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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.

Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series


Learning the dynamics of complex systems features a large
number of applications in data science. Graph-based modeling
and inference underpins the most prominent family of
approaches to learn complex dynamics due to their ability to
capture the intrinsic sparsity of direct interactions in such systems.
They also provide the user with interpretable graphs
that unveil behavioral patterns and changes. To cope with
the time-varying nature of interactions, this paper develops
an estimation criterion and a solver to learn the parameters

Paper Details

Authors:
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano
Submitted On:
11 December 2018 - 4:54am
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Dynamic network identification poster

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[1] Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano, "Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3841. Accessed: Dec. 16, 2018.
@article{3841-18,
url = {http://sigport.org/3841},
author = {Daniel Romero; Bakht Zaman; Baltasar Beferull-Lozano },
publisher = {IEEE SigPort},
title = {Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series},
year = {2018} }
TY - EJOUR
T1 - Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series
AU - Daniel Romero; Bakht Zaman; Baltasar Beferull-Lozano
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3841
ER -
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. (2018). Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series. IEEE SigPort. http://sigport.org/3841
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano, 2018. Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series. Available at: http://sigport.org/3841.
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. (2018). "Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series." Web.
1. Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3841

Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing


In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data.

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Authors:
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi
Submitted On:
4 December 2018 - 11:04pm
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GlobalSIP2018_talk.pdf

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[1] Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi, "Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3840. Accessed: Dec. 16, 2018.
@article{3840-18,
url = {http://sigport.org/3840},
author = {Gonzalo Cucho-Padin; Yue Wang; Lara Waldrop; Zhi Tian; Farzad Kamalabadi },
publisher = {IEEE SigPort},
title = {Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing},
year = {2018} }
TY - EJOUR
T1 - Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing
AU - Gonzalo Cucho-Padin; Yue Wang; Lara Waldrop; Zhi Tian; Farzad Kamalabadi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3840
ER -
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. (2018). Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing. IEEE SigPort. http://sigport.org/3840
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi, 2018. Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing. Available at: http://sigport.org/3840.
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. (2018). "Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing." Web.
1. Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3840

Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods


Compressing unstructured mesh data from computer simulations poses several challenges that are not encountered in the compression of images or videos. Since the spatial locations of the points are not on a regular grid, as in an image, it is difficult to identify near neighbors of a point whose values can be exploited for compression.

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Authors:
Chandrika Kamath, Ya Ju Fan
Submitted On:
3 December 2018 - 5:12pm
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Kamath_ComparingCompression_final.pdf

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[1] Chandrika Kamath, Ya Ju Fan, "Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3839. Accessed: Dec. 16, 2018.
@article{3839-18,
url = {http://sigport.org/3839},
author = {Chandrika Kamath; Ya Ju Fan },
publisher = {IEEE SigPort},
title = {Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods},
year = {2018} }
TY - EJOUR
T1 - Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods
AU - Chandrika Kamath; Ya Ju Fan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3839
ER -
Chandrika Kamath, Ya Ju Fan. (2018). Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods. IEEE SigPort. http://sigport.org/3839
Chandrika Kamath, Ya Ju Fan, 2018. Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods. Available at: http://sigport.org/3839.
Chandrika Kamath, Ya Ju Fan. (2018). "Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods." Web.
1. Chandrika Kamath, Ya Ju Fan. Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3839

TRANSMISSION DESIGN FOR A JOINT MIMO RADAR AND MU-MIMO DOWNLINK COMMUNICATION SYSTEM


We study a cooperative transmission scheme for a joint multiple-input-multiple-output (MIMO) radar and multi-user (MU) MIMO downlink communication system, where both systems operate on the same frequency band simultaneously. Maximization of the total weighted system mutual information or sum rate is considered with the presence of an extended target and environmental clutter. An alternating optimization based iterative algorithm is proposed to find the transmit covariance matrices for both radar and communication applications.

GlobalSIP.pdf

PDF icon GlobalSIP.pdf (529 downloads)

Paper Details

Authors:
Mohammad Saquib
Submitted On:
2 December 2018 - 6:45pm
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GlobalSIP.pdf

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[1] Mohammad Saquib, "TRANSMISSION DESIGN FOR A JOINT MIMO RADAR AND MU-MIMO DOWNLINK COMMUNICATION SYSTEM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3838. Accessed: Dec. 16, 2018.
@article{3838-18,
url = {http://sigport.org/3838},
author = {Mohammad Saquib },
publisher = {IEEE SigPort},
title = {TRANSMISSION DESIGN FOR A JOINT MIMO RADAR AND MU-MIMO DOWNLINK COMMUNICATION SYSTEM},
year = {2018} }
TY - EJOUR
T1 - TRANSMISSION DESIGN FOR A JOINT MIMO RADAR AND MU-MIMO DOWNLINK COMMUNICATION SYSTEM
AU - Mohammad Saquib
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3838
ER -
Mohammad Saquib. (2018). TRANSMISSION DESIGN FOR A JOINT MIMO RADAR AND MU-MIMO DOWNLINK COMMUNICATION SYSTEM. IEEE SigPort. http://sigport.org/3838
Mohammad Saquib, 2018. TRANSMISSION DESIGN FOR A JOINT MIMO RADAR AND MU-MIMO DOWNLINK COMMUNICATION SYSTEM. Available at: http://sigport.org/3838.
Mohammad Saquib. (2018). "TRANSMISSION DESIGN FOR A JOINT MIMO RADAR AND MU-MIMO DOWNLINK COMMUNICATION SYSTEM." Web.
1. Mohammad Saquib. TRANSMISSION DESIGN FOR A JOINT MIMO RADAR AND MU-MIMO DOWNLINK COMMUNICATION SYSTEM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3838

SECURITY ISSUES IN SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS


To satisfy the increasing consumer demand for mobile data,
regulatory bodies have set forward to allow commercial
wireless systems to operate on spectrum bands that until
recently were reserved for military radar. Such co-existence
would require mechanisms for controlling the interference.
One such mechanism is to assign a precoder to the communication
system, designed to meet certain interference related
objectives. This paper looks into whether the implicit radar
information contained in such precoder can be exploited by

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Submitted On:
1 December 2018 - 10:38am
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Globalsip2018_Petropulu.pdf

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[1] , "SECURITY ISSUES IN SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3837. Accessed: Dec. 16, 2018.
@article{3837-18,
url = {http://sigport.org/3837},
author = { },
publisher = {IEEE SigPort},
title = {SECURITY ISSUES IN SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS},
year = {2018} }
TY - EJOUR
T1 - SECURITY ISSUES IN SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3837
ER -
. (2018). SECURITY ISSUES IN SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS. IEEE SigPort. http://sigport.org/3837
, 2018. SECURITY ISSUES IN SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS. Available at: http://sigport.org/3837.
. (2018). "SECURITY ISSUES IN SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS." Web.
1. . SECURITY ISSUES IN SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3837

Security in the Internet of Things: Information Theoretic Insights


The emerging Internet of Things (IoT) has several salient characteristics that differentiate it from existing wireless networking architectures. These include the deployment of very large numbers of (possibly) low-complexity terminals; the need for low-latency, short-packet communications (e.g., to support automation); light or no infrastructure; and primary applications of data gathering, inference and control.

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Submitted On:
30 November 2018 - 6:01pm
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globalsip18.pdf

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[1] , "Security in the Internet of Things: Information Theoretic Insights", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3836. Accessed: Dec. 16, 2018.
@article{3836-18,
url = {http://sigport.org/3836},
author = { },
publisher = {IEEE SigPort},
title = {Security in the Internet of Things: Information Theoretic Insights},
year = {2018} }
TY - EJOUR
T1 - Security in the Internet of Things: Information Theoretic Insights
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3836
ER -
. (2018). Security in the Internet of Things: Information Theoretic Insights. IEEE SigPort. http://sigport.org/3836
, 2018. Security in the Internet of Things: Information Theoretic Insights. Available at: http://sigport.org/3836.
. (2018). "Security in the Internet of Things: Information Theoretic Insights." Web.
1. . Security in the Internet of Things: Information Theoretic Insights [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3836

Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding


We describe a general framework for designing and embedding a fingerprint at the physical layer of a wireless network to achieve authentication with enhanced security and stealth. Fingerprint embedding is a key-aided process of superimposing a low-power tag to the primary message waveform for the purpose of authenticating the transmission. The tag is uniquely created from the message and key, and successful authentication is achieved when the correct tag is detected by the receiver.

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Submitted On:
10 December 2018 - 11:28am
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PHY Security Seminar GlobalSIP 18 27 Nov 2018.pdf

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[1] , "Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3835. Accessed: Dec. 16, 2018.
@article{3835-18,
url = {http://sigport.org/3835},
author = { },
publisher = {IEEE SigPort},
title = {Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding},
year = {2018} }
TY - EJOUR
T1 - Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3835
ER -
. (2018). Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding. IEEE SigPort. http://sigport.org/3835
, 2018. Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding. Available at: http://sigport.org/3835.
. (2018). "Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding." Web.
1. . Cryptographic Side-Channel Signaling and Authentication via Fingerprint Embedding [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3835

Self-Supervised Anomaly Detection for Narrowband SETI Presentation

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Authors:
Ki Hyun, Seungwoo Son, Steve Croft, Andrew Siemion
Submitted On:
29 November 2018 - 8:40pm
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zhang_globalsip2018.pdf

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[1] Ki Hyun, Seungwoo Son, Steve Croft, Andrew Siemion, "Self-Supervised Anomaly Detection for Narrowband SETI Presentation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3833. Accessed: Dec. 16, 2018.
@article{3833-18,
url = {http://sigport.org/3833},
author = {Ki Hyun; Seungwoo Son; Steve Croft; Andrew Siemion },
publisher = {IEEE SigPort},
title = {Self-Supervised Anomaly Detection for Narrowband SETI Presentation},
year = {2018} }
TY - EJOUR
T1 - Self-Supervised Anomaly Detection for Narrowband SETI Presentation
AU - Ki Hyun; Seungwoo Son; Steve Croft; Andrew Siemion
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3833
ER -
Ki Hyun, Seungwoo Son, Steve Croft, Andrew Siemion. (2018). Self-Supervised Anomaly Detection for Narrowband SETI Presentation. IEEE SigPort. http://sigport.org/3833
Ki Hyun, Seungwoo Son, Steve Croft, Andrew Siemion, 2018. Self-Supervised Anomaly Detection for Narrowband SETI Presentation. Available at: http://sigport.org/3833.
Ki Hyun, Seungwoo Son, Steve Croft, Andrew Siemion. (2018). "Self-Supervised Anomaly Detection for Narrowband SETI Presentation." Web.
1. Ki Hyun, Seungwoo Son, Steve Croft, Andrew Siemion. Self-Supervised Anomaly Detection for Narrowband SETI Presentation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3833

PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION


With the development of augmented reality, the delivery and storage of 3D content have become an important research area. Among the proposals for point cloud compression collected by MPEG, Apple’s Test Model Category 2 (TMC2) achieves the highest quality for 3D sequences under a bitrate constraint. However, the TMC2 framework is not spatially scalable. In this paper, we add interpolation compo- nents which make TMC2 suitable for flexible resolution. We apply a patch-aware averaging filter to eliminate most outliers which result from the interpolation.

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Submitted On:
29 November 2018 - 2:08pm
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GlobalSIP_Poster_revised.pdf

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[1] , "PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3832. Accessed: Dec. 16, 2018.
@article{3832-18,
url = {http://sigport.org/3832},
author = { },
publisher = {IEEE SigPort},
title = {PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION},
year = {2018} }
TY - EJOUR
T1 - PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3832
ER -
. (2018). PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION. IEEE SigPort. http://sigport.org/3832
, 2018. PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION. Available at: http://sigport.org/3832.
. (2018). "PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION." Web.
1. . PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3832

Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks

Paper Details

Authors:
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li
Submitted On:
29 November 2018 - 3:44am
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GlobalSIP_poster_Final.pdf

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[1] Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li, "Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3830. Accessed: Dec. 16, 2018.
@article{3830-18,
url = {http://sigport.org/3830},
author = {Jian Zheng; Yifan Wang; Xiaonan Zhang; Xiaohua Li },
publisher = {IEEE SigPort},
title = {Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks},
year = {2018} }
TY - EJOUR
T1 - Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks
AU - Jian Zheng; Yifan Wang; Xiaonan Zhang; Xiaohua Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3830
ER -
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li. (2018). Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks. IEEE SigPort. http://sigport.org/3830
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li, 2018. Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks. Available at: http://sigport.org/3830.
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li. (2018). "Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks." Web.
1. Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li. Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3830

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