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Wang, Jianfeng
Alternative names
Publications (7 of 7) Show all publications
Wang, J., Zhou, Z. & Yu, J. (2019). Statistical inference for block sparsity of complex signals.
Open this publication in new window or tab >>Statistical inference for block sparsity of complex signals
2019 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Block sparsity is an important parameter in many algorithms to successfully recover block sparse signals under the framework of compressive sensing. However, it is often unknown and needs to beestimated. Recently there emerges a few research work about how to estimate block sparsity of real-valued signals, while there is, to the best of our knowledge, no investigation that has been conductedfor complex-valued signals. In this paper, we propose a new method to estimate the block sparsity of complex-valued signal. Its statistical properties are obtained and verified by simulations. In addition,we demonstrate the importance of accurately estimating the block sparsity in signal recovery through asensitivity analysis.

Keywords
Block sparsity, Complex-valued signals, Multivariate isotropic symmetric α-stable distribution
National Category
Signal Processing Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-162998 (URN)
Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-09-06
Wang, J., Fonseca, R. M., Rutledge, K., Martin-Torres, J. & Yu, J. (2019). Weather Simulation Uncertainty Estimation using Bayesian Hierarchical Model. Journal of Applied Meteorology and Climatology, 58(3), 585-603
Open this publication in new window or tab >>Weather Simulation Uncertainty Estimation using Bayesian Hierarchical Model
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2019 (English)In: Journal of Applied Meteorology and Climatology, ISSN 1558-8424, E-ISSN 1558-8432, Vol. 58, no 3, p. 585-603Article in journal (Refereed) Published
Abstract [en]

Estimates of the uncertainty of model output fields (e.g. 2-meter temperature, surface radiation fluxes or wind speed) are of great value to the weather and climate communities. The traditional approach for the uncertainty estimation is to conduct an ensemble of simulations where the model configuration is perturbed, and/or different models are considered. This procedure is very computationally expensive and may not be feasible in particular for higher resolution experiments. In this paper a new method based on Bayesian Hierarchical Models (BHM) that requires just one model run is proposed. It is applied to the Weather Research and Forecasting (WRF) model’s 2-meter temperature in the Botnia-Atlantica region in Scandinavia for a 10-day period in the winter and summer seasons. For both seasons, the estimated uncertainty using the BHM is found to be comparable to that obtained from an ensemble of experiments in which different Planetary Boundary Layer (PBL) schemes are employed. While WRF-BHM is not capable of generating the full set of products obtained from an ensemble of simulations, it can be used to extract commonly used diagnostics including the uncertainty estimation which is the focus of this work. The methodology proposed here is fully general and can easily be extended to any other output variable and numerical model.

Place, publisher, year, edition, pages
American Meteorological Society, 2019
Keywords
WRF, Uncertainty, Bayesian Hierarchical Model, Matérn Covariance, Planetary Boundary Layer, Botnia-Atlantica
National Category
Probability Theory and Statistics Meteorology and Atmospheric Sciences
Research subject
Mathematical Statistics; Meteorology
Identifiers
urn:nbn:se:umu:diva-155617 (URN)10.1175/JAMC-D-18-0018.1 (DOI)000460652900002 ()
Projects
WindCoE
Available from: 2019-01-24 Created: 2019-01-24 Last updated: 2019-04-08Bibliographically approved
Wang, J., Zhou, Z., Garpebring, A. & Yu, J. (2017). Sparsity estimation in compressive sensing with application to MR images.
Open this publication in new window or tab >>Sparsity estimation in compressive sensing with application to MR images
2017 (English)Manuscript (preprint) (Other academic)
Abstract [en]

The theory of compressive sensing (CS) asserts that an unknown signal x in C^N canbe accurately recovered from m measurements with m << N provided that x is sparse. Most of the recovery algorithms need the sparsity s = ||x||_0 as an input. However,generally s is unknown, and directly estimating the sparsity has been an open problem.In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. Inthe simulation study and real data study, magnetic resonance image data is used asinput signal, which becomes sparse after sparsified transformation. The results fromthe simulation study confirm the theoretical properties of the estimator. In practice, theestimate from a real MR image can be used for recovering future MR images under theframework of CS if they are believed to have the same sparsity level after sparsification.

Publisher
p. 17
Keywords
Compressive sensing, Sparsity, Bayesian hierarchical model, Matérn covariance, MRI
National Category
Probability Theory and Statistics Medical Image Processing Computational Mathematics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-141548 (URN)
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2018-06-09
Wang, J. & Yu, J. (2017). Sparsity Estimation of MR images in Compressive Sensing. In: : . Paper presented at IRSYSC 2017 – 3rd International Researchers, Statisticians and Young Statisticians Congress, Konya, Turkey, May 2017..
Open this publication in new window or tab >>Sparsity Estimation of MR images in Compressive Sensing
2017 (English)Conference paper, Oral presentation with published abstract (Other academic)
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-141530 (URN)
Conference
IRSYSC 2017 – 3rd International Researchers, Statisticians and Young Statisticians Congress, Konya, Turkey, May 2017.
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2018-06-09
Wang, J., Garpebring, A., Brynolfsson, P. & Yu, J. (2016). Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using Bayesian hierarchical model. In: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling: . Paper presented at METMA VIII - 8th International Workshop on Spatio-temporal Modelling, 1-3 June, Valencia, Spain (pp. 217-217).
Open this publication in new window or tab >>Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using Bayesian hierarchical model
2016 (English)In: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling, 2016, p. 217-217Conference paper, Poster (with or without abstract) (Other academic)
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-141538 (URN)978-84-608-8468-2 (ISBN)
Conference
METMA VIII - 8th International Workshop on Spatio-temporal Modelling, 1-3 June, Valencia, Spain
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2018-06-09
Wang, J., Garpebring, A., Brynolfsson, P., Liu, X. & Yu, J. (2016). Contrast agent quantification by using spatial information in dynamic contrast enhanced MRI.
Open this publication in new window or tab >>Contrast agent quantification by using spatial information in dynamic contrast enhanced MRI
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2016 (English)Manuscript (preprint) (Other academic)
Abstract [en]

The purpose of this study is to investigate a method, using simulations, toimprove contrast agent quantication in Dynamic Contrast Enhanced MRI.Bayesian hierarchical models (BHMs) are applied to smaller images such that spatial information can be incorporated. Then exploratory analysisis done for larger images by using maximum a posteriori (MAP).

For smaller images: the estimators of proposed BHMs show improvementsin terms of the root mean squared error compared to the estimators in existingmethod for a noise level equivalent of a 12-channel head coil at 3T. Moreover,Leroux model outperforms Besag models. For larger images: MAP estimatorsalso show improvements by assigning Leroux prior.

Publisher
p. 12
Keywords
Contrast agent quantication, BHM, Besag, Leroux, INLA, MAP
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-141525 (URN)
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2018-06-09
wang, j., Garpebring, A., Brynolfsson, P., Liu, X. & Yu, J.Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI.
Open this publication in new window or tab >>Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI
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(English)Manuscript (preprint) (Other academic)
National Category
Natural Sciences
Identifiers
urn:nbn:se:umu:diva-133609 (URN)
Available from: 2017-04-13 Created: 2017-04-13 Last updated: 2018-06-09Bibliographically approved
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