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Signal Processing Theory and Methods

ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS

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Authors:
François Glineur
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13 October 2019 - 10:40pm
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[1] François Glineur, "ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4867. Accessed: Oct. 17, 2019.
@article{4867-19,
url = {http://sigport.org/4867},
author = {François Glineur },
publisher = {IEEE SigPort},
title = {ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS},
year = {2019} }
TY - EJOUR
T1 - ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS
AU - François Glineur
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4867
ER -
François Glineur. (2019). ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS. IEEE SigPort. http://sigport.org/4867
François Glineur, 2019. ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS. Available at: http://sigport.org/4867.
François Glineur. (2019). "ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS." Web.
1. François Glineur. ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4867

Computing Vessel Velocity from Single Perspective Projection Images


We present an image-based approach to estimate the velocity of moving vessels from their traces on the water surface. Vessels moving at constant heading and speed display a familiar V-shaped pattern which only differs from one to another by the wavelength of their transverse and divergent components. Such wavelength is related to vessel velocity. We use planar homography and natural constraints on the geometry of ships’ wake crests to compute vessel velocity from single optical images acquired by conventional cameras.

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12 September 2019 - 2:35pm
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[1] , "Computing Vessel Velocity from Single Perspective Projection Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4572. Accessed: Oct. 17, 2019.
@article{4572-19,
url = {http://sigport.org/4572},
author = { },
publisher = {IEEE SigPort},
title = {Computing Vessel Velocity from Single Perspective Projection Images},
year = {2019} }
TY - EJOUR
T1 - Computing Vessel Velocity from Single Perspective Projection Images
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4572
ER -
. (2019). Computing Vessel Velocity from Single Perspective Projection Images. IEEE SigPort. http://sigport.org/4572
, 2019. Computing Vessel Velocity from Single Perspective Projection Images. Available at: http://sigport.org/4572.
. (2019). "Computing Vessel Velocity from Single Perspective Projection Images." Web.
1. . Computing Vessel Velocity from Single Perspective Projection Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4572

PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS


In this paper, we analyze the asymptotic performance of a convex optimization-based discrete-valued vector reconstruction from linear measurements. We firstly propose a box-constrained version of the conventional sum of absolute values (SOAV) optimization, which uses a weighted sum of L1 regularizers as a regularizer for the discrete-valued vector. We then derive the asymptotic symbol error rate (SER) performance of the box-constrained SOAV (Box-SOAV) optimization theoretically by using convex Gaussian min-max theorem.

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Authors:
Ryo Hayakawa, Kazunori Hayashi
Submitted On:
15 May 2019 - 5:52pm
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[1] Ryo Hayakawa, Kazunori Hayashi, "PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4532. Accessed: Oct. 17, 2019.
@article{4532-19,
url = {http://sigport.org/4532},
author = {Ryo Hayakawa; Kazunori Hayashi },
publisher = {IEEE SigPort},
title = {PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS},
year = {2019} }
TY - EJOUR
T1 - PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS
AU - Ryo Hayakawa; Kazunori Hayashi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4532
ER -
Ryo Hayakawa, Kazunori Hayashi. (2019). PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS. IEEE SigPort. http://sigport.org/4532
Ryo Hayakawa, Kazunori Hayashi, 2019. PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS. Available at: http://sigport.org/4532.
Ryo Hayakawa, Kazunori Hayashi. (2019). "PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS." Web.
1. Ryo Hayakawa, Kazunori Hayashi. PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4532

Robust least squares estimation of graph signals


Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS with respect to mismatches in the presumed graph topology. It builds on the fact that graph LMS converges faster when the graph topology is specified correctly. We consider two measures of convergence speed, based on which we develop randomized greedy algorithms for robust interpolation of graph signals.

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Authors:
Jari Miettinen, Sergiy Vorobyov, Esa Ollila
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13 May 2019 - 12:55pm
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[1] Jari Miettinen, Sergiy Vorobyov, Esa Ollila, "Robust least squares estimation of graph signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4489. Accessed: Oct. 17, 2019.
@article{4489-19,
url = {http://sigport.org/4489},
author = {Jari Miettinen; Sergiy Vorobyov; Esa Ollila },
publisher = {IEEE SigPort},
title = {Robust least squares estimation of graph signals},
year = {2019} }
TY - EJOUR
T1 - Robust least squares estimation of graph signals
AU - Jari Miettinen; Sergiy Vorobyov; Esa Ollila
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4489
ER -
Jari Miettinen, Sergiy Vorobyov, Esa Ollila. (2019). Robust least squares estimation of graph signals. IEEE SigPort. http://sigport.org/4489
Jari Miettinen, Sergiy Vorobyov, Esa Ollila, 2019. Robust least squares estimation of graph signals. Available at: http://sigport.org/4489.
Jari Miettinen, Sergiy Vorobyov, Esa Ollila. (2019). "Robust least squares estimation of graph signals." Web.
1. Jari Miettinen, Sergiy Vorobyov, Esa Ollila. Robust least squares estimation of graph signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4489

SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL


Community detection from graphs has many applications
in machine learning, biological and social sciences. While
there is a broad spectrum of literature based on various
approaches, recently there has been a significant focus on
inference algorithms for statistical models of community
structure. These algorithms strive to solve an inference
problem based on a generative model of the network. Recent
advances in stochastic gradient MCMC have played a crucial
role in improving the scalability of these techniques. In this

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Authors:
Soumyasundar Pal, Mark Coates
Submitted On:
10 May 2019 - 1:51pm
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[1] Soumyasundar Pal, Mark Coates, "SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4382. Accessed: Oct. 17, 2019.
@article{4382-19,
url = {http://sigport.org/4382},
author = {Soumyasundar Pal; Mark Coates },
publisher = {IEEE SigPort},
title = {SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL},
year = {2019} }
TY - EJOUR
T1 - SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL
AU - Soumyasundar Pal; Mark Coates
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4382
ER -
Soumyasundar Pal, Mark Coates. (2019). SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL. IEEE SigPort. http://sigport.org/4382
Soumyasundar Pal, Mark Coates, 2019. SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL. Available at: http://sigport.org/4382.
Soumyasundar Pal, Mark Coates. (2019). "SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL." Web.
1. Soumyasundar Pal, Mark Coates. SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4382

POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS


Distributed estimation of a parameter vector in a network of sensor nodes with ambiguous measurements is considered. The ambiguities are modelled by following a set-theoretic approach, that leads to each sensor employing a non-convex constraint set on the parameter vector. Consensus can be used to reach an estimate consistent with the measurements of all nodes, assuming that such an estimate exists, but unfortunately, such an approach leads to a non-convex problem.

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Authors:
Dimitris Ampeliotis, Kostas Berberidis
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10 May 2019 - 9:52am
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[1] Dimitris Ampeliotis, Kostas Berberidis, "POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4335. Accessed: Oct. 17, 2019.
@article{4335-19,
url = {http://sigport.org/4335},
author = {Dimitris Ampeliotis; Kostas Berberidis },
publisher = {IEEE SigPort},
title = {POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS},
year = {2019} }
TY - EJOUR
T1 - POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS
AU - Dimitris Ampeliotis; Kostas Berberidis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4335
ER -
Dimitris Ampeliotis, Kostas Berberidis. (2019). POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS. IEEE SigPort. http://sigport.org/4335
Dimitris Ampeliotis, Kostas Berberidis, 2019. POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS. Available at: http://sigport.org/4335.
Dimitris Ampeliotis, Kostas Berberidis. (2019). "POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS." Web.
1. Dimitris Ampeliotis, Kostas Berberidis. POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4335

Provably Accelerated Randomized Gossip Algorithms


In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method - a popular method for solving linear systems. In each gossip iteration all nodes of the network update their values but only a pair of them exchange their private information. Numerical experiments on popular wireless sensor networks showing the benefits of our protocols are also presented.

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Authors:
Nicolas Loizou, Michael Rabbat, Peter Richtarik
Submitted On:
9 May 2019 - 4:21pm
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[1] Nicolas Loizou, Michael Rabbat, Peter Richtarik, "Provably Accelerated Randomized Gossip Algorithms", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4237. Accessed: Oct. 17, 2019.
@article{4237-19,
url = {http://sigport.org/4237},
author = {Nicolas Loizou; Michael Rabbat; Peter Richtarik },
publisher = {IEEE SigPort},
title = {Provably Accelerated Randomized Gossip Algorithms},
year = {2019} }
TY - EJOUR
T1 - Provably Accelerated Randomized Gossip Algorithms
AU - Nicolas Loizou; Michael Rabbat; Peter Richtarik
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4237
ER -
Nicolas Loizou, Michael Rabbat, Peter Richtarik. (2019). Provably Accelerated Randomized Gossip Algorithms. IEEE SigPort. http://sigport.org/4237
Nicolas Loizou, Michael Rabbat, Peter Richtarik, 2019. Provably Accelerated Randomized Gossip Algorithms. Available at: http://sigport.org/4237.
Nicolas Loizou, Michael Rabbat, Peter Richtarik. (2019). "Provably Accelerated Randomized Gossip Algorithms." Web.
1. Nicolas Loizou, Michael Rabbat, Peter Richtarik. Provably Accelerated Randomized Gossip Algorithms [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4237

A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm

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7 May 2019 - 5:09pm
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[1] , "A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3956. Accessed: Oct. 17, 2019.
@article{3956-19,
url = {http://sigport.org/3956},
author = { },
publisher = {IEEE SigPort},
title = {A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm},
year = {2019} }
TY - EJOUR
T1 - A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3956
ER -
. (2019). A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm. IEEE SigPort. http://sigport.org/3956
, 2019. A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm. Available at: http://sigport.org/3956.
. (2019). "A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm." Web.
1. . A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3956

Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing


In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data.

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Authors:
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi
Submitted On:
4 December 2018 - 11:04pm
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[1] Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi, "Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3840. Accessed: Oct. 17, 2019.
@article{3840-18,
url = {http://sigport.org/3840},
author = {Gonzalo Cucho-Padin; Yue Wang; Lara Waldrop; Zhi Tian; Farzad Kamalabadi },
publisher = {IEEE SigPort},
title = {Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing},
year = {2018} }
TY - EJOUR
T1 - Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing
AU - Gonzalo Cucho-Padin; Yue Wang; Lara Waldrop; Zhi Tian; Farzad Kamalabadi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3840
ER -
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. (2018). Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing. IEEE SigPort. http://sigport.org/3840
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi, 2018. Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing. Available at: http://sigport.org/3840.
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. (2018). "Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing." Web.
1. Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3840

MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING


In this paper, we discuss the problem of modeling a graph signal on a directed graph when observing only partially the graph signal. The graph signal is recovered using a learned graph filter. The novelty is to use the random walk operator associated to an ergodic random walk on the graph, so as to define and learn a graph filter, expressed as a polynomial of this operator. Through the study of different cases, we show the efficiency of the signal modeling using the random walk operator compared to existing methods using the adjacency matrix or ignoring the directions in the graph.

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Authors:
Harry Sevi, Gabriel Rilling, Pierre Borgnat
Submitted On:
27 November 2018 - 9:53am
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[1] Harry Sevi, Gabriel Rilling, Pierre Borgnat, "MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3812. Accessed: Oct. 17, 2019.
@article{3812-18,
url = {http://sigport.org/3812},
author = {Harry Sevi; Gabriel Rilling; Pierre Borgnat },
publisher = {IEEE SigPort},
title = {MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING},
year = {2018} }
TY - EJOUR
T1 - MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING
AU - Harry Sevi; Gabriel Rilling; Pierre Borgnat
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3812
ER -
Harry Sevi, Gabriel Rilling, Pierre Borgnat. (2018). MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING. IEEE SigPort. http://sigport.org/3812
Harry Sevi, Gabriel Rilling, Pierre Borgnat, 2018. MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING. Available at: http://sigport.org/3812.
Harry Sevi, Gabriel Rilling, Pierre Borgnat. (2018). "MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING." Web.
1. Harry Sevi, Gabriel Rilling, Pierre Borgnat. MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3812

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