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Signal Processing Theory and Methods

UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM


Multiple-object tracking (MOT) and classification are core technologies for processing moving point clouds in radar or lidar applications. For accurate object classification, the one-to-one association relationship between the model of each objects' motion (trackers) and the observation sequences including auxiliary features (e.g., radar cross section) is important.

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Authors:
Yuta Kawachi, Teppei Suzuki
Submitted On:
4 June 2020 - 7:59am
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ICASSP2020_A0_vert_ykawachi_submit_20200415_3.pdf

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[1] Yuta Kawachi, Teppei Suzuki, "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5452. Accessed: Jul. 04, 2020.
@article{5452-20,
url = {http://sigport.org/5452},
author = {Yuta Kawachi; Teppei Suzuki },
publisher = {IEEE SigPort},
title = {UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM},
year = {2020} }
TY - EJOUR
T1 - UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM
AU - Yuta Kawachi; Teppei Suzuki
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5452
ER -
Yuta Kawachi, Teppei Suzuki. (2020). UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. IEEE SigPort. http://sigport.org/5452
Yuta Kawachi, Teppei Suzuki, 2020. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. Available at: http://sigport.org/5452.
Yuta Kawachi, Teppei Suzuki. (2020). "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM." Web.
1. Yuta Kawachi, Teppei Suzuki. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5452

UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM


Multiple-object tracking (MOT) and classification are core technologies for processing moving point clouds in radar or lidar applications. For accurate object classification, the one-to-one association relationship between the model of each objects' motion (trackers) and the observation sequences including auxiliary features (e.g., radar cross section) is important.

Paper Details

Authors:
Yuta Kawachi, Teppei Suzuki
Submitted On:
4 June 2020 - 7:59am
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ICASSP2020_A0_vert_ykawachi_submit_20200415_3.pdf

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[1] Yuta Kawachi, Teppei Suzuki, "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5451. Accessed: Jul. 04, 2020.
@article{5451-20,
url = {http://sigport.org/5451},
author = {Yuta Kawachi; Teppei Suzuki },
publisher = {IEEE SigPort},
title = {UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM},
year = {2020} }
TY - EJOUR
T1 - UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM
AU - Yuta Kawachi; Teppei Suzuki
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5451
ER -
Yuta Kawachi, Teppei Suzuki. (2020). UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. IEEE SigPort. http://sigport.org/5451
Yuta Kawachi, Teppei Suzuki, 2020. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. Available at: http://sigport.org/5451.
Yuta Kawachi, Teppei Suzuki. (2020). "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM." Web.
1. Yuta Kawachi, Teppei Suzuki. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5451

On the Stability of Polynomial Spectral Graph Filters

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Authors:
Henry Kenlay, Dorina Thanou, Xiaowen Dong
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22 May 2020 - 5:17am
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[1] Henry Kenlay, Dorina Thanou, Xiaowen Dong, "On the Stability of Polynomial Spectral Graph Filters", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5431. Accessed: Jul. 04, 2020.
@article{5431-20,
url = {http://sigport.org/5431},
author = {Henry Kenlay; Dorina Thanou; Xiaowen Dong },
publisher = {IEEE SigPort},
title = {On the Stability of Polynomial Spectral Graph Filters},
year = {2020} }
TY - EJOUR
T1 - On the Stability of Polynomial Spectral Graph Filters
AU - Henry Kenlay; Dorina Thanou; Xiaowen Dong
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5431
ER -
Henry Kenlay, Dorina Thanou, Xiaowen Dong. (2020). On the Stability of Polynomial Spectral Graph Filters. IEEE SigPort. http://sigport.org/5431
Henry Kenlay, Dorina Thanou, Xiaowen Dong, 2020. On the Stability of Polynomial Spectral Graph Filters. Available at: http://sigport.org/5431.
Henry Kenlay, Dorina Thanou, Xiaowen Dong. (2020). "On the Stability of Polynomial Spectral Graph Filters." Web.
1. Henry Kenlay, Dorina Thanou, Xiaowen Dong. On the Stability of Polynomial Spectral Graph Filters [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5431

Recovery of binary sparse signals from compressed linear measurements via polynomial optimization

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20 May 2020 - 5:38am
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[1] , "Recovery of binary sparse signals from compressed linear measurements via polynomial optimization", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5405. Accessed: Jul. 04, 2020.
@article{5405-20,
url = {http://sigport.org/5405},
author = { },
publisher = {IEEE SigPort},
title = {Recovery of binary sparse signals from compressed linear measurements via polynomial optimization},
year = {2020} }
TY - EJOUR
T1 - Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5405
ER -
. (2020). Recovery of binary sparse signals from compressed linear measurements via polynomial optimization. IEEE SigPort. http://sigport.org/5405
, 2020. Recovery of binary sparse signals from compressed linear measurements via polynomial optimization. Available at: http://sigport.org/5405.
. (2020). "Recovery of binary sparse signals from compressed linear measurements via polynomial optimization." Web.
1. . Recovery of binary sparse signals from compressed linear measurements via polynomial optimization [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5405

PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION

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Authors:
Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic
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16 May 2020 - 8:06am
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[1] Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic, "PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5373. Accessed: Jul. 04, 2020.
@article{5373-20,
url = {http://sigport.org/5373},
author = {Bruno Scalzo; Ljubisa Stankovic; Anthony G. Constantinides; Danilo P. Mandic },
publisher = {IEEE SigPort},
title = {PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION},
year = {2020} }
TY - EJOUR
T1 - PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION
AU - Bruno Scalzo; Ljubisa Stankovic; Anthony G. Constantinides; Danilo P. Mandic
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5373
ER -
Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic. (2020). PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION. IEEE SigPort. http://sigport.org/5373
Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic, 2020. PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION. Available at: http://sigport.org/5373.
Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic. (2020). "PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION." Web.
1. Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic. PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5373

Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach


Economic and financial decision-making may cause a significant impact on government, society, and industries. Due to the increasing volume of data, decision science has become an interdisciplinary field of study, supported by efficient methods and models of data analysis. Our contributions lie exactly in the intersection of signal processing, tensorial algebra, and decision science. More precisely, we introduce a novel approach in which the data taken into account in the decision process is modeled as a tensor.

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Authors:
Betania S.C. Campello, Leonardo T. Duarte, João M. T. Romano
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15 May 2020 - 5:45pm
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[1] Betania S.C. Campello, Leonardo T. Duarte, João M. T. Romano, "Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5356. Accessed: Jul. 04, 2020.
@article{5356-20,
url = {http://sigport.org/5356},
author = {Betania S.C. Campello; Leonardo T. Duarte; João M. T. Romano },
publisher = {IEEE SigPort},
title = {Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach},
year = {2020} }
TY - EJOUR
T1 - Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach
AU - Betania S.C. Campello; Leonardo T. Duarte; João M. T. Romano
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5356
ER -
Betania S.C. Campello, Leonardo T. Duarte, João M. T. Romano. (2020). Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach. IEEE SigPort. http://sigport.org/5356
Betania S.C. Campello, Leonardo T. Duarte, João M. T. Romano, 2020. Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach. Available at: http://sigport.org/5356.
Betania S.C. Campello, Leonardo T. Duarte, João M. T. Romano. (2020). "Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach." Web.
1. Betania S.C. Campello, Leonardo T. Duarte, João M. T. Romano. Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5356

β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR


In Nuclear Magnetic Resonance (NMR) spectroscopy, an efficient analysis and a relevant extraction of different molecule properties from a given chemical mixture are important tasks, especially when processing bidimensional NMR data. To that end, using a blind source separation approach based on a variational formulation seems to be a good strategy. However, the poor resolution of NMR spectra and their large dimension require a new and modern blind source separation method.

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Authors:
Sandrine ANTHOINE, Caroline CHAUX
Submitted On:
15 May 2020 - 4:42am
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[1] Sandrine ANTHOINE, Caroline CHAUX, "β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5342. Accessed: Jul. 04, 2020.
@article{5342-20,
url = {http://sigport.org/5342},
author = {Sandrine ANTHOINE; Caroline CHAUX },
publisher = {IEEE SigPort},
title = {β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR},
year = {2020} }
TY - EJOUR
T1 - β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR
AU - Sandrine ANTHOINE; Caroline CHAUX
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5342
ER -
Sandrine ANTHOINE, Caroline CHAUX. (2020). β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR. IEEE SigPort. http://sigport.org/5342
Sandrine ANTHOINE, Caroline CHAUX, 2020. β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR. Available at: http://sigport.org/5342.
Sandrine ANTHOINE, Caroline CHAUX. (2020). "β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR." Web.
1. Sandrine ANTHOINE, Caroline CHAUX. β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5342

LEARNING SIGNED GRAPHS FROM DATA


Signed graphs have recently been found to offer advantages over unsigned graphs in a variety of tasks. However, the problem of learning graph topologies has only been considered for the unsigned case. In this paper, we propose a conceptually simple and flexible approach to signed graph learning via signed smoothness metrics. Learning the graph amounts to solving a convex optimization problem, which we show can be reduced to an efficiently solvable quadratic problem. Applications to signal reconstruction and clustering corroborate the effectiveness of the proposed method.

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Authors:
Thomas Dittrich
Submitted On:
14 May 2020 - 5:04am
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SignedGraphLearning.pdf

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[1] Thomas Dittrich, "LEARNING SIGNED GRAPHS FROM DATA", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5274. Accessed: Jul. 04, 2020.
@article{5274-20,
url = {http://sigport.org/5274},
author = {Thomas Dittrich },
publisher = {IEEE SigPort},
title = {LEARNING SIGNED GRAPHS FROM DATA},
year = {2020} }
TY - EJOUR
T1 - LEARNING SIGNED GRAPHS FROM DATA
AU - Thomas Dittrich
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5274
ER -
Thomas Dittrich. (2020). LEARNING SIGNED GRAPHS FROM DATA. IEEE SigPort. http://sigport.org/5274
Thomas Dittrich, 2020. LEARNING SIGNED GRAPHS FROM DATA. Available at: http://sigport.org/5274.
Thomas Dittrich. (2020). "LEARNING SIGNED GRAPHS FROM DATA." Web.
1. Thomas Dittrich. LEARNING SIGNED GRAPHS FROM DATA [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5274

Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition


Graph signal processing on directed graphs poses theoretical challenges since an eigendecomposition of filters is in general not available. Instead, Fourier analysis requires a Jordan decomposition and the frequency response is given by the Jordan normal form, whose computation is numerically unstable for large sizes. In this paper, we propose to replace a given adjacency shift A by a diagonalizable shift A D obtained via the Jordan-Chevalley decomposition.

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Authors:
Chris Wendler, Markus Püschel
Submitted On:
14 May 2020 - 2:21am
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[1] Chris Wendler, Markus Püschel, "Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5234. Accessed: Jul. 04, 2020.
@article{5234-20,
url = {http://sigport.org/5234},
author = {Chris Wendler; Markus Püschel },
publisher = {IEEE SigPort},
title = {Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition},
year = {2020} }
TY - EJOUR
T1 - Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition
AU - Chris Wendler; Markus Püschel
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5234
ER -
Chris Wendler, Markus Püschel. (2020). Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition. IEEE SigPort. http://sigport.org/5234
Chris Wendler, Markus Püschel, 2020. Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition. Available at: http://sigport.org/5234.
Chris Wendler, Markus Püschel. (2020). "Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition." Web.
1. Chris Wendler, Markus Püschel. Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5234

Blind Source Separation of Graph Signals

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Authors:
Esa Ollila
Submitted On:
14 May 2020 - 1:45am
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[1] Esa Ollila, "Blind Source Separation of Graph Signals", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5226. Accessed: Jul. 04, 2020.
@article{5226-20,
url = {http://sigport.org/5226},
author = {Esa Ollila },
publisher = {IEEE SigPort},
title = {Blind Source Separation of Graph Signals},
year = {2020} }
TY - EJOUR
T1 - Blind Source Separation of Graph Signals
AU - Esa Ollila
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5226
ER -
Esa Ollila. (2020). Blind Source Separation of Graph Signals. IEEE SigPort. http://sigport.org/5226
Esa Ollila, 2020. Blind Source Separation of Graph Signals. Available at: http://sigport.org/5226.
Esa Ollila. (2020). "Blind Source Separation of Graph Signals." Web.
1. Esa Ollila. Blind Source Separation of Graph Signals [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5226

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