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Emerging: Big Data

Introduction to the Special Session on Topological Data Analysis


Topological Data Analysis (TDA) is a topic which has recently seen many applications. The goal of this special session is to highlight the bridge between signal processing, machine learning and techniques in topological data analysis. In this way, we hope to encourage more engineers to start exploring TDA and its applications. This paper briefly introduces the standard techniques used in this area, delineates the common theme connecting the works presented in this session, and concludes with a brief summary of each of the papers presented.

Paper Details

Authors:
Harish Chintakunta, Michael Robinson, Hamid Krim
Submitted On:
19 March 2016 - 8:29pm
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20160324_ICASSP.pdf

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[1] Harish Chintakunta, Michael Robinson, Hamid Krim, "Introduction to the Special Session on Topological Data Analysis", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/839. Accessed: Dec. 16, 2017.
@article{839-16,
url = {http://sigport.org/839},
author = {Harish Chintakunta; Michael Robinson; Hamid Krim },
publisher = {IEEE SigPort},
title = {Introduction to the Special Session on Topological Data Analysis},
year = {2016} }
TY - EJOUR
T1 - Introduction to the Special Session on Topological Data Analysis
AU - Harish Chintakunta; Michael Robinson; Hamid Krim
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/839
ER -
Harish Chintakunta, Michael Robinson, Hamid Krim. (2016). Introduction to the Special Session on Topological Data Analysis. IEEE SigPort. http://sigport.org/839
Harish Chintakunta, Michael Robinson, Hamid Krim, 2016. Introduction to the Special Session on Topological Data Analysis. Available at: http://sigport.org/839.
Harish Chintakunta, Michael Robinson, Hamid Krim. (2016). "Introduction to the Special Session on Topological Data Analysis." Web.
1. Harish Chintakunta, Michael Robinson, Hamid Krim. Introduction to the Special Session on Topological Data Analysis [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/839

D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization

Paper Details

Authors:
Jean Lafond, Hoi-To Wai, Eric Moulines
Submitted On:
18 March 2016 - 2:56pm
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d_fw_slides.pdf

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[1] Jean Lafond, Hoi-To Wai, Eric Moulines, "D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/775. Accessed: Dec. 16, 2017.
@article{775-16,
url = {http://sigport.org/775},
author = {Jean Lafond; Hoi-To Wai; Eric Moulines },
publisher = {IEEE SigPort},
title = {D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization},
year = {2016} }
TY - EJOUR
T1 - D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization
AU - Jean Lafond; Hoi-To Wai; Eric Moulines
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/775
ER -
Jean Lafond, Hoi-To Wai, Eric Moulines. (2016). D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization. IEEE SigPort. http://sigport.org/775
Jean Lafond, Hoi-To Wai, Eric Moulines, 2016. D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization. Available at: http://sigport.org/775.
Jean Lafond, Hoi-To Wai, Eric Moulines. (2016). "D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization." Web.
1. Jean Lafond, Hoi-To Wai, Eric Moulines. D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/775

Effecient Sensor Position Selection Using Graph Signal Sampling Theory


We consider the problem of selecting optimal sensor placements. The proposed approach is based on the sampling theorem of graph signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the existing methods in the context of graph signal processing and clarify the relationship between these methods and the proposed approach. The effectiveness of our approach is verified through numerical experiments, showing advantages in prediction error and execution time.

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Authors:
Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega
Submitted On:
18 March 2016 - 2:54am
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Sakiyama_etal_ICASSP2016_3.pdf

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[1] Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega, "Effecient Sensor Position Selection Using Graph Signal Sampling Theory", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/761. Accessed: Dec. 16, 2017.
@article{761-16,
url = {http://sigport.org/761},
author = {Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega },
publisher = {IEEE SigPort},
title = {Effecient Sensor Position Selection Using Graph Signal Sampling Theory},
year = {2016} }
TY - EJOUR
T1 - Effecient Sensor Position Selection Using Graph Signal Sampling Theory
AU - Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/761
ER -
Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega. (2016). Effecient Sensor Position Selection Using Graph Signal Sampling Theory. IEEE SigPort. http://sigport.org/761
Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega, 2016. Effecient Sensor Position Selection Using Graph Signal Sampling Theory. Available at: http://sigport.org/761.
Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega. (2016). "Effecient Sensor Position Selection Using Graph Signal Sampling Theory." Web.
1. Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega. Effecient Sensor Position Selection Using Graph Signal Sampling Theory [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/761

Signal Processing on Graphs: Performance of Graph Structure Estimation

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Authors:
Jonathan Mei, José M F Moura
Submitted On:
15 March 2016 - 4:02pm
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icassp_jmei2016.pptx

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[1] Jonathan Mei, José M F Moura, "Signal Processing on Graphs: Performance of Graph Structure Estimation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/696. Accessed: Dec. 16, 2017.
@article{696-16,
url = {http://sigport.org/696},
author = {Jonathan Mei; José M F Moura },
publisher = {IEEE SigPort},
title = {Signal Processing on Graphs: Performance of Graph Structure Estimation},
year = {2016} }
TY - EJOUR
T1 - Signal Processing on Graphs: Performance of Graph Structure Estimation
AU - Jonathan Mei; José M F Moura
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/696
ER -
Jonathan Mei, José M F Moura. (2016). Signal Processing on Graphs: Performance of Graph Structure Estimation. IEEE SigPort. http://sigport.org/696
Jonathan Mei, José M F Moura, 2016. Signal Processing on Graphs: Performance of Graph Structure Estimation. Available at: http://sigport.org/696.
Jonathan Mei, José M F Moura. (2016). "Signal Processing on Graphs: Performance of Graph Structure Estimation." Web.
1. Jonathan Mei, José M F Moura. Signal Processing on Graphs: Performance of Graph Structure Estimation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/696

SmartRoadSense - A collaborative project for monitoring road surface conditions


SmartRoadSense is a collaborative project aimed at monitoring road surface conditions exploiting the sensors, i.e., accelerometers and GPS, commonly embedded in most mobile devices. Given a smartphone connected to the cabin of a car travelling on a road, the SmartRoadSense app analyzes the accelerometer signals and extracts a roughness index (based on a linear predictive coding analysis) characterizing the quality of the road.

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Authors:
Emanuele Lattanzi, Valerio Freschi, Alessandro Bogliolo
Submitted On:
5 April 2016 - 5:31am
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SmartRoadSense.pdf

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[1] Emanuele Lattanzi, Valerio Freschi, Alessandro Bogliolo, "SmartRoadSense - A collaborative project for monitoring road surface conditions", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/677. Accessed: Dec. 16, 2017.
@article{677-16,
url = {http://sigport.org/677},
author = {Emanuele Lattanzi; Valerio Freschi; Alessandro Bogliolo },
publisher = {IEEE SigPort},
title = {SmartRoadSense - A collaborative project for monitoring road surface conditions},
year = {2016} }
TY - EJOUR
T1 - SmartRoadSense - A collaborative project for monitoring road surface conditions
AU - Emanuele Lattanzi; Valerio Freschi; Alessandro Bogliolo
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/677
ER -
Emanuele Lattanzi, Valerio Freschi, Alessandro Bogliolo. (2016). SmartRoadSense - A collaborative project for monitoring road surface conditions. IEEE SigPort. http://sigport.org/677
Emanuele Lattanzi, Valerio Freschi, Alessandro Bogliolo, 2016. SmartRoadSense - A collaborative project for monitoring road surface conditions. Available at: http://sigport.org/677.
Emanuele Lattanzi, Valerio Freschi, Alessandro Bogliolo. (2016). "SmartRoadSense - A collaborative project for monitoring road surface conditions." Web.
1. Emanuele Lattanzi, Valerio Freschi, Alessandro Bogliolo. SmartRoadSense - A collaborative project for monitoring road surface conditions [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/677

Non-monotone Quadratic Potential Games with single Quadratic constraints


We consider the problem of solving a quadratic potential game with single quadratic constraints, under no monotonicity condition of the game, nor convexity in any of the player's problem. We show existence of Nash equilibria (NE) in the game, and propose a framework to calculate Pareto efficient solutions. Regarding the corresponding non-convex potential function, we show that strong duality holds with its corresponding dual problem, give existence results of solutions and present conditions for global optimality. Finally, we propose a centralized method to solve the potential problem, and a distributed version for compact constraints. We also present simulations showing convergence behavior of the proposed distributed algorithm.

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Authors:
Santiago Zazo, Sergio Valcarcel Macua
Submitted On:
29 March 2016 - 9:57am
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quadratic_poster.pdf

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[1] Santiago Zazo, Sergio Valcarcel Macua, "Non-monotone Quadratic Potential Games with single Quadratic constraints", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/632. Accessed: Dec. 16, 2017.
@article{632-16,
url = {http://sigport.org/632},
author = {Santiago Zazo; Sergio Valcarcel Macua },
publisher = {IEEE SigPort},
title = {Non-monotone Quadratic Potential Games with single Quadratic constraints},
year = {2016} }
TY - EJOUR
T1 - Non-monotone Quadratic Potential Games with single Quadratic constraints
AU - Santiago Zazo; Sergio Valcarcel Macua
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/632
ER -
Santiago Zazo, Sergio Valcarcel Macua. (2016). Non-monotone Quadratic Potential Games with single Quadratic constraints. IEEE SigPort. http://sigport.org/632
Santiago Zazo, Sergio Valcarcel Macua, 2016. Non-monotone Quadratic Potential Games with single Quadratic constraints. Available at: http://sigport.org/632.
Santiago Zazo, Sergio Valcarcel Macua. (2016). "Non-monotone Quadratic Potential Games with single Quadratic constraints." Web.
1. Santiago Zazo, Sergio Valcarcel Macua. Non-monotone Quadratic Potential Games with single Quadratic constraints [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/632

Panel Discussion: Algorithms vs. Architectures: Opportunities and Challenges in Multicore/GPU DSP


In the 1960s, Marshall McLuhan published the book entitled, The Extensions of Man focusing primarily on television, an electronic media as being the outward extension of human nervous system, which from contemporary interpretation marks the previous stage of Big Data.

Paper Details

Authors:
Lee Barford, Paul Blinzer, Joe Cavallaro, Hong, Jiang, Nick Moore, Yinglong Xia
Submitted On:
23 February 2016 - 1:44pm
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GlobalSIPS 2015 Panel Discussion Presentation.pdf

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[1] Lee Barford, Paul Blinzer, Joe Cavallaro, Hong, Jiang, Nick Moore, Yinglong Xia, "Panel Discussion: Algorithms vs. Architectures: Opportunities and Challenges in Multicore/GPU DSP", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/569. Accessed: Dec. 16, 2017.
@article{569-15,
url = {http://sigport.org/569},
author = {Lee Barford; Paul Blinzer; Joe Cavallaro; Hong; Jiang; Nick Moore; Yinglong Xia },
publisher = {IEEE SigPort},
title = {Panel Discussion: Algorithms vs. Architectures: Opportunities and Challenges in Multicore/GPU DSP},
year = {2015} }
TY - EJOUR
T1 - Panel Discussion: Algorithms vs. Architectures: Opportunities and Challenges in Multicore/GPU DSP
AU - Lee Barford; Paul Blinzer; Joe Cavallaro; Hong; Jiang; Nick Moore; Yinglong Xia
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/569
ER -
Lee Barford, Paul Blinzer, Joe Cavallaro, Hong, Jiang, Nick Moore, Yinglong Xia. (2015). Panel Discussion: Algorithms vs. Architectures: Opportunities and Challenges in Multicore/GPU DSP. IEEE SigPort. http://sigport.org/569
Lee Barford, Paul Blinzer, Joe Cavallaro, Hong, Jiang, Nick Moore, Yinglong Xia, 2015. Panel Discussion: Algorithms vs. Architectures: Opportunities and Challenges in Multicore/GPU DSP. Available at: http://sigport.org/569.
Lee Barford, Paul Blinzer, Joe Cavallaro, Hong, Jiang, Nick Moore, Yinglong Xia. (2015). "Panel Discussion: Algorithms vs. Architectures: Opportunities and Challenges in Multicore/GPU DSP." Web.
1. Lee Barford, Paul Blinzer, Joe Cavallaro, Hong, Jiang, Nick Moore, Yinglong Xia. Panel Discussion: Algorithms vs. Architectures: Opportunities and Challenges in Multicore/GPU DSP [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/569

Models for Spectral Clustering and Their Applications


Microarray Spectral Clustering.

Ph.D. Thesis by Donald McCuan (advisor Andrew Knyazev), Department of Mathematical and Statistical Sciences, University of Colorado Denver, 2012, originally posted at http://math.ucdenver.edu/theses/McCuan_PhdThesis.pdf (543 downloads)

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Authors:
Donald Donald
Submitted On:
23 February 2016 - 1:44pm
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McCuan_PhdThesis.pdf

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[1] Donald Donald, "Models for Spectral Clustering and Their Applications", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/564. Accessed: Dec. 16, 2017.
@article{564-15,
url = {http://sigport.org/564},
author = {Donald Donald },
publisher = {IEEE SigPort},
title = {Models for Spectral Clustering and Their Applications},
year = {2015} }
TY - EJOUR
T1 - Models for Spectral Clustering and Their Applications
AU - Donald Donald
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/564
ER -
Donald Donald. (2015). Models for Spectral Clustering and Their Applications. IEEE SigPort. http://sigport.org/564
Donald Donald, 2015. Models for Spectral Clustering and Their Applications. Available at: http://sigport.org/564.
Donald Donald. (2015). "Models for Spectral Clustering and Their Applications." Web.
1. Donald Donald. Models for Spectral Clustering and Their Applications [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/564

Multigrid Eigensolvers for Image Segmentation


Spectral Image Segmentation

Presentation at LANL and UC Davis, 2009. Originally posted at http://math.ucdenver.edu/~aknyazev/research/conf/

LANL09.ppt

Office presentation icon LANL09.ppt (299 downloads)

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23 February 2016 - 1:44pm
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LANL09.ppt

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[1] , "Multigrid Eigensolvers for Image Segmentation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/562. Accessed: Dec. 16, 2017.
@article{562-15,
url = {http://sigport.org/562},
author = { },
publisher = {IEEE SigPort},
title = {Multigrid Eigensolvers for Image Segmentation},
year = {2015} }
TY - EJOUR
T1 - Multigrid Eigensolvers for Image Segmentation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/562
ER -
. (2015). Multigrid Eigensolvers for Image Segmentation. IEEE SigPort. http://sigport.org/562
, 2015. Multigrid Eigensolvers for Image Segmentation. Available at: http://sigport.org/562.
. (2015). "Multigrid Eigensolvers for Image Segmentation." Web.
1. . Multigrid Eigensolvers for Image Segmentation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/562

Novel data clustering for microarrays and image segmentation


Spectral Clustering Eigenvalue Problem

We develop novel algorithms and software on parallel computers for data clustering of large datasets. We are interested in applying our approach, e.g., for analysis of large datasets of microarrays or tiling arrays in molecular biology and for segmentation of high resolution images.

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23 February 2016 - 1:44pm
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camready-1031.ppt

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[1] , "Novel data clustering for microarrays and image segmentation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/561. Accessed: Dec. 16, 2017.
@article{561-15,
url = {http://sigport.org/561},
author = { },
publisher = {IEEE SigPort},
title = {Novel data clustering for microarrays and image segmentation},
year = {2015} }
TY - EJOUR
T1 - Novel data clustering for microarrays and image segmentation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/561
ER -
. (2015). Novel data clustering for microarrays and image segmentation. IEEE SigPort. http://sigport.org/561
, 2015. Novel data clustering for microarrays and image segmentation. Available at: http://sigport.org/561.
. (2015). "Novel data clustering for microarrays and image segmentation." Web.
1. . Novel data clustering for microarrays and image segmentation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/561

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