Sorry, you need to enable JavaScript to visit this website.

Signal and System Modeling, Representation and Estimation

Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition


It is well known that empirical mode decomposition can suffer from computational instabilities at the signal boundaries. These ``end effects'' cause two problems: 1) sifting termination issues, i.e.~convergence and 2) estimation error, i.e.~accuracy. In this paper, we propose to use linear prediction in conjunction with a previous method to address end effects, to further mitigate these problems.

Paper Details

Authors:
Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon
Submitted On:
26 November 2018 - 4:23pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP_2018_Poster___Mitigating_End_Effects.pdf

Subscribe

[1] Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon, "Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3735. Accessed: Mar. 22, 2019.
@article{3735-18,
url = {http://sigport.org/3735},
author = {Steven Sandoval; Matthew Bredin; and Phillip L.~De Leon },
publisher = {IEEE SigPort},
title = {Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition},
year = {2018} }
TY - EJOUR
T1 - Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition
AU - Steven Sandoval; Matthew Bredin; and Phillip L.~De Leon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3735
ER -
Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon. (2018). Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition. IEEE SigPort. http://sigport.org/3735
Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon, 2018. Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition. Available at: http://sigport.org/3735.
Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon. (2018). "Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition." Web.
1. Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon. Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3735

ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING


Without relative motion or beam scanning, orbitalangular-momentum (OAM)-based radar is shown to be able to estimate azimuth of targets, which opens a new perspective for traditional radar techniques. However, the existing application of two-dimensional (2-D) fast Fourier transform (FFT) and multiple signal classification (MUSIC) algorithms in OAM-based radar targets detection doesn’t realize 2-D super-resolution and robust estimation.

Paper Details

Authors:
Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li
Submitted On:
22 November 2018 - 10:45pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL.pdf

Subscribe

[1] Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li, "ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3729. Accessed: Mar. 22, 2019.
@article{3729-18,
url = {http://sigport.org/3729},
author = {Rui Chen; Wen-xuan Long; Yue Gao and Jiandong Li },
publisher = {IEEE SigPort},
title = {ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING},
year = {2018} }
TY - EJOUR
T1 - ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING
AU - Rui Chen; Wen-xuan Long; Yue Gao and Jiandong Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3729
ER -
Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li. (2018). ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING. IEEE SigPort. http://sigport.org/3729
Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li, 2018. ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING. Available at: http://sigport.org/3729.
Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li. (2018). "ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING." Web.
1. Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li. ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3729

Learning Flexible Representations of Stochastic Processes on Graphs


Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs. We propose both a generalization of the underlying graph and a class of linear operations for stochastic (time-varying) processes on directed (or undirected) graphs to be used in graph convolutional networks.

Paper Details

Authors:
Brian M. Sadler, Radu V. Balan
Submitted On:
30 May 2018 - 1:35pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster

Subscribe

[1] Brian M. Sadler, Radu V. Balan, "Learning Flexible Representations of Stochastic Processes on Graphs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3220. Accessed: Mar. 22, 2019.
@article{3220-18,
url = {http://sigport.org/3220},
author = {Brian M. Sadler; Radu V. Balan },
publisher = {IEEE SigPort},
title = {Learning Flexible Representations of Stochastic Processes on Graphs},
year = {2018} }
TY - EJOUR
T1 - Learning Flexible Representations of Stochastic Processes on Graphs
AU - Brian M. Sadler; Radu V. Balan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3220
ER -
Brian M. Sadler, Radu V. Balan. (2018). Learning Flexible Representations of Stochastic Processes on Graphs. IEEE SigPort. http://sigport.org/3220
Brian M. Sadler, Radu V. Balan, 2018. Learning Flexible Representations of Stochastic Processes on Graphs. Available at: http://sigport.org/3220.
Brian M. Sadler, Radu V. Balan. (2018). "Learning Flexible Representations of Stochastic Processes on Graphs." Web.
1. Brian M. Sadler, Radu V. Balan. Learning Flexible Representations of Stochastic Processes on Graphs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3220

Global Optimality in Inductive Matrix Completion


Inductive matrix completion (IMC) is a model for incorporating side information in form of “features” of the row and column entities of an unknown matrix in the matrix completion problem. As side information, features can substantially reduce the number of observed entries required for reconstructing an unknown matrix from its given entries. The IMC problem can be formulated as a low-rank matrix recovery problem where the observed entries are seen as measurements of a smaller matrix that models the interaction between the column and row features.

Paper Details

Authors:
Mohsen Ghassemi, Anand D. Sarwate, Naveen goela
Submitted On:
1 May 2018 - 11:04pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018.pdf

Subscribe

[1] Mohsen Ghassemi, Anand D. Sarwate, Naveen goela, "Global Optimality in Inductive Matrix Completion", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3171. Accessed: Mar. 22, 2019.
@article{3171-18,
url = {http://sigport.org/3171},
author = {Mohsen Ghassemi; Anand D. Sarwate; Naveen goela },
publisher = {IEEE SigPort},
title = {Global Optimality in Inductive Matrix Completion},
year = {2018} }
TY - EJOUR
T1 - Global Optimality in Inductive Matrix Completion
AU - Mohsen Ghassemi; Anand D. Sarwate; Naveen goela
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3171
ER -
Mohsen Ghassemi, Anand D. Sarwate, Naveen goela. (2018). Global Optimality in Inductive Matrix Completion. IEEE SigPort. http://sigport.org/3171
Mohsen Ghassemi, Anand D. Sarwate, Naveen goela, 2018. Global Optimality in Inductive Matrix Completion. Available at: http://sigport.org/3171.
Mohsen Ghassemi, Anand D. Sarwate, Naveen goela. (2018). "Global Optimality in Inductive Matrix Completion." Web.
1. Mohsen Ghassemi, Anand D. Sarwate, Naveen goela. Global Optimality in Inductive Matrix Completion [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3171

ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements

Paper Details

Authors:
Submitted On:
20 April 2018 - 4:30pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_MW.pdf

Subscribe

[1] , "ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3113. Accessed: Mar. 22, 2019.
@article{3113-18,
url = {http://sigport.org/3113},
author = { },
publisher = {IEEE SigPort},
title = {ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements},
year = {2018} }
TY - EJOUR
T1 - ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3113
ER -
. (2018). ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements. IEEE SigPort. http://sigport.org/3113
, 2018. ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements. Available at: http://sigport.org/3113.
. (2018). "ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements." Web.
1. . ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3113

Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers

Paper Details

Authors:
Pramod Varshney
Submitted On:
20 April 2018 - 1:17am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp-2018_QLi.pdf

Subscribe

[1] Pramod Varshney, "Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3076. Accessed: Mar. 22, 2019.
@article{3076-18,
url = {http://sigport.org/3076},
author = {Pramod Varshney },
publisher = {IEEE SigPort},
title = {Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers},
year = {2018} }
TY - EJOUR
T1 - Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers
AU - Pramod Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3076
ER -
Pramod Varshney. (2018). Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers. IEEE SigPort. http://sigport.org/3076
Pramod Varshney, 2018. Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers. Available at: http://sigport.org/3076.
Pramod Varshney. (2018). "Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers." Web.
1. Pramod Varshney. Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3076

PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA

Paper Details

Authors:
Sandeep Kumar, Daniel P. Palomar
Submitted On:
20 April 2018 - 1:08am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_ICASSP2018.pdf

Subscribe

[1] Sandeep Kumar, Daniel P. Palomar, "PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3073. Accessed: Mar. 22, 2019.
@article{3073-18,
url = {http://sigport.org/3073},
author = {Sandeep Kumar; Daniel P. Palomar },
publisher = {IEEE SigPort},
title = {PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA},
year = {2018} }
TY - EJOUR
T1 - PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA
AU - Sandeep Kumar; Daniel P. Palomar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3073
ER -
Sandeep Kumar, Daniel P. Palomar. (2018). PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA. IEEE SigPort. http://sigport.org/3073
Sandeep Kumar, Daniel P. Palomar, 2018. PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA. Available at: http://sigport.org/3073.
Sandeep Kumar, Daniel P. Palomar. (2018). "PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA." Web.
1. Sandeep Kumar, Daniel P. Palomar. PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3073

PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA

Paper Details

Authors:
Sandeep Kumar, Daniel P. Palomar
Submitted On:
20 April 2018 - 1:08am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_ICASSP2018.pdf

Subscribe

[1] Sandeep Kumar, Daniel P. Palomar, "PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3070. Accessed: Mar. 22, 2019.
@article{3070-18,
url = {http://sigport.org/3070},
author = {Sandeep Kumar; Daniel P. Palomar },
publisher = {IEEE SigPort},
title = {PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA},
year = {2018} }
TY - EJOUR
T1 - PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA
AU - Sandeep Kumar; Daniel P. Palomar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3070
ER -
Sandeep Kumar, Daniel P. Palomar. (2018). PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA. IEEE SigPort. http://sigport.org/3070
Sandeep Kumar, Daniel P. Palomar, 2018. PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA. Available at: http://sigport.org/3070.
Sandeep Kumar, Daniel P. Palomar. (2018). "PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA." Web.
1. Sandeep Kumar, Daniel P. Palomar. PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3070

Demixing and blind deconvolution of graph-diffused signals


We extend the classical joint problem of signal demixing, blind deconvolution,
and filter identification to the realm of graphs. The model is that
each mixing signal is generated by a sparse input diffused via a graph filter.
Then, the sum of diffused signals is observed. We identify and address
two problems: 1) each sparse input is diffused in a different graph; and 2)
all signals are diffused in the same graph. These tasks amount to finding
the collections of sources and filter coefficients producing the observation.

Paper Details

Authors:
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez
Submitted On:
19 April 2018 - 4:51pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018_demixing_GSP_poster_v2.pdf

Subscribe

[1] Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez, "Demixing and blind deconvolution of graph-diffused signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3027. Accessed: Mar. 22, 2019.
@article{3027-18,
url = {http://sigport.org/3027},
author = {Fernando J. Iglesias; Santiago Segarra; Samuel Rey-Escudero; Antonio G. Marques; David Ramirez },
publisher = {IEEE SigPort},
title = {Demixing and blind deconvolution of graph-diffused signals},
year = {2018} }
TY - EJOUR
T1 - Demixing and blind deconvolution of graph-diffused signals
AU - Fernando J. Iglesias; Santiago Segarra; Samuel Rey-Escudero; Antonio G. Marques; David Ramirez
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3027
ER -
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. (2018). Demixing and blind deconvolution of graph-diffused signals. IEEE SigPort. http://sigport.org/3027
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez, 2018. Demixing and blind deconvolution of graph-diffused signals. Available at: http://sigport.org/3027.
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. (2018). "Demixing and blind deconvolution of graph-diffused signals." Web.
1. Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. Demixing and blind deconvolution of graph-diffused signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3027

A Dimension-Independent Discriminant between Distributions


Henze-Penrose divergence is a non-parametric divergence measure that can be used to estimate a bound on the Bayes error in a binary classification problem. In this paper, we show that a cross- match statistic based on optimal weighted matching can be used to directly estimate Henze-Penrose divergence. Unlike an earlier approach based on the Friedman-Rafsky minimal spanning tree statistic, the proposed method is dimension-independent. The new approach is evaluated using simulation and applied to real datasets to obtain Bayes error estimates.

Paper Details

Authors:
Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero
Submitted On:
19 April 2018 - 12:32pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp2018.pdf

Subscribe

[1] Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero, "A Dimension-Independent Discriminant between Distributions", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2982. Accessed: Mar. 22, 2019.
@article{2982-18,
url = {http://sigport.org/2982},
author = {Salimeh Yasaei-Sekeh; Brandon Oselio; Alfred Hero },
publisher = {IEEE SigPort},
title = {A Dimension-Independent Discriminant between Distributions},
year = {2018} }
TY - EJOUR
T1 - A Dimension-Independent Discriminant between Distributions
AU - Salimeh Yasaei-Sekeh; Brandon Oselio; Alfred Hero
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2982
ER -
Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero. (2018). A Dimension-Independent Discriminant between Distributions. IEEE SigPort. http://sigport.org/2982
Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero, 2018. A Dimension-Independent Discriminant between Distributions. Available at: http://sigport.org/2982.
Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero. (2018). "A Dimension-Independent Discriminant between Distributions." Web.
1. Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero. A Dimension-Independent Discriminant between Distributions [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2982

Pages