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Signal and System Modeling, Representation and Estimation

ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW


In this paper, we propose an adaptive STFT (ASTFT) with adaptive chirp-modulated Gaussian window. The window is obtained from rotating Gaussian function in time-frequency plane by fractional Fourier transform (FRFT). It is completely adaptive where the two parameters, FRFT rotation angle and Gaussian variance, are signal-dependent. The angle dependents on the chirp rate of the signal. The variance is determined by the chirp rate and its first derivative.

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Authors:
Soo-Chang Pei, Shih-Gu Huang
Submitted On:
12 April 2018 - 1:20pm
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[1] Soo-Chang Pei, Shih-Gu Huang, "ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2450. Accessed: Jul. 17, 2019.
@article{2450-18,
url = {http://sigport.org/2450},
author = {Soo-Chang Pei; Shih-Gu Huang },
publisher = {IEEE SigPort},
title = {ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW},
year = {2018} }
TY - EJOUR
T1 - ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW
AU - Soo-Chang Pei; Shih-Gu Huang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2450
ER -
Soo-Chang Pei, Shih-Gu Huang. (2018). ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW. IEEE SigPort. http://sigport.org/2450
Soo-Chang Pei, Shih-Gu Huang, 2018. ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW. Available at: http://sigport.org/2450.
Soo-Chang Pei, Shih-Gu Huang. (2018). "ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW." Web.
1. Soo-Chang Pei, Shih-Gu Huang. ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2450

Convolutional group-sparse coding and source localization


In this paper, we present a new interpretation of non-negatively constrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging, and provide a visual example on an image derived from data captured by the Hubble telescope.

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Authors:
Pol del Aguila Pla, Joakim Jaldén
Submitted On:
12 April 2018 - 12:59pm
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[1] Pol del Aguila Pla, Joakim Jaldén, "Convolutional group-sparse coding and source localization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2444. Accessed: Jul. 17, 2019.
@article{2444-18,
url = {http://sigport.org/2444},
author = {Pol del Aguila Pla; Joakim Jaldén },
publisher = {IEEE SigPort},
title = {Convolutional group-sparse coding and source localization},
year = {2018} }
TY - EJOUR
T1 - Convolutional group-sparse coding and source localization
AU - Pol del Aguila Pla; Joakim Jaldén
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2444
ER -
Pol del Aguila Pla, Joakim Jaldén. (2018). Convolutional group-sparse coding and source localization. IEEE SigPort. http://sigport.org/2444
Pol del Aguila Pla, Joakim Jaldén, 2018. Convolutional group-sparse coding and source localization. Available at: http://sigport.org/2444.
Pol del Aguila Pla, Joakim Jaldén. (2018). "Convolutional group-sparse coding and source localization." Web.
1. Pol del Aguila Pla, Joakim Jaldén. Convolutional group-sparse coding and source localization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2444

Sparse overcomplete denoising: aggregation versus global optimization

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Authors:
Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg
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12 April 2018 - 11:41am
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2017_SPARS_Poster_Convolutional_vs_Cycle.pdf

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[1] Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg, "Sparse overcomplete denoising: aggregation versus global optimization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2411. Accessed: Jul. 17, 2019.
@article{2411-18,
url = {http://sigport.org/2411},
author = {Diego Carrera; Giacomo Boracchi; Alessandro Foi; Brendt Wohlberg },
publisher = {IEEE SigPort},
title = {Sparse overcomplete denoising: aggregation versus global optimization},
year = {2018} }
TY - EJOUR
T1 - Sparse overcomplete denoising: aggregation versus global optimization
AU - Diego Carrera; Giacomo Boracchi; Alessandro Foi; Brendt Wohlberg
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2411
ER -
Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg. (2018). Sparse overcomplete denoising: aggregation versus global optimization. IEEE SigPort. http://sigport.org/2411
Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg, 2018. Sparse overcomplete denoising: aggregation versus global optimization. Available at: http://sigport.org/2411.
Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg. (2018). "Sparse overcomplete denoising: aggregation versus global optimization." Web.
1. Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg. Sparse overcomplete denoising: aggregation versus global optimization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2411

Fast Projection onto the $\ell_{\infty,1}$-Mixed Norm Ball using Steffensen Root Search

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Authors:
Paul Rodriguez
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12 April 2018 - 11:06am
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[1] Paul Rodriguez , "Fast Projection onto the $\ell_{\infty,1}$-Mixed Norm Ball using Steffensen Root Search", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2379. Accessed: Jul. 17, 2019.
@article{2379-18,
url = {http://sigport.org/2379},
author = {Paul Rodriguez },
publisher = {IEEE SigPort},
title = {Fast Projection onto the $\ell_{\infty,1}$-Mixed Norm Ball using Steffensen Root Search},
year = {2018} }
TY - EJOUR
T1 - Fast Projection onto the $\ell_{\infty,1}$-Mixed Norm Ball using Steffensen Root Search
AU - Paul Rodriguez
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2379
ER -
Paul Rodriguez . (2018). Fast Projection onto the $\ell_{\infty,1}$-Mixed Norm Ball using Steffensen Root Search. IEEE SigPort. http://sigport.org/2379
Paul Rodriguez , 2018. Fast Projection onto the $\ell_{\infty,1}$-Mixed Norm Ball using Steffensen Root Search. Available at: http://sigport.org/2379.
Paul Rodriguez . (2018). "Fast Projection onto the $\ell_{\infty,1}$-Mixed Norm Ball using Steffensen Root Search." Web.
1. Paul Rodriguez . Fast Projection onto the $\ell_{\infty,1}$-Mixed Norm Ball using Steffensen Root Search [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2379

Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering

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Authors:
Quanyan Zhu
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13 November 2017 - 11:10am
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conference_presentation_GlobalSIP2017.pdf

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[1] Quanyan Zhu, "Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2338. Accessed: Jul. 17, 2019.
@article{2338-17,
url = {http://sigport.org/2338},
author = {Quanyan Zhu },
publisher = {IEEE SigPort},
title = {Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering},
year = {2017} }
TY - EJOUR
T1 - Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering
AU - Quanyan Zhu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2338
ER -
Quanyan Zhu. (2017). Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering. IEEE SigPort. http://sigport.org/2338
Quanyan Zhu, 2017. Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering. Available at: http://sigport.org/2338.
Quanyan Zhu. (2017). "Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering." Web.
1. Quanyan Zhu. Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2338

MULTIPLE SOURCES IDENTIFICATION IN NETWORKS WITH PARTIAL TIMESTAMPS

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Authors:
Feng Ji
Submitted On:
10 November 2017 - 12:08am
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[1] Feng Ji, "MULTIPLE SOURCES IDENTIFICATION IN NETWORKS WITH PARTIAL TIMESTAMPS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2283. Accessed: Jul. 17, 2019.
@article{2283-17,
url = {http://sigport.org/2283},
author = {Feng Ji },
publisher = {IEEE SigPort},
title = {MULTIPLE SOURCES IDENTIFICATION IN NETWORKS WITH PARTIAL TIMESTAMPS},
year = {2017} }
TY - EJOUR
T1 - MULTIPLE SOURCES IDENTIFICATION IN NETWORKS WITH PARTIAL TIMESTAMPS
AU - Feng Ji
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2283
ER -
Feng Ji. (2017). MULTIPLE SOURCES IDENTIFICATION IN NETWORKS WITH PARTIAL TIMESTAMPS. IEEE SigPort. http://sigport.org/2283
Feng Ji, 2017. MULTIPLE SOURCES IDENTIFICATION IN NETWORKS WITH PARTIAL TIMESTAMPS. Available at: http://sigport.org/2283.
Feng Ji. (2017). "MULTIPLE SOURCES IDENTIFICATION IN NETWORKS WITH PARTIAL TIMESTAMPS." Web.
1. Feng Ji. MULTIPLE SOURCES IDENTIFICATION IN NETWORKS WITH PARTIAL TIMESTAMPS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2283

DICTIONARY LEARNING IN THE ANALYSIS SPARSE REPRESENTATION WITH OPTIMIZATION ON STIEFEL MANIFOLD

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Authors:
Yujie Li, Shuxue Ding, Zhenni Li, Xiang Li, Benying Tan
Submitted On:
9 November 2017 - 11:18pm
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[1] Yujie Li, Shuxue Ding, Zhenni Li, Xiang Li, Benying Tan, "DICTIONARY LEARNING IN THE ANALYSIS SPARSE REPRESENTATION WITH OPTIMIZATION ON STIEFEL MANIFOLD", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2282. Accessed: Jul. 17, 2019.
@article{2282-17,
url = {http://sigport.org/2282},
author = {Yujie Li; Shuxue Ding; Zhenni Li; Xiang Li; Benying Tan },
publisher = {IEEE SigPort},
title = {DICTIONARY LEARNING IN THE ANALYSIS SPARSE REPRESENTATION WITH OPTIMIZATION ON STIEFEL MANIFOLD},
year = {2017} }
TY - EJOUR
T1 - DICTIONARY LEARNING IN THE ANALYSIS SPARSE REPRESENTATION WITH OPTIMIZATION ON STIEFEL MANIFOLD
AU - Yujie Li; Shuxue Ding; Zhenni Li; Xiang Li; Benying Tan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2282
ER -
Yujie Li, Shuxue Ding, Zhenni Li, Xiang Li, Benying Tan. (2017). DICTIONARY LEARNING IN THE ANALYSIS SPARSE REPRESENTATION WITH OPTIMIZATION ON STIEFEL MANIFOLD. IEEE SigPort. http://sigport.org/2282
Yujie Li, Shuxue Ding, Zhenni Li, Xiang Li, Benying Tan, 2017. DICTIONARY LEARNING IN THE ANALYSIS SPARSE REPRESENTATION WITH OPTIMIZATION ON STIEFEL MANIFOLD. Available at: http://sigport.org/2282.
Yujie Li, Shuxue Ding, Zhenni Li, Xiang Li, Benying Tan. (2017). "DICTIONARY LEARNING IN THE ANALYSIS SPARSE REPRESENTATION WITH OPTIMIZATION ON STIEFEL MANIFOLD." Web.
1. Yujie Li, Shuxue Ding, Zhenni Li, Xiang Li, Benying Tan. DICTIONARY LEARNING IN THE ANALYSIS SPARSE REPRESENTATION WITH OPTIMIZATION ON STIEFEL MANIFOLD [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2282

ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS


The power of particle filters in tracking the state of non-linear and non-Gaussian systems stems not only from their simple numerical implementation but also from their optimality and convergence properties. In particle filtering, the posterior distribution of the state is approximated by a discrete mass of samples, called particles, that stochastically evolve in time according to the dynamics of the model and the observations. Particle filters have been shown to converge almost surely toward the optimal filter as the number of particles increases.

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Authors:
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi
Submitted On:
9 November 2017 - 11:53am
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nesrine GLOBALSIP2018 - Copy.pdf

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[1] Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi, "ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2272. Accessed: Jul. 17, 2019.
@article{2272-17,
url = {http://sigport.org/2272},
author = {Nesrine Amor; Nidhal Carla Bouaynaya; Roman Shterenberg and Souad Chebbi },
publisher = {IEEE SigPort},
title = {ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS},
year = {2017} }
TY - EJOUR
T1 - ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS
AU - Nesrine Amor; Nidhal Carla Bouaynaya; Roman Shterenberg and Souad Chebbi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2272
ER -
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi. (2017). ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS. IEEE SigPort. http://sigport.org/2272
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi, 2017. ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS. Available at: http://sigport.org/2272.
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi. (2017). "ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS." Web.
1. Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi. ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2272

ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding


The alternating direction method of multipliers (ADMM) has been widely used for a very wide variety of imaging inverse problems. One of the disadvantages of this method, however, is the need to select an algorithm parameter, the penalty parameter, that has a significant effect on the rate of convergence of the algorithm. Although a number of heuristic methods have been proposed, as yet there is no general theory providing a good choice of this parameter for all problems.

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Authors:
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov
Submitted On:
3 October 2017 - 6:45pm
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[1] Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov, "ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2254. Accessed: Jul. 17, 2019.
@article{2254-17,
url = {http://sigport.org/2254},
author = {Youzuo Lin; Brendt Wohlberg; Velimir Vesselinov },
publisher = {IEEE SigPort},
title = {ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding},
year = {2017} }
TY - EJOUR
T1 - ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding
AU - Youzuo Lin; Brendt Wohlberg; Velimir Vesselinov
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2254
ER -
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. (2017). ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding. IEEE SigPort. http://sigport.org/2254
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov, 2017. ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding. Available at: http://sigport.org/2254.
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. (2017). "ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding." Web.
1. Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2254

QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING


Large-scale networks of computing units, often characterised by the absence of central control, have become commonplace in many applications. To facilitate data processing in these large-scale networks, distributed signal processing is required. The iterative behaviour of distributed processing algorithms combined with energy, computational power, and bandwidth limitations imposed by such networks, place tight constraints on the transmission capacities of the individual nodes.

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Authors:
Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens
Submitted On:
14 March 2017 - 5:54am
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Schellekens 2017 - Poster - QUANTISATION EFFECTS IN PDMM A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING.pdf

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[1] Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens, "QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1764. Accessed: Jul. 17, 2019.
@article{1764-17,
url = {http://sigport.org/1764},
author = {Daan H. M. Schellekens; Thomas Sherson; Richard Heusdens },
publisher = {IEEE SigPort},
title = {QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING},
year = {2017} }
TY - EJOUR
T1 - QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING
AU - Daan H. M. Schellekens; Thomas Sherson; Richard Heusdens
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1764
ER -
Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens. (2017). QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING. IEEE SigPort. http://sigport.org/1764
Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens, 2017. QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING. Available at: http://sigport.org/1764.
Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens. (2017). "QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING." Web.
1. Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens. QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1764

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