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GlobalSIP 2018

The 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP)  focuses on signal and information processing with an emphasis on up-and-coming signal processing themes. The conference features world-class plenary speeches, distinguished symposium talks, tutorials, exhibits, oral and poster sessions, and panels. GlobalSIP is comprised of co-located General Symposium and symposia selected based on responses to the call-for-symposia proposals.

A Generalizable Model for Seizure Prediction based on Deep Learning using CNN-LSTM Architecture

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Authors:
Mohamad Shahbazi, Hamid K. Aghajan
Submitted On:
20 November 2018 - 11:16am
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Presentation Slides

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[1] Mohamad Shahbazi, Hamid K. Aghajan, "A Generalizable Model for Seizure Prediction based on Deep Learning using CNN-LSTM Architecture", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3678. Accessed: Dec. 16, 2018.
@article{3678-18,
url = {http://sigport.org/3678},
author = {Mohamad Shahbazi; Hamid K. Aghajan },
publisher = {IEEE SigPort},
title = {A Generalizable Model for Seizure Prediction based on Deep Learning using CNN-LSTM Architecture},
year = {2018} }
TY - EJOUR
T1 - A Generalizable Model for Seizure Prediction based on Deep Learning using CNN-LSTM Architecture
AU - Mohamad Shahbazi; Hamid K. Aghajan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3678
ER -
Mohamad Shahbazi, Hamid K. Aghajan. (2018). A Generalizable Model for Seizure Prediction based on Deep Learning using CNN-LSTM Architecture. IEEE SigPort. http://sigport.org/3678
Mohamad Shahbazi, Hamid K. Aghajan, 2018. A Generalizable Model for Seizure Prediction based on Deep Learning using CNN-LSTM Architecture. Available at: http://sigport.org/3678.
Mohamad Shahbazi, Hamid K. Aghajan. (2018). "A Generalizable Model for Seizure Prediction based on Deep Learning using CNN-LSTM Architecture." Web.
1. Mohamad Shahbazi, Hamid K. Aghajan. A Generalizable Model for Seizure Prediction based on Deep Learning using CNN-LSTM Architecture [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3678

Sketching Discrete Valued Sparse Matrices


The problem of recovering a sparse matrix X from its sketchAXB T is referred to as the matrix sketching problem. Typically, the sketch is a lower dimensional matrix compared to X, and the sketching matrices A and B are known. Matrix sketching algorithms have been developed in the past to recover matrices from a continuous valued vectorspace (e.g., R^N×N ). However, employing such algorithms to recover discrete valued matrices may not be optimal. In this paper, we propose two novel algorithms that can efficiently recover a discrete valued sparse matrix from its sketch.

slides.pdf

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18 November 2018 - 12:01pm
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[1] , "Sketching Discrete Valued Sparse Matrices", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3677. Accessed: Dec. 16, 2018.
@article{3677-18,
url = {http://sigport.org/3677},
author = { },
publisher = {IEEE SigPort},
title = {Sketching Discrete Valued Sparse Matrices},
year = {2018} }
TY - EJOUR
T1 - Sketching Discrete Valued Sparse Matrices
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3677
ER -
. (2018). Sketching Discrete Valued Sparse Matrices. IEEE SigPort. http://sigport.org/3677
, 2018. Sketching Discrete Valued Sparse Matrices. Available at: http://sigport.org/3677.
. (2018). "Sketching Discrete Valued Sparse Matrices." Web.
1. . Sketching Discrete Valued Sparse Matrices [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3677

Three-dimentional Convolution Neural Network based Encrypted Traffic Classifier for Wireless Communications


Network traffic classification, working by associating traffic flows with specific categories or intruders, plays an important role in network management and security. For network traffic classification in wireless communications, the major challenge is encrypted data. Researchers are usually not authorized to get inner information of the traffic flows, and have to analyze traffic features. Machine learning algorithms are widely used as classifiers, and represent learning makes feature extraction more accurate by avoiding manual operation.

Ran-ppt.pdf

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Authors:
Jing Ran,Yexin Chen,Shulan Li
Submitted On:
18 November 2018 - 2:48pm
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[1] Jing Ran,Yexin Chen,Shulan Li, "Three-dimentional Convolution Neural Network based Encrypted Traffic Classifier for Wireless Communications", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3675. Accessed: Dec. 16, 2018.
@article{3675-18,
url = {http://sigport.org/3675},
author = {Jing Ran;Yexin Chen;Shulan Li },
publisher = {IEEE SigPort},
title = {Three-dimentional Convolution Neural Network based Encrypted Traffic Classifier for Wireless Communications},
year = {2018} }
TY - EJOUR
T1 - Three-dimentional Convolution Neural Network based Encrypted Traffic Classifier for Wireless Communications
AU - Jing Ran;Yexin Chen;Shulan Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3675
ER -
Jing Ran,Yexin Chen,Shulan Li. (2018). Three-dimentional Convolution Neural Network based Encrypted Traffic Classifier for Wireless Communications. IEEE SigPort. http://sigport.org/3675
Jing Ran,Yexin Chen,Shulan Li, 2018. Three-dimentional Convolution Neural Network based Encrypted Traffic Classifier for Wireless Communications. Available at: http://sigport.org/3675.
Jing Ran,Yexin Chen,Shulan Li. (2018). "Three-dimentional Convolution Neural Network based Encrypted Traffic Classifier for Wireless Communications." Web.
1. Jing Ran,Yexin Chen,Shulan Li. Three-dimentional Convolution Neural Network based Encrypted Traffic Classifier for Wireless Communications [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3675

Sequential Closed-Form Semiblind Receiver for Space-Time Coded Multihop Relaying Systems


In this letter, we present a sequential closed-form
semiblind receiver for a one-way multihop amplify-and-forward
relaying system. Assuming Khatri–Rao space-time coding at each
relay, it is shown that the system with K relays can be modeled
by means of a generalized nested PARAFAC model. Decomposing
this model intoK + 1 third-order PARAFAC models, we develop
a closed-form semiblind receiver for jointly estimating the information
symbols and the individual channels, at the destination node.

Paper Details

Authors:
Walter da C. Freitas Jr., Gérard Favier , and André L. F. de Almeida
Submitted On:
19 November 2018 - 8:08am
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globalsip2018_v03.pdf

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[1] Walter da C. Freitas Jr., Gérard Favier , and André L. F. de Almeida, "Sequential Closed-Form Semiblind Receiver for Space-Time Coded Multihop Relaying Systems", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3674. Accessed: Dec. 16, 2018.
@article{3674-18,
url = {http://sigport.org/3674},
author = {Walter da C. Freitas Jr.; Gérard Favier ; and André L. F. de Almeida },
publisher = {IEEE SigPort},
title = {Sequential Closed-Form Semiblind Receiver for Space-Time Coded Multihop Relaying Systems},
year = {2018} }
TY - EJOUR
T1 - Sequential Closed-Form Semiblind Receiver for Space-Time Coded Multihop Relaying Systems
AU - Walter da C. Freitas Jr.; Gérard Favier ; and André L. F. de Almeida
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3674
ER -
Walter da C. Freitas Jr., Gérard Favier , and André L. F. de Almeida. (2018). Sequential Closed-Form Semiblind Receiver for Space-Time Coded Multihop Relaying Systems. IEEE SigPort. http://sigport.org/3674
Walter da C. Freitas Jr., Gérard Favier , and André L. F. de Almeida, 2018. Sequential Closed-Form Semiblind Receiver for Space-Time Coded Multihop Relaying Systems. Available at: http://sigport.org/3674.
Walter da C. Freitas Jr., Gérard Favier , and André L. F. de Almeida. (2018). "Sequential Closed-Form Semiblind Receiver for Space-Time Coded Multihop Relaying Systems." Web.
1. Walter da C. Freitas Jr., Gérard Favier , and André L. F. de Almeida. Sequential Closed-Form Semiblind Receiver for Space-Time Coded Multihop Relaying Systems [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3674

Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications


In order to realize the vision of intelligent connected vehicles, it is necessary to model the vehicle-to-vehicle (V2V) channels in various realistic environments, especially when the line-of-sight (LOS) between transmitter (Tx) and receiver (Rx) is obstructed. In this paper, we model obstructed vehicle-to-vehicle (V2V) channels for the 5-GHz band through measurement-validated ray-tracing (RT) simulations. To begin, we establish a realistic V2V RT simulator through integrating three key channel features: small-scale structures (e.g.

Paper Details

Authors:
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner
Submitted On:
18 November 2018 - 2:17am
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Obstructed V2V channel modeling for intelligent vehicular communications_BJTU.pdf

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[1] Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner, "Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3673. Accessed: Dec. 16, 2018.
@article{3673-18,
url = {http://sigport.org/3673},
author = {Ke Guan; Bo Ai; Danping He; David W. Matolak; Qi Wang; Zhangdui Zhong; Thomas Kuerner },
publisher = {IEEE SigPort},
title = {Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications},
year = {2018} }
TY - EJOUR
T1 - Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications
AU - Ke Guan; Bo Ai; Danping He; David W. Matolak; Qi Wang; Zhangdui Zhong; Thomas Kuerner
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3673
ER -
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner. (2018). Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications. IEEE SigPort. http://sigport.org/3673
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner, 2018. Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications. Available at: http://sigport.org/3673.
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner. (2018). "Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications." Web.
1. Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner. Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3673

Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays


This work addresses the problem of Port-Starboard (PS) beamforming for low-frequency active sonar (LFAS) with a triplet receiver array.

The work presents a new algorithm for sub-bands beam-space adaptive beamforming with twist compensation and evaluates its performance with experimental data collected at sea.

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Authors:
Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa
Submitted On:
17 November 2018 - 5:20am
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Adaptive Beamformer

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[1] Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa, "Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3672. Accessed: Dec. 16, 2018.
@article{3672-18,
url = {http://sigport.org/3672},
author = {Alessandra Tesei; Alain Maguer; Fabrizio Ferraioli; Valerio Latini; Luca Pesa },
publisher = {IEEE SigPort},
title = {Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays},
year = {2018} }
TY - EJOUR
T1 - Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays
AU - Alessandra Tesei; Alain Maguer; Fabrizio Ferraioli; Valerio Latini; Luca Pesa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3672
ER -
Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa. (2018). Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays. IEEE SigPort. http://sigport.org/3672
Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa, 2018. Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays. Available at: http://sigport.org/3672.
Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa. (2018). "Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays." Web.
1. Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa. Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3672

l0-norm Feature LMS Algorithms


A class of algorithms known as feature least-mean-square (FLMS) has been proposed recently to exploit hidden sparsity

Paper Details

Authors:
Hamed Yazdanpanah, José Antonio Apolinário Jr., Paulo Sergio Ramirez Diniz, Markus Vinicius Santos Lima
Submitted On:
16 November 2018 - 4:29pm
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PosterGlobalSIP_2018.pdf

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[1] Hamed Yazdanpanah, José Antonio Apolinário Jr., Paulo Sergio Ramirez Diniz, Markus Vinicius Santos Lima, "l0-norm Feature LMS Algorithms", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3671. Accessed: Dec. 16, 2018.
@article{3671-18,
url = {http://sigport.org/3671},
author = {Hamed Yazdanpanah; José Antonio Apolinário Jr.; Paulo Sergio Ramirez Diniz; Markus Vinicius Santos Lima },
publisher = {IEEE SigPort},
title = {l0-norm Feature LMS Algorithms},
year = {2018} }
TY - EJOUR
T1 - l0-norm Feature LMS Algorithms
AU - Hamed Yazdanpanah; José Antonio Apolinário Jr.; Paulo Sergio Ramirez Diniz; Markus Vinicius Santos Lima
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3671
ER -
Hamed Yazdanpanah, José Antonio Apolinário Jr., Paulo Sergio Ramirez Diniz, Markus Vinicius Santos Lima. (2018). l0-norm Feature LMS Algorithms. IEEE SigPort. http://sigport.org/3671
Hamed Yazdanpanah, José Antonio Apolinário Jr., Paulo Sergio Ramirez Diniz, Markus Vinicius Santos Lima, 2018. l0-norm Feature LMS Algorithms. Available at: http://sigport.org/3671.
Hamed Yazdanpanah, José Antonio Apolinário Jr., Paulo Sergio Ramirez Diniz, Markus Vinicius Santos Lima. (2018). "l0-norm Feature LMS Algorithms." Web.
1. Hamed Yazdanpanah, José Antonio Apolinário Jr., Paulo Sergio Ramirez Diniz, Markus Vinicius Santos Lima. l0-norm Feature LMS Algorithms [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3671

Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence


Most existing work in designing sensing matrices for compressive recovery is based on optimizing some quality factor, such as mutual coherence, average coherence or the restricted isometry constant (RIC), of the sensing matrix. In this paper, we report anomalous results that show that such a design is not always guaranteed to improve reconstruction results.

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Authors:
Dhruv Shah, Alankar Kotwal, Ajit Rajwade
Submitted On:
20 November 2018 - 2:42am
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globalsip2018_poster_v3.pdf

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[1] Dhruv Shah, Alankar Kotwal, Ajit Rajwade, "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3670. Accessed: Dec. 16, 2018.
@article{3670-18,
url = {http://sigport.org/3670},
author = {Dhruv Shah; Alankar Kotwal; Ajit Rajwade },
publisher = {IEEE SigPort},
title = {Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence},
year = {2018} }
TY - EJOUR
T1 - Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence
AU - Dhruv Shah; Alankar Kotwal; Ajit Rajwade
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3670
ER -
Dhruv Shah, Alankar Kotwal, Ajit Rajwade. (2018). Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence. IEEE SigPort. http://sigport.org/3670
Dhruv Shah, Alankar Kotwal, Ajit Rajwade, 2018. Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence. Available at: http://sigport.org/3670.
Dhruv Shah, Alankar Kotwal, Ajit Rajwade. (2018). "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence." Web.
1. Dhruv Shah, Alankar Kotwal, Ajit Rajwade. Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3670

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