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Signal Processing Theory and Methods

Online Empirical Mode Decomposition


The success of Empirical Mode Decomposition (EMD) resides in its practical approach to dissect non-stationary data. EMD repetitively goes through the entire data span to iteratively extract Intrinsic Mode Functions (IMFs). This approach, however, is not suitable for data stream as the entire data set has to be reconsidered every time a new point is added. To overcome this, we propose Online EMD, an algorithm that extracts IMFs on the fly.

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Authors:
Romain Fontugne, Pierre Borgnat, Patrick Flandrin
Submitted On:
28 February 2017 - 4:29am
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icassp17_poster_onlineEMD.pdf

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[1] Romain Fontugne, Pierre Borgnat, Patrick Flandrin, "Online Empirical Mode Decomposition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1491. Accessed: Dec. 18, 2017.
@article{1491-17,
url = {http://sigport.org/1491},
author = {Romain Fontugne; Pierre Borgnat; Patrick Flandrin },
publisher = {IEEE SigPort},
title = {Online Empirical Mode Decomposition},
year = {2017} }
TY - EJOUR
T1 - Online Empirical Mode Decomposition
AU - Romain Fontugne; Pierre Borgnat; Patrick Flandrin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1491
ER -
Romain Fontugne, Pierre Borgnat, Patrick Flandrin. (2017). Online Empirical Mode Decomposition. IEEE SigPort. http://sigport.org/1491
Romain Fontugne, Pierre Borgnat, Patrick Flandrin, 2017. Online Empirical Mode Decomposition. Available at: http://sigport.org/1491.
Romain Fontugne, Pierre Borgnat, Patrick Flandrin. (2017). "Online Empirical Mode Decomposition." Web.
1. Romain Fontugne, Pierre Borgnat, Patrick Flandrin. Online Empirical Mode Decomposition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1491

Linear Systems on Graphs

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Authors:
Oguzhan Teke, Palghat P. Vaidyanathan
Submitted On:
11 December 2016 - 12:04am
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linearSystems_globalsip_presentation.pdf

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[1] Oguzhan Teke, Palghat P. Vaidyanathan, "Linear Systems on Graphs", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1437. Accessed: Dec. 18, 2017.
@article{1437-16,
url = {http://sigport.org/1437},
author = {Oguzhan Teke; Palghat P. Vaidyanathan },
publisher = {IEEE SigPort},
title = {Linear Systems on Graphs},
year = {2016} }
TY - EJOUR
T1 - Linear Systems on Graphs
AU - Oguzhan Teke; Palghat P. Vaidyanathan
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1437
ER -
Oguzhan Teke, Palghat P. Vaidyanathan. (2016). Linear Systems on Graphs. IEEE SigPort. http://sigport.org/1437
Oguzhan Teke, Palghat P. Vaidyanathan, 2016. Linear Systems on Graphs. Available at: http://sigport.org/1437.
Oguzhan Teke, Palghat P. Vaidyanathan. (2016). "Linear Systems on Graphs." Web.
1. Oguzhan Teke, Palghat P. Vaidyanathan. Linear Systems on Graphs [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1437

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets


Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolving in time, such as spreading or propagation of waves on a network. The analysis of this type of data requires a new set of methods that takes into account the time and graph dimensions. We propose a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex evolution follows a dynamic process.

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Authors:
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud
Submitted On:
8 December 2016 - 5:01pm
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globalsip_grassi.pdf

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[1] Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud, "Tracking Time-Vertex Propagation using Dynamic Graph Wavelets", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1428. Accessed: Dec. 18, 2017.
@article{1428-16,
url = {http://sigport.org/1428},
author = {Francesco Grassi; Nathanael Perraudin; Benjamin Ricaud },
publisher = {IEEE SigPort},
title = {Tracking Time-Vertex Propagation using Dynamic Graph Wavelets},
year = {2016} }
TY - EJOUR
T1 - Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
AU - Francesco Grassi; Nathanael Perraudin; Benjamin Ricaud
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1428
ER -
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud. (2016). Tracking Time-Vertex Propagation using Dynamic Graph Wavelets. IEEE SigPort. http://sigport.org/1428
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud, 2016. Tracking Time-Vertex Propagation using Dynamic Graph Wavelets. Available at: http://sigport.org/1428.
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud. (2016). "Tracking Time-Vertex Propagation using Dynamic Graph Wavelets." Web.
1. Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud. Tracking Time-Vertex Propagation using Dynamic Graph Wavelets [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1428

Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems


Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

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Authors:
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro
Submitted On:
8 December 2016 - 3:51pm
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sketching-globalsip16-presentation.pdf

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[1] Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1415. Accessed: Dec. 18, 2017.
@article{1415-16,
url = {http://sigport.org/1415},
author = {Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems},
year = {2016} }
TY - EJOUR
T1 - Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems
AU - Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1415
ER -
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. IEEE SigPort. http://sigport.org/1415
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, 2016. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. Available at: http://sigport.org/1415.
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems." Web.
1. Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1415

Neighborhood-Preserving Translations on Graphs

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Authors:
Nicolas Grelier, Bastien Pasdeloup, Jean-Charles Vialatte, Vincent Gripon
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6 December 2016 - 7:58pm
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poster.pdf

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[1] Nicolas Grelier, Bastien Pasdeloup, Jean-Charles Vialatte, Vincent Gripon, "Neighborhood-Preserving Translations on Graphs", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1380. Accessed: Dec. 18, 2017.
@article{1380-16,
url = {http://sigport.org/1380},
author = {Nicolas Grelier; Bastien Pasdeloup; Jean-Charles Vialatte; Vincent Gripon },
publisher = {IEEE SigPort},
title = {Neighborhood-Preserving Translations on Graphs},
year = {2016} }
TY - EJOUR
T1 - Neighborhood-Preserving Translations on Graphs
AU - Nicolas Grelier; Bastien Pasdeloup; Jean-Charles Vialatte; Vincent Gripon
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1380
ER -
Nicolas Grelier, Bastien Pasdeloup, Jean-Charles Vialatte, Vincent Gripon. (2016). Neighborhood-Preserving Translations on Graphs. IEEE SigPort. http://sigport.org/1380
Nicolas Grelier, Bastien Pasdeloup, Jean-Charles Vialatte, Vincent Gripon, 2016. Neighborhood-Preserving Translations on Graphs. Available at: http://sigport.org/1380.
Nicolas Grelier, Bastien Pasdeloup, Jean-Charles Vialatte, Vincent Gripon. (2016). "Neighborhood-Preserving Translations on Graphs." Web.
1. Nicolas Grelier, Bastien Pasdeloup, Jean-Charles Vialatte, Vincent Gripon. Neighborhood-Preserving Translations on Graphs [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1380

MORPHOLOGICAL PDES ON GRAPHS FOR ANALYZING UNORGANIZED DATA IN 3D AND HIGHER

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Authors:
Abderrahim Elmoataz, François Lozes, Hugues Talbot
Submitted On:
6 December 2016 - 2:04pm
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globalsip.pdf

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[1] Abderrahim Elmoataz, François Lozes, Hugues Talbot, "MORPHOLOGICAL PDES ON GRAPHS FOR ANALYZING UNORGANIZED DATA IN 3D AND HIGHER", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1373. Accessed: Dec. 18, 2017.
@article{1373-16,
url = {http://sigport.org/1373},
author = {Abderrahim Elmoataz; François Lozes; Hugues Talbot },
publisher = {IEEE SigPort},
title = {MORPHOLOGICAL PDES ON GRAPHS FOR ANALYZING UNORGANIZED DATA IN 3D AND HIGHER},
year = {2016} }
TY - EJOUR
T1 - MORPHOLOGICAL PDES ON GRAPHS FOR ANALYZING UNORGANIZED DATA IN 3D AND HIGHER
AU - Abderrahim Elmoataz; François Lozes; Hugues Talbot
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1373
ER -
Abderrahim Elmoataz, François Lozes, Hugues Talbot. (2016). MORPHOLOGICAL PDES ON GRAPHS FOR ANALYZING UNORGANIZED DATA IN 3D AND HIGHER. IEEE SigPort. http://sigport.org/1373
Abderrahim Elmoataz, François Lozes, Hugues Talbot, 2016. MORPHOLOGICAL PDES ON GRAPHS FOR ANALYZING UNORGANIZED DATA IN 3D AND HIGHER. Available at: http://sigport.org/1373.
Abderrahim Elmoataz, François Lozes, Hugues Talbot. (2016). "MORPHOLOGICAL PDES ON GRAPHS FOR ANALYZING UNORGANIZED DATA IN 3D AND HIGHER." Web.
1. Abderrahim Elmoataz, François Lozes, Hugues Talbot. MORPHOLOGICAL PDES ON GRAPHS FOR ANALYZING UNORGANIZED DATA IN 3D AND HIGHER [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1373

Graph Frequency Analysis of Brain Signals


This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations.

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Authors:
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro
Submitted On:
19 July 2016 - 1:47pm
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projectSummary_2016_IEEE.pdf

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[1] Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro, "Graph Frequency Analysis of Brain Signals", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1134. Accessed: Dec. 18, 2017.
@article{1134-16,
url = {http://sigport.org/1134},
author = {Weiyu Huang; Leah Goldsberry; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; and Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Graph Frequency Analysis of Brain Signals},
year = {2016} }
TY - EJOUR
T1 - Graph Frequency Analysis of Brain Signals
AU - Weiyu Huang; Leah Goldsberry; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; and Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1134
ER -
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro. (2016). Graph Frequency Analysis of Brain Signals. IEEE SigPort. http://sigport.org/1134
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro, 2016. Graph Frequency Analysis of Brain Signals. Available at: http://sigport.org/1134.
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro. (2016). "Graph Frequency Analysis of Brain Signals." Web.
1. Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro. Graph Frequency Analysis of Brain Signals [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1134

IEEE SP Cup 2016 Project Report by Team "10Hertz": Exploring Power Signals for Location Forensics of Media Recordings


Electric Network Frequency is the frequency of power distribution networks in power grids that fluctuates about a nominal value with respect to the changing loads.Its ubiquitous nature has made notable contributions to forensic analysis that has substantiated its use as a significant tool in this area. In this paper we have proposed a technique to identify the power grid in which the ENF containing signal was recorded, without the assistance of concurrent power references.

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Authors:
Abhijith A., Abhinash V., Sreekiran A.R.,Tom Jose
Submitted On:
12 July 2016 - 10:53am
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[1] Abhijith A., Abhinash V., Sreekiran A.R.,Tom Jose, "IEEE SP Cup 2016 Project Report by Team "10Hertz": Exploring Power Signals for Location Forensics of Media Recordings", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1132. Accessed: Dec. 18, 2017.
@article{1132-16,
url = {http://sigport.org/1132},
author = {Abhijith A.; Abhinash V.; Sreekiran A.R.;Tom Jose },
publisher = {IEEE SigPort},
title = {IEEE SP Cup 2016 Project Report by Team "10Hertz": Exploring Power Signals for Location Forensics of Media Recordings},
year = {2016} }
TY - EJOUR
T1 - IEEE SP Cup 2016 Project Report by Team "10Hertz": Exploring Power Signals for Location Forensics of Media Recordings
AU - Abhijith A.; Abhinash V.; Sreekiran A.R.;Tom Jose
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1132
ER -
Abhijith A., Abhinash V., Sreekiran A.R.,Tom Jose. (2016). IEEE SP Cup 2016 Project Report by Team "10Hertz": Exploring Power Signals for Location Forensics of Media Recordings. IEEE SigPort. http://sigport.org/1132
Abhijith A., Abhinash V., Sreekiran A.R.,Tom Jose, 2016. IEEE SP Cup 2016 Project Report by Team "10Hertz": Exploring Power Signals for Location Forensics of Media Recordings. Available at: http://sigport.org/1132.
Abhijith A., Abhinash V., Sreekiran A.R.,Tom Jose. (2016). "IEEE SP Cup 2016 Project Report by Team "10Hertz": Exploring Power Signals for Location Forensics of Media Recordings." Web.
1. Abhijith A., Abhinash V., Sreekiran A.R.,Tom Jose. IEEE SP Cup 2016 Project Report by Team "10Hertz": Exploring Power Signals for Location Forensics of Media Recordings [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1132

A HIGH PERFORMANCE BASEBAND INSTRUMENT


Testing complex digital signal processors (DSPs) requires a development platform with sufficient
signal bandwidth and system performance to fully exercise the DSP. Without a development plat-
form, verification of DSPs would be limited to monitoring test output signals for an indication of
performance and successful operation. In addition, a development platform with high-speed analog
input and output interfaces to the DSP system allows it to be used directly in many sophisticated

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Authors:
Jeremy Webb, Bevan Baas
Submitted On:
30 June 2016 - 2:14am
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jwwebb.sigport.6.29.2016.pdf

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[1] Jeremy Webb, Bevan Baas, "A HIGH PERFORMANCE BASEBAND INSTRUMENT", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1130. Accessed: Dec. 18, 2017.
@article{1130-16,
url = {http://sigport.org/1130},
author = {Jeremy Webb; Bevan Baas },
publisher = {IEEE SigPort},
title = {A HIGH PERFORMANCE BASEBAND INSTRUMENT},
year = {2016} }
TY - EJOUR
T1 - A HIGH PERFORMANCE BASEBAND INSTRUMENT
AU - Jeremy Webb; Bevan Baas
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1130
ER -
Jeremy Webb, Bevan Baas. (2016). A HIGH PERFORMANCE BASEBAND INSTRUMENT. IEEE SigPort. http://sigport.org/1130
Jeremy Webb, Bevan Baas, 2016. A HIGH PERFORMANCE BASEBAND INSTRUMENT. Available at: http://sigport.org/1130.
Jeremy Webb, Bevan Baas. (2016). "A HIGH PERFORMANCE BASEBAND INSTRUMENT." Web.
1. Jeremy Webb, Bevan Baas. A HIGH PERFORMANCE BASEBAND INSTRUMENT [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1130

Robust Beamforming based on Minimum Dispersion Criterion


In this presentation, the topic of robust beamforming is studied. We devise the minimum dispersion criterion which extends the minimum variance criterion from l2‐norm to lp‐norm. Formulations with different linear and nonlinear constraints are examined. The proposed framework generalizes existing approaches including the Capon and linearly constrained minimum variance beamformers as well as the method based on worst-case performance optimization. Computationally attractive algorithm realizations are also developed.

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Submitted On:
26 June 2016 - 10:03am
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[1] , "Robust Beamforming based on Minimum Dispersion Criterion", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1128. Accessed: Dec. 18, 2017.
@article{1128-16,
url = {http://sigport.org/1128},
author = { },
publisher = {IEEE SigPort},
title = {Robust Beamforming based on Minimum Dispersion Criterion},
year = {2016} }
TY - EJOUR
T1 - Robust Beamforming based on Minimum Dispersion Criterion
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1128
ER -
. (2016). Robust Beamforming based on Minimum Dispersion Criterion. IEEE SigPort. http://sigport.org/1128
, 2016. Robust Beamforming based on Minimum Dispersion Criterion. Available at: http://sigport.org/1128.
. (2016). "Robust Beamforming based on Minimum Dispersion Criterion." Web.
1. . Robust Beamforming based on Minimum Dispersion Criterion [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1128

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