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Statistical Signal Processing

Sketching for Sequential Change-Point Detection

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Authors:
Meng Wang, Andrew Thompson, Yang Cao
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23 February 2016 - 1:38pm
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GLOBALSIP2015.pdf

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[1] Meng Wang, Andrew Thompson, Yang Cao, "Sketching for Sequential Change-Point Detection", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/330. Accessed: Feb. 23, 2019.
@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.

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Authors:
Vladislav Tadic, Alin Achim
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23 February 2016 - 1:44pm
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GlobalSIP2015.pdf

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[1] Vladislav Tadic, Alin Achim, "Better than l0 Recovery via Blind Identification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/318. Accessed: Feb. 23, 2019.
@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.

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Authors:
Bingpeng Zhou
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23 February 2016 - 1:44pm
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GlobalSIP_2015_Presentation.pdf

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[1] Bingpeng Zhou, "On the Particle-Assisted Stochastic Search In Cooperative Wireless Network Localization", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/263. Accessed: Feb. 23, 2019.
@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
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GlobalSIP_2015_Presentation.pdf

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[1] Bingpeng Zhou, "On the particle-assisted stochasitc search in cooperative wireless network localization", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/262. Accessed: Feb. 23, 2019.
@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

Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target


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Authors:
Tianyao Huang
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23 February 2016 - 1:43pm
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ppt.pdf

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[1] Tianyao Huang, "Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/241. Accessed: Feb. 23, 2019.
@article{241-15,
url = {http://sigport.org/241},
author = {Tianyao Huang },
publisher = {IEEE SigPort},
title = {Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target},
year = {2015} }
TY - EJOUR
T1 - Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target
AU - Tianyao Huang
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/241
ER -
Tianyao Huang. (2015). Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target. IEEE SigPort. http://sigport.org/241
Tianyao Huang, 2015. Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target. Available at: http://sigport.org/241.
Tianyao Huang. (2015). "Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target." Web.
1. Tianyao Huang. Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/241

A novel wavelet based shock wave detector


In this paper, the detection of shock wave that generated by supersonic bullet is considered. We present a new framework based on wavelet multi-scale products method. We analyze the method under the standard likelihood ratio test. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An optimal detector under the standard likelihood ratio test is proposed.

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23 February 2016 - 1:43pm
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ChinaSIP Poster_final.pptx

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An improved wavelet based shock wave detector.pdf

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[1] , "A novel wavelet based shock wave detector", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/211. Accessed: Feb. 23, 2019.
@article{211-15,
url = {http://sigport.org/211},
author = { },
publisher = {IEEE SigPort},
title = {A novel wavelet based shock wave detector},
year = {2015} }
TY - EJOUR
T1 - A novel wavelet based shock wave detector
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/211
ER -
. (2015). A novel wavelet based shock wave detector. IEEE SigPort. http://sigport.org/211
, 2015. A novel wavelet based shock wave detector. Available at: http://sigport.org/211.
. (2015). "A novel wavelet based shock wave detector." Web.
1. . A novel wavelet based shock wave detector [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/211

A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering


In this manuscript the application of a factor graph approach to the filtering problem for a mixed linear/nonlinear state-space model is investigated. In particular, after developing a factor graph for the considered model, a novel approximate recursive technique for solving such a problem is derived applying the sum-product algorithm and a specific scheduling procedure for message passing to this graph.

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Authors:
Francesco Montorsi, Matteo Sola, Marco Casparriello
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23 February 2016 - 1:43pm
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turbo_filtering_report.pdf

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[1] Francesco Montorsi, Matteo Sola, Marco Casparriello, "A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/208. Accessed: Feb. 23, 2019.
@article{208-15,
url = {http://sigport.org/208},
author = {Francesco Montorsi; Matteo Sola; Marco Casparriello },
publisher = {IEEE SigPort},
title = {A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering},
year = {2015} }
TY - EJOUR
T1 - A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering
AU - Francesco Montorsi; Matteo Sola; Marco Casparriello
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/208
ER -
Francesco Montorsi, Matteo Sola, Marco Casparriello. (2015). A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering. IEEE SigPort. http://sigport.org/208
Francesco Montorsi, Matteo Sola, Marco Casparriello, 2015. A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering. Available at: http://sigport.org/208.
Francesco Montorsi, Matteo Sola, Marco Casparriello. (2015). "A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering." Web.
1. Francesco Montorsi, Matteo Sola, Marco Casparriello. A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/208

Simple and Accurate Algorithms for Sinusoidal Frequency Estimation


The problem of estimating the frequencies of sinusoidal components from a finite number of noisy discrete-time measurements has attracted a great deal of attention and still is an active research area to date, because of its wide applications in science and engineering. In this presentation, simple and accurate solutions for sinusoidal frequency estimation of 1D and 2D signals in the presence of additive white Gaussian noise are presented.

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23 February 2016 - 1:43pm
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Sinusodial_Frequency_Estimation.pdf

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[1] , "Simple and Accurate Algorithms for Sinusoidal Frequency Estimation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/201. Accessed: Feb. 23, 2019.
@article{201-15,
url = {http://sigport.org/201},
author = { },
publisher = {IEEE SigPort},
title = {Simple and Accurate Algorithms for Sinusoidal Frequency Estimation},
year = {2015} }
TY - EJOUR
T1 - Simple and Accurate Algorithms for Sinusoidal Frequency Estimation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/201
ER -
. (2015). Simple and Accurate Algorithms for Sinusoidal Frequency Estimation. IEEE SigPort. http://sigport.org/201
, 2015. Simple and Accurate Algorithms for Sinusoidal Frequency Estimation. Available at: http://sigport.org/201.
. (2015). "Simple and Accurate Algorithms for Sinusoidal Frequency Estimation." Web.
1. . Simple and Accurate Algorithms for Sinusoidal Frequency Estimation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/201

Source Localization: Applications and Algorithms


Finding the position of a target based on measurements from an array of spatially separated sensors has been an important problem in radar, sonar, global positioning system, mobile communications, multimedia and wireless sensor networks. Time-of-arrival (TOA), time-difference-of-arrival (TDOA), received signal strength (RSS) and direction-of-arrival (DOA) of the emitted signal are commonly used measurements for source localization. Basically, TOAs, TDOAs and RSSs provide the distance information between the source and sensors while DOAs are the source bearings relative to the receivers.

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23 February 2016 - 1:38pm
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Source_Localization.pdf

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[1] , "Source Localization: Applications and Algorithms", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/200. Accessed: Feb. 23, 2019.
@article{200-15,
url = {http://sigport.org/200},
author = { },
publisher = {IEEE SigPort},
title = {Source Localization: Applications and Algorithms},
year = {2015} }
TY - EJOUR
T1 - Source Localization: Applications and Algorithms
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/200
ER -
. (2015). Source Localization: Applications and Algorithms. IEEE SigPort. http://sigport.org/200
, 2015. Source Localization: Applications and Algorithms. Available at: http://sigport.org/200.
. (2015). "Source Localization: Applications and Algorithms." Web.
1. . Source Localization: Applications and Algorithms [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/200

How to Derive Bias and Mean Square Error for an Estimator?


Analyzing the performance of estimators is a typical task in signal processing. Two fundamental performance measures in the aspect of accuracy are bias and mean square error (MSE). In this presentation, we revisit a simple technique to produce the bias and MSE of an estimator that minimizes or maximizes an unconstrained differentiable cost function over a continuous space of the parameter vector under the small error conditions. This presentation is a companion work of: H. C. So, Y. T. Chan, K. C. Ho and Y.

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23 February 2016 - 1:43pm
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compute_bias_mse.pdf

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[1] , "How to Derive Bias and Mean Square Error for an Estimator?", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/194. Accessed: Feb. 23, 2019.
@article{194-15,
url = {http://sigport.org/194},
author = { },
publisher = {IEEE SigPort},
title = {How to Derive Bias and Mean Square Error for an Estimator?},
year = {2015} }
TY - EJOUR
T1 - How to Derive Bias and Mean Square Error for an Estimator?
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/194
ER -
. (2015). How to Derive Bias and Mean Square Error for an Estimator?. IEEE SigPort. http://sigport.org/194
, 2015. How to Derive Bias and Mean Square Error for an Estimator?. Available at: http://sigport.org/194.
. (2015). "How to Derive Bias and Mean Square Error for an Estimator?." Web.
1. . How to Derive Bias and Mean Square Error for an Estimator? [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/194

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