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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.

Kernel-Based Learning for Smart Inverter Control


Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation. Smart inverters have been advocated as a fast-responding means to regulate voltage and minimize ohmic losses. Since optimal inverter coordination may be computationally challenging and preset local control rules are subpar, the approach of customized control rules designed in a quasi-static fashion features as a golden middle. Departing from affine control rules, this work puts forth non-linear inverter control policies.

Paper Details

Authors:
Aditie Garg, Mana Jalali, Vassilis Kekatos
Submitted On:
27 November 2018 - 4:30am
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presentation-kekatos.pdf

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[1] Aditie Garg, Mana Jalali, Vassilis Kekatos, "Kernel-Based Learning for Smart Inverter Control", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3808. Accessed: Dec. 16, 2018.
@article{3808-18,
url = {http://sigport.org/3808},
author = {Aditie Garg; Mana Jalali; Vassilis Kekatos },
publisher = {IEEE SigPort},
title = {Kernel-Based Learning for Smart Inverter Control},
year = {2018} }
TY - EJOUR
T1 - Kernel-Based Learning for Smart Inverter Control
AU - Aditie Garg; Mana Jalali; Vassilis Kekatos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3808
ER -
Aditie Garg, Mana Jalali, Vassilis Kekatos. (2018). Kernel-Based Learning for Smart Inverter Control. IEEE SigPort. http://sigport.org/3808
Aditie Garg, Mana Jalali, Vassilis Kekatos, 2018. Kernel-Based Learning for Smart Inverter Control. Available at: http://sigport.org/3808.
Aditie Garg, Mana Jalali, Vassilis Kekatos. (2018). "Kernel-Based Learning for Smart Inverter Control." Web.
1. Aditie Garg, Mana Jalali, Vassilis Kekatos. Kernel-Based Learning for Smart Inverter Control [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3808

Fast phase-difference-based DoA estimation using random ferns


Direction of arrival (DOA) information of a signal is important in communications, localization, object tracking and so on. Frequency-domain-based time-delay estimation is capable of achieving DOA in subsample accuracy; however, it suffers from the phase wrapping problem. In this paper, a frequency-diversity based method is proposed to overcome the phase wrapping problem. Inspired by the machine learning technique of random ferns, an algorithm is proposed to speed up the search procedure.

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Authors:
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri
Submitted On:
27 November 2018 - 3:31am
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GlobalSIP_poster.pdf

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[1] Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri, "Fast phase-difference-based DoA estimation using random ferns", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3807. Accessed: Dec. 16, 2018.
@article{3807-18,
url = {http://sigport.org/3807},
author = {Hui Chen; Tarig Ballal; Tareq Y. Al-Naffouri },
publisher = {IEEE SigPort},
title = {Fast phase-difference-based DoA estimation using random ferns},
year = {2018} }
TY - EJOUR
T1 - Fast phase-difference-based DoA estimation using random ferns
AU - Hui Chen; Tarig Ballal; Tareq Y. Al-Naffouri
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3807
ER -
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri. (2018). Fast phase-difference-based DoA estimation using random ferns. IEEE SigPort. http://sigport.org/3807
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri, 2018. Fast phase-difference-based DoA estimation using random ferns. Available at: http://sigport.org/3807.
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri. (2018). "Fast phase-difference-based DoA estimation using random ferns." Web.
1. Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri. Fast phase-difference-based DoA estimation using random ferns [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3807

GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION

Paper Details

Authors:
Xiaoming Tao, Mai Xu, Chaoyi Han, Jianhua Lu
Submitted On:
27 November 2018 - 2:22am
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段一平GlobalSIP_poster_final.pdf

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[1] Xiaoming Tao, Mai Xu, Chaoyi Han, Jianhua Lu, "GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3806. Accessed: Dec. 16, 2018.
@article{3806-18,
url = {http://sigport.org/3806},
author = {Xiaoming Tao; Mai Xu; Chaoyi Han; Jianhua Lu },
publisher = {IEEE SigPort},
title = {GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION},
year = {2018} }
TY - EJOUR
T1 - GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION
AU - Xiaoming Tao; Mai Xu; Chaoyi Han; Jianhua Lu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3806
ER -
Xiaoming Tao, Mai Xu, Chaoyi Han, Jianhua Lu. (2018). GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION. IEEE SigPort. http://sigport.org/3806
Xiaoming Tao, Mai Xu, Chaoyi Han, Jianhua Lu, 2018. GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION. Available at: http://sigport.org/3806.
Xiaoming Tao, Mai Xu, Chaoyi Han, Jianhua Lu. (2018). "GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION." Web.
1. Xiaoming Tao, Mai Xu, Chaoyi Han, Jianhua Lu. GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3806

GlobalSIP 2018 Poster: A Low-Complexity LS Turbo Channel Estimation Technique for MU-MIMO Systems

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Submitted On:
27 November 2018 - 2:05am
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[1] , "GlobalSIP 2018 Poster: A Low-Complexity LS Turbo Channel Estimation Technique for MU-MIMO Systems", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3805. Accessed: Dec. 16, 2018.
@article{3805-18,
url = {http://sigport.org/3805},
author = { },
publisher = {IEEE SigPort},
title = {GlobalSIP 2018 Poster: A Low-Complexity LS Turbo Channel Estimation Technique for MU-MIMO Systems},
year = {2018} }
TY - EJOUR
T1 - GlobalSIP 2018 Poster: A Low-Complexity LS Turbo Channel Estimation Technique for MU-MIMO Systems
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3805
ER -
. (2018). GlobalSIP 2018 Poster: A Low-Complexity LS Turbo Channel Estimation Technique for MU-MIMO Systems. IEEE SigPort. http://sigport.org/3805
, 2018. GlobalSIP 2018 Poster: A Low-Complexity LS Turbo Channel Estimation Technique for MU-MIMO Systems. Available at: http://sigport.org/3805.
. (2018). "GlobalSIP 2018 Poster: A Low-Complexity LS Turbo Channel Estimation Technique for MU-MIMO Systems." Web.
1. . GlobalSIP 2018 Poster: A Low-Complexity LS Turbo Channel Estimation Technique for MU-MIMO Systems [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3805

Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs

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Authors:
Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic
Submitted On:
27 November 2018 - 1:27am
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[1] Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic, "Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3804. Accessed: Dec. 16, 2018.
@article{3804-18,
url = {http://sigport.org/3804},
author = {Anuva Kulkarni; Franz Franchetti; Jelena Kovacevic },
publisher = {IEEE SigPort},
title = {Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs},
year = {2018} }
TY - EJOUR
T1 - Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs
AU - Anuva Kulkarni; Franz Franchetti; Jelena Kovacevic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3804
ER -
Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic. (2018). Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs. IEEE SigPort. http://sigport.org/3804
Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic, 2018. Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs. Available at: http://sigport.org/3804.
Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic. (2018). "Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs." Web.
1. Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic. Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3804

Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation


Neural spiking responses are generated by both extrinsic covariates such as sensory variables and intrinsic covariates such as those rep-resenting the state of a system. Although the external covariates can be directly controlled or measured; the internal factors are hard, if not impossible, to control or even observe. This study provides a statistical framework that enables characterization of the unobserved factors controlling neuronal response variability induced by behavior, with the model parameters fitted directly to real spiking data.

Paper Details

Authors:
Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh
Submitted On:
26 November 2018 - 11:32pm
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Niknam

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[1] Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh, "Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3803. Accessed: Dec. 16, 2018.
@article{3803-18,
url = {http://sigport.org/3803},
author = {Kaiser Niknam; Amir Akbarian; Behrad Noudoost; Neda Nategh },
publisher = {IEEE SigPort},
title = {Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation},
year = {2018} }
TY - EJOUR
T1 - Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation
AU - Kaiser Niknam; Amir Akbarian; Behrad Noudoost; Neda Nategh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3803
ER -
Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh. (2018). Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation. IEEE SigPort. http://sigport.org/3803
Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh, 2018. Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation. Available at: http://sigport.org/3803.
Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh. (2018). "Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation." Web.
1. Kaiser Niknam, Amir Akbarian, Behrad Noudoost, Neda Nategh. Characterizing unobserved factors driving local field potential dynamics underlying a time-varying spike generation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3803

Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs

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Authors:
Madeleine S. Kotzagiannidis, Mike E. Davies
Submitted On:
8 December 2018 - 1:41pm
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[1] Madeleine S. Kotzagiannidis, Mike E. Davies, "Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3802. Accessed: Dec. 16, 2018.
@article{3802-18,
url = {http://sigport.org/3802},
author = {Madeleine S. Kotzagiannidis; Mike E. Davies },
publisher = {IEEE SigPort},
title = {Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs},
year = {2018} }
TY - EJOUR
T1 - Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs
AU - Madeleine S. Kotzagiannidis; Mike E. Davies
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3802
ER -
Madeleine S. Kotzagiannidis, Mike E. Davies. (2018). Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs. IEEE SigPort. http://sigport.org/3802
Madeleine S. Kotzagiannidis, Mike E. Davies, 2018. Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs. Available at: http://sigport.org/3802.
Madeleine S. Kotzagiannidis, Mike E. Davies. (2018). "Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs." Web.
1. Madeleine S. Kotzagiannidis, Mike E. Davies. Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3802

Predicting Power Outages Using Graph Neural Networks


Power outages have a major impact on economic development due to the dependence of (virtually all) productive sectors on electric power. Thus, many resources within the scientific and engineering communities have been employed to improve the efficiency and reliability of power grids. In particular, we consider the problem of predicting power outages based on the current weather conditions. Weather measurements taken by a sensor network naturally fit within the graph signal processing framework since the measurements are related by the relative position of the sensors.

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Authors:
Damian Owerko, Fernando Gama, Alejandro Ribeiro
Submitted On:
26 November 2018 - 10:11pm
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[1] Damian Owerko, Fernando Gama, Alejandro Ribeiro, "Predicting Power Outages Using Graph Neural Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3800. Accessed: Dec. 16, 2018.
@article{3800-18,
url = {http://sigport.org/3800},
author = {Damian Owerko; Fernando Gama; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Predicting Power Outages Using Graph Neural Networks},
year = {2018} }
TY - EJOUR
T1 - Predicting Power Outages Using Graph Neural Networks
AU - Damian Owerko; Fernando Gama; Alejandro Ribeiro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3800
ER -
Damian Owerko, Fernando Gama, Alejandro Ribeiro. (2018). Predicting Power Outages Using Graph Neural Networks. IEEE SigPort. http://sigport.org/3800
Damian Owerko, Fernando Gama, Alejandro Ribeiro, 2018. Predicting Power Outages Using Graph Neural Networks. Available at: http://sigport.org/3800.
Damian Owerko, Fernando Gama, Alejandro Ribeiro. (2018). "Predicting Power Outages Using Graph Neural Networks." Web.
1. Damian Owerko, Fernando Gama, Alejandro Ribeiro. Predicting Power Outages Using Graph Neural Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3800

Fast phase-difference-based DoA estimation using random ferns

Paper Details

Authors:
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri
Submitted On:
27 November 2018 - 3:31am
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GlobalSIP_poster.pdf

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[1] Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri, "Fast phase-difference-based DoA estimation using random ferns", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3798. Accessed: Dec. 16, 2018.
@article{3798-18,
url = {http://sigport.org/3798},
author = {Hui Chen; Tarig Ballal; Tareq Y. Al-Naffouri },
publisher = {IEEE SigPort},
title = {Fast phase-difference-based DoA estimation using random ferns},
year = {2018} }
TY - EJOUR
T1 - Fast phase-difference-based DoA estimation using random ferns
AU - Hui Chen; Tarig Ballal; Tareq Y. Al-Naffouri
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3798
ER -
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri. (2018). Fast phase-difference-based DoA estimation using random ferns. IEEE SigPort. http://sigport.org/3798
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri, 2018. Fast phase-difference-based DoA estimation using random ferns. Available at: http://sigport.org/3798.
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri. (2018). "Fast phase-difference-based DoA estimation using random ferns." Web.
1. Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri. Fast phase-difference-based DoA estimation using random ferns [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3798

PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES

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Authors:
Hanwei Wu, Qiwen Wang, Markus Flierl
Submitted On:
27 November 2018 - 6:31pm
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[1] Hanwei Wu, Qiwen Wang, Markus Flierl, " PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3796. Accessed: Dec. 16, 2018.
@article{3796-18,
url = {http://sigport.org/3796},
author = {Hanwei Wu; Qiwen Wang; Markus Flierl },
publisher = {IEEE SigPort},
title = { PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES},
year = {2018} }
TY - EJOUR
T1 - PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES
AU - Hanwei Wu; Qiwen Wang; Markus Flierl
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3796
ER -
Hanwei Wu, Qiwen Wang, Markus Flierl. (2018). PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES. IEEE SigPort. http://sigport.org/3796
Hanwei Wu, Qiwen Wang, Markus Flierl, 2018. PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES. Available at: http://sigport.org/3796.
Hanwei Wu, Qiwen Wang, Markus Flierl. (2018). " PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES." Web.
1. Hanwei Wu, Qiwen Wang, Markus Flierl. PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3796

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