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Signal and System Modeling, Representation and Estimation

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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[1] Quanyan Zhu, "Slides: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2338. Accessed: Dec. 18, 2017.
@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
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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: Dec. 18, 2017.
@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


globalsip.pdf

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Authors:
Yujie Li, Shuxue Ding, Zhenni Li, Xiang Li, Benying Tan
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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: Dec. 18, 2017.
@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
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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: Dec. 18, 2017.
@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: Dec. 18, 2017.
@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: Dec. 18, 2017.
@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

Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model


We introduce a new estimation algorithm specifically designed for the latent high-order autoregressive models. It implements the concept of the filter-based maximum likelihood. Our approach is fully deterministic and is less computationally demanding than the traditional Monte Carlo Markov chain techniques. The simulation experiments confirm the interest of our approach.

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Authors:
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski
Submitted On:
7 March 2017 - 6:12am
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[1] Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski, "Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1684. Accessed: Dec. 18, 2017.
@article{1684-17,
url = {http://sigport.org/1684},
author = {Ivan Gorynin; Emmanuel Monfrini; Wojciech Pieczynski },
publisher = {IEEE SigPort},
title = {Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model},
year = {2017} }
TY - EJOUR
T1 - Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model
AU - Ivan Gorynin; Emmanuel Monfrini; Wojciech Pieczynski
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1684
ER -
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. (2017). Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model. IEEE SigPort. http://sigport.org/1684
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski, 2017. Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model. Available at: http://sigport.org/1684.
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. (2017). "Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model." Web.
1. Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1684

Energy Blowup for Truncated Stable LTI Systems


In this paper we analyze the convergence behavior of a sampling based system approximation process, where the time variable is in the argument of the signal and not in the argument of the bandlimited impulse response. We consider the Paley-Wiener space $PW_\pi^2$ of bandlimited signals with finite energy and stable linear time-invariant (LTI) systems, and show that there are signals and systems such that the approximation process diverges in the $L^2$-norm, i.e., the norm of the signal space. We prove that the sets of signals and systems creating divergence are jointly spaceable, i.e., there exists an infinite dimensional closed subspace of $PW_\pi^2$ and an infinite dimensional closed subspace of the space of all stable LTI systems, such that the approximation process diverges for any non-zero pair of signal and system from these subspaces.

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Authors:
Holger Boche, Ullrich Mönich
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2 March 2017 - 5:57am
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[1] Holger Boche, Ullrich Mönich, "Energy Blowup for Truncated Stable LTI Systems", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1580. Accessed: Dec. 18, 2017.
@article{1580-17,
url = {http://sigport.org/1580},
author = {Holger Boche; Ullrich Mönich },
publisher = {IEEE SigPort},
title = {Energy Blowup for Truncated Stable LTI Systems},
year = {2017} }
TY - EJOUR
T1 - Energy Blowup for Truncated Stable LTI Systems
AU - Holger Boche; Ullrich Mönich
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1580
ER -
Holger Boche, Ullrich Mönich. (2017). Energy Blowup for Truncated Stable LTI Systems. IEEE SigPort. http://sigport.org/1580
Holger Boche, Ullrich Mönich, 2017. Energy Blowup for Truncated Stable LTI Systems. Available at: http://sigport.org/1580.
Holger Boche, Ullrich Mönich. (2017). "Energy Blowup for Truncated Stable LTI Systems." Web.
1. Holger Boche, Ullrich Mönich. Energy Blowup for Truncated Stable LTI Systems [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1580

Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity


In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domains are sparse. However, it is assumed here that there exist additional structures over the sparse coefficients. Accordingly, we propose new problem formulation for the blind image deconvolution, which utilize the structural

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Authors:
Deokyoung Kang, Suk I. Yoo
Submitted On:
13 March 2017 - 4:21am
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icassp2017_jeon_3855_IVMSP-P9.8.pdf

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[1] Deokyoung Kang, Suk I. Yoo, "Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1577. Accessed: Dec. 18, 2017.
@article{1577-17,
url = {http://sigport.org/1577},
author = {Deokyoung Kang; Suk I. Yoo },
publisher = {IEEE SigPort},
title = {Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity},
year = {2017} }
TY - EJOUR
T1 - Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity
AU - Deokyoung Kang; Suk I. Yoo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1577
ER -
Deokyoung Kang, Suk I. Yoo. (2017). Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity. IEEE SigPort. http://sigport.org/1577
Deokyoung Kang, Suk I. Yoo, 2017. Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity. Available at: http://sigport.org/1577.
Deokyoung Kang, Suk I. Yoo. (2017). "Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity." Web.
1. Deokyoung Kang, Suk I. Yoo. Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1577

AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID


It is well known that the positive- and negative frequency components of a real sinusoid spectrally interact with each other; thus, introducing bias in frequency estimation based on the periodogram maximization. We propose to filter out the negative-frequency component. To that end, a coarse frequency estimation is obtained using the windowing approach, known to reduce the estimation bias, and then used to filter out the negative frequency component via modulation and discrete Fourier transform

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Submitted On:
28 February 2017 - 5:41am
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[1] , "AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1498. Accessed: Dec. 18, 2017.
@article{1498-17,
url = {http://sigport.org/1498},
author = { },
publisher = {IEEE SigPort},
title = {AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID},
year = {2017} }
TY - EJOUR
T1 - AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1498
ER -
. (2017). AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID. IEEE SigPort. http://sigport.org/1498
, 2017. AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID. Available at: http://sigport.org/1498.
. (2017). "AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID." Web.
1. . AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1498

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