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Signal and System Modeling, Representation and Estimation

$\alpha$ Belief Propagation as Fully Factorized Approximation

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Authors:
Dong Liu, Nima N. Moghadam, Lars Kildehoj Rasmussen, Saikat ChatterjeeJingliang Huang,
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10 November 2019 - 12:06pm
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Poster

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[1] Dong Liu, Nima N. Moghadam, Lars Kildehoj Rasmussen, Saikat ChatterjeeJingliang Huang, , "$\alpha$ Belief Propagation as Fully Factorized Approximation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4944. Accessed: Dec. 13, 2019.
@article{4944-19,
url = {http://sigport.org/4944},
author = {Dong Liu; Nima N. Moghadam; Lars Kildehoj Rasmussen; Saikat ChatterjeeJingliang Huang; },
publisher = {IEEE SigPort},
title = {$\alpha$ Belief Propagation as Fully Factorized Approximation},
year = {2019} }
TY - EJOUR
T1 - $\alpha$ Belief Propagation as Fully Factorized Approximation
AU - Dong Liu; Nima N. Moghadam; Lars Kildehoj Rasmussen; Saikat ChatterjeeJingliang Huang;
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4944
ER -
Dong Liu, Nima N. Moghadam, Lars Kildehoj Rasmussen, Saikat ChatterjeeJingliang Huang, . (2019). $\alpha$ Belief Propagation as Fully Factorized Approximation. IEEE SigPort. http://sigport.org/4944
Dong Liu, Nima N. Moghadam, Lars Kildehoj Rasmussen, Saikat ChatterjeeJingliang Huang, , 2019. $\alpha$ Belief Propagation as Fully Factorized Approximation. Available at: http://sigport.org/4944.
Dong Liu, Nima N. Moghadam, Lars Kildehoj Rasmussen, Saikat ChatterjeeJingliang Huang, . (2019). "$\alpha$ Belief Propagation as Fully Factorized Approximation." Web.
1. Dong Liu, Nima N. Moghadam, Lars Kildehoj Rasmussen, Saikat ChatterjeeJingliang Huang, . $\alpha$ Belief Propagation as Fully Factorized Approximation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4944

Estimating Correlation Coefficients for Quantum Radar and Noise Radar

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Authors:
David Luong, Sreeraman Rajan, Bhashyam Balaji
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8 November 2019 - 11:03am
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GlobalSIP 2019 Presentation: Estimating Correlation Coefficients for Quantum Radar and Noise Radar

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[1] David Luong, Sreeraman Rajan, Bhashyam Balaji, "Estimating Correlation Coefficients for Quantum Radar and Noise Radar", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4931. Accessed: Dec. 13, 2019.
@article{4931-19,
url = {http://sigport.org/4931},
author = {David Luong; Sreeraman Rajan; Bhashyam Balaji },
publisher = {IEEE SigPort},
title = {Estimating Correlation Coefficients for Quantum Radar and Noise Radar},
year = {2019} }
TY - EJOUR
T1 - Estimating Correlation Coefficients for Quantum Radar and Noise Radar
AU - David Luong; Sreeraman Rajan; Bhashyam Balaji
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4931
ER -
David Luong, Sreeraman Rajan, Bhashyam Balaji. (2019). Estimating Correlation Coefficients for Quantum Radar and Noise Radar. IEEE SigPort. http://sigport.org/4931
David Luong, Sreeraman Rajan, Bhashyam Balaji, 2019. Estimating Correlation Coefficients for Quantum Radar and Noise Radar. Available at: http://sigport.org/4931.
David Luong, Sreeraman Rajan, Bhashyam Balaji. (2019). "Estimating Correlation Coefficients for Quantum Radar and Noise Radar." Web.
1. David Luong, Sreeraman Rajan, Bhashyam Balaji. Estimating Correlation Coefficients for Quantum Radar and Noise Radar [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4931

AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications


For their analysis with conventional signal processing tools, non-stationary signals are assumed to be stationary (or at least wide-sense stationary) in short intervals. While this approach allows them to be studied, it disregards the temporal evolution of their statistics. As such, to analyze this type of signals, it is desirable to use a representation that registers and characterizes the temporal changes in the frequency content of the signals, as these changes may occur in single or multiple periodic ways.

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Authors:
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk
Submitted On:
7 November 2019 - 7:21pm
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[1] Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk, "AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4927. Accessed: Dec. 13, 2019.
@article{4927-19,
url = {http://sigport.org/4927},
author = {Raymundo Cassani; Isabela Albuquerque; Joao Monteiro; Tiago H. Falk },
publisher = {IEEE SigPort},
title = {AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications},
year = {2019} }
TY - EJOUR
T1 - AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications
AU - Raymundo Cassani; Isabela Albuquerque; Joao Monteiro; Tiago H. Falk
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4927
ER -
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. (2019). AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications. IEEE SigPort. http://sigport.org/4927
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk, 2019. AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications. Available at: http://sigport.org/4927.
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. (2019). "AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications." Web.
1. Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4927

On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery


Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals.

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4 November 2019 - 10:52pm
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GlobalSIP 2019 jianchen zhu(1)(1).pdf

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[1] , "On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4911. Accessed: Dec. 13, 2019.
@article{4911-19,
url = {http://sigport.org/4911},
author = { },
publisher = {IEEE SigPort},
title = {On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery},
year = {2019} }
TY - EJOUR
T1 - On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4911
ER -
. (2019). On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery. IEEE SigPort. http://sigport.org/4911
, 2019. On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery. Available at: http://sigport.org/4911.
. (2019). "On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery." Web.
1. . On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4911

Incentivizing Crowdsourced Workers via Truth Detection


Crowdsourcing platforms often want to incentivize workers to finish tasks with high quality and truthfully report their solutions. A high-quality solution requires a worker to exert effort; a platform can motivate such effort exertion and truthful reporting by providing a reward.

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Authors:
Haoran Yu, Jianwei Huang, Randall A Berry
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4 November 2019 - 10:19pm
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Chao_GlobalSIP_slides.pdf

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[1] Haoran Yu, Jianwei Huang, Randall A Berry, "Incentivizing Crowdsourced Workers via Truth Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4910. Accessed: Dec. 13, 2019.
@article{4910-19,
url = {http://sigport.org/4910},
author = {Haoran Yu; Jianwei Huang; Randall A Berry },
publisher = {IEEE SigPort},
title = {Incentivizing Crowdsourced Workers via Truth Detection},
year = {2019} }
TY - EJOUR
T1 - Incentivizing Crowdsourced Workers via Truth Detection
AU - Haoran Yu; Jianwei Huang; Randall A Berry
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4910
ER -
Haoran Yu, Jianwei Huang, Randall A Berry. (2019). Incentivizing Crowdsourced Workers via Truth Detection. IEEE SigPort. http://sigport.org/4910
Haoran Yu, Jianwei Huang, Randall A Berry, 2019. Incentivizing Crowdsourced Workers via Truth Detection. Available at: http://sigport.org/4910.
Haoran Yu, Jianwei Huang, Randall A Berry. (2019). "Incentivizing Crowdsourced Workers via Truth Detection." Web.
1. Haoran Yu, Jianwei Huang, Randall A Berry. Incentivizing Crowdsourced Workers via Truth Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4910

Automatic neural network search method for Open Set Recognition


Real-world recognition or classification tasks in computer vision are not apparent in controlled environments and often get involved in open set. Previous research work on real-world recognition problem is knowledge- and labor-intensive to pursue good performance for there are numbers of task domains. Auto Machine Learning (AutoML) approaches supply an easier way to apply advanced machine learning technologies, reduce the demand for experienced human experts and improve classification performance on close set.

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Authors:
Li Sun, Xiaoyi Yu, Liuan Wang, Jun Sun, Hiroya Inakoshi, Ken Kobayashi, Hiromichi Kobashi
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17 September 2019 - 4:35am
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ICIP_sunli_v3.pdf

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[1] Li Sun, Xiaoyi Yu, Liuan Wang, Jun Sun, Hiroya Inakoshi, Ken Kobayashi, Hiromichi Kobashi, "Automatic neural network search method for Open Set Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4657. Accessed: Dec. 13, 2019.
@article{4657-19,
url = {http://sigport.org/4657},
author = {Li Sun; Xiaoyi Yu; Liuan Wang; Jun Sun; Hiroya Inakoshi; Ken Kobayashi; Hiromichi Kobashi },
publisher = {IEEE SigPort},
title = {Automatic neural network search method for Open Set Recognition},
year = {2019} }
TY - EJOUR
T1 - Automatic neural network search method for Open Set Recognition
AU - Li Sun; Xiaoyi Yu; Liuan Wang; Jun Sun; Hiroya Inakoshi; Ken Kobayashi; Hiromichi Kobashi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4657
ER -
Li Sun, Xiaoyi Yu, Liuan Wang, Jun Sun, Hiroya Inakoshi, Ken Kobayashi, Hiromichi Kobashi. (2019). Automatic neural network search method for Open Set Recognition. IEEE SigPort. http://sigport.org/4657
Li Sun, Xiaoyi Yu, Liuan Wang, Jun Sun, Hiroya Inakoshi, Ken Kobayashi, Hiromichi Kobashi, 2019. Automatic neural network search method for Open Set Recognition. Available at: http://sigport.org/4657.
Li Sun, Xiaoyi Yu, Liuan Wang, Jun Sun, Hiroya Inakoshi, Ken Kobayashi, Hiromichi Kobashi. (2019). "Automatic neural network search method for Open Set Recognition." Web.
1. Li Sun, Xiaoyi Yu, Liuan Wang, Jun Sun, Hiroya Inakoshi, Ken Kobayashi, Hiromichi Kobashi. Automatic neural network search method for Open Set Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4657

A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties


Stochastic mirror descent (SMD) algorithms have recently garnered a great deal of attention in optimization, signal processing, and machine learning. They are similar to stochastic gradient descent (SGD), in that they perform updates along the negative gradient of an instantaneous (or stochastically chosen) loss function. However, rather than update the parameter (or weight) vector directly, they update it in a "mirrored" domain whose transformation is given by the gradient of a strictly convex differentiable potential function.

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Authors:
Navid Azizan, Babak Hassibi
Submitted On:
13 May 2019 - 8:33pm
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ICASSP-SMD-Poster.pdf

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[1] Navid Azizan, Babak Hassibi, "A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4498. Accessed: Dec. 13, 2019.
@article{4498-19,
url = {http://sigport.org/4498},
author = {Navid Azizan; Babak Hassibi },
publisher = {IEEE SigPort},
title = {A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties},
year = {2019} }
TY - EJOUR
T1 - A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties
AU - Navid Azizan; Babak Hassibi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4498
ER -
Navid Azizan, Babak Hassibi. (2019). A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties. IEEE SigPort. http://sigport.org/4498
Navid Azizan, Babak Hassibi, 2019. A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties. Available at: http://sigport.org/4498.
Navid Azizan, Babak Hassibi. (2019). "A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties." Web.
1. Navid Azizan, Babak Hassibi. A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4498

Sparse Recovery and Non-stationary Blind Demodulation

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Authors:
Youye Xie, Michael B. Wakin, Gongguo Tang
Submitted On:
13 May 2019 - 5:51pm
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[1] Youye Xie, Michael B. Wakin, Gongguo Tang, "Sparse Recovery and Non-stationary Blind Demodulation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4494. Accessed: Dec. 13, 2019.
@article{4494-19,
url = {http://sigport.org/4494},
author = {Youye Xie; Michael B. Wakin; Gongguo Tang },
publisher = {IEEE SigPort},
title = {Sparse Recovery and Non-stationary Blind Demodulation},
year = {2019} }
TY - EJOUR
T1 - Sparse Recovery and Non-stationary Blind Demodulation
AU - Youye Xie; Michael B. Wakin; Gongguo Tang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4494
ER -
Youye Xie, Michael B. Wakin, Gongguo Tang. (2019). Sparse Recovery and Non-stationary Blind Demodulation. IEEE SigPort. http://sigport.org/4494
Youye Xie, Michael B. Wakin, Gongguo Tang, 2019. Sparse Recovery and Non-stationary Blind Demodulation. Available at: http://sigport.org/4494.
Youye Xie, Michael B. Wakin, Gongguo Tang. (2019). "Sparse Recovery and Non-stationary Blind Demodulation." Web.
1. Youye Xie, Michael B. Wakin, Gongguo Tang. Sparse Recovery and Non-stationary Blind Demodulation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4494

A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM

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12 May 2019 - 4:31am
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ICASSP2019_poster_deepMTT.pdf

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[1] , "A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4459. Accessed: Dec. 13, 2019.
@article{4459-19,
url = {http://sigport.org/4459},
author = { },
publisher = {IEEE SigPort},
title = {A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM},
year = {2019} }
TY - EJOUR
T1 - A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4459
ER -
. (2019). A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM. IEEE SigPort. http://sigport.org/4459
, 2019. A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM. Available at: http://sigport.org/4459.
. (2019). "A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM." Web.
1. . A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4459

DISJUNCT MATRICES FOR COMPRESSED SENSING

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Authors:
Pradip Sasmal, Sai Subramanyam Thoota, Chandra R. Murthy
Submitted On:
11 May 2019 - 2:51am
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DISJUNCT MATRICES FOR COMPRESSED SENSING

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[1] Pradip Sasmal, Sai Subramanyam Thoota, Chandra R. Murthy, "DISJUNCT MATRICES FOR COMPRESSED SENSING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4438. Accessed: Dec. 13, 2019.
@article{4438-19,
url = {http://sigport.org/4438},
author = {Pradip Sasmal; Sai Subramanyam Thoota; Chandra R. Murthy },
publisher = {IEEE SigPort},
title = {DISJUNCT MATRICES FOR COMPRESSED SENSING},
year = {2019} }
TY - EJOUR
T1 - DISJUNCT MATRICES FOR COMPRESSED SENSING
AU - Pradip Sasmal; Sai Subramanyam Thoota; Chandra R. Murthy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4438
ER -
Pradip Sasmal, Sai Subramanyam Thoota, Chandra R. Murthy. (2019). DISJUNCT MATRICES FOR COMPRESSED SENSING. IEEE SigPort. http://sigport.org/4438
Pradip Sasmal, Sai Subramanyam Thoota, Chandra R. Murthy, 2019. DISJUNCT MATRICES FOR COMPRESSED SENSING. Available at: http://sigport.org/4438.
Pradip Sasmal, Sai Subramanyam Thoota, Chandra R. Murthy. (2019). "DISJUNCT MATRICES FOR COMPRESSED SENSING." Web.
1. Pradip Sasmal, Sai Subramanyam Thoota, Chandra R. Murthy. DISJUNCT MATRICES FOR COMPRESSED SENSING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4438

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