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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.

IMPLEMENTATION OF A HDL-CODER BASED TELECOMMAND RECEIVER APPLICATION FOR MICROSATELLITE COMMUNICATION


In this paper the development and implementation of a Telecommand (TC) receiver application for microsatellite
communication is presented. The TC receiver application is executed and operated by a highly integrated Generic
Software-Defined Radio (GSDR) platform. This platform architecture is designed for the reliable operation of
multiple radio frequency applications on spacecraft. For the development and implementation process of the TC receiver application, a new model-based development workflow by Matlab/Simulink is used and evaluated

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19 November 2018 - 2:09am
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GlobalSIP 2018 Poster HDC TC GSDR.pdf

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[1] , "IMPLEMENTATION OF A HDL-CODER BASED TELECOMMAND RECEIVER APPLICATION FOR MICROSATELLITE COMMUNICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3683. Accessed: Apr. 21, 2019.
@article{3683-18,
url = {http://sigport.org/3683},
author = { },
publisher = {IEEE SigPort},
title = {IMPLEMENTATION OF A HDL-CODER BASED TELECOMMAND RECEIVER APPLICATION FOR MICROSATELLITE COMMUNICATION},
year = {2018} }
TY - EJOUR
T1 - IMPLEMENTATION OF A HDL-CODER BASED TELECOMMAND RECEIVER APPLICATION FOR MICROSATELLITE COMMUNICATION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3683
ER -
. (2018). IMPLEMENTATION OF A HDL-CODER BASED TELECOMMAND RECEIVER APPLICATION FOR MICROSATELLITE COMMUNICATION. IEEE SigPort. http://sigport.org/3683
, 2018. IMPLEMENTATION OF A HDL-CODER BASED TELECOMMAND RECEIVER APPLICATION FOR MICROSATELLITE COMMUNICATION. Available at: http://sigport.org/3683.
. (2018). "IMPLEMENTATION OF A HDL-CODER BASED TELECOMMAND RECEIVER APPLICATION FOR MICROSATELLITE COMMUNICATION." Web.
1. . IMPLEMENTATION OF A HDL-CODER BASED TELECOMMAND RECEIVER APPLICATION FOR MICROSATELLITE COMMUNICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3683

Statistical detection and classification of transient signals in low-bit sampling time-domain signals


We investigate the performance of the generalized Spectral Kurtosis (SK) estimator in detecting and discriminating natural and artificial very short duration transients in the 2-bit sampling time domain Very-Long-Baseline Interferometry (VLBI) data. We demonstrate that, after a 32-bit FFT operation is performed on the 2-bit time domain voltages, these two types of transients become distinguishable from each other in the spectral domain.

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Authors:
Gelu M. Nita, Aard Keimpema, Zsolt Paragi
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18 November 2018 - 4:48pm
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[1] Gelu M. Nita, Aard Keimpema, Zsolt Paragi, "Statistical detection and classification of transient signals in low-bit sampling time-domain signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3682. Accessed: Apr. 21, 2019.
@article{3682-18,
url = {http://sigport.org/3682},
author = {Gelu M. Nita; Aard Keimpema; Zsolt Paragi },
publisher = {IEEE SigPort},
title = {Statistical detection and classification of transient signals in low-bit sampling time-domain signals},
year = {2018} }
TY - EJOUR
T1 - Statistical detection and classification of transient signals in low-bit sampling time-domain signals
AU - Gelu M. Nita; Aard Keimpema; Zsolt Paragi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3682
ER -
Gelu M. Nita, Aard Keimpema, Zsolt Paragi. (2018). Statistical detection and classification of transient signals in low-bit sampling time-domain signals. IEEE SigPort. http://sigport.org/3682
Gelu M. Nita, Aard Keimpema, Zsolt Paragi, 2018. Statistical detection and classification of transient signals in low-bit sampling time-domain signals. Available at: http://sigport.org/3682.
Gelu M. Nita, Aard Keimpema, Zsolt Paragi. (2018). "Statistical detection and classification of transient signals in low-bit sampling time-domain signals." Web.
1. Gelu M. Nita, Aard Keimpema, Zsolt Paragi. Statistical detection and classification of transient signals in low-bit sampling time-domain signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3682

NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER


Monitoring sleep quality and status is important to learn health condition for improvement and prevent sleep apnea. A bed-mounted seismometer system is proposed to monitor the heart and respiratory rates, and body movement and posture, during the sleep. To effectively monitor sleep status, an innovative local maxima statistics based approach and an instantaneous property based method are developed to estimate heart and respiratory rates, respectively. These methods are more robust and stable compared to previous works.

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Authors:
Fangyu Li, Jose Clemente, WenZhan Song
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18 November 2018 - 4:25pm
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globalsip-sleep-monitoring.pdf

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[1] Fangyu Li, Jose Clemente, WenZhan Song, "NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3681. Accessed: Apr. 21, 2019.
@article{3681-18,
url = {http://sigport.org/3681},
author = {Fangyu Li; Jose Clemente; WenZhan Song },
publisher = {IEEE SigPort},
title = {NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER},
year = {2018} }
TY - EJOUR
T1 - NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER
AU - Fangyu Li; Jose Clemente; WenZhan Song
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3681
ER -
Fangyu Li, Jose Clemente, WenZhan Song. (2018). NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER. IEEE SigPort. http://sigport.org/3681
Fangyu Li, Jose Clemente, WenZhan Song, 2018. NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER. Available at: http://sigport.org/3681.
Fangyu Li, Jose Clemente, WenZhan Song. (2018). "NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER." Web.
1. Fangyu Li, Jose Clemente, WenZhan Song. NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3681

LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION


The rapid convergence rate, high fidelity learning outcome and low computational cost are key targets in solving the learning problem of the complex physical system. Guided

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Authors:
Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song
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18 November 2018 - 4:23pm
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GlobalSIP2018_FWI 16.07.19.pdf

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[1] Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song, "LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3680. Accessed: Apr. 21, 2019.
@article{3680-18,
url = {http://sigport.org/3680},
author = {Rui Xie; Fangyu Li; Zengyan Wang; WenZhan Song },
publisher = {IEEE SigPort},
title = {LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION},
year = {2018} }
TY - EJOUR
T1 - LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION
AU - Rui Xie; Fangyu Li; Zengyan Wang; WenZhan Song
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3680
ER -
Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song. (2018). LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION. IEEE SigPort. http://sigport.org/3680
Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song, 2018. LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION. Available at: http://sigport.org/3680.
Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song. (2018). "LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION." Web.
1. Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song. LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3680

OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS


In this paper, the problem of how to balance the energy consumption during data processing in networks is investigated using a fog middleware. We first demonstrate that for a fog network with different kind of nodes, balancing the energy relies on a combinatorial optimization that is solved using graph theory. We propose a transformation of the transshipment graph problem to formulate an optimization problem that we solve with linear programming (LP).

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Authors:
Jose Clemente, Fangyu Li, WenZhan Song
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18 November 2018 - 4:03pm
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GlobalSIP_Jose.pdf

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[1] Jose Clemente, Fangyu Li, WenZhan Song, "OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3679. Accessed: Apr. 21, 2019.
@article{3679-18,
url = {http://sigport.org/3679},
author = {Jose Clemente; Fangyu Li; WenZhan Song },
publisher = {IEEE SigPort},
title = {OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS},
year = {2018} }
TY - EJOUR
T1 - OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS
AU - Jose Clemente; Fangyu Li; WenZhan Song
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3679
ER -
Jose Clemente, Fangyu Li, WenZhan Song. (2018). OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS. IEEE SigPort. http://sigport.org/3679
Jose Clemente, Fangyu Li, WenZhan Song, 2018. OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS. Available at: http://sigport.org/3679.
Jose Clemente, Fangyu Li, WenZhan Song. (2018). "OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS." Web.
1. Jose Clemente, Fangyu Li, WenZhan Song. OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3679

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

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Authors:
Mohamad Shahbazi, Hamid K. Aghajan
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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: Apr. 21, 2019.
@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.

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18 November 2018 - 12:01pm
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slides.pdf

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[1] , "Sketching Discrete Valued Sparse Matrices", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3677. Accessed: Apr. 21, 2019.
@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.

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Authors:
Jing Ran,Yexin Chen,Shulan Li
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18 November 2018 - 2:48pm
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Ran-ppt.pdf

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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: Apr. 21, 2019.
@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.

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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: Apr. 21, 2019.
@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.

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Authors:
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner
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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: Apr. 21, 2019.
@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

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