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Signal Processing Theory and Methods

ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR

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Authors:
Layla Majzoobi, Farshad Lahouti
Submitted On:
20 March 2016 - 3:28am
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ICASSP2016Poster.pdf

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[1] Layla Majzoobi, Farshad Lahouti, "ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/858. Accessed: Jun. 22, 2017.
@article{858-16,
url = {http://sigport.org/858},
author = {Layla Majzoobi; Farshad Lahouti },
publisher = {IEEE SigPort},
title = {ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR},
year = {2016} }
TY - EJOUR
T1 - ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR
AU - Layla Majzoobi; Farshad Lahouti
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/858
ER -
Layla Majzoobi, Farshad Lahouti. (2016). ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR. IEEE SigPort. http://sigport.org/858
Layla Majzoobi, Farshad Lahouti, 2016. ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR. Available at: http://sigport.org/858.
Layla Majzoobi, Farshad Lahouti. (2016). "ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR." Web.
1. Layla Majzoobi, Farshad Lahouti. ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/858

Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information


We consider an oligopoly dynamic pricing problem where the demand model is unknown and the sellers have different marginal costs. We formulate the problem as a repeated game with incomplete information. We develop a dynamic pricing strategy that leads to a Pareto-efficient and subgame-perfect equilibrium and offers a bounded regret over an infinite horizon, where regret is defined as the expected cumulative profit loss as compared to the ideal scenario with a known demand model.

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Authors:
Qing Zhao
Submitted On:
19 March 2016 - 9:33pm
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ICASSP16-poster.pdf

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[1] Qing Zhao, "Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/848. Accessed: Jun. 22, 2017.
@article{848-16,
url = {http://sigport.org/848},
author = {Qing Zhao },
publisher = {IEEE SigPort},
title = {Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information},
year = {2016} }
TY - EJOUR
T1 - Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information
AU - Qing Zhao
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/848
ER -
Qing Zhao. (2016). Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information. IEEE SigPort. http://sigport.org/848
Qing Zhao, 2016. Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information. Available at: http://sigport.org/848.
Qing Zhao. (2016). "Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information." Web.
1. Qing Zhao. Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/848

BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING


2LMM.pdf

PDF icon 2LMM.pdf (169 downloads)

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Authors:
Chiara Ravazzi, Enrico Magli
Submitted On:
19 March 2016 - 9:13am
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2LMM.pdf

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[1] Chiara Ravazzi, Enrico Magli, "BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/811. Accessed: Jun. 22, 2017.
@article{811-16,
url = {http://sigport.org/811},
author = {Chiara Ravazzi; Enrico Magli },
publisher = {IEEE SigPort},
title = {BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING},
year = {2016} }
TY - EJOUR
T1 - BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING
AU - Chiara Ravazzi; Enrico Magli
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/811
ER -
Chiara Ravazzi, Enrico Magli. (2016). BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING. IEEE SigPort. http://sigport.org/811
Chiara Ravazzi, Enrico Magli, 2016. BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING. Available at: http://sigport.org/811.
Chiara Ravazzi, Enrico Magli. (2016). "BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING." Web.
1. Chiara Ravazzi, Enrico Magli. BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/811

Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices

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Authors:
Chiara Ravazzi, Sophie M. Fosson, Tiziano Bianchi, Enrico Magli
Submitted On:
19 March 2016 - 9:10am
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Sparsity_estimation.pdf

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[1] Chiara Ravazzi, Sophie M. Fosson, Tiziano Bianchi, Enrico Magli, "Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/810. Accessed: Jun. 22, 2017.
@article{810-16,
url = {http://sigport.org/810},
author = {Chiara Ravazzi; Sophie M. Fosson; Tiziano Bianchi; Enrico Magli },
publisher = {IEEE SigPort},
title = {Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices},
year = {2016} }
TY - EJOUR
T1 - Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices
AU - Chiara Ravazzi; Sophie M. Fosson; Tiziano Bianchi; Enrico Magli
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/810
ER -
Chiara Ravazzi, Sophie M. Fosson, Tiziano Bianchi, Enrico Magli. (2016). Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices. IEEE SigPort. http://sigport.org/810
Chiara Ravazzi, Sophie M. Fosson, Tiziano Bianchi, Enrico Magli, 2016. Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices. Available at: http://sigport.org/810.
Chiara Ravazzi, Sophie M. Fosson, Tiziano Bianchi, Enrico Magli. (2016). "Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices." Web.
1. Chiara Ravazzi, Sophie M. Fosson, Tiziano Bianchi, Enrico Magli. Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/810

The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs

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Authors:
M. S. Kotzagiannidis, P.L. Dragotti
Submitted On:
18 March 2016 - 9:29pm
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MKotzagiannidisICASSP.pdf

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[1] M. S. Kotzagiannidis, P.L. Dragotti, "The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/780. Accessed: Jun. 22, 2017.
@article{780-16,
url = {http://sigport.org/780},
author = {M. S. Kotzagiannidis; P.L. Dragotti },
publisher = {IEEE SigPort},
title = {The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs},
year = {2016} }
TY - EJOUR
T1 - The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs
AU - M. S. Kotzagiannidis; P.L. Dragotti
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/780
ER -
M. S. Kotzagiannidis, P.L. Dragotti. (2016). The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs. IEEE SigPort. http://sigport.org/780
M. S. Kotzagiannidis, P.L. Dragotti, 2016. The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs. Available at: http://sigport.org/780.
M. S. Kotzagiannidis, P.L. Dragotti. (2016). "The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs." Web.
1. M. S. Kotzagiannidis, P.L. Dragotti. The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/780

Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs


This paper focuses on the problem of distributed composite
hypothesis testing in a network of sparsely interconnected
agents, in which only a small section of the field modeling
parametric alternatives is observable at each agent. A recursive
generalized likelihood ratio test (GLRT) type algorithm
in a distributed setup of the consensus-plus-innovations form
is proposed, in which the agents update their parameter estimates
and decision statistics by simultaneously processing
the latest sensed information (innovations) and information

Paper Details

Authors:
Anit Kumar Sahu, Soummya Kar
Submitted On:
16 March 2016 - 5:22pm
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ICASSP_poster.pdf

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[1] Anit Kumar Sahu, Soummya Kar, "Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/726. Accessed: Jun. 22, 2017.
@article{726-16,
url = {http://sigport.org/726},
author = {Anit Kumar Sahu; Soummya Kar },
publisher = {IEEE SigPort},
title = {Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs},
year = {2016} }
TY - EJOUR
T1 - Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs
AU - Anit Kumar Sahu; Soummya Kar
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/726
ER -
Anit Kumar Sahu, Soummya Kar. (2016). Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs. IEEE SigPort. http://sigport.org/726
Anit Kumar Sahu, Soummya Kar, 2016. Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs. Available at: http://sigport.org/726.
Anit Kumar Sahu, Soummya Kar. (2016). "Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs." Web.
1. Anit Kumar Sahu, Soummya Kar. Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/726

Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters


ICASSP 2016 presentation, Session: SPTM-P14 - Compressed Sampling and Sparsity, Friday, March 25, 8:30-10:30

ICASSP2016.pdf

PDF icon ICASSP2016.pdf (138 downloads)

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Authors:
Mahdi Shaghaghi and Sergiy A. Vorobyov
Submitted On:
16 March 2016 - 10:46am
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ICASSP2016.pdf

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[1] Mahdi Shaghaghi and Sergiy A. Vorobyov, "Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/716. Accessed: Jun. 22, 2017.
@article{716-16,
url = {http://sigport.org/716},
author = {Mahdi Shaghaghi and Sergiy A. Vorobyov },
publisher = {IEEE SigPort},
title = {Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters},
year = {2016} }
TY - EJOUR
T1 - Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters
AU - Mahdi Shaghaghi and Sergiy A. Vorobyov
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/716
ER -
Mahdi Shaghaghi and Sergiy A. Vorobyov. (2016). Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters. IEEE SigPort. http://sigport.org/716
Mahdi Shaghaghi and Sergiy A. Vorobyov, 2016. Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters. Available at: http://sigport.org/716.
Mahdi Shaghaghi and Sergiy A. Vorobyov. (2016). "Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters." Web.
1. Mahdi Shaghaghi and Sergiy A. Vorobyov. Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/716

Time Delay Estimation: Applications and Algorithms


Time delay estimation refers to finding the time-differences-of-arrival between signals received at an array of sensors. In this presentation, representative applications of time delay estimation are first described. Algorithms for accurately estimating the time difference between two sensor outputs using random and deterministic signals are then presented and analyzed.

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Submitted On:
23 February 2016 - 1:44pm
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Time_Delay_Estimation.pdf

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[1] , "Time Delay Estimation: Applications and Algorithms", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/565. Accessed: Jun. 22, 2017.
@article{565-15,
url = {http://sigport.org/565},
author = { },
publisher = {IEEE SigPort},
title = {Time Delay Estimation: Applications and Algorithms},
year = {2015} }
TY - EJOUR
T1 - Time Delay Estimation: Applications and Algorithms
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/565
ER -
. (2015). Time Delay Estimation: Applications and Algorithms. IEEE SigPort. http://sigport.org/565
, 2015. Time Delay Estimation: Applications and Algorithms. Available at: http://sigport.org/565.
. (2015). "Time Delay Estimation: Applications and Algorithms." Web.
1. . Time Delay Estimation: Applications and Algorithms [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/565

Nonconvex ADMM for Distributed Sparse PCA

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Authors:
Mingyi Hong
Submitted On:
23 February 2016 - 1:44pm
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Presentation_Davood.pdf

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[1] Mingyi Hong, "Nonconvex ADMM for Distributed Sparse PCA", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/553. Accessed: Jun. 22, 2017.
@article{553-15,
url = {http://sigport.org/553},
author = {Mingyi Hong },
publisher = {IEEE SigPort},
title = {Nonconvex ADMM for Distributed Sparse PCA},
year = {2015} }
TY - EJOUR
T1 - Nonconvex ADMM for Distributed Sparse PCA
AU - Mingyi Hong
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/553
ER -
Mingyi Hong. (2015). Nonconvex ADMM for Distributed Sparse PCA. IEEE SigPort. http://sigport.org/553
Mingyi Hong, 2015. Nonconvex ADMM for Distributed Sparse PCA. Available at: http://sigport.org/553.
Mingyi Hong. (2015). "Nonconvex ADMM for Distributed Sparse PCA." Web.
1. Mingyi Hong. Nonconvex ADMM for Distributed Sparse PCA [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/553

Optimizing Spectral Diversity for Graph Signal Coarsening

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Authors:
Yuantao Gu
Submitted On:
23 February 2016 - 1:44pm
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globalsip2015_lpf_v5.pdf

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[1] Yuantao Gu, "Optimizing Spectral Diversity for Graph Signal Coarsening", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/478. Accessed: Jun. 22, 2017.
@article{478-15,
url = {http://sigport.org/478},
author = {Yuantao Gu },
publisher = {IEEE SigPort},
title = {Optimizing Spectral Diversity for Graph Signal Coarsening},
year = {2015} }
TY - EJOUR
T1 - Optimizing Spectral Diversity for Graph Signal Coarsening
AU - Yuantao Gu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/478
ER -
Yuantao Gu. (2015). Optimizing Spectral Diversity for Graph Signal Coarsening. IEEE SigPort. http://sigport.org/478
Yuantao Gu, 2015. Optimizing Spectral Diversity for Graph Signal Coarsening. Available at: http://sigport.org/478.
Yuantao Gu. (2015). "Optimizing Spectral Diversity for Graph Signal Coarsening." Web.
1. Yuantao Gu. Optimizing Spectral Diversity for Graph Signal Coarsening [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/478

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