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Signal and System Modeling, Representation and Estimation

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. 19, 2019.
@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

Two interesting and easy-to-follow tutorials: signal energy and ZCR


Both the articles I referenced in this document play the role of tutorials that introduce some feature extraction (FE) approaches based on signal energy and zero-crossing rates (ZCRs). They offer cutting-edge algorithms in which the feasibility of a balance among creativity, simplicity, and accuracy constitutes the main motivation. The theory presented, smoothly shown and accompanied by numerical examples, is complemented with source-codes in C/C++ programming language and interesting applications on neurophysiological signal processing, speech processing and image processing.

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Authors:
Guido, R.C.
Submitted On:
13 July 2016 - 7:11pm
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[1] Guido, R.C., "Two interesting and easy-to-follow tutorials: signal energy and ZCR", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1133. Accessed: Jul. 19, 2019.
@article{1133-16,
url = {http://sigport.org/1133},
author = {Guido; R.C. },
publisher = {IEEE SigPort},
title = {Two interesting and easy-to-follow tutorials: signal energy and ZCR},
year = {2016} }
TY - EJOUR
T1 - Two interesting and easy-to-follow tutorials: signal energy and ZCR
AU - Guido; R.C.
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1133
ER -
Guido, R.C.. (2016). Two interesting and easy-to-follow tutorials: signal energy and ZCR. IEEE SigPort. http://sigport.org/1133
Guido, R.C., 2016. Two interesting and easy-to-follow tutorials: signal energy and ZCR. Available at: http://sigport.org/1133.
Guido, R.C.. (2016). "Two interesting and easy-to-follow tutorials: signal energy and ZCR." Web.
1. Guido, R.C.. Two interesting and easy-to-follow tutorials: signal energy and ZCR [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1133

Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold


Parameter estimation using quantized observations is of importance in many practical applications. Under a symmetric 1-bit setup, consisting of a zero-threshold hard limiter, it is well known that the large sample performance loss for low signal-to-noise ratios (SNRs) is moderate (2/pi or -1.96dB). This makes low-complexity analog-to-digital converters (ADCs) with 1-bit resolution a promising solution for future wireless communications and signal processing devices.

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Authors:
Manuel Stein, Shahar Bar, Josef A. Nossek, Joseph Tabrikian
Submitted On:
10 April 2016 - 12:38am
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[1] Manuel Stein, Shahar Bar, Josef A. Nossek, Joseph Tabrikian, "Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1088. Accessed: Jul. 19, 2019.
@article{1088-16,
url = {http://sigport.org/1088},
author = {Manuel Stein; Shahar Bar; Josef A. Nossek; Joseph Tabrikian },
publisher = {IEEE SigPort},
title = {Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold},
year = {2016} }
TY - EJOUR
T1 - Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold
AU - Manuel Stein; Shahar Bar; Josef A. Nossek; Joseph Tabrikian
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1088
ER -
Manuel Stein, Shahar Bar, Josef A. Nossek, Joseph Tabrikian. (2016). Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold. IEEE SigPort. http://sigport.org/1088
Manuel Stein, Shahar Bar, Josef A. Nossek, Joseph Tabrikian, 2016. Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold. Available at: http://sigport.org/1088.
Manuel Stein, Shahar Bar, Josef A. Nossek, Joseph Tabrikian. (2016). "Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold." Web.
1. Manuel Stein, Shahar Bar, Josef A. Nossek, Joseph Tabrikian. Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1088

Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition

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Authors:
Frank Ong, Michael Lustig
Submitted On:
30 March 2016 - 11:35pm
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[1] Frank Ong, Michael Lustig, "Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1073. Accessed: Jul. 19, 2019.
@article{1073-16,
url = {http://sigport.org/1073},
author = {Frank Ong; Michael Lustig },
publisher = {IEEE SigPort},
title = {Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition},
year = {2016} }
TY - EJOUR
T1 - Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition
AU - Frank Ong; Michael Lustig
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1073
ER -
Frank Ong, Michael Lustig. (2016). Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition. IEEE SigPort. http://sigport.org/1073
Frank Ong, Michael Lustig, 2016. Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition. Available at: http://sigport.org/1073.
Frank Ong, Michael Lustig. (2016). "Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition." Web.
1. Frank Ong, Michael Lustig. Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1073

ON THE DETECTION OF NON-STATIONARY SIGNALS IN THE MATCHED SIGNAL TRANSFORM DOMAIN

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Authors:
Gabriel Vasile, Cornel Ioana, Remus Cacoveanu, Silviu Ciochina
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25 March 2016 - 7:01am
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[1] Gabriel Vasile, Cornel Ioana, Remus Cacoveanu, Silviu Ciochina, "ON THE DETECTION OF NON-STATIONARY SIGNALS IN THE MATCHED SIGNAL TRANSFORM DOMAIN", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1045. Accessed: Jul. 19, 2019.
@article{1045-16,
url = {http://sigport.org/1045},
author = {Gabriel Vasile; Cornel Ioana; Remus Cacoveanu; Silviu Ciochina },
publisher = {IEEE SigPort},
title = {ON THE DETECTION OF NON-STATIONARY SIGNALS IN THE MATCHED SIGNAL TRANSFORM DOMAIN},
year = {2016} }
TY - EJOUR
T1 - ON THE DETECTION OF NON-STATIONARY SIGNALS IN THE MATCHED SIGNAL TRANSFORM DOMAIN
AU - Gabriel Vasile; Cornel Ioana; Remus Cacoveanu; Silviu Ciochina
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1045
ER -
Gabriel Vasile, Cornel Ioana, Remus Cacoveanu, Silviu Ciochina. (2016). ON THE DETECTION OF NON-STATIONARY SIGNALS IN THE MATCHED SIGNAL TRANSFORM DOMAIN. IEEE SigPort. http://sigport.org/1045
Gabriel Vasile, Cornel Ioana, Remus Cacoveanu, Silviu Ciochina, 2016. ON THE DETECTION OF NON-STATIONARY SIGNALS IN THE MATCHED SIGNAL TRANSFORM DOMAIN. Available at: http://sigport.org/1045.
Gabriel Vasile, Cornel Ioana, Remus Cacoveanu, Silviu Ciochina. (2016). "ON THE DETECTION OF NON-STATIONARY SIGNALS IN THE MATCHED SIGNAL TRANSFORM DOMAIN." Web.
1. Gabriel Vasile, Cornel Ioana, Remus Cacoveanu, Silviu Ciochina. ON THE DETECTION OF NON-STATIONARY SIGNALS IN THE MATCHED SIGNAL TRANSFORM DOMAIN [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1045

Non-linear regression for bivariate self-similarity identification - application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts


Internet traffic monitoring is a crucial task for network security. Self-similarity, a key property for a relevant description of internet traffic statistics, has already been massively and successfully involved in anomaly detection. Self-similar analysis was however so far applied either to byte or Packet count time series independently, while both signals are jointly collected and technically deeply related. The present contribution elaborates on a recently proposed multivariate self-similar model, Operator fractional Brownian Motion (OfBm), to

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Authors:
Jordan Frecon , Romain Fontugne , Gustavo Didier , Nelly Pustelnik , Kensuke Fukuda , Patrice Abry
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24 March 2016 - 10:00am
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[1] Jordan Frecon , Romain Fontugne , Gustavo Didier , Nelly Pustelnik , Kensuke Fukuda , Patrice Abry, "Non-linear regression for bivariate self-similarity identification - application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1023. Accessed: Jul. 19, 2019.
@article{1023-16,
url = {http://sigport.org/1023},
author = {Jordan Frecon ; Romain Fontugne ; Gustavo Didier ; Nelly Pustelnik ; Kensuke Fukuda ; Patrice Abry },
publisher = {IEEE SigPort},
title = {Non-linear regression for bivariate self-similarity identification - application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts},
year = {2016} }
TY - EJOUR
T1 - Non-linear regression for bivariate self-similarity identification - application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts
AU - Jordan Frecon ; Romain Fontugne ; Gustavo Didier ; Nelly Pustelnik ; Kensuke Fukuda ; Patrice Abry
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1023
ER -
Jordan Frecon , Romain Fontugne , Gustavo Didier , Nelly Pustelnik , Kensuke Fukuda , Patrice Abry. (2016). Non-linear regression for bivariate self-similarity identification - application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts. IEEE SigPort. http://sigport.org/1023
Jordan Frecon , Romain Fontugne , Gustavo Didier , Nelly Pustelnik , Kensuke Fukuda , Patrice Abry, 2016. Non-linear regression for bivariate self-similarity identification - application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts. Available at: http://sigport.org/1023.
Jordan Frecon , Romain Fontugne , Gustavo Didier , Nelly Pustelnik , Kensuke Fukuda , Patrice Abry. (2016). "Non-linear regression for bivariate self-similarity identification - application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts." Web.
1. Jordan Frecon , Romain Fontugne , Gustavo Didier , Nelly Pustelnik , Kensuke Fukuda , Patrice Abry. Non-linear regression for bivariate self-similarity identification - application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1023

Distributed Estimation of Latent Parameters in State Space Models Using Separable Likelihoods


This work is a part of our research on scalable and/or distributed fusion and sensor calibration. We address parameter estimation in multi-sensor state space models which underpins surveillance applications with sensor networks. The parameter likelihood of the problem involves centralised Bayesian filtering of multi-sensor data, which lacks scalability with the number of sensors and induces a large communication load. We propose separable likelihoods which approximate the centralised likelihood with single sensor filtering terms.

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Authors:
Murat Uney, Bernard Mulgrew, Daniel E. Clark
Submitted On:
22 March 2016 - 4:44pm
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[1] Murat Uney, Bernard Mulgrew, Daniel E. Clark, "Distributed Estimation of Latent Parameters in State Space Models Using Separable Likelihoods", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/975. Accessed: Jul. 19, 2019.
@article{975-16,
url = {http://sigport.org/975},
author = {Murat Uney; Bernard Mulgrew; Daniel E. Clark },
publisher = {IEEE SigPort},
title = {Distributed Estimation of Latent Parameters in State Space Models Using Separable Likelihoods},
year = {2016} }
TY - EJOUR
T1 - Distributed Estimation of Latent Parameters in State Space Models Using Separable Likelihoods
AU - Murat Uney; Bernard Mulgrew; Daniel E. Clark
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/975
ER -
Murat Uney, Bernard Mulgrew, Daniel E. Clark. (2016). Distributed Estimation of Latent Parameters in State Space Models Using Separable Likelihoods. IEEE SigPort. http://sigport.org/975
Murat Uney, Bernard Mulgrew, Daniel E. Clark, 2016. Distributed Estimation of Latent Parameters in State Space Models Using Separable Likelihoods. Available at: http://sigport.org/975.
Murat Uney, Bernard Mulgrew, Daniel E. Clark. (2016). "Distributed Estimation of Latent Parameters in State Space Models Using Separable Likelihoods." Web.
1. Murat Uney, Bernard Mulgrew, Daniel E. Clark. Distributed Estimation of Latent Parameters in State Space Models Using Separable Likelihoods [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/975

Infrared Small Target Detection with Compressive Measurements


A novel scheme for infrared small target detection in compressive domain is presented. First, the original image is separated into two components, i.e., the target and the background. Next, we compress them individually. Finally, the compressed target image is utilized to construct the corresponding compressive detector to perform detection in compressive domain.

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Authors:
Lijuan Xie, Li Kang, Jingxiong Huang
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21 March 2016 - 10:03pm
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[1] Lijuan Xie, Li Kang, Jingxiong Huang, "Infrared Small Target Detection with Compressive Measurements", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/948. Accessed: Jul. 19, 2019.
@article{948-16,
url = {http://sigport.org/948},
author = {Lijuan Xie; Li Kang; Jingxiong Huang },
publisher = {IEEE SigPort},
title = {Infrared Small Target Detection with Compressive Measurements},
year = {2016} }
TY - EJOUR
T1 - Infrared Small Target Detection with Compressive Measurements
AU - Lijuan Xie; Li Kang; Jingxiong Huang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/948
ER -
Lijuan Xie, Li Kang, Jingxiong Huang. (2016). Infrared Small Target Detection with Compressive Measurements. IEEE SigPort. http://sigport.org/948
Lijuan Xie, Li Kang, Jingxiong Huang, 2016. Infrared Small Target Detection with Compressive Measurements. Available at: http://sigport.org/948.
Lijuan Xie, Li Kang, Jingxiong Huang. (2016). "Infrared Small Target Detection with Compressive Measurements." Web.
1. Lijuan Xie, Li Kang, Jingxiong Huang. Infrared Small Target Detection with Compressive Measurements [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/948

ON THE NULL SPACE CONSTANT FOR LP MINIMIZATION

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Authors:
Laming Chen, Yuantao Gu
Submitted On:
21 March 2016 - 11:09am
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[1] Laming Chen, Yuantao Gu, "ON THE NULL SPACE CONSTANT FOR LP MINIMIZATION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/928. Accessed: Jul. 19, 2019.
@article{928-16,
url = {http://sigport.org/928},
author = {Laming Chen; Yuantao Gu },
publisher = {IEEE SigPort},
title = {ON THE NULL SPACE CONSTANT FOR LP MINIMIZATION},
year = {2016} }
TY - EJOUR
T1 - ON THE NULL SPACE CONSTANT FOR LP MINIMIZATION
AU - Laming Chen; Yuantao Gu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/928
ER -
Laming Chen, Yuantao Gu. (2016). ON THE NULL SPACE CONSTANT FOR LP MINIMIZATION. IEEE SigPort. http://sigport.org/928
Laming Chen, Yuantao Gu, 2016. ON THE NULL SPACE CONSTANT FOR LP MINIMIZATION. Available at: http://sigport.org/928.
Laming Chen, Yuantao Gu. (2016). "ON THE NULL SPACE CONSTANT FOR LP MINIMIZATION." Web.
1. Laming Chen, Yuantao Gu. ON THE NULL SPACE CONSTANT FOR LP MINIMIZATION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/928

Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed

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Authors:
François Auger, Patrick Flandrin
Submitted On:
21 March 2016 - 5:51am
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[1] François Auger, Patrick Flandrin, "Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/919. Accessed: Jul. 19, 2019.
@article{919-16,
url = {http://sigport.org/919},
author = {François Auger; Patrick Flandrin },
publisher = {IEEE SigPort},
title = {Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed},
year = {2016} }
TY - EJOUR
T1 - Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed
AU - François Auger; Patrick Flandrin
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/919
ER -
François Auger, Patrick Flandrin. (2016). Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed. IEEE SigPort. http://sigport.org/919
François Auger, Patrick Flandrin, 2016. Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed. Available at: http://sigport.org/919.
François Auger, Patrick Flandrin. (2016). "Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed." Web.
1. François Auger, Patrick Flandrin. Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/919

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