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Nonlinear Systems and Signal Processing

Insense: Incoherent Sensor Selection for Sparse Signals


Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal.

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Authors:
Amirali Aghazadeh; Mohammad Golbabaee; Andrew Lan; Richard Baraniuk
Submitted On:
12 April 2018 - 8:24pm
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ICASSP_Insense_Poster.pdf

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[1] Amirali Aghazadeh; Mohammad Golbabaee; Andrew Lan; Richard Baraniuk, "Insense: Incoherent Sensor Selection for Sparse Signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2528. Accessed: Jul. 16, 2018.
@article{2528-18,
url = {http://sigport.org/2528},
author = {Amirali Aghazadeh; Mohammad Golbabaee; Andrew Lan; Richard Baraniuk },
publisher = {IEEE SigPort},
title = {Insense: Incoherent Sensor Selection for Sparse Signals},
year = {2018} }
TY - EJOUR
T1 - Insense: Incoherent Sensor Selection for Sparse Signals
AU - Amirali Aghazadeh; Mohammad Golbabaee; Andrew Lan; Richard Baraniuk
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2528
ER -
Amirali Aghazadeh; Mohammad Golbabaee; Andrew Lan; Richard Baraniuk. (2018). Insense: Incoherent Sensor Selection for Sparse Signals. IEEE SigPort. http://sigport.org/2528
Amirali Aghazadeh; Mohammad Golbabaee; Andrew Lan; Richard Baraniuk, 2018. Insense: Incoherent Sensor Selection for Sparse Signals. Available at: http://sigport.org/2528.
Amirali Aghazadeh; Mohammad Golbabaee; Andrew Lan; Richard Baraniuk. (2018). "Insense: Incoherent Sensor Selection for Sparse Signals." Web.
1. Amirali Aghazadeh; Mohammad Golbabaee; Andrew Lan; Richard Baraniuk. Insense: Incoherent Sensor Selection for Sparse Signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2528

IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS


In this paper, we propose a scheme to simplify the selection of kernel adaptive filters in a multikernel structure.
By multiplying the output of each kernel filter by an adaptive biasing factor between zero and one, the degrading effects of poorly adjusted kernel filters can be minimized, increasing the robustness of the multikernel scheme. This approach is able to deal with the lack of the necessary statistical information for an optimal adjustment of the filter and its structure.

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Authors:
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz
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12 April 2018 - 1:24pm
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Poster_ICASSP2018_Paper#4215.pdf

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[1] Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz, "IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2449. Accessed: Jul. 16, 2018.
@article{2449-18,
url = {http://sigport.org/2449},
author = {Magno T. M. Silva; Renato Candido; Jerónimo Arenas-García; Luis A. Azpicueta-Ruiz },
publisher = {IEEE SigPort},
title = {IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS},
year = {2018} }
TY - EJOUR
T1 - IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS
AU - Magno T. M. Silva; Renato Candido; Jerónimo Arenas-García; Luis A. Azpicueta-Ruiz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2449
ER -
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz. (2018). IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS. IEEE SigPort. http://sigport.org/2449
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz, 2018. IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS. Available at: http://sigport.org/2449.
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz. (2018). "IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS." Web.
1. Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz. IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2449

Assessing cross-dependencies using bivariate multifractal analysis


Multifractal analysis, notably with its recent wavelet-leader based formulation, has nowadays become a reference tool to characterize scale-free temporal dynamics in time series. It proved successful in numerous applications very diverse in nature. However, such successes remained restricted to univariate analysis while many recent applications call for the joint analysis of several components. Surprisingly, multivariate multifractal analysis remained mostly overlooked.

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Authors:
Herwig Wendt, Roberto Leonarduzzi, Patrice Abry, Stephane Roux, Stephane Jaffard, Stephane Seuret
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12 April 2018 - 11:11am
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poster_ICASSP_2018_bivar_v00.pdf

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[1] Herwig Wendt, Roberto Leonarduzzi, Patrice Abry, Stephane Roux, Stephane Jaffard, Stephane Seuret, "Assessing cross-dependencies using bivariate multifractal analysis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2377. Accessed: Jul. 16, 2018.
@article{2377-18,
url = {http://sigport.org/2377},
author = {Herwig Wendt; Roberto Leonarduzzi; Patrice Abry; Stephane Roux; Stephane Jaffard; Stephane Seuret },
publisher = {IEEE SigPort},
title = {Assessing cross-dependencies using bivariate multifractal analysis},
year = {2018} }
TY - EJOUR
T1 - Assessing cross-dependencies using bivariate multifractal analysis
AU - Herwig Wendt; Roberto Leonarduzzi; Patrice Abry; Stephane Roux; Stephane Jaffard; Stephane Seuret
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2377
ER -
Herwig Wendt, Roberto Leonarduzzi, Patrice Abry, Stephane Roux, Stephane Jaffard, Stephane Seuret. (2018). Assessing cross-dependencies using bivariate multifractal analysis. IEEE SigPort. http://sigport.org/2377
Herwig Wendt, Roberto Leonarduzzi, Patrice Abry, Stephane Roux, Stephane Jaffard, Stephane Seuret, 2018. Assessing cross-dependencies using bivariate multifractal analysis. Available at: http://sigport.org/2377.
Herwig Wendt, Roberto Leonarduzzi, Patrice Abry, Stephane Roux, Stephane Jaffard, Stephane Seuret. (2018). "Assessing cross-dependencies using bivariate multifractal analysis." Web.
1. Herwig Wendt, Roberto Leonarduzzi, Patrice Abry, Stephane Roux, Stephane Jaffard, Stephane Seuret. Assessing cross-dependencies using bivariate multifractal analysis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2377

A Theory of Generalized Proximity for ADMM


The alternating direction method of multipliers has become in recent years the most widely used proximal method for signal processing. In this paper, we lay the groundwork for a new notion of proximity and use it to illustrate that the method (ADMM) is actually somewhat of a maladroit rearrangement of a new, more practical procedure that generalizes the Douglas-Rachford algorithm. Compared to ADMM, the algorithm that we propose enjoys not only a more sensible form, but also a more general convergence result.

Poster.pdf

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Authors:
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross
Submitted On:
12 November 2017 - 9:44pm
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[1] François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross, "A Theory of Generalized Proximity for ADMM", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2326. Accessed: Jul. 16, 2018.
@article{2326-17,
url = {http://sigport.org/2326},
author = {François D. Côté; Ioannis N. Psaromiligkos; Warren J. Gross },
publisher = {IEEE SigPort},
title = {A Theory of Generalized Proximity for ADMM},
year = {2017} }
TY - EJOUR
T1 - A Theory of Generalized Proximity for ADMM
AU - François D. Côté; Ioannis N. Psaromiligkos; Warren J. Gross
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2326
ER -
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. (2017). A Theory of Generalized Proximity for ADMM. IEEE SigPort. http://sigport.org/2326
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross, 2017. A Theory of Generalized Proximity for ADMM. Available at: http://sigport.org/2326.
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. (2017). "A Theory of Generalized Proximity for ADMM." Web.
1. François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. A Theory of Generalized Proximity for ADMM [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2326

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: Jul. 16, 2018.
@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

APPROXIMATE SIMULATION OF LINEAR CONTINUOUS TIME MODELS DRIVEN BY ASYMMETRIC STABLE LÉVY PROCESSES


In this paper we extend to the multidimensional case the modified Poisson series representation of linear stochastic processes driven by $\alpha$-stable innovations. The latter has been recently introduced in the literature and it involves a Gaussian approximation of the residuals of the series, via the exact characterization of their moments. This allows for Bayesian techniques for parameter or state inference that would not be available otherwise, due to the lack of a closed-form likelihood function for the $\alpha$-stable distribution.

ICASSP.pdf

PDF icon ICASSP.pdf (811 downloads)

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Authors:
Marina Riabiz, Simon Godsill
Submitted On:
7 March 2017 - 12:52pm
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[1] Marina Riabiz, Simon Godsill, "APPROXIMATE SIMULATION OF LINEAR CONTINUOUS TIME MODELS DRIVEN BY ASYMMETRIC STABLE LÉVY PROCESSES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1689. Accessed: Jul. 16, 2018.
@article{1689-17,
url = {http://sigport.org/1689},
author = {Marina Riabiz; Simon Godsill },
publisher = {IEEE SigPort},
title = {APPROXIMATE SIMULATION OF LINEAR CONTINUOUS TIME MODELS DRIVEN BY ASYMMETRIC STABLE LÉVY PROCESSES},
year = {2017} }
TY - EJOUR
T1 - APPROXIMATE SIMULATION OF LINEAR CONTINUOUS TIME MODELS DRIVEN BY ASYMMETRIC STABLE LÉVY PROCESSES
AU - Marina Riabiz; Simon Godsill
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1689
ER -
Marina Riabiz, Simon Godsill. (2017). APPROXIMATE SIMULATION OF LINEAR CONTINUOUS TIME MODELS DRIVEN BY ASYMMETRIC STABLE LÉVY PROCESSES. IEEE SigPort. http://sigport.org/1689
Marina Riabiz, Simon Godsill, 2017. APPROXIMATE SIMULATION OF LINEAR CONTINUOUS TIME MODELS DRIVEN BY ASYMMETRIC STABLE LÉVY PROCESSES. Available at: http://sigport.org/1689.
Marina Riabiz, Simon Godsill. (2017). "APPROXIMATE SIMULATION OF LINEAR CONTINUOUS TIME MODELS DRIVEN BY ASYMMETRIC STABLE LÉVY PROCESSES." Web.
1. Marina Riabiz, Simon Godsill. APPROXIMATE SIMULATION OF LINEAR CONTINUOUS TIME MODELS DRIVEN BY ASYMMETRIC STABLE LÉVY PROCESSES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1689

Introducing Complex Functional Link Polynomial Filters


The paper introduces a novel class of complex nonlinear filters, the complex functional link polynomial (CFLiP) filters.
These filters present many interesting properties. They are a sub-class of linear-in-the-parameter nonlinear filters.
They satisfy all the conditions of Stone-Weirstrass theorem and thus are universal approximators for causal, time-invariant, discrete-time, finite-memory, complex, continuous systems defined on a compact domain.

file.pdf

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Authors:
Alberto Carini, Danilo Comminiello
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28 February 2017 - 6:21am
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[1] Alberto Carini, Danilo Comminiello, "Introducing Complex Functional Link Polynomial Filters", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1501. Accessed: Jul. 16, 2018.
@article{1501-17,
url = {http://sigport.org/1501},
author = {Alberto Carini; Danilo Comminiello },
publisher = {IEEE SigPort},
title = {Introducing Complex Functional Link Polynomial Filters},
year = {2017} }
TY - EJOUR
T1 - Introducing Complex Functional Link Polynomial Filters
AU - Alberto Carini; Danilo Comminiello
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1501
ER -
Alberto Carini, Danilo Comminiello. (2017). Introducing Complex Functional Link Polynomial Filters. IEEE SigPort. http://sigport.org/1501
Alberto Carini, Danilo Comminiello, 2017. Introducing Complex Functional Link Polynomial Filters. Available at: http://sigport.org/1501.
Alberto Carini, Danilo Comminiello. (2017). "Introducing Complex Functional Link Polynomial Filters." Web.
1. Alberto Carini, Danilo Comminiello. Introducing Complex Functional Link Polynomial Filters [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1501

A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations


In this paper, we propose an iterative algorithm based on hard thresholding
for demixing a pair of signals from nonlinear observations of
their superposition. We focus on the under-determined case where
the number of available observations is far less than the ambient dimension
of the signals. We derive nearly-tight upper bounds on the
sample complexity of the algorithm to achieve stable recovery of the
component signals. Moreover, we show that the algorithm enjoys
a linear convergence rate. We provide a range of simulations to illustrate

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Authors:
Mohammadreza Soltani, Chinmay Hegde
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4 December 2016 - 5:15pm
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[1] Mohammadreza Soltani, Chinmay Hegde, "A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1340. Accessed: Jul. 16, 2018.
@article{1340-16,
url = {http://sigport.org/1340},
author = {Mohammadreza Soltani; Chinmay Hegde },
publisher = {IEEE SigPort},
title = {A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations},
year = {2016} }
TY - EJOUR
T1 - A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations
AU - Mohammadreza Soltani; Chinmay Hegde
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1340
ER -
Mohammadreza Soltani, Chinmay Hegde. (2016). A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations. IEEE SigPort. http://sigport.org/1340
Mohammadreza Soltani, Chinmay Hegde, 2016. A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations. Available at: http://sigport.org/1340.
Mohammadreza Soltani, Chinmay Hegde. (2016). "A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations." Web.
1. Mohammadreza Soltani, Chinmay Hegde. A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1340

Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling


We consider the problem of estimating discrete self- exciting point process models from limited binary observations, where the history of the process serves as the covariate. We analyze the performance of two classes of estimators: l1-regularized maximum likelihood and greedy estimation for a discrete version of the Hawkes process and characterize the sampling tradeoffs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear and generalized linear models with i.i.d.

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Authors:
Abbas Kazemipour, Min Wu and Behtash Babadi
Submitted On:
12 December 2016 - 9:35am
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[1] Abbas Kazemipour, Min Wu and Behtash Babadi, "Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1261. Accessed: Jul. 16, 2018.
@article{1261-16,
url = {http://sigport.org/1261},
author = {Abbas Kazemipour; Min Wu and Behtash Babadi },
publisher = {IEEE SigPort},
title = {Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling},
year = {2016} }
TY - EJOUR
T1 - Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling
AU - Abbas Kazemipour; Min Wu and Behtash Babadi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1261
ER -
Abbas Kazemipour, Min Wu and Behtash Babadi. (2016). Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling. IEEE SigPort. http://sigport.org/1261
Abbas Kazemipour, Min Wu and Behtash Babadi, 2016. Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling. Available at: http://sigport.org/1261.
Abbas Kazemipour, Min Wu and Behtash Babadi. (2016). "Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling." Web.
1. Abbas Kazemipour, Min Wu and Behtash Babadi. Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1261

A Bayesian framework for the multifractal analysis of images using data augmentation and a Whittle approximation


Texture analysis is an image processing task that can be conducted using the mathematical framework of multifractal analysis to study the regularity fluctuations of image intensity and the practical tools for their assessment, such as (wavelet) leaders. A recently introduced statistical model for leaders enables the Bayesian estimation of multifractal parameters. It significantly improves performance over standard (linear regression based) estimation. However, the computational cost induced by the associated nonstandard posterior distributions limits its application.

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Authors:
Herwig Wendt, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Patrice Abry
Submitted On:
24 March 2016 - 10:09pm
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[1] Herwig Wendt, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Patrice Abry, "A Bayesian framework for the multifractal analysis of images using data augmentation and a Whittle approximation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1038. Accessed: Jul. 16, 2018.
@article{1038-16,
url = {http://sigport.org/1038},
author = {Herwig Wendt; Yoann Altmann; Jean-Yves Tourneret; Stephen McLaughlin; Patrice Abry },
publisher = {IEEE SigPort},
title = {A Bayesian framework for the multifractal analysis of images using data augmentation and a Whittle approximation},
year = {2016} }
TY - EJOUR
T1 - A Bayesian framework for the multifractal analysis of images using data augmentation and a Whittle approximation
AU - Herwig Wendt; Yoann Altmann; Jean-Yves Tourneret; Stephen McLaughlin; Patrice Abry
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1038
ER -
Herwig Wendt, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Patrice Abry. (2016). A Bayesian framework for the multifractal analysis of images using data augmentation and a Whittle approximation. IEEE SigPort. http://sigport.org/1038
Herwig Wendt, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Patrice Abry, 2016. A Bayesian framework for the multifractal analysis of images using data augmentation and a Whittle approximation. Available at: http://sigport.org/1038.
Herwig Wendt, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Patrice Abry. (2016). "A Bayesian framework for the multifractal analysis of images using data augmentation and a Whittle approximation." Web.
1. Herwig Wendt, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Patrice Abry. A Bayesian framework for the multifractal analysis of images using data augmentation and a Whittle approximation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1038

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