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Nonlinear Systems and Signal Processing

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

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Authors:
Marina Riabiz, Simon Godsill
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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. 27, 2017.
@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.

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Authors:
Alberto Carini, Danilo Comminiello
Submitted On:
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. 27, 2017.
@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
Submitted On:
4 December 2016 - 5:15pm
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MrSChinGlobalSip2016.pdf

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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. 27, 2017.
@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. 27, 2017.
@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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Combrexelle_ICASSP_Shanghai_2016.pdf.zip

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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. 27, 2017.
@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

PARAMETER ESTIMATION OF POLYNOMIAL PHASE SIGNAL BASED ON LOW-COMPLEXITY LSU-EKF ALGORITHM IN ENTIRE IDENTIFIABLE REGION


Fast implementation of parameter estimation for polynomial phase signal (PPS) is considered in this paper. A method which combines the least squares unwrapping (LSU) estimator and the extended Kalman filter (EKF) is proposed. A small number of initial samples are used to estimate the PPS’s parameters and then these coarse estimates are used to initial the EKF. The proposed LSU-EKF estimator greatly reduces the computation complexity of the LSU estimator and can work in entire identifiable region which inherits from the LSU estimator.

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Authors:
Zhen-miao Deng, Rong-rong Xu, Yi-xiong Zhang, Ping-ping Pan, and Ru-jia Hong
Submitted On:
12 March 2016 - 4:39am
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ICASSP poster.pdf

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[1] Zhen-miao Deng, Rong-rong Xu, Yi-xiong Zhang, Ping-ping Pan, and Ru-jia Hong, "PARAMETER ESTIMATION OF POLYNOMIAL PHASE SIGNAL BASED ON LOW-COMPLEXITY LSU-EKF ALGORITHM IN ENTIRE IDENTIFIABLE REGION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/633. Accessed: Jul. 27, 2017.
@article{633-16,
url = {http://sigport.org/633},
author = {Zhen-miao Deng; Rong-rong Xu; Yi-xiong Zhang; Ping-ping Pan; and Ru-jia Hong },
publisher = {IEEE SigPort},
title = {PARAMETER ESTIMATION OF POLYNOMIAL PHASE SIGNAL BASED ON LOW-COMPLEXITY LSU-EKF ALGORITHM IN ENTIRE IDENTIFIABLE REGION},
year = {2016} }
TY - EJOUR
T1 - PARAMETER ESTIMATION OF POLYNOMIAL PHASE SIGNAL BASED ON LOW-COMPLEXITY LSU-EKF ALGORITHM IN ENTIRE IDENTIFIABLE REGION
AU - Zhen-miao Deng; Rong-rong Xu; Yi-xiong Zhang; Ping-ping Pan; and Ru-jia Hong
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/633
ER -
Zhen-miao Deng, Rong-rong Xu, Yi-xiong Zhang, Ping-ping Pan, and Ru-jia Hong. (2016). PARAMETER ESTIMATION OF POLYNOMIAL PHASE SIGNAL BASED ON LOW-COMPLEXITY LSU-EKF ALGORITHM IN ENTIRE IDENTIFIABLE REGION. IEEE SigPort. http://sigport.org/633
Zhen-miao Deng, Rong-rong Xu, Yi-xiong Zhang, Ping-ping Pan, and Ru-jia Hong, 2016. PARAMETER ESTIMATION OF POLYNOMIAL PHASE SIGNAL BASED ON LOW-COMPLEXITY LSU-EKF ALGORITHM IN ENTIRE IDENTIFIABLE REGION. Available at: http://sigport.org/633.
Zhen-miao Deng, Rong-rong Xu, Yi-xiong Zhang, Ping-ping Pan, and Ru-jia Hong. (2016). "PARAMETER ESTIMATION OF POLYNOMIAL PHASE SIGNAL BASED ON LOW-COMPLEXITY LSU-EKF ALGORITHM IN ENTIRE IDENTIFIABLE REGION." Web.
1. Zhen-miao Deng, Rong-rong Xu, Yi-xiong Zhang, Ping-ping Pan, and Ru-jia Hong. PARAMETER ESTIMATION OF POLYNOMIAL PHASE SIGNAL BASED ON LOW-COMPLEXITY LSU-EKF ALGORITHM IN ENTIRE IDENTIFIABLE REGION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/633

Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements


This is an overview presentation about developing accurate prior models that can capture non-Gaussian characteristics of images. The slides use tunable diode laser absorption tomography (TDLAT) as an application to show the results.
For more information, please check out the publication at IEEE Xplore:

Zeeshan Nadir, Michael S. Brown, Mary L. Comer, Charles A. Bouman, “Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements” , 2015 IEEE GlobalSIP Conference, Dec 14-16

Paper Details

Authors:
Michael S. Brown, Mary L. Comer, Charles A. Bouman
Submitted On:
23 February 2016 - 1:38pm
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[1] Michael S. Brown, Mary L. Comer, Charles A. Bouman, "Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/442. Accessed: Jul. 27, 2017.
@article{442-15,
url = {http://sigport.org/442},
author = {Michael S. Brown; Mary L. Comer; Charles A. Bouman },
publisher = {IEEE SigPort},
title = {Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements},
year = {2015} }
TY - EJOUR
T1 - Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements
AU - Michael S. Brown; Mary L. Comer; Charles A. Bouman
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/442
ER -
Michael S. Brown, Mary L. Comer, Charles A. Bouman. (2015). Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements. IEEE SigPort. http://sigport.org/442
Michael S. Brown, Mary L. Comer, Charles A. Bouman, 2015. Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements. Available at: http://sigport.org/442.
Michael S. Brown, Mary L. Comer, Charles A. Bouman. (2015). "Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements." Web.
1. Michael S. Brown, Mary L. Comer, Charles A. Bouman. Gaussian Mixture Prior Models for Imaging of Flow Cross Sections from Sparse Hyperspectral Measurements [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/442