

Lightning Impulse Parameter Estimation Using Nonlinear Least Squares Algorithm

2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) ◽

10.1109/jeeit.2019.8717454 ◽

2019 ◽

Author(s):

Mohammad I. Al Saaideh ◽

Eyad A. Feilat ◽

Dia I. Abu-Al-Nadi ◽

Amer S. Al-Hinai

Keyword(s):

Parameter Estimation ◽

Least Squares ◽

Nonlinear Least Squares ◽

Lightning Impulse ◽

Least Squares Algorithm

High-speed FPGA implementation of orthogonal matching pursuit for compressive sensing signal reconstruction

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2018.07.017 ◽

2018 ◽

Vol 71 ◽

pp. 173-190 ◽

Cited By ~ 1

Author(s):

Önder Polat ◽

Sema Koç Kayhan

Keyword(s):

Compressive Sensing ◽

High Speed ◽

Matching Pursuit ◽

Signal Reconstruction ◽

Fpga Implementation ◽

Orthogonal Matching Pursuit

Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

International Agrophysics ◽

10.1515/intag-2016-0056 ◽

2017 ◽

Vol 31 (3) ◽

pp. 433-445

Author(s):

Yifan Yan ◽

Jianli Liu ◽

Jiabao Zhang ◽

Xiaopeng Li ◽

Yongchao Zhao

Keyword(s):

Confidence Interval ◽

Least Squares ◽

Water Content ◽

Soil Water ◽

Nonlinear Least Squares ◽

Uncertainty Estimation ◽

Model Parameters ◽

Retention Curve ◽

Least Squares Algorithm ◽

Soil Hydraulic

AbstractNonlinear least squares algorithm is commonly used to fit the evaporation experiment data and to obtain the ‘optimal’ soil hydraulic model parameters. But the major defects of nonlinear least squares algorithm include non-uniqueness of the solution to inverse problems and its inability to quantify uncertainties associated with the simulation model. In this study, it is clarified by applying retention curve and a modified generalised likelihood uncertainty estimation method to model calibration. Results show that nonlinear least squares gives good fits to soil water retention curve and unsaturated water conductivity based on data observed by Wind method. And meanwhile, the application of generalised likelihood uncertainty estimation clearly demonstrates that a much wider range of parameters can fit the observations well. Using the ‘optimal’ solution to predict soil water content and conductivity is very risky. Whereas, 95% confidence interval generated by generalised likelihood uncertainty estimation quantifies well the uncertainty of the observed data. With a decrease of water content, the maximum of nash and sutcliffe value generated by generalised likelihood uncertainty estimation performs better and better than the counterpart of nonlinear least squares. 95% confidence interval quantifies well the uncertainties and provides preliminary sensitivities of parameters.