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GlobalSIP 2019

The 7th 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 Worst Case Performance Optimization Based Design Approach to Robust Symbol Level Precoding for Downlink MU MIMO


This paper addresses the optimization problem of symbol-level precoding (SLP) in the downlink of a multiuser multiple-input multiple-output (MU-MIMO) wireless system while the precoder’s output is subject to partially-known distortions. In particular, we assume a linear distortion model with bounded additive noise. The original signal-to-interference-plus-noise ratio (SINR) -constrained SLP problem minimizing the total transmit power is first reformulated as a penalized unconstrained problem, which is referred to as the relaxed robust formulation. We

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Authors:
Shahram Shahbazpanahi, Bjorn Ottersten
Submitted On:
18 November 2019 - 1:04pm
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WCSLP_GSIP.pdf

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[1] Shahram Shahbazpanahi, Bjorn Ottersten, "A Worst Case Performance Optimization Based Design Approach to Robust Symbol Level Precoding for Downlink MU MIMO", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4960. Accessed: Dec. 07, 2019.
@article{4960-19,
url = {http://sigport.org/4960},
author = {Shahram Shahbazpanahi; Bjorn Ottersten },
publisher = {IEEE SigPort},
title = {A Worst Case Performance Optimization Based Design Approach to Robust Symbol Level Precoding for Downlink MU MIMO},
year = {2019} }
TY - EJOUR
T1 - A Worst Case Performance Optimization Based Design Approach to Robust Symbol Level Precoding for Downlink MU MIMO
AU - Shahram Shahbazpanahi; Bjorn Ottersten
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4960
ER -
Shahram Shahbazpanahi, Bjorn Ottersten. (2019). A Worst Case Performance Optimization Based Design Approach to Robust Symbol Level Precoding for Downlink MU MIMO. IEEE SigPort. http://sigport.org/4960
Shahram Shahbazpanahi, Bjorn Ottersten, 2019. A Worst Case Performance Optimization Based Design Approach to Robust Symbol Level Precoding for Downlink MU MIMO. Available at: http://sigport.org/4960.
Shahram Shahbazpanahi, Bjorn Ottersten. (2019). "A Worst Case Performance Optimization Based Design Approach to Robust Symbol Level Precoding for Downlink MU MIMO." Web.
1. Shahram Shahbazpanahi, Bjorn Ottersten. A Worst Case Performance Optimization Based Design Approach to Robust Symbol Level Precoding for Downlink MU MIMO [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4960

Bayesian Design of Sampling Set for Bandlimited Graph Signals


The design of sampling set (DoS) for bandlimited graph signals (GS) has been extensively studied in recent years, but few of them

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15 November 2019 - 8:03am
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[1] , "Bayesian Design of Sampling Set for Bandlimited Graph Signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4959. Accessed: Dec. 07, 2019.
@article{4959-19,
url = {http://sigport.org/4959},
author = { },
publisher = {IEEE SigPort},
title = {Bayesian Design of Sampling Set for Bandlimited Graph Signals},
year = {2019} }
TY - EJOUR
T1 - Bayesian Design of Sampling Set for Bandlimited Graph Signals
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4959
ER -
. (2019). Bayesian Design of Sampling Set for Bandlimited Graph Signals. IEEE SigPort. http://sigport.org/4959
, 2019. Bayesian Design of Sampling Set for Bandlimited Graph Signals. Available at: http://sigport.org/4959.
. (2019). "Bayesian Design of Sampling Set for Bandlimited Graph Signals." Web.
1. . Bayesian Design of Sampling Set for Bandlimited Graph Signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4959

Improved Subspace K-Means Performance via a Randomized Matrix Decomposition


Subspace clustering algorithms provide the capability
to project a dataset onto bases that facilitate clustering.
Proposed in 2017, the subspace k-means algorithm simultaneously
performs clustering and dimensionality reduction with the goal
of finding the optimal subspace for the cluster structure; this
is accomplished by incorporating a trade-off between cluster
and noise subspaces in the objective function. In this study,
we improve subspace k-means by estimating a critical transformation

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Authors:
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley
Submitted On:
14 November 2019 - 7:39pm
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Improved Subspace K-means Performance via a Randomized Matrix Decomposition.pdf

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[1] Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley, "Improved Subspace K-Means Performance via a Randomized Matrix Decomposition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4958. Accessed: Dec. 07, 2019.
@article{4958-19,
url = {http://sigport.org/4958},
author = {Trevor Vannoy; Jacob Senecal; Veronika Strnadova-Neeley },
publisher = {IEEE SigPort},
title = {Improved Subspace K-Means Performance via a Randomized Matrix Decomposition},
year = {2019} }
TY - EJOUR
T1 - Improved Subspace K-Means Performance via a Randomized Matrix Decomposition
AU - Trevor Vannoy; Jacob Senecal; Veronika Strnadova-Neeley
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4958
ER -
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. (2019). Improved Subspace K-Means Performance via a Randomized Matrix Decomposition. IEEE SigPort. http://sigport.org/4958
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley, 2019. Improved Subspace K-Means Performance via a Randomized Matrix Decomposition. Available at: http://sigport.org/4958.
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. (2019). "Improved Subspace K-Means Performance via a Randomized Matrix Decomposition." Web.
1. Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. Improved Subspace K-Means Performance via a Randomized Matrix Decomposition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4958

Scene Text Aware Image Retargeting


Extensive use of text labels and symbols available in the digital media for interpretation and communication of information has gained a lot of attention in the era of digital media. Access of the images with scene text in it through different display devices tend to deform the scene text region while resizing for better viewing experience. We propose an image retargeting operator, which is aware of the scene text present in the image. We perform the normal seam carving depending on the content of the image for the non-text region.

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Authors:
Diptiben Patel, Shanmuganathan Raman
Submitted On:
14 November 2019 - 8:02am
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Poster_Scene_Text_aware_Image_Retargeting.pdf

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[1] Diptiben Patel, Shanmuganathan Raman, "Scene Text Aware Image Retargeting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4957. Accessed: Dec. 07, 2019.
@article{4957-19,
url = {http://sigport.org/4957},
author = {Diptiben Patel; Shanmuganathan Raman },
publisher = {IEEE SigPort},
title = {Scene Text Aware Image Retargeting},
year = {2019} }
TY - EJOUR
T1 - Scene Text Aware Image Retargeting
AU - Diptiben Patel; Shanmuganathan Raman
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4957
ER -
Diptiben Patel, Shanmuganathan Raman. (2019). Scene Text Aware Image Retargeting. IEEE SigPort. http://sigport.org/4957
Diptiben Patel, Shanmuganathan Raman, 2019. Scene Text Aware Image Retargeting. Available at: http://sigport.org/4957.
Diptiben Patel, Shanmuganathan Raman. (2019). "Scene Text Aware Image Retargeting." Web.
1. Diptiben Patel, Shanmuganathan Raman. Scene Text Aware Image Retargeting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4957

Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver


Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling prospect to offer exponential speedup to process computationally expensive operations, such as the ones verified in SR imaging.

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14 November 2019 - 4:13pm
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GlobalSIP Presentation (Ystallonne Alves).pdf

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[1] , "Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4956. Accessed: Dec. 07, 2019.
@article{4956-19,
url = {http://sigport.org/4956},
author = { },
publisher = {IEEE SigPort},
title = {Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver},
year = {2019} }
TY - EJOUR
T1 - Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4956
ER -
. (2019). Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver. IEEE SigPort. http://sigport.org/4956
, 2019. Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver. Available at: http://sigport.org/4956.
. (2019). "Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver." Web.
1. . Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4956

Learning About Loads to Improve Power System Operation and Control

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Submitted On:
13 November 2019 - 11:31am
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MathieuGlobalSIP

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[1] , "Learning About Loads to Improve Power System Operation and Control", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4955. Accessed: Dec. 07, 2019.
@article{4955-19,
url = {http://sigport.org/4955},
author = { },
publisher = {IEEE SigPort},
title = {Learning About Loads to Improve Power System Operation and Control},
year = {2019} }
TY - EJOUR
T1 - Learning About Loads to Improve Power System Operation and Control
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4955
ER -
. (2019). Learning About Loads to Improve Power System Operation and Control. IEEE SigPort. http://sigport.org/4955
, 2019. Learning About Loads to Improve Power System Operation and Control. Available at: http://sigport.org/4955.
. (2019). "Learning About Loads to Improve Power System Operation and Control." Web.
1. . Learning About Loads to Improve Power System Operation and Control [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4955

Adaptation of an EMG-Based Speech Recognizer via Meta-Learning


In nonacoustic speech recognition based on electromyography, i.e. on electrical muscle activity captured by noninvasive surface electrodes, differences between recording sessions are known to cause deteriorating system accuracy. Efficient adaptation of an existing system to an unseen recording session is therefore imperative for practical usage scenarios. We report on a meta-learning approach to pretrain a deep neural network frontend for a myoelectric speech recognizer in a way that it can be easily adapted to a new session.

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Authors:
Krsto Proroković, Michael Wand, Tanja Schultz, Jürgen Schmidhuber
Submitted On:
6 December 2019 - 2:25pm
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Adaptation of an EMG-Based Speech Recognizer via Meta-Learning.pdf

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[1] Krsto Proroković, Michael Wand, Tanja Schultz, Jürgen Schmidhuber, "Adaptation of an EMG-Based Speech Recognizer via Meta-Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4954. Accessed: Dec. 07, 2019.
@article{4954-19,
url = {http://sigport.org/4954},
author = {Krsto Proroković; Michael Wand; Tanja Schultz; Jürgen Schmidhuber },
publisher = {IEEE SigPort},
title = {Adaptation of an EMG-Based Speech Recognizer via Meta-Learning},
year = {2019} }
TY - EJOUR
T1 - Adaptation of an EMG-Based Speech Recognizer via Meta-Learning
AU - Krsto Proroković; Michael Wand; Tanja Schultz; Jürgen Schmidhuber
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4954
ER -
Krsto Proroković, Michael Wand, Tanja Schultz, Jürgen Schmidhuber. (2019). Adaptation of an EMG-Based Speech Recognizer via Meta-Learning. IEEE SigPort. http://sigport.org/4954
Krsto Proroković, Michael Wand, Tanja Schultz, Jürgen Schmidhuber, 2019. Adaptation of an EMG-Based Speech Recognizer via Meta-Learning. Available at: http://sigport.org/4954.
Krsto Proroković, Michael Wand, Tanja Schultz, Jürgen Schmidhuber. (2019). "Adaptation of an EMG-Based Speech Recognizer via Meta-Learning." Web.
1. Krsto Proroković, Michael Wand, Tanja Schultz, Jürgen Schmidhuber. Adaptation of an EMG-Based Speech Recognizer via Meta-Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4954

Single RF Chain Hybrid Analog/Digital Beamforming for mmWave Massive-MIMO


Hybrid beamforming has attracted considerable attention in recent years as an efficient and promising technique for the practical implementation of millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) wireless systems. In this paper, we investigate hybrid analog/digital beamforming designs based on a single RF chain architecture (SRCA) for mmWave massive-MIMO. We first revisit the SRCA and then explore its shortcomings.

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Authors:
Alireza Morsali, Sara Norouzi, and Benoit Champagne
Submitted On:
12 November 2019 - 11:07pm
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[1] Alireza Morsali, Sara Norouzi, and Benoit Champagne, "Single RF Chain Hybrid Analog/Digital Beamforming for mmWave Massive-MIMO", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4953. Accessed: Dec. 07, 2019.
@article{4953-19,
url = {http://sigport.org/4953},
author = {Alireza Morsali; Sara Norouzi; and Benoit Champagne },
publisher = {IEEE SigPort},
title = {Single RF Chain Hybrid Analog/Digital Beamforming for mmWave Massive-MIMO},
year = {2019} }
TY - EJOUR
T1 - Single RF Chain Hybrid Analog/Digital Beamforming for mmWave Massive-MIMO
AU - Alireza Morsali; Sara Norouzi; and Benoit Champagne
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4953
ER -
Alireza Morsali, Sara Norouzi, and Benoit Champagne. (2019). Single RF Chain Hybrid Analog/Digital Beamforming for mmWave Massive-MIMO. IEEE SigPort. http://sigport.org/4953
Alireza Morsali, Sara Norouzi, and Benoit Champagne, 2019. Single RF Chain Hybrid Analog/Digital Beamforming for mmWave Massive-MIMO. Available at: http://sigport.org/4953.
Alireza Morsali, Sara Norouzi, and Benoit Champagne. (2019). "Single RF Chain Hybrid Analog/Digital Beamforming for mmWave Massive-MIMO." Web.
1. Alireza Morsali, Sara Norouzi, and Benoit Champagne. Single RF Chain Hybrid Analog/Digital Beamforming for mmWave Massive-MIMO [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4953

A Comparison of Boosted Deep Neural Networks for Voice Activity Detection


Voice activity detection (VAD) is an integral part of speech processing for real world problems, and a lot of work has been done to improve VAD performance. Of late, deep neural networks have been used to detect the presence of speech and this has offered tremendous gains. Unfortunately, these efforts have been either restricted to feed-forward neural networks that do not adequately capture frequency and temporal correlations, or the recurrent architectures have not been adequately tested in noisy environments.

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Authors:
Harshit Krishnakumar, Donald S. Williamson
Submitted On:
12 November 2019 - 10:09pm
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[1] Harshit Krishnakumar, Donald S. Williamson, "A Comparison of Boosted Deep Neural Networks for Voice Activity Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4952. Accessed: Dec. 07, 2019.
@article{4952-19,
url = {http://sigport.org/4952},
author = {Harshit Krishnakumar; Donald S. Williamson },
publisher = {IEEE SigPort},
title = {A Comparison of Boosted Deep Neural Networks for Voice Activity Detection},
year = {2019} }
TY - EJOUR
T1 - A Comparison of Boosted Deep Neural Networks for Voice Activity Detection
AU - Harshit Krishnakumar; Donald S. Williamson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4952
ER -
Harshit Krishnakumar, Donald S. Williamson. (2019). A Comparison of Boosted Deep Neural Networks for Voice Activity Detection. IEEE SigPort. http://sigport.org/4952
Harshit Krishnakumar, Donald S. Williamson, 2019. A Comparison of Boosted Deep Neural Networks for Voice Activity Detection. Available at: http://sigport.org/4952.
Harshit Krishnakumar, Donald S. Williamson. (2019). "A Comparison of Boosted Deep Neural Networks for Voice Activity Detection." Web.
1. Harshit Krishnakumar, Donald S. Williamson. A Comparison of Boosted Deep Neural Networks for Voice Activity Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4952

Sampling Signals on Meet/Join Lattices


We present a novel sampling theorem, and prototypical applications, for Fourier-sparse lattice signals, i.e., data indexed by a finite semi-lattice. A semilattice is a partially ordered set endowed with a meet (or join) operation that returns the greatest lower bound (smallest upper bound) of two elements. Semilattices can be viewed as a special class of directed graphs with a strictly triangular adjacency matrix , which thus cannot be diagonalized.

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Authors:
Markus Püschel
Submitted On:
15 November 2019 - 12:58pm
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globalsip2019-dlsp-sampling.pdf

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globalsip2019-dlsp-sampling.pdf

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[1] Markus Püschel, "Sampling Signals on Meet/Join Lattices", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4951. Accessed: Dec. 07, 2019.
@article{4951-19,
url = {http://sigport.org/4951},
author = {Markus Püschel },
publisher = {IEEE SigPort},
title = {Sampling Signals on Meet/Join Lattices},
year = {2019} }
TY - EJOUR
T1 - Sampling Signals on Meet/Join Lattices
AU - Markus Püschel
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4951
ER -
Markus Püschel. (2019). Sampling Signals on Meet/Join Lattices. IEEE SigPort. http://sigport.org/4951
Markus Püschel, 2019. Sampling Signals on Meet/Join Lattices. Available at: http://sigport.org/4951.
Markus Püschel. (2019). "Sampling Signals on Meet/Join Lattices." Web.
1. Markus Püschel. Sampling Signals on Meet/Join Lattices [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4951

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