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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 Model-Driven Deep Learning Network for MIMO Detection

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26 November 2018 - 8:03pm
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IEEE_Globalsip_2018.pdf

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[1] , "A Model-Driven Deep Learning Network for MIMO Detection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3795. Accessed: Dec. 16, 2018.
@article{3795-18,
url = {http://sigport.org/3795},
author = { },
publisher = {IEEE SigPort},
title = {A Model-Driven Deep Learning Network for MIMO Detection},
year = {2018} }
TY - EJOUR
T1 - A Model-Driven Deep Learning Network for MIMO Detection
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3795
ER -
. (2018). A Model-Driven Deep Learning Network for MIMO Detection. IEEE SigPort. http://sigport.org/3795
, 2018. A Model-Driven Deep Learning Network for MIMO Detection. Available at: http://sigport.org/3795.
. (2018). "A Model-Driven Deep Learning Network for MIMO Detection." Web.
1. . A Model-Driven Deep Learning Network for MIMO Detection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3795

Robust Multi-User Analog Beamforming in mmWave MIMO Systems


In this paper, we propose a robust analog-only beamforming scheme for the downlink multi-user systems, which not only suppresses the interference and enhances the beamform- ing gain, but also provides robustness against imperfect channel state information (CSI). We strike a balance between the average beamforming gain and the inter-user interference by formulating a multi-objective problem. A probabilistic objective of leakage interference power is formulated to alleviate the effects of the channel estimation and feedback quantization errors.

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Authors:
Lisi Jiang, Hamid Jafarkhani
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26 November 2018 - 7:06pm
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Robust Multi-User Analog Beamforming in mmWave MIMO Systems.pdf

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[1] Lisi Jiang, Hamid Jafarkhani, "Robust Multi-User Analog Beamforming in mmWave MIMO Systems", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3794. Accessed: Dec. 16, 2018.
@article{3794-18,
url = {http://sigport.org/3794},
author = {Lisi Jiang; Hamid Jafarkhani },
publisher = {IEEE SigPort},
title = {Robust Multi-User Analog Beamforming in mmWave MIMO Systems},
year = {2018} }
TY - EJOUR
T1 - Robust Multi-User Analog Beamforming in mmWave MIMO Systems
AU - Lisi Jiang; Hamid Jafarkhani
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3794
ER -
Lisi Jiang, Hamid Jafarkhani. (2018). Robust Multi-User Analog Beamforming in mmWave MIMO Systems. IEEE SigPort. http://sigport.org/3794
Lisi Jiang, Hamid Jafarkhani, 2018. Robust Multi-User Analog Beamforming in mmWave MIMO Systems. Available at: http://sigport.org/3794.
Lisi Jiang, Hamid Jafarkhani. (2018). "Robust Multi-User Analog Beamforming in mmWave MIMO Systems." Web.
1. Lisi Jiang, Hamid Jafarkhani. Robust Multi-User Analog Beamforming in mmWave MIMO Systems [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3794

Reconstruction-free deep convolutional neural networks for partially observed images


Conventional image discrimination tasks are performed on fully observed images. In challenging real imaging scenarios, where sensing systems are energy demanding or need to operate with limited bandwidth and exposure-time budgets, or defective pixels, where the data collected often suffers from missing information, and this makes the task extremely hard. In this paper, we leverage Convolutional Neural Networks (CNNs) to extract information from partially observed images.

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Authors:
Arun Asokan Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, Muyinatu A. Lediju Bell, Trac D. Tran
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26 November 2018 - 8:14pm
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GlobalSIP_Poster_v2.pptx

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[1] Arun Asokan Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, Muyinatu A. Lediju Bell, Trac D. Tran, "Reconstruction-free deep convolutional neural networks for partially observed images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3789. Accessed: Dec. 16, 2018.
@article{3789-18,
url = {http://sigport.org/3789},
author = {Arun Asokan Nair; Luoluo Liu; Akshay Rangamani; Peter Chin; Muyinatu A. Lediju Bell; Trac D. Tran },
publisher = {IEEE SigPort},
title = {Reconstruction-free deep convolutional neural networks for partially observed images},
year = {2018} }
TY - EJOUR
T1 - Reconstruction-free deep convolutional neural networks for partially observed images
AU - Arun Asokan Nair; Luoluo Liu; Akshay Rangamani; Peter Chin; Muyinatu A. Lediju Bell; Trac D. Tran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3789
ER -
Arun Asokan Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, Muyinatu A. Lediju Bell, Trac D. Tran. (2018). Reconstruction-free deep convolutional neural networks for partially observed images. IEEE SigPort. http://sigport.org/3789
Arun Asokan Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, Muyinatu A. Lediju Bell, Trac D. Tran, 2018. Reconstruction-free deep convolutional neural networks for partially observed images. Available at: http://sigport.org/3789.
Arun Asokan Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, Muyinatu A. Lediju Bell, Trac D. Tran. (2018). "Reconstruction-free deep convolutional neural networks for partially observed images." Web.
1. Arun Asokan Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, Muyinatu A. Lediju Bell, Trac D. Tran. Reconstruction-free deep convolutional neural networks for partially observed images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3789

Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals


In this paper we consider the generalized approxi- mate message passing (GAMP) algorithm for recovering a sparse signal from modulo samples of randomized projections of the unknown signal. The modulo samples are obtained by a self-reset (SR) analog to digital converter (ADC). Additionally, in contrast to previous work on SR ADC, we consider a scenario where the compressed sensing (CS) measurements (i.e., randomized projections) are sent through a communication channel, namely an additive white Gaussian noise (AWGN) channel before being quantized by a SR ADC.

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Authors:
Osman Musa, Peter Jung, Norbert Goertz
Submitted On:
26 November 2018 - 3:57pm
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globalsip2018-poster.pdf

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[1] Osman Musa, Peter Jung, Norbert Goertz, " Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3788. Accessed: Dec. 16, 2018.
@article{3788-18,
url = {http://sigport.org/3788},
author = {Osman Musa; Peter Jung; Norbert Goertz },
publisher = {IEEE SigPort},
title = { Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals},
year = {2018} }
TY - EJOUR
T1 - Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals
AU - Osman Musa; Peter Jung; Norbert Goertz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3788
ER -
Osman Musa, Peter Jung, Norbert Goertz. (2018). Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals. IEEE SigPort. http://sigport.org/3788
Osman Musa, Peter Jung, Norbert Goertz, 2018. Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals. Available at: http://sigport.org/3788.
Osman Musa, Peter Jung, Norbert Goertz. (2018). " Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals." Web.
1. Osman Musa, Peter Jung, Norbert Goertz. Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3788

Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing


Cell-free Massive multiple-input multiple-output
(MIMO) ensures ubiquitous communication at high spectral
efficiency (SE) thanks to increased macro-diversity as compared
cellular communications. However, system scalability and performance
are limited by fronthauling traffic and interference.
Unlike conventional precoding schemes that only suppress intra-cell
interference, full-pilot zero-forcing (fpZF), introduced in [1],
actively suppresses also inter-cell interference, without sharing

Paper Details

Authors:
Giovanni Interdonato, Marcus Karlsson, Emil Björnson, Erik G. Larsson
Submitted On:
26 November 2018 - 3:28pm
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interdonato-globalsip.pdf

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[1] Giovanni Interdonato, Marcus Karlsson, Emil Björnson, Erik G. Larsson, "Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3786. Accessed: Dec. 16, 2018.
@article{3786-18,
url = {http://sigport.org/3786},
author = {Giovanni Interdonato; Marcus Karlsson; Emil Björnson; Erik G. Larsson },
publisher = {IEEE SigPort},
title = {Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing},
year = {2018} }
TY - EJOUR
T1 - Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing
AU - Giovanni Interdonato; Marcus Karlsson; Emil Björnson; Erik G. Larsson
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3786
ER -
Giovanni Interdonato, Marcus Karlsson, Emil Björnson, Erik G. Larsson. (2018). Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing. IEEE SigPort. http://sigport.org/3786
Giovanni Interdonato, Marcus Karlsson, Emil Björnson, Erik G. Larsson, 2018. Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing. Available at: http://sigport.org/3786.
Giovanni Interdonato, Marcus Karlsson, Emil Björnson, Erik G. Larsson. (2018). "Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing." Web.
1. Giovanni Interdonato, Marcus Karlsson, Emil Björnson, Erik G. Larsson. Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3786

Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization

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Authors:
Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini
Submitted On:
26 November 2018 - 3:18pm
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globalsip18_ZOPSGD

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[1] Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini, "Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3784. Accessed: Dec. 16, 2018.
@article{3784-18,
url = {http://sigport.org/3784},
author = {Xingguo Li; Pin-Yu Chen; Jarvis Haupt; Lisa Amini },
publisher = {IEEE SigPort},
title = {Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization},
year = {2018} }
TY - EJOUR
T1 - Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization
AU - Xingguo Li; Pin-Yu Chen; Jarvis Haupt; Lisa Amini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3784
ER -
Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini. (2018). Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization. IEEE SigPort. http://sigport.org/3784
Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini, 2018. Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization. Available at: http://sigport.org/3784.
Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini. (2018). "Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization." Web.
1. Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini. Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3784

A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION


Deep neural networks (DNNs) have found applications in diverse signal processing (SP) problems. Most efforts either directly adopt the DNN as a black-box approach to perform certain SP tasks without taking into account of any known properties of the signal models, or insert a pre-defined SP operator into a DNN as an add-on data processing stage. This paper presents a novel hybrid-NN framework in which one or more SP layers are inserted into the DNN architecture in a coherent manner to enhance the network capability and efficiency in feature extraction.

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Authors:
Zhe Zhang, Xiang Chen, Zhi Tian
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26 November 2018 - 3:14pm
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Hybrid_NN_Poster_Zhe_new_2.pdf

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[1] Zhe Zhang, Xiang Chen, Zhi Tian, "A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3783. Accessed: Dec. 16, 2018.
@article{3783-18,
url = {http://sigport.org/3783},
author = {Zhe Zhang; Xiang Chen; Zhi Tian },
publisher = {IEEE SigPort},
title = {A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION
AU - Zhe Zhang; Xiang Chen; Zhi Tian
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3783
ER -
Zhe Zhang, Xiang Chen, Zhi Tian. (2018). A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION. IEEE SigPort. http://sigport.org/3783
Zhe Zhang, Xiang Chen, Zhi Tian, 2018. A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION. Available at: http://sigport.org/3783.
Zhe Zhang, Xiang Chen, Zhi Tian. (2018). "A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION." Web.
1. Zhe Zhang, Xiang Chen, Zhi Tian. A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3783

StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures


A novel non-stationarity visualization tool known as StationPlot is developed for deciphering the chaotic behavior of a dynamical time series. A family of analytic measures enumerating geometrical aspects of the non-stationarity & degree of variability is formulated by convex hull geometry (CHG) on StationPlot. In the Euclidean space, both trend-stationary (TS) & difference-stationary (DS) perturbations are comprehended by the asymmetric structure of StationPlot's region of interest (ROI).

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Authors:
Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee
Submitted On:
26 November 2018 - 2:05pm
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Oral_StationPlot_GlobalSIP_2018

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[1] Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee, "StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3778. Accessed: Dec. 16, 2018.
@article{3778-18,
url = {http://sigport.org/3778},
author = {Sawon Pratiher; Subhankar Chattoraj; Rajdeep Mukherjee },
publisher = {IEEE SigPort},
title = {StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures},
year = {2018} }
TY - EJOUR
T1 - StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures
AU - Sawon Pratiher; Subhankar Chattoraj; Rajdeep Mukherjee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3778
ER -
Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee. (2018). StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures. IEEE SigPort. http://sigport.org/3778
Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee, 2018. StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures. Available at: http://sigport.org/3778.
Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee. (2018). "StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures." Web.
1. Sawon Pratiher, Subhankar Chattoraj, Rajdeep Mukherjee. StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3778

Interactive Object Segmentation with Noisy Binary Inputs

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Authors:
Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell
Submitted On:
26 November 2018 - 12:51pm
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Canal_globalSIP_poster.pdf

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[1] Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell, "Interactive Object Segmentation with Noisy Binary Inputs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3777. Accessed: Dec. 16, 2018.
@article{3777-18,
url = {http://sigport.org/3777},
author = {Gregory Canal; Sivabalan Manivasagam; Shaoheng Liang; Christopher Rozell },
publisher = {IEEE SigPort},
title = {Interactive Object Segmentation with Noisy Binary Inputs},
year = {2018} }
TY - EJOUR
T1 - Interactive Object Segmentation with Noisy Binary Inputs
AU - Gregory Canal; Sivabalan Manivasagam; Shaoheng Liang; Christopher Rozell
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3777
ER -
Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell. (2018). Interactive Object Segmentation with Noisy Binary Inputs. IEEE SigPort. http://sigport.org/3777
Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell, 2018. Interactive Object Segmentation with Noisy Binary Inputs. Available at: http://sigport.org/3777.
Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell. (2018). "Interactive Object Segmentation with Noisy Binary Inputs." Web.
1. Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell. Interactive Object Segmentation with Noisy Binary Inputs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3777

RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES

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26 November 2018 - 3:06am
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Poster for GlobalSIP

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[1] , "RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3776. Accessed: Dec. 16, 2018.
@article{3776-18,
url = {http://sigport.org/3776},
author = { },
publisher = {IEEE SigPort},
title = {RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES},
year = {2018} }
TY - EJOUR
T1 - RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3776
ER -
. (2018). RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES. IEEE SigPort. http://sigport.org/3776
, 2018. RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES. Available at: http://sigport.org/3776.
. (2018). "RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES." Web.
1. . RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3776

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