Sorry, you need to enable JavaScript to visit this website.

Statistical Signal Processing

Deviation Detection with Continuous Observations


This paper considers the detection of possible deviation from a nominal distribution for continuously valued random variables. Specifically, under the null hypothesis, samples are distributed approximately according to a nominal distribution. Any significant departure from this nominal distribution constitutes the alternative hypothesis. It is established that for such deviation detection where the nominal distribution is only specified under the null hypothesis, Kullback-Leibler distance is not a suitable measure for deviation.

PPT.pdf

PDF icon PPT.pdf (484 downloads)

Paper Details

Authors:
Biao Chen
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

PPT.pdf

(484 downloads)

Keywords

Additional Categories

Subscribe

[1] Biao Chen, "Deviation Detection with Continuous Observations", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/447. Accessed: Sep. 20, 2018.
@article{447-15,
url = {http://sigport.org/447},
author = {Biao Chen },
publisher = {IEEE SigPort},
title = {Deviation Detection with Continuous Observations},
year = {2015} }
TY - EJOUR
T1 - Deviation Detection with Continuous Observations
AU - Biao Chen
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/447
ER -
Biao Chen. (2015). Deviation Detection with Continuous Observations. IEEE SigPort. http://sigport.org/447
Biao Chen, 2015. Deviation Detection with Continuous Observations. Available at: http://sigport.org/447.
Biao Chen. (2015). "Deviation Detection with Continuous Observations." Web.
1. Biao Chen. Deviation Detection with Continuous Observations [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/447

Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements


This is an overview presentation about developing accurate prior models that can capture non-Gaussian characteristics of images. The slides use tunable diode laser absorption tomography (TDLAT) as an application to show the results.
For more information, please check out the publication at IEEE Xplore:

Zeeshan Nadir, Michael S. Brown, Mary L. Comer, Charles A. Bouman, “Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements” , 2015 IEEE GlobalSIP Conference, Dec 14-16

Paper Details

Authors:
Michael S. Brown, Mary L. Comer, Charles A. Bouman
Submitted On:
23 February 2016 - 1:38pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Globalsip 2015.pdf

(345 downloads)

Subscribe

[1] Michael S. Brown, Mary L. Comer, Charles A. Bouman, "Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/442. Accessed: Sep. 20, 2018.
@article{442-15,
url = {http://sigport.org/442},
author = {Michael S. Brown; Mary L. Comer; Charles A. Bouman },
publisher = {IEEE SigPort},
title = {Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements},
year = {2015} }
TY - EJOUR
T1 - Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements
AU - Michael S. Brown; Mary L. Comer; Charles A. Bouman
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/442
ER -
Michael S. Brown, Mary L. Comer, Charles A. Bouman. (2015). Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements. IEEE SigPort. http://sigport.org/442
Michael S. Brown, Mary L. Comer, Charles A. Bouman, 2015. Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements. Available at: http://sigport.org/442.
Michael S. Brown, Mary L. Comer, Charles A. Bouman. (2015). "Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements." Web.
1. Michael S. Brown, Mary L. Comer, Charles A. Bouman. Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/442

Joint Composite Detection and Bayesian Estimation: A Neyman-Pearson Approach


The paper considers the composite detection problem where both detection and parameter estimation are of primary interest. Based on a Neyman-Pearson type of formulation, our goal is to find the joint detector and estimator that minimizes a decision-dependent Bayesian estimation risk subject to the detection error probability constraints. The optimal joint solution not only yields lower Bayesian estimation risk compared to the conventional method, which combines the likelihood ratio test and the Bayesian estimator in sequence, but

Paper Details

Authors:
Xiaodong Wang
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP_JDE.pdf

(396 downloads)

Subscribe

[1] Xiaodong Wang, "Joint Composite Detection and Bayesian Estimation: A Neyman-Pearson Approach", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/441. Accessed: Sep. 20, 2018.
@article{441-15,
url = {http://sigport.org/441},
author = {Xiaodong Wang },
publisher = {IEEE SigPort},
title = {Joint Composite Detection and Bayesian Estimation: A Neyman-Pearson Approach},
year = {2015} }
TY - EJOUR
T1 - Joint Composite Detection and Bayesian Estimation: A Neyman-Pearson Approach
AU - Xiaodong Wang
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/441
ER -
Xiaodong Wang. (2015). Joint Composite Detection and Bayesian Estimation: A Neyman-Pearson Approach. IEEE SigPort. http://sigport.org/441
Xiaodong Wang, 2015. Joint Composite Detection and Bayesian Estimation: A Neyman-Pearson Approach. Available at: http://sigport.org/441.
Xiaodong Wang. (2015). "Joint Composite Detection and Bayesian Estimation: A Neyman-Pearson Approach." Web.
1. Xiaodong Wang. Joint Composite Detection and Bayesian Estimation: A Neyman-Pearson Approach [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/441

Multi-Sensor Generalized Sequential Probability Ratio Test Using Level-Triggered Sampling


This paper investigates the generalized sequential probability ratio test (GSPRT) with multiple sensors. Focusing on the communication-constrained scenario, where sensors transmit one-bit messages to the fusion center, we propose a decentralized GSRPT based on level-triggered sampling scheme (LTS-GSPRT). The proposed LTS-GSPRT amounts to the algorithm where each sensor successively reports the decisions of local GSPRTs to the fusion center.

Paper Details

Authors:
Xiaoou Li;Xiaodong Wang;Jingchen Liu
Submitted On:
2 June 2016 - 1:58pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP_DSHT.pdf

(370 downloads)

Subscribe

[1] Xiaoou Li;Xiaodong Wang;Jingchen Liu, "Multi-Sensor Generalized Sequential Probability Ratio Test Using Level-Triggered Sampling", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/440. Accessed: Sep. 20, 2018.
@article{440-15,
url = {http://sigport.org/440},
author = {Xiaoou Li;Xiaodong Wang;Jingchen Liu },
publisher = {IEEE SigPort},
title = {Multi-Sensor Generalized Sequential Probability Ratio Test Using Level-Triggered Sampling},
year = {2015} }
TY - EJOUR
T1 - Multi-Sensor Generalized Sequential Probability Ratio Test Using Level-Triggered Sampling
AU - Xiaoou Li;Xiaodong Wang;Jingchen Liu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/440
ER -
Xiaoou Li;Xiaodong Wang;Jingchen Liu. (2015). Multi-Sensor Generalized Sequential Probability Ratio Test Using Level-Triggered Sampling. IEEE SigPort. http://sigport.org/440
Xiaoou Li;Xiaodong Wang;Jingchen Liu, 2015. Multi-Sensor Generalized Sequential Probability Ratio Test Using Level-Triggered Sampling. Available at: http://sigport.org/440.
Xiaoou Li;Xiaodong Wang;Jingchen Liu. (2015). "Multi-Sensor Generalized Sequential Probability Ratio Test Using Level-Triggered Sampling." Web.
1. Xiaoou Li;Xiaodong Wang;Jingchen Liu. Multi-Sensor Generalized Sequential Probability Ratio Test Using Level-Triggered Sampling [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/440

Energy-Aware Sensor Selection in Field Reconstruction

Paper Details

Authors:
Sijia Liu, Makan Fardad, Engin Masazade, Pramod Varshney
Submitted On:
23 February 2016 - 1:38pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP_SPL_Slides.pdf

(395 downloads)

Subscribe

[1] Sijia Liu, Makan Fardad, Engin Masazade, Pramod Varshney, "Energy-Aware Sensor Selection in Field Reconstruction", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/439. Accessed: Sep. 20, 2018.
@article{439-15,
url = {http://sigport.org/439},
author = {Sijia Liu; Makan Fardad; Engin Masazade; Pramod Varshney },
publisher = {IEEE SigPort},
title = {Energy-Aware Sensor Selection in Field Reconstruction},
year = {2015} }
TY - EJOUR
T1 - Energy-Aware Sensor Selection in Field Reconstruction
AU - Sijia Liu; Makan Fardad; Engin Masazade; Pramod Varshney
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/439
ER -
Sijia Liu, Makan Fardad, Engin Masazade, Pramod Varshney. (2015). Energy-Aware Sensor Selection in Field Reconstruction. IEEE SigPort. http://sigport.org/439
Sijia Liu, Makan Fardad, Engin Masazade, Pramod Varshney, 2015. Energy-Aware Sensor Selection in Field Reconstruction. Available at: http://sigport.org/439.
Sijia Liu, Makan Fardad, Engin Masazade, Pramod Varshney. (2015). "Energy-Aware Sensor Selection in Field Reconstruction." Web.
1. Sijia Liu, Makan Fardad, Engin Masazade, Pramod Varshney. Energy-Aware Sensor Selection in Field Reconstruction [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/439

Estimating Multi-Resolution Dependency Graphs within a Locally Stationary Wavelet Framework


Estimated partial correlation structure throughout an epilepsy seizure

Estimation of sparse partial correlation graphs is discussed within the multivariate locally-stationary wavelet framework. We discuss the requirement for regularisation in such a framework and how this effects estimation. We observe that sparse model selection in the framework promotes more robust estimates of multivariate LSW processes, and improves interpretation through graph selection. The method is applied to study evolving correlation dynamics throughout an epileptic seizure.

globalSIP.pdf

PDF icon globalSIP.pdf (1751 downloads)

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

globalSIP.pdf

(1751 downloads)

Subscribe

[1] , "Estimating Multi-Resolution Dependency Graphs within a Locally Stationary Wavelet Framework", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/429. Accessed: Sep. 20, 2018.
@article{429-15,
url = {http://sigport.org/429},
author = { },
publisher = {IEEE SigPort},
title = {Estimating Multi-Resolution Dependency Graphs within a Locally Stationary Wavelet Framework},
year = {2015} }
TY - EJOUR
T1 - Estimating Multi-Resolution Dependency Graphs within a Locally Stationary Wavelet Framework
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/429
ER -
. (2015). Estimating Multi-Resolution Dependency Graphs within a Locally Stationary Wavelet Framework. IEEE SigPort. http://sigport.org/429
, 2015. Estimating Multi-Resolution Dependency Graphs within a Locally Stationary Wavelet Framework. Available at: http://sigport.org/429.
. (2015). "Estimating Multi-Resolution Dependency Graphs within a Locally Stationary Wavelet Framework." Web.
1. . Estimating Multi-Resolution Dependency Graphs within a Locally Stationary Wavelet Framework [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/429

Sketching for Sequential Change-Point Detection

Paper Details

Authors:
Meng Wang, Andrew Thompson, Yang Cao
Submitted On:
23 February 2016 - 1:38pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GLOBALSIP2015.pdf

(1590 downloads)

Subscribe

[1] Meng Wang, Andrew Thompson, Yang Cao, "Sketching for Sequential Change-Point Detection", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/330. Accessed: Sep. 20, 2018.
@article{330-15,
url = {http://sigport.org/330},
author = {Meng Wang; Andrew Thompson; Yang Cao },
publisher = {IEEE SigPort},
title = {Sketching for Sequential Change-Point Detection},
year = {2015} }
TY - EJOUR
T1 - Sketching for Sequential Change-Point Detection
AU - Meng Wang; Andrew Thompson; Yang Cao
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/330
ER -
Meng Wang, Andrew Thompson, Yang Cao. (2015). Sketching for Sequential Change-Point Detection. IEEE SigPort. http://sigport.org/330
Meng Wang, Andrew Thompson, Yang Cao, 2015. Sketching for Sequential Change-Point Detection. Available at: http://sigport.org/330.
Meng Wang, Andrew Thompson, Yang Cao. (2015). "Sketching for Sequential Change-Point Detection." Web.
1. Meng Wang, Andrew Thompson, Yang Cao. Sketching for Sequential Change-Point Detection [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/330

Better than l0 Recovery via Blind Identification


In this work, we propose a novel approach to multiple measurement vector (MMV) compressed sensing. We show that by exploiting the statistical properties of the sources, we can do better than previously derived lower bounds in this context. We show that in the MMV case, we can identify the active sources with fewer sensors than sources. We first develop a general framework for recovering the sparsity profile of the sources by combining ideas from compressed sensing with blind identification methods.

Paper Details

Authors:
Vladislav Tadic, Alin Achim
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP2015.pdf

(1590 downloads)

Subscribe

[1] Vladislav Tadic, Alin Achim, "Better than l0 Recovery via Blind Identification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/318. Accessed: Sep. 20, 2018.
@article{318-15,
url = {http://sigport.org/318},
author = {Vladislav Tadic; Alin Achim },
publisher = {IEEE SigPort},
title = {Better than l0 Recovery via Blind Identification},
year = {2015} }
TY - EJOUR
T1 - Better than l0 Recovery via Blind Identification
AU - Vladislav Tadic; Alin Achim
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/318
ER -
Vladislav Tadic, Alin Achim. (2015). Better than l0 Recovery via Blind Identification. IEEE SigPort. http://sigport.org/318
Vladislav Tadic, Alin Achim, 2015. Better than l0 Recovery via Blind Identification. Available at: http://sigport.org/318.
Vladislav Tadic, Alin Achim. (2015). "Better than l0 Recovery via Blind Identification." Web.
1. Vladislav Tadic, Alin Achim. Better than l0 Recovery via Blind Identification [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/318

On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization


Cooperative localization plays a key role in location-aware service of wireless networks. However, the statistical-based estimator of network localization, e.g., the maximum likelihood estimator or the maximum a posterior estimator, is commonly non-convex due to nonlinear measurement function and/or non-Gaussian system disturbance, which complicates the localization of network nodes. In this presentation, a novel particle-assisted stochastic search (PASS) algorithm is proposed to find out the optimal node locations based on its non-convex objective function.

Paper Details

Authors:
Bingpeng Zhou
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP_2015_Presentation.pdf

(358 downloads)

Subscribe

[1] Bingpeng Zhou, "On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/263. Accessed: Sep. 20, 2018.
@article{263-15,
url = {http://sigport.org/263},
author = {Bingpeng Zhou },
publisher = {IEEE SigPort},
title = {On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization},
year = {2015} }
TY - EJOUR
T1 - On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization
AU - Bingpeng Zhou
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/263
ER -
Bingpeng Zhou. (2015). On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization. IEEE SigPort. http://sigport.org/263
Bingpeng Zhou, 2015. On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization. Available at: http://sigport.org/263.
Bingpeng Zhou. (2015). "On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization." Web.
1. Bingpeng Zhou. On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/263

On the particle-assisted stochasitc search in cooperative wireless network localization


Cooperative localization plays a key role in locationaware service of wireless networks. However, the statistical-based estimator of network localization, e.g., the maximum likelihood estimator or the maximum a posterior estimator, is commonly non-convex due to nonlinear measurement function and/or non-Gaussian system disturbance, which complicates the localization of network nodes. In this presentation, a novel particle-assisted stochastic search (PASS) algorithm is proposed to find out the optimal node locations based on its non-convex objective function.

Paper Details

Authors:
Bingpeng Zhou
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP_2015_Presentation.pdf

(340 downloads)

Subscribe

[1] Bingpeng Zhou, "On the particle-assisted stochasitc search in cooperative wireless network localization", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/262. Accessed: Sep. 20, 2018.
@article{262-15,
url = {http://sigport.org/262},
author = {Bingpeng Zhou },
publisher = {IEEE SigPort},
title = {On the particle-assisted stochasitc search in cooperative wireless network localization},
year = {2015} }
TY - EJOUR
T1 - On the particle-assisted stochasitc search in cooperative wireless network localization
AU - Bingpeng Zhou
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/262
ER -
Bingpeng Zhou. (2015). On the particle-assisted stochasitc search in cooperative wireless network localization. IEEE SigPort. http://sigport.org/262
Bingpeng Zhou, 2015. On the particle-assisted stochasitc search in cooperative wireless network localization. Available at: http://sigport.org/262.
Bingpeng Zhou. (2015). "On the particle-assisted stochasitc search in cooperative wireless network localization." Web.
1. Bingpeng Zhou. On the particle-assisted stochasitc search in cooperative wireless network localization [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/262

Pages