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Signal Processing Theory and Methods

Tutorial Slides for Convex Optimization Techniques for Super-Resolution Parameter Estimation

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Authors:
Yuejie Chi, Gongguo Tang
Submitted On:
30 March 2016 - 3:28am
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ICASSP2016tutorial_revised.pdf

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[1] Yuejie Chi, Gongguo Tang, "Tutorial Slides for Convex Optimization Techniques for Super-Resolution Parameter Estimation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1069. Accessed: Nov. 21, 2017.
@article{1069-16,
url = {http://sigport.org/1069},
author = {Yuejie Chi; Gongguo Tang },
publisher = {IEEE SigPort},
title = {Tutorial Slides for Convex Optimization Techniques for Super-Resolution Parameter Estimation},
year = {2016} }
TY - EJOUR
T1 - Tutorial Slides for Convex Optimization Techniques for Super-Resolution Parameter Estimation
AU - Yuejie Chi; Gongguo Tang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1069
ER -
Yuejie Chi, Gongguo Tang. (2016). Tutorial Slides for Convex Optimization Techniques for Super-Resolution Parameter Estimation. IEEE SigPort. http://sigport.org/1069
Yuejie Chi, Gongguo Tang, 2016. Tutorial Slides for Convex Optimization Techniques for Super-Resolution Parameter Estimation. Available at: http://sigport.org/1069.
Yuejie Chi, Gongguo Tang. (2016). "Tutorial Slides for Convex Optimization Techniques for Super-Resolution Parameter Estimation." Web.
1. Yuejie Chi, Gongguo Tang. Tutorial Slides for Convex Optimization Techniques for Super-Resolution Parameter Estimation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1069

Are there approximate Fast Fourier Transforms on graphs ?

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Authors:
Luc Le Magoarou, Rémi Gribonval
Submitted On:
29 March 2016 - 3:34am
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poster_ICASSP2.pdf

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[1] Luc Le Magoarou, Rémi Gribonval, "Are there approximate Fast Fourier Transforms on graphs ?", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1063. Accessed: Nov. 21, 2017.
@article{1063-16,
url = {http://sigport.org/1063},
author = {Luc Le Magoarou; Rémi Gribonval },
publisher = {IEEE SigPort},
title = {Are there approximate Fast Fourier Transforms on graphs ?},
year = {2016} }
TY - EJOUR
T1 - Are there approximate Fast Fourier Transforms on graphs ?
AU - Luc Le Magoarou; Rémi Gribonval
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1063
ER -
Luc Le Magoarou, Rémi Gribonval. (2016). Are there approximate Fast Fourier Transforms on graphs ?. IEEE SigPort. http://sigport.org/1063
Luc Le Magoarou, Rémi Gribonval, 2016. Are there approximate Fast Fourier Transforms on graphs ?. Available at: http://sigport.org/1063.
Luc Le Magoarou, Rémi Gribonval. (2016). "Are there approximate Fast Fourier Transforms on graphs ?." Web.
1. Luc Le Magoarou, Rémi Gribonval. Are there approximate Fast Fourier Transforms on graphs ? [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1063

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

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Authors:
Nicolas TREMBLAY, Gilles PUY, Pierre BORGNAT, Rémi GRIBONVAL, Pierre VANDERGHEYNST
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24 March 2016 - 12:06am
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talk_ICASSP_2016.pdf

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[1] Nicolas TREMBLAY, Gilles PUY, Pierre BORGNAT, Rémi GRIBONVAL, Pierre VANDERGHEYNST, "Accelerated Spectral Clustering Using Graph Filtering of Random Signals", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1017. Accessed: Nov. 21, 2017.
@article{1017-16,
url = {http://sigport.org/1017},
author = {Nicolas TREMBLAY; Gilles PUY; Pierre BORGNAT; Rémi GRIBONVAL; Pierre VANDERGHEYNST },
publisher = {IEEE SigPort},
title = {Accelerated Spectral Clustering Using Graph Filtering of Random Signals},
year = {2016} }
TY - EJOUR
T1 - Accelerated Spectral Clustering Using Graph Filtering of Random Signals
AU - Nicolas TREMBLAY; Gilles PUY; Pierre BORGNAT; Rémi GRIBONVAL; Pierre VANDERGHEYNST
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1017
ER -
Nicolas TREMBLAY, Gilles PUY, Pierre BORGNAT, Rémi GRIBONVAL, Pierre VANDERGHEYNST. (2016). Accelerated Spectral Clustering Using Graph Filtering of Random Signals. IEEE SigPort. http://sigport.org/1017
Nicolas TREMBLAY, Gilles PUY, Pierre BORGNAT, Rémi GRIBONVAL, Pierre VANDERGHEYNST, 2016. Accelerated Spectral Clustering Using Graph Filtering of Random Signals. Available at: http://sigport.org/1017.
Nicolas TREMBLAY, Gilles PUY, Pierre BORGNAT, Rémi GRIBONVAL, Pierre VANDERGHEYNST. (2016). "Accelerated Spectral Clustering Using Graph Filtering of Random Signals." Web.
1. Nicolas TREMBLAY, Gilles PUY, Pierre BORGNAT, Rémi GRIBONVAL, Pierre VANDERGHEYNST. Accelerated Spectral Clustering Using Graph Filtering of Random Signals [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1017

ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise

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Authors:
Jon Onativia, Yue M. Lu, Pier Luigi Dragotti
Submitted On:
21 March 2016 - 10:31am
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IcasspProSparse16.pdf

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[1] Jon Onativia, Yue M. Lu, Pier Luigi Dragotti, "ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/927. Accessed: Nov. 21, 2017.
@article{927-16,
url = {http://sigport.org/927},
author = {Jon Onativia; Yue M. Lu; Pier Luigi Dragotti },
publisher = {IEEE SigPort},
title = {ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise},
year = {2016} }
TY - EJOUR
T1 - ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise
AU - Jon Onativia; Yue M. Lu; Pier Luigi Dragotti
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/927
ER -
Jon Onativia, Yue M. Lu, Pier Luigi Dragotti. (2016). ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise. IEEE SigPort. http://sigport.org/927
Jon Onativia, Yue M. Lu, Pier Luigi Dragotti, 2016. ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise. Available at: http://sigport.org/927.
Jon Onativia, Yue M. Lu, Pier Luigi Dragotti. (2016). "ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise." Web.
1. Jon Onativia, Yue M. Lu, Pier Luigi Dragotti. ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/927

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: Nov. 21, 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: Nov. 21, 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 (217 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: Nov. 21, 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: Nov. 21, 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: Nov. 21, 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: Nov. 21, 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

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