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Statistical Signal Processing

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: Nov. 25, 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

A watermarking technique to secure printed QR-Codes


The QR (Quick Response) code is a two-dimensional barcode, which was designed for storage information and high speed reading applications. Being cheap to produce and fast to read, it becomes actually a popular solution for product labeling.
Ones try to make QR code a solution against counterfeiting. We present a novel technique that permits to create a secure printed QR code which is robust

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Authors:
Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier
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14 November 2017 - 9:39pm
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[1] Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier, "A watermarking technique to secure printed QR-Codes", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2266. Accessed: Nov. 25, 2017.
@article{2266-17,
url = {http://sigport.org/2266},
author = {Hoai Phuong Nguyen; Angès Delahaies; Florent Retraint; Dục Huy Nguyen; Marc Pic; Frédéric Morain-Nicolier },
publisher = {IEEE SigPort},
title = {A watermarking technique to secure printed QR-Codes},
year = {2017} }
TY - EJOUR
T1 - A watermarking technique to secure printed QR-Codes
AU - Hoai Phuong Nguyen; Angès Delahaies; Florent Retraint; Dục Huy Nguyen; Marc Pic; Frédéric Morain-Nicolier
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2266
ER -
Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier. (2017). A watermarking technique to secure printed QR-Codes. IEEE SigPort. http://sigport.org/2266
Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier, 2017. A watermarking technique to secure printed QR-Codes. Available at: http://sigport.org/2266.
Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier. (2017). "A watermarking technique to secure printed QR-Codes." Web.
1. Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier. A watermarking technique to secure printed QR-Codes [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2266

COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION

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Authors:
Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup
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9 November 2017 - 11:02am
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[1] Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup, "COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2264. Accessed: Nov. 25, 2017.
@article{2264-17,
url = {http://sigport.org/2264},
author = {Huynh Van Luong; Nikos Deligiannis; Jurgen Seiler; Soren Forchhammer; Andre Kaup },
publisher = {IEEE SigPort},
title = {COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION},
year = {2017} }
TY - EJOUR
T1 - COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION
AU - Huynh Van Luong; Nikos Deligiannis; Jurgen Seiler; Soren Forchhammer; Andre Kaup
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2264
ER -
Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup. (2017). COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION. IEEE SigPort. http://sigport.org/2264
Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup, 2017. COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION. Available at: http://sigport.org/2264.
Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup. (2017). "COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION." Web.
1. Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup. COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2264

Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation


The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) type I and type II error probabilities and (ii) mean squared error performance.

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Authors:
M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor
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13 March 2017 - 12:12pm
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[1] M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor, "Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1759. Accessed: Nov. 25, 2017.
@article{1759-17,
url = {http://sigport.org/1759},
author = {M. Fauß; K. G. Nagananda; A. M. Zoubir; H. V. Poor },
publisher = {IEEE SigPort},
title = {Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation},
year = {2017} }
TY - EJOUR
T1 - Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation
AU - M. Fauß; K. G. Nagananda; A. M. Zoubir; H. V. Poor
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1759
ER -
M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor. (2017). Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation. IEEE SigPort. http://sigport.org/1759
M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor, 2017. Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation. Available at: http://sigport.org/1759.
M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor. (2017). "Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation." Web.
1. M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor. Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1759

Robust Particle Filter by Dynamic Averaging of Multiple Noise Models


State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing techniques usually adopt a Gaussian noise assumption which may result in a major degradation in performance when the measurements are with the presence of outliers. A robust algorithm immune to the presence of outliers is desirable.

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11 March 2017 - 11:15am
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[1] , "Robust Particle Filter by Dynamic Averaging of Multiple Noise Models", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1743. Accessed: Nov. 25, 2017.
@article{1743-17,
url = {http://sigport.org/1743},
author = { },
publisher = {IEEE SigPort},
title = {Robust Particle Filter by Dynamic Averaging of Multiple Noise Models},
year = {2017} }
TY - EJOUR
T1 - Robust Particle Filter by Dynamic Averaging of Multiple Noise Models
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1743
ER -
. (2017). Robust Particle Filter by Dynamic Averaging of Multiple Noise Models. IEEE SigPort. http://sigport.org/1743
, 2017. Robust Particle Filter by Dynamic Averaging of Multiple Noise Models. Available at: http://sigport.org/1743.
. (2017). "Robust Particle Filter by Dynamic Averaging of Multiple Noise Models." Web.
1. . Robust Particle Filter by Dynamic Averaging of Multiple Noise Models [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1743

Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction

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Authors:
Leonardo M. Millefiori, P. Braca and P. Willett
Submitted On:
6 March 2017 - 6:55pm
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[1] Leonardo M. Millefiori, P. Braca and P. Willett, "Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1670. Accessed: Nov. 25, 2017.
@article{1670-17,
url = {http://sigport.org/1670},
author = {Leonardo M. Millefiori; P. Braca and P. Willett },
publisher = {IEEE SigPort},
title = {Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction},
year = {2017} }
TY - EJOUR
T1 - Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction
AU - Leonardo M. Millefiori; P. Braca and P. Willett
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1670
ER -
Leonardo M. Millefiori, P. Braca and P. Willett. (2017). Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction. IEEE SigPort. http://sigport.org/1670
Leonardo M. Millefiori, P. Braca and P. Willett, 2017. Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction. Available at: http://sigport.org/1670.
Leonardo M. Millefiori, P. Braca and P. Willett. (2017). "Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction." Web.
1. Leonardo M. Millefiori, P. Braca and P. Willett. Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1670

AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering


One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC.

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Authors:
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero
Submitted On:
5 March 2017 - 11:06pm
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[1] Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1643. Accessed: Nov. 25, 2017.
@article{1643-17,
url = {http://sigport.org/1643},
author = {Pin-Yu Chen; Thibaut Gensollen; Alfred Hero },
publisher = {IEEE SigPort},
title = {AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering},
year = {2017} }
TY - EJOUR
T1 - AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
AU - Pin-Yu Chen; Thibaut Gensollen; Alfred Hero
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1643
ER -
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. IEEE SigPort. http://sigport.org/1643
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, 2017. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. Available at: http://sigport.org/1643.
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering." Web.
1. Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1643

Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory


In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In
the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced
into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and,

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Authors:
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri
Submitted On:
2 March 2017 - 1:17pm
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[1] Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri, "Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1588. Accessed: Nov. 25, 2017.
@article{1588-17,
url = {http://sigport.org/1588},
author = {Mohamed Suliman; Tarig Ballal; Abla Kammoun; Tareq Y. Al-Naffouri },
publisher = {IEEE SigPort},
title = {Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory},
year = {2017} }
TY - EJOUR
T1 - Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory
AU - Mohamed Suliman; Tarig Ballal; Abla Kammoun; Tareq Y. Al-Naffouri
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1588
ER -
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri. (2017). Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory. IEEE SigPort. http://sigport.org/1588
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri, 2017. Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory. Available at: http://sigport.org/1588.
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri. (2017). "Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory." Web.
1. Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri. Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1588

Weak Law of Large Numbers for Stationary Graph Processes


The ability to obtain accurate estimators from a set of measurements is a key factor in science and engineering. Typically, there is an inherent assumption that the measurements were taken in a sequential order, be it in space or time. However, data is increasingly irregular so this assumption of sequentially obtained measurements no longer holds. By leveraging notions of graph signal processing to account for these irregular domains, we propose an unbiased estimator for the mean of a wide sense stationary graph process based on the diffusion of a single realization.

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Authors:
Fernando Gama, Alejandro Ribeiro
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2 March 2017 - 9:47am
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[1] Fernando Gama, Alejandro Ribeiro, "Weak Law of Large Numbers for Stationary Graph Processes", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1585. Accessed: Nov. 25, 2017.
@article{1585-17,
url = {http://sigport.org/1585},
author = {Fernando Gama; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Weak Law of Large Numbers for Stationary Graph Processes},
year = {2017} }
TY - EJOUR
T1 - Weak Law of Large Numbers for Stationary Graph Processes
AU - Fernando Gama; Alejandro Ribeiro
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1585
ER -
Fernando Gama, Alejandro Ribeiro. (2017). Weak Law of Large Numbers for Stationary Graph Processes. IEEE SigPort. http://sigport.org/1585
Fernando Gama, Alejandro Ribeiro, 2017. Weak Law of Large Numbers for Stationary Graph Processes. Available at: http://sigport.org/1585.
Fernando Gama, Alejandro Ribeiro. (2017). "Weak Law of Large Numbers for Stationary Graph Processes." Web.
1. Fernando Gama, Alejandro Ribeiro. Weak Law of Large Numbers for Stationary Graph Processes [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1585

Estimation accuracy of non-standard maximum likelihood estimators

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Authors:
Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal
Submitted On:
1 March 2017 - 5:08am
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[1] Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal, "Estimation accuracy of non-standard maximum likelihood estimators", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1542. Accessed: Nov. 25, 2017.
@article{1542-17,
url = {http://sigport.org/1542},
author = {Nabil Kbayer; Jerome Galy; Eric Chaumette; Francois Vincent; Alexandre Renaux; Pascal Larzabal },
publisher = {IEEE SigPort},
title = {Estimation accuracy of non-standard maximum likelihood estimators},
year = {2017} }
TY - EJOUR
T1 - Estimation accuracy of non-standard maximum likelihood estimators
AU - Nabil Kbayer; Jerome Galy; Eric Chaumette; Francois Vincent; Alexandre Renaux; Pascal Larzabal
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1542
ER -
Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal. (2017). Estimation accuracy of non-standard maximum likelihood estimators. IEEE SigPort. http://sigport.org/1542
Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal, 2017. Estimation accuracy of non-standard maximum likelihood estimators. Available at: http://sigport.org/1542.
Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal. (2017). "Estimation accuracy of non-standard maximum likelihood estimators." Web.
1. Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal. Estimation accuracy of non-standard maximum likelihood estimators [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1542

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