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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.

Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos


Smartphone video-based measurement of heart rate typically uses photoplethysmography (PPG). Prior accuracy studies report low mean absolute errors for apps based on contact PPG on a fingertip, but substantial errors on a troubling percentage of measurements. In this study, we aimed to reduce the rate of substantial heart rate estimation errors by introducing a novel signal present in fingertip videos: fingertip contact surface area.

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Authors:
Sara Fridovich-Keil, Peter J. Ramadge
Submitted On:
27 November 2018 - 1:59am
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GlobalSIP2018_archived_v4.pdf

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[1] Sara Fridovich-Keil, Peter J. Ramadge, "Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3753. Accessed: Dec. 16, 2018.
@article{3753-18,
url = {http://sigport.org/3753},
author = {Sara Fridovich-Keil; Peter J. Ramadge },
publisher = {IEEE SigPort},
title = {Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos},
year = {2018} }
TY - EJOUR
T1 - Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos
AU - Sara Fridovich-Keil; Peter J. Ramadge
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3753
ER -
Sara Fridovich-Keil, Peter J. Ramadge. (2018). Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos. IEEE SigPort. http://sigport.org/3753
Sara Fridovich-Keil, Peter J. Ramadge, 2018. Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos. Available at: http://sigport.org/3753.
Sara Fridovich-Keil, Peter J. Ramadge. (2018). "Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos." Web.
1. Sara Fridovich-Keil, Peter J. Ramadge. Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3753

PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEM FOR IOT USING TIME-FREQUENCY ALOHA SCHEME


One of the main issues in using a Low Earth Orbit (LEO) satellite constellation to extend a Low-Powered Wide Area Network is the frequency synchronization. Using a link based on random access solves this concern, but also prevents delivery guarantees,
and implies less predictable performance. This paper concerns the estimation of Bit Error Rate (BER) and Packet Error Rate (PER) using physical layer abstractions under a time and frequency random scheme, namely Time and Frequency Aloha. We first derive a BER

Paper Details

Authors:
Mathieu Dervin, José Radzik, Sonia Cazalens, Cédric Baudoin, Daniela Dragomirescu
Submitted On:
23 November 2018 - 1:13pm
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CLUZEL_poster.pdf

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[1] Mathieu Dervin, José Radzik, Sonia Cazalens, Cédric Baudoin, Daniela Dragomirescu, "PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEM FOR IOT USING TIME-FREQUENCY ALOHA SCHEME", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3752. Accessed: Dec. 16, 2018.
@article{3752-18,
url = {http://sigport.org/3752},
author = {Mathieu Dervin; José Radzik; Sonia Cazalens; Cédric Baudoin; Daniela Dragomirescu },
publisher = {IEEE SigPort},
title = {PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEM FOR IOT USING TIME-FREQUENCY ALOHA SCHEME},
year = {2018} }
TY - EJOUR
T1 - PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEM FOR IOT USING TIME-FREQUENCY ALOHA SCHEME
AU - Mathieu Dervin; José Radzik; Sonia Cazalens; Cédric Baudoin; Daniela Dragomirescu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3752
ER -
Mathieu Dervin, José Radzik, Sonia Cazalens, Cédric Baudoin, Daniela Dragomirescu. (2018). PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEM FOR IOT USING TIME-FREQUENCY ALOHA SCHEME. IEEE SigPort. http://sigport.org/3752
Mathieu Dervin, José Radzik, Sonia Cazalens, Cédric Baudoin, Daniela Dragomirescu, 2018. PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEM FOR IOT USING TIME-FREQUENCY ALOHA SCHEME. Available at: http://sigport.org/3752.
Mathieu Dervin, José Radzik, Sonia Cazalens, Cédric Baudoin, Daniela Dragomirescu. (2018). "PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEM FOR IOT USING TIME-FREQUENCY ALOHA SCHEME." Web.
1. Mathieu Dervin, José Radzik, Sonia Cazalens, Cédric Baudoin, Daniela Dragomirescu. PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEM FOR IOT USING TIME-FREQUENCY ALOHA SCHEME [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3752

Tensor Ensemble Learning


In big data applications, classical ensemble learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view ensemble learning to multidimensional tensor- valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor ensemble learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple learning algorithms in order to improve test performance.

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Authors:
Ahmad Moniri
Submitted On:
23 November 2018 - 1:09pm
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IK_AM_DPM_GlobalSIP_2018_presentation.pdf

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[1] Ahmad Moniri, "Tensor Ensemble Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3751. Accessed: Dec. 16, 2018.
@article{3751-18,
url = {http://sigport.org/3751},
author = {Ahmad Moniri },
publisher = {IEEE SigPort},
title = {Tensor Ensemble Learning},
year = {2018} }
TY - EJOUR
T1 - Tensor Ensemble Learning
AU - Ahmad Moniri
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3751
ER -
Ahmad Moniri. (2018). Tensor Ensemble Learning. IEEE SigPort. http://sigport.org/3751
Ahmad Moniri, 2018. Tensor Ensemble Learning. Available at: http://sigport.org/3751.
Ahmad Moniri. (2018). "Tensor Ensemble Learning." Web.
1. Ahmad Moniri. Tensor Ensemble Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3751

Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR


Many state-of-the-art machine learning models such as deep neural networks have recently shown to be vulnerable to adversarial perturbations, especially in classification tasks. Motivated by adversarial machine learning, in this paper we investigate the robustness of sparse regression models with strongly correlated covariates to adversarially designed measurement noises. Specifically, we consider the family of ordered weighted L1 (OWL) regularized regression methods and study the case of OSCAR (octagonal shrinkage clustering algorithm for regression) in the adversarial setting.

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Authors:
Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu
Submitted On:
23 November 2018 - 1:03pm
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globalsip(3).pdf

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[1] Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu, "Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3749. Accessed: Dec. 16, 2018.
@article{3749-18,
url = {http://sigport.org/3749},
author = {Pin-Yu Chen; Bhanukiran Vinzamuri and Sijia Liu },
publisher = {IEEE SigPort},
title = {Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR},
year = {2018} }
TY - EJOUR
T1 - Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR
AU - Pin-Yu Chen; Bhanukiran Vinzamuri and Sijia Liu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3749
ER -
Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu. (2018). Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR. IEEE SigPort. http://sigport.org/3749
Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu, 2018. Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR. Available at: http://sigport.org/3749.
Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu. (2018). "Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR." Web.
1. Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu. Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3749

Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks

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Submitted On:
28 November 2018 - 2:50am
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Global_sip_presentation.pdf

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ver2_Global_sip_presentation.pdf

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ver3_Global_sip_presentation.pdf

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Ardeshiri_Global_sip_presentation.pdf

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[1] , "Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3747. Accessed: Dec. 16, 2018.
@article{3747-18,
url = {http://sigport.org/3747},
author = { },
publisher = {IEEE SigPort},
title = {Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks},
year = {2018} }
TY - EJOUR
T1 - Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3747
ER -
. (2018). Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks. IEEE SigPort. http://sigport.org/3747
, 2018. Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks. Available at: http://sigport.org/3747.
. (2018). "Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks." Web.
1. . Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3747

ON THE BEHAVIOR OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR MIXTURE MODELS

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Authors:
Babak Barazandeh, Meisam Razaviyayn
Submitted On:
23 November 2018 - 11:48am
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Globalsip.pdf

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[1] Babak Barazandeh, Meisam Razaviyayn, "ON THE BEHAVIOR OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR MIXTURE MODELS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3746. Accessed: Dec. 16, 2018.
@article{3746-18,
url = {http://sigport.org/3746},
author = {Babak Barazandeh; Meisam Razaviyayn },
publisher = {IEEE SigPort},
title = {ON THE BEHAVIOR OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR MIXTURE MODELS},
year = {2018} }
TY - EJOUR
T1 - ON THE BEHAVIOR OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR MIXTURE MODELS
AU - Babak Barazandeh; Meisam Razaviyayn
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3746
ER -
Babak Barazandeh, Meisam Razaviyayn. (2018). ON THE BEHAVIOR OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR MIXTURE MODELS. IEEE SigPort. http://sigport.org/3746
Babak Barazandeh, Meisam Razaviyayn, 2018. ON THE BEHAVIOR OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR MIXTURE MODELS. Available at: http://sigport.org/3746.
Babak Barazandeh, Meisam Razaviyayn. (2018). "ON THE BEHAVIOR OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR MIXTURE MODELS." Web.
1. Babak Barazandeh, Meisam Razaviyayn. ON THE BEHAVIOR OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR MIXTURE MODELS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3746

Presentation slides for GlobalSIP 2018


In this paper, we investigate the impact of multiple-antenna
deployment at access points (APs) and users on the performance
of cell-free massive multiple-input multiple-output
(MIMO). The transmission is done via time-division duplex
(TDD) protocol. With this protocol, the channels are first
estimated at each AP based on the received pilot signals
in the training phase. Then these channel information will
be used to decode the symbols before sending to all users.
The simple and distributed conjugate beamforming technique

Paper Details

Authors:
Trang C. Mai, Hien Quoc Ngo, Trung Q. Duong
Submitted On:
23 November 2018 - 11:09am
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Trang Mai_Globalsip_2018.pdf

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[1] Trang C. Mai, Hien Quoc Ngo, Trung Q. Duong, "Presentation slides for GlobalSIP 2018", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3744. Accessed: Dec. 16, 2018.
@article{3744-18,
url = {http://sigport.org/3744},
author = {Trang C. Mai; Hien Quoc Ngo; Trung Q. Duong },
publisher = {IEEE SigPort},
title = {Presentation slides for GlobalSIP 2018},
year = {2018} }
TY - EJOUR
T1 - Presentation slides for GlobalSIP 2018
AU - Trang C. Mai; Hien Quoc Ngo; Trung Q. Duong
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3744
ER -
Trang C. Mai, Hien Quoc Ngo, Trung Q. Duong. (2018). Presentation slides for GlobalSIP 2018. IEEE SigPort. http://sigport.org/3744
Trang C. Mai, Hien Quoc Ngo, Trung Q. Duong, 2018. Presentation slides for GlobalSIP 2018. Available at: http://sigport.org/3744.
Trang C. Mai, Hien Quoc Ngo, Trung Q. Duong. (2018). "Presentation slides for GlobalSIP 2018." Web.
1. Trang C. Mai, Hien Quoc Ngo, Trung Q. Duong. Presentation slides for GlobalSIP 2018 [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3744

NEURAL LATTICE DECODERS


Lattice decoders constructed with neural networks are presented.
Firstly, we show how the fundamental parallelotope
is used as a compact set for the approximation by a neural lattice
decoder. Secondly, we introduce the notion of Voronoi reduced
lattice basis. As a consequence, a first optimal neural
lattice decoder is built from Boolean equations and the facets
of the Voronoi cell. This decoder needs no learning. Finally,
we present two neural decoders with learning. It is shown

Paper Details

Authors:
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, and Loïc Brunel
Submitted On:
23 November 2018 - 10:45am
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talk_globalSIP.pdf

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[1] Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, and Loïc Brunel, "NEURAL LATTICE DECODERS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3743. Accessed: Dec. 16, 2018.
@article{3743-18,
url = {http://sigport.org/3743},
author = {Vincent Corlay; Joseph J. Boutros; Philippe Ciblat; and Loïc Brunel },
publisher = {IEEE SigPort},
title = {NEURAL LATTICE DECODERS},
year = {2018} }
TY - EJOUR
T1 - NEURAL LATTICE DECODERS
AU - Vincent Corlay; Joseph J. Boutros; Philippe Ciblat; and Loïc Brunel
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3743
ER -
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, and Loïc Brunel. (2018). NEURAL LATTICE DECODERS. IEEE SigPort. http://sigport.org/3743
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, and Loïc Brunel, 2018. NEURAL LATTICE DECODERS. Available at: http://sigport.org/3743.
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, and Loïc Brunel. (2018). "NEURAL LATTICE DECODERS." Web.
1. Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, and Loïc Brunel. NEURAL LATTICE DECODERS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3743

A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels


The downlink capacity of a unicast network with a large number of users than the base station transmit antennas depends on user selection and interference among the selected users. Various suboptimal selection schemes in combination with suboptimal or optimal precoding have been proposed in the literature, and some of these techniques asymptotically achieve the sum capacity of DPC, as the number of users goes to infinity. In the previous works, the joint design problem is addressed as a decoupled problem of selection and precoding either at the design level or the solution level.

Paper Details

Authors:
Ashok BANDI, Bhavani Shankar Mysore R, Sina Maleki, Symeon chatzinotas, Bjorn Ottersten
Submitted On:
23 November 2018 - 10:15am
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GlobalSIP2018.pdf

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GlobalSIP2018.pdf

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[1] Ashok BANDI, Bhavani Shankar Mysore R, Sina Maleki, Symeon chatzinotas, Bjorn Ottersten, "A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3742. Accessed: Dec. 16, 2018.
@article{3742-18,
url = {http://sigport.org/3742},
author = {Ashok BANDI; Bhavani Shankar Mysore R; Sina Maleki; Symeon chatzinotas; Bjorn Ottersten },
publisher = {IEEE SigPort},
title = {A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels},
year = {2018} }
TY - EJOUR
T1 - A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels
AU - Ashok BANDI; Bhavani Shankar Mysore R; Sina Maleki; Symeon chatzinotas; Bjorn Ottersten
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3742
ER -
Ashok BANDI, Bhavani Shankar Mysore R, Sina Maleki, Symeon chatzinotas, Bjorn Ottersten. (2018). A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels. IEEE SigPort. http://sigport.org/3742
Ashok BANDI, Bhavani Shankar Mysore R, Sina Maleki, Symeon chatzinotas, Bjorn Ottersten, 2018. A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels. Available at: http://sigport.org/3742.
Ashok BANDI, Bhavani Shankar Mysore R, Sina Maleki, Symeon chatzinotas, Bjorn Ottersten. (2018). "A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels." Web.
1. Ashok BANDI, Bhavani Shankar Mysore R, Sina Maleki, Symeon chatzinotas, Bjorn Ottersten. A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3742

A Performance Analysis on the Optimal Number of Measurements for Coded Compressive Imaging


In this paper, we consider two practical coded compressive imaging techniques. We investigate the optimal number of measurements under quadratic signal-to-noise-ratio (SNR) decrease. We focus on imaging scenarios in both real and complex vector spaces. In real vector spaces, we consider focal plane array (FPA) based super-resolution imaging with a constant measurement time constraint. Our model is comprised of a spatial light modulator and a low resolution FPA for modulating and sampling the incoming light intensity, respectively.

Paper Details

Authors:
Oğuzhan Fatih Kar, Alper Güngör, Serhat Ilbey, Can Barış Top, H. Emre Güven
Submitted On:
23 November 2018 - 6:01am
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GLOBALSIP_Powerpoint.pdf

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[1] Oğuzhan Fatih Kar, Alper Güngör, Serhat Ilbey, Can Barış Top, H. Emre Güven, "A Performance Analysis on the Optimal Number of Measurements for Coded Compressive Imaging", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3741. Accessed: Dec. 16, 2018.
@article{3741-18,
url = {http://sigport.org/3741},
author = {Oğuzhan Fatih Kar; Alper Güngör; Serhat Ilbey; Can Barış Top; H. Emre Güven },
publisher = {IEEE SigPort},
title = {A Performance Analysis on the Optimal Number of Measurements for Coded Compressive Imaging},
year = {2018} }
TY - EJOUR
T1 - A Performance Analysis on the Optimal Number of Measurements for Coded Compressive Imaging
AU - Oğuzhan Fatih Kar; Alper Güngör; Serhat Ilbey; Can Barış Top; H. Emre Güven
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3741
ER -
Oğuzhan Fatih Kar, Alper Güngör, Serhat Ilbey, Can Barış Top, H. Emre Güven. (2018). A Performance Analysis on the Optimal Number of Measurements for Coded Compressive Imaging. IEEE SigPort. http://sigport.org/3741
Oğuzhan Fatih Kar, Alper Güngör, Serhat Ilbey, Can Barış Top, H. Emre Güven, 2018. A Performance Analysis on the Optimal Number of Measurements for Coded Compressive Imaging. Available at: http://sigport.org/3741.
Oğuzhan Fatih Kar, Alper Güngör, Serhat Ilbey, Can Barış Top, H. Emre Güven. (2018). "A Performance Analysis on the Optimal Number of Measurements for Coded Compressive Imaging." Web.
1. Oğuzhan Fatih Kar, Alper Güngör, Serhat Ilbey, Can Barış Top, H. Emre Güven. A Performance Analysis on the Optimal Number of Measurements for Coded Compressive Imaging [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3741

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