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Statistical Signal Processing

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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poster1_icassp_2017_fauss.pdf

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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: May. 28, 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: May. 28, 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
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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: May. 28, 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
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5 March 2017 - 11:06pm
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ICASSP_AMOS_2017.pdf

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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: May. 28, 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
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2 March 2017 - 1:17pm
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Suliman_ICASSP2017.pdf

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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: May. 28, 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: May. 28, 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
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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: May. 28, 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

Generalized Barankin-Type Lower Bounds for Misspecified Models

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Authors:
Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent
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1 March 2017 - 5:03am
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[1] Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent, "Generalized Barankin-Type Lower Bounds for Misspecified Models", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1541. Accessed: May. 28, 2017.
@article{1541-17,
url = {http://sigport.org/1541},
author = {Mouhamadou Lamine Diong; Eric Chaumette; Francois Vincent },
publisher = {IEEE SigPort},
title = {Generalized Barankin-Type Lower Bounds for Misspecified Models},
year = {2017} }
TY - EJOUR
T1 - Generalized Barankin-Type Lower Bounds for Misspecified Models
AU - Mouhamadou Lamine Diong; Eric Chaumette; Francois Vincent
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1541
ER -
Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent. (2017). Generalized Barankin-Type Lower Bounds for Misspecified Models. IEEE SigPort. http://sigport.org/1541
Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent, 2017. Generalized Barankin-Type Lower Bounds for Misspecified Models. Available at: http://sigport.org/1541.
Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent. (2017). "Generalized Barankin-Type Lower Bounds for Misspecified Models." Web.
1. Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent. Generalized Barankin-Type Lower Bounds for Misspecified Models [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1541

Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference

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Eric Chaumette, Francois Vincent
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1 March 2017 - 4:59am
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[1] Eric Chaumette, Francois Vincent, "Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1540. Accessed: May. 28, 2017.
@article{1540-17,
url = {http://sigport.org/1540},
author = {Eric Chaumette; Francois Vincent },
publisher = {IEEE SigPort},
title = {Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference},
year = {2017} }
TY - EJOUR
T1 - Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference
AU - Eric Chaumette; Francois Vincent
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1540
ER -
Eric Chaumette, Francois Vincent. (2017). Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference. IEEE SigPort. http://sigport.org/1540
Eric Chaumette, Francois Vincent, 2017. Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference. Available at: http://sigport.org/1540.
Eric Chaumette, Francois Vincent. (2017). "Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference." Web.
1. Eric Chaumette, Francois Vincent. Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1540

Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval


The recently reported Wirtinger flow (WF) algorithm has been demonstrated as a promising method for solving the problem of phase retrieval by applying a gradient descent scheme. An empirical choice of stepsize is suggested in practice. However, this heuristic stepsize selection rule is not optimal. In order to accelerate the convergence rate, we propose an improved WF with optimal stepsize. It is revealed that this optimal stepsize is the solution of a univariate cubic equation with real-valued coefficients.

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Authors:
Sreeraman Rajan, Xingzhao Liu
Submitted On:
28 February 2017 - 3:58am
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ICASSP2017 poster.pdf

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[1] Sreeraman Rajan, Xingzhao Liu, "Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1488. Accessed: May. 28, 2017.
@article{1488-17,
url = {http://sigport.org/1488},
author = {Sreeraman Rajan; Xingzhao Liu },
publisher = {IEEE SigPort},
title = {Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval},
year = {2017} }
TY - EJOUR
T1 - Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval
AU - Sreeraman Rajan; Xingzhao Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1488
ER -
Sreeraman Rajan, Xingzhao Liu. (2017). Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval. IEEE SigPort. http://sigport.org/1488
Sreeraman Rajan, Xingzhao Liu, 2017. Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval. Available at: http://sigport.org/1488.
Sreeraman Rajan, Xingzhao Liu. (2017). "Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval." Web.
1. Sreeraman Rajan, Xingzhao Liu. Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1488

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