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Signal and System Modeling, Representation and Estimation

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
Submitted On:
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: Oct. 16, 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: Oct. 16, 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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ICASSPposter36x48.pdf

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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: Oct. 16, 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: Oct. 16, 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: Oct. 16, 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

Rethinking Super-resolution: The Bandwidth Selection Problem


Super-resolution is the art of recovering spikes from their low-pass projections. Over the last decade specifically, several significant advancements linked with mathematical guarantees and recovery algorithms have been made. Most super-resolution algorithms rely on a two-step procedure: deconvolution followed by high-resolution frequency estimation. However, for this to work, exact bandwidth of low-pass filter must be known; an assumption that is central to the mathematical model of super-resolution.

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Authors:
Dmitry Batenkov, Ayush Bhandari, Thierry Blu
Submitted On:
10 May 2019 - 11:17pm
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Poster

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[1] Dmitry Batenkov, Ayush Bhandari, Thierry Blu, "Rethinking Super-resolution: The Bandwidth Selection Problem", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4430. Accessed: Oct. 16, 2019.
@article{4430-19,
url = {http://sigport.org/4430},
author = {Dmitry Batenkov; Ayush Bhandari; Thierry Blu },
publisher = {IEEE SigPort},
title = {Rethinking Super-resolution: The Bandwidth Selection Problem},
year = {2019} }
TY - EJOUR
T1 - Rethinking Super-resolution: The Bandwidth Selection Problem
AU - Dmitry Batenkov; Ayush Bhandari; Thierry Blu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4430
ER -
Dmitry Batenkov, Ayush Bhandari, Thierry Blu. (2019). Rethinking Super-resolution: The Bandwidth Selection Problem. IEEE SigPort. http://sigport.org/4430
Dmitry Batenkov, Ayush Bhandari, Thierry Blu, 2019. Rethinking Super-resolution: The Bandwidth Selection Problem. Available at: http://sigport.org/4430.
Dmitry Batenkov, Ayush Bhandari, Thierry Blu. (2019). "Rethinking Super-resolution: The Bandwidth Selection Problem." Web.
1. Dmitry Batenkov, Ayush Bhandari, Thierry Blu. Rethinking Super-resolution: The Bandwidth Selection Problem [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4430

Tropical Modeling of Weighted Transducer Algorithms on Graphs

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Authors:
Petros Maragos
Submitted On:
10 May 2019 - 2:39pm
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ICASSP 2019 paper poster.

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[1] Petros Maragos, "Tropical Modeling of Weighted Transducer Algorithms on Graphs", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4390. Accessed: Oct. 16, 2019.
@article{4390-19,
url = {http://sigport.org/4390},
author = {Petros Maragos },
publisher = {IEEE SigPort},
title = {Tropical Modeling of Weighted Transducer Algorithms on Graphs},
year = {2019} }
TY - EJOUR
T1 - Tropical Modeling of Weighted Transducer Algorithms on Graphs
AU - Petros Maragos
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4390
ER -
Petros Maragos. (2019). Tropical Modeling of Weighted Transducer Algorithms on Graphs. IEEE SigPort. http://sigport.org/4390
Petros Maragos, 2019. Tropical Modeling of Weighted Transducer Algorithms on Graphs. Available at: http://sigport.org/4390.
Petros Maragos. (2019). "Tropical Modeling of Weighted Transducer Algorithms on Graphs." Web.
1. Petros Maragos. Tropical Modeling of Weighted Transducer Algorithms on Graphs [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4390

Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition


The Second-order Sequential Best Rotation (SBR2) algorithm, used for Eigenvalue Decomposition (EVD) on para-Hermitian polynomial matrices typically encountered in wideband signal processing applications like multichannel Wiener filtering and channel coding, involves a series of delay and rotation operations to achieve diagonalisation. In this paper, we proposed the use of Householder transformations to reduce polynomial matrices to tridiagonal form before zeroing the dominant element with rotation.

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Authors:
Vincent W. Neo, Patrick A. Naylor
Submitted On:
10 May 2019 - 12:30pm
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SBR2HT_Neo2019.pdf

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[1] Vincent W. Neo, Patrick A. Naylor, "Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4356. Accessed: Oct. 16, 2019.
@article{4356-19,
url = {http://sigport.org/4356},
author = {Vincent W. Neo; Patrick A. Naylor },
publisher = {IEEE SigPort},
title = {Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition},
year = {2019} }
TY - EJOUR
T1 - Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition
AU - Vincent W. Neo; Patrick A. Naylor
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4356
ER -
Vincent W. Neo, Patrick A. Naylor. (2019). Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition. IEEE SigPort. http://sigport.org/4356
Vincent W. Neo, Patrick A. Naylor, 2019. Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition. Available at: http://sigport.org/4356.
Vincent W. Neo, Patrick A. Naylor. (2019). "Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition." Web.
1. Vincent W. Neo, Patrick A. Naylor. Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4356

Presentation

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10 May 2019 - 10:47am
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[1] , "Presentation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4347. Accessed: Oct. 16, 2019.
@article{4347-19,
url = {http://sigport.org/4347},
author = { },
publisher = {IEEE SigPort},
title = {Presentation},
year = {2019} }
TY - EJOUR
T1 - Presentation
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4347
ER -
. (2019). Presentation. IEEE SigPort. http://sigport.org/4347
, 2019. Presentation. Available at: http://sigport.org/4347.
. (2019). "Presentation." Web.
1. . Presentation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4347

A DISCRETE SIGNAL PROCESSING FRAMEWORK FOR MEET/JOIN LATTICES WITH APPLICATIONS TO HYPERGRAPHS AND TREES


We introduce a novel discrete signal processing framework, called discrete-lattice SP, for signals indexed by a finite lattice. A lattice is a partially ordered set that supports a meet (or join) operation that returns the greatest element below two given elements. Discrete-lattice SP chooses the meet as shift operation and derives associated notion of (meet-invariant) convolution, Fourier transform, frequency response, and a convolution theorem. Examples of lattices include sets of sets that are closed under intersection and trees.

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10 May 2019 - 8:23am
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Poster for publication DSP on meet/join lattices

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[1] , "A DISCRETE SIGNAL PROCESSING FRAMEWORK FOR MEET/JOIN LATTICES WITH APPLICATIONS TO HYPERGRAPHS AND TREES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4319. Accessed: Oct. 16, 2019.
@article{4319-19,
url = {http://sigport.org/4319},
author = { },
publisher = {IEEE SigPort},
title = {A DISCRETE SIGNAL PROCESSING FRAMEWORK FOR MEET/JOIN LATTICES WITH APPLICATIONS TO HYPERGRAPHS AND TREES},
year = {2019} }
TY - EJOUR
T1 - A DISCRETE SIGNAL PROCESSING FRAMEWORK FOR MEET/JOIN LATTICES WITH APPLICATIONS TO HYPERGRAPHS AND TREES
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4319
ER -
. (2019). A DISCRETE SIGNAL PROCESSING FRAMEWORK FOR MEET/JOIN LATTICES WITH APPLICATIONS TO HYPERGRAPHS AND TREES. IEEE SigPort. http://sigport.org/4319
, 2019. A DISCRETE SIGNAL PROCESSING FRAMEWORK FOR MEET/JOIN LATTICES WITH APPLICATIONS TO HYPERGRAPHS AND TREES. Available at: http://sigport.org/4319.
. (2019). "A DISCRETE SIGNAL PROCESSING FRAMEWORK FOR MEET/JOIN LATTICES WITH APPLICATIONS TO HYPERGRAPHS AND TREES." Web.
1. . A DISCRETE SIGNAL PROCESSING FRAMEWORK FOR MEET/JOIN LATTICES WITH APPLICATIONS TO HYPERGRAPHS AND TREES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4319

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