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Adaptive Signal Processing

PERFORMANCE LIMITS OF SINGLE-AGENT AND MULTI-AGENT SUB-GRADIENT STOCHASTIC LEARNING

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Authors:
Ali H. Sayed
Submitted On:
19 March 2016 - 11:41am
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Poster_Bicheng_2016_ICASSP.pdf

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[1] Ali H. Sayed, "PERFORMANCE LIMITS OF SINGLE-AGENT AND MULTI-AGENT SUB-GRADIENT STOCHASTIC LEARNING", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/820. Accessed: Oct. 17, 2018.
@article{820-16,
url = {http://sigport.org/820},
author = {Ali H. Sayed },
publisher = {IEEE SigPort},
title = {PERFORMANCE LIMITS OF SINGLE-AGENT AND MULTI-AGENT SUB-GRADIENT STOCHASTIC LEARNING},
year = {2016} }
TY - EJOUR
T1 - PERFORMANCE LIMITS OF SINGLE-AGENT AND MULTI-AGENT SUB-GRADIENT STOCHASTIC LEARNING
AU - Ali H. Sayed
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/820
ER -
Ali H. Sayed. (2016). PERFORMANCE LIMITS OF SINGLE-AGENT AND MULTI-AGENT SUB-GRADIENT STOCHASTIC LEARNING. IEEE SigPort. http://sigport.org/820
Ali H. Sayed, 2016. PERFORMANCE LIMITS OF SINGLE-AGENT AND MULTI-AGENT SUB-GRADIENT STOCHASTIC LEARNING. Available at: http://sigport.org/820.
Ali H. Sayed. (2016). "PERFORMANCE LIMITS OF SINGLE-AGENT AND MULTI-AGENT SUB-GRADIENT STOCHASTIC LEARNING." Web.
1. Ali H. Sayed. PERFORMANCE LIMITS OF SINGLE-AGENT AND MULTI-AGENT SUB-GRADIENT STOCHASTIC LEARNING [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/820

Slides for I-SM-PUAP algorithm presentation


In this presentation, we present an improved set-membership partial-update
affine projection (I-SM-PUAP) algorithm, aiming at
accelerating the convergence, and decreasing the update rates
and the computational complexity of the set-membership
partial-update affine projection (SM-PUAP) algorithm. To
meet these targets, we constrain the weight vector perturbation
to be bounded by a hypersphere instead of the threshold
hyperplanes as in the standard algorithm. We use the distance
between the present weight vector and the expected update

Paper Details

Authors:
Paulo S. R. Diniz, Hamed Yazdanpanah
Submitted On:
15 March 2016 - 8:58pm
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ICASSP2016_Presentation_diniz.pdf

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[1] Paulo S. R. Diniz, Hamed Yazdanpanah, "Slides for I-SM-PUAP algorithm presentation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/699. Accessed: Oct. 17, 2018.
@article{699-16,
url = {http://sigport.org/699},
author = {Paulo S. R. Diniz; Hamed Yazdanpanah },
publisher = {IEEE SigPort},
title = {Slides for I-SM-PUAP algorithm presentation},
year = {2016} }
TY - EJOUR
T1 - Slides for I-SM-PUAP algorithm presentation
AU - Paulo S. R. Diniz; Hamed Yazdanpanah
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/699
ER -
Paulo S. R. Diniz, Hamed Yazdanpanah. (2016). Slides for I-SM-PUAP algorithm presentation. IEEE SigPort. http://sigport.org/699
Paulo S. R. Diniz, Hamed Yazdanpanah, 2016. Slides for I-SM-PUAP algorithm presentation. Available at: http://sigport.org/699.
Paulo S. R. Diniz, Hamed Yazdanpanah. (2016). "Slides for I-SM-PUAP algorithm presentation." Web.
1. Paulo S. R. Diniz, Hamed Yazdanpanah. Slides for I-SM-PUAP algorithm presentation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/699

Locating Salient Group-Structured Image Features via Adaptive Compressive Sensing


In this paper we consider the task of locating salient group-structured features in potentially high-dimensional images; the salient feature detection here is modeled as a Robust Principal Component Analysis problem, in which the aim is to locate groups of outlier columns embedded in an otherwise low rank matrix.

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Authors:
Jarvis Haupt
Submitted On:
23 February 2016 - 1:44pm
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globalsip_XingguoLi.pdf

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[1] Jarvis Haupt, "Locating Salient Group-Structured Image Features via Adaptive Compressive Sensing", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/495. Accessed: Oct. 17, 2018.
@article{495-15,
url = {http://sigport.org/495},
author = {Jarvis Haupt },
publisher = {IEEE SigPort},
title = {Locating Salient Group-Structured Image Features via Adaptive Compressive Sensing},
year = {2015} }
TY - EJOUR
T1 - Locating Salient Group-Structured Image Features via Adaptive Compressive Sensing
AU - Jarvis Haupt
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/495
ER -
Jarvis Haupt. (2015). Locating Salient Group-Structured Image Features via Adaptive Compressive Sensing. IEEE SigPort. http://sigport.org/495
Jarvis Haupt, 2015. Locating Salient Group-Structured Image Features via Adaptive Compressive Sensing. Available at: http://sigport.org/495.
Jarvis Haupt. (2015). "Locating Salient Group-Structured Image Features via Adaptive Compressive Sensing." Web.
1. Jarvis Haupt. Locating Salient Group-Structured Image Features via Adaptive Compressive Sensing [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/495

Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing


4 TI_ADC

Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing

Paper Details

Authors:
Simran Singh, Michael Epp, Wolfgang Schlecker and Mikko Valkama
Submitted On:
23 February 2016 - 1:44pm
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2015_Dec_GlobalSIP_4_TI-ADCs_Upload.ppt

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[1] Simran Singh, Michael Epp, Wolfgang Schlecker and Mikko Valkama, "Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/433. Accessed: Oct. 17, 2018.
@article{433-15,
url = {http://sigport.org/433},
author = {Simran Singh; Michael Epp; Wolfgang Schlecker and Mikko Valkama },
publisher = {IEEE SigPort},
title = {Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing},
year = {2015} }
TY - EJOUR
T1 - Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing
AU - Simran Singh; Michael Epp; Wolfgang Schlecker and Mikko Valkama
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/433
ER -
Simran Singh, Michael Epp, Wolfgang Schlecker and Mikko Valkama. (2015). Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing. IEEE SigPort. http://sigport.org/433
Simran Singh, Michael Epp, Wolfgang Schlecker and Mikko Valkama, 2015. Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing. Available at: http://sigport.org/433.
Simran Singh, Michael Epp, Wolfgang Schlecker and Mikko Valkama. (2015). "Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing." Web.
1. Simran Singh, Michael Epp, Wolfgang Schlecker and Mikko Valkama. Low-Complexity Digital Correction of 4-Channel Time-Interleaved ADC Frequency Response Mismatch using Adaptive I/Q Signal Processing [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/433

Recursive Filters with Bayesian Quadratic Network Game Fusion


Distributed filter in networks mainly involves two stages, local estimation by private observation and information fusion with neighbor nodes based on the underlying topology. Since Bayesian game is a powerful tool to analyze the interaction equilibrium of multi-player with incomplete information in networks, we combine the recursive LMMSE filter with network game of quadratic utilities under the Bayesian filtering framework. In our algorithm, the nodes update their local beliefs on the unknown state by private observations and historical actions from neighbors in network.

slides.pdf

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Paper Details

Authors:
Muyuan Zhai, Tao Yang, Bo Hu
Submitted On:
23 February 2016 - 1:38pm
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slides.pdf

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[1] Muyuan Zhai, Tao Yang, Bo Hu, "Recursive Filters with Bayesian Quadratic Network Game Fusion", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/287. Accessed: Oct. 17, 2018.
@article{287-15,
url = {http://sigport.org/287},
author = {Muyuan Zhai; Tao Yang; Bo Hu },
publisher = {IEEE SigPort},
title = {Recursive Filters with Bayesian Quadratic Network Game Fusion},
year = {2015} }
TY - EJOUR
T1 - Recursive Filters with Bayesian Quadratic Network Game Fusion
AU - Muyuan Zhai; Tao Yang; Bo Hu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/287
ER -
Muyuan Zhai, Tao Yang, Bo Hu. (2015). Recursive Filters with Bayesian Quadratic Network Game Fusion. IEEE SigPort. http://sigport.org/287
Muyuan Zhai, Tao Yang, Bo Hu, 2015. Recursive Filters with Bayesian Quadratic Network Game Fusion. Available at: http://sigport.org/287.
Muyuan Zhai, Tao Yang, Bo Hu. (2015). "Recursive Filters with Bayesian Quadratic Network Game Fusion." Web.
1. Muyuan Zhai, Tao Yang, Bo Hu. Recursive Filters with Bayesian Quadratic Network Game Fusion [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/287

Bags of Affine Subspaces for Robust Object Tracking


object tracking results

We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames. In contrast to linear subspaces, affine subspaces explicitly model the origin of subspaces. Furthermore, instead of using a brittle point-to-subspace distance during the search for the object in a new frame, we propose to use a subspace-to-subspace distance by representing candidate image areas also as affine subspaces.

report.pdf

PDF icon report.pdf (951 downloads)

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Authors:
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi
Submitted On:
23 February 2016 - 1:43pm
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report.pdf

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[1] Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi, "Bags of Affine Subspaces for Robust Object Tracking", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/185. Accessed: Oct. 17, 2018.
@article{185-15,
url = {http://sigport.org/185},
author = {Sareh Shirazi; Conrad Sanderson; Chris McCool; Mehrtash Harandi },
publisher = {IEEE SigPort},
title = {Bags of Affine Subspaces for Robust Object Tracking},
year = {2015} }
TY - EJOUR
T1 - Bags of Affine Subspaces for Robust Object Tracking
AU - Sareh Shirazi; Conrad Sanderson; Chris McCool; Mehrtash Harandi
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/185
ER -
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. (2015). Bags of Affine Subspaces for Robust Object Tracking. IEEE SigPort. http://sigport.org/185
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi, 2015. Bags of Affine Subspaces for Robust Object Tracking. Available at: http://sigport.org/185.
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. (2015). "Bags of Affine Subspaces for Robust Object Tracking." Web.
1. Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. Bags of Affine Subspaces for Robust Object Tracking [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/185

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