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MLSP 2015

The 25th MLSP 2015 workshop in the series of workshops organized by the IEEE Signal Processing Society MLSP Technical Committee will take place in Boston USA and present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions.

Full-duplex vs. Half-duplex Secret-key Generation

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Authors:
Hendrik Vogt
Submitted On:
23 February 2016 - 1:44pm
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WIFS2015_Vogt.pdf

(268 downloads)

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[1] Hendrik Vogt, "Full-duplex vs. Half-duplex Secret-key Generation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/597. Accessed: Jun. 23, 2017.
@article{597-16,
url = {http://sigport.org/597},
author = {Hendrik Vogt },
publisher = {IEEE SigPort},
title = {Full-duplex vs. Half-duplex Secret-key Generation},
year = {2016} }
TY - EJOUR
T1 - Full-duplex vs. Half-duplex Secret-key Generation
AU - Hendrik Vogt
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/597
ER -
Hendrik Vogt. (2016). Full-duplex vs. Half-duplex Secret-key Generation. IEEE SigPort. http://sigport.org/597
Hendrik Vogt, 2016. Full-duplex vs. Half-duplex Secret-key Generation. Available at: http://sigport.org/597.
Hendrik Vogt. (2016). "Full-duplex vs. Half-duplex Secret-key Generation." Web.
1. Hendrik Vogt. Full-duplex vs. Half-duplex Secret-key Generation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/597

Accelerated graph-based spectral polynomial filters


BF, GF and CG filters on 1D signals

Graph-based spectral denoising is a low-pass filtering using the eigendecomposition of the graph Laplacian matrix of a noisy signal. Polynomial filtering avoids costly computation of the eigendecomposition by projections onto suitable Krylov subspaces. Polynomial filters can be based, e.g., on the bilateral and guided filters. We propose constructing accelerated polynomial filters by running flexible Krylov subspace based linear and eigenvalue solvers such as the Block Locally Optimal Preconditioned Conjugate Gradient (LOBPCG) method.

MLSP2015.pdf

PDF icon MLSP2015.pdf (267 downloads)

Paper Details

Authors:
Alexander Malyshev
Submitted On:
23 February 2016 - 1:44pm
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MLSP2015.pdf

(267 downloads)

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[1] Alexander Malyshev, "Accelerated graph-based spectral polynomial filters", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/297. Accessed: Jun. 23, 2017.
@article{297-15,
url = {http://sigport.org/297},
author = {Alexander Malyshev },
publisher = {IEEE SigPort},
title = {Accelerated graph-based spectral polynomial filters},
year = {2015} }
TY - EJOUR
T1 - Accelerated graph-based spectral polynomial filters
AU - Alexander Malyshev
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/297
ER -
Alexander Malyshev. (2015). Accelerated graph-based spectral polynomial filters. IEEE SigPort. http://sigport.org/297
Alexander Malyshev, 2015. Accelerated graph-based spectral polynomial filters. Available at: http://sigport.org/297.
Alexander Malyshev. (2015). "Accelerated graph-based spectral polynomial filters." Web.
1. Alexander Malyshev. Accelerated graph-based spectral polynomial filters [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/297

Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition


Electroencephalogram (EEG) is a gold standard in epilepsy diagnosis and has been widely studied for epilepsy-related signal classification, such as seizure detection or focus localization. In the past few years, discrete wavelet transform (DWT) has been widely used to analyze epileptic EEG. However, one practical question unanswered is the optimal levels of wavelet decomposition. Deeper DWT can yield a more detailed depiction of signals but it requires substantially more computational time.

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Authors:
Suiren Wan
Submitted On:
23 February 2016 - 1:38pm
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epileptic-focus-localization.pdf

(336 downloads)

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[1] Suiren Wan, "Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/216. Accessed: Jun. 23, 2017.
@article{216-15,
url = {http://sigport.org/216},
author = {Suiren Wan },
publisher = {IEEE SigPort},
title = {Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition},
year = {2015} }
TY - EJOUR
T1 - Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition
AU - Suiren Wan
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/216
ER -
Suiren Wan. (2015). Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition. IEEE SigPort. http://sigport.org/216
Suiren Wan, 2015. Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition. Available at: http://sigport.org/216.
Suiren Wan. (2015). "Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition." Web.
1. Suiren Wan. Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/216

A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation


Precise estimation of synchronization parameters is essential for reliable
data detection in digital communications and phase errors can
result in significant performance degradation. The literature on estimation
of synchronization parameters, including the carrier frequency
offset, are based on approximations or heuristics because
the optimal estimation problem is analytically intractable for most
cases of interest. We develop an online Bayesian inference procedure
for blind estimation of the frequency offset, for arbitrary signal

Paper Details

Authors:
Keith W. Forsythe
Submitted On:
23 February 2016 - 1:43pm
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SeqBayesInfBlindFreqEst_sigport.pdf

(365 downloads)

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[1] Keith W. Forsythe, "A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/213. Accessed: Jun. 23, 2017.
@article{213-15,
url = {http://sigport.org/213},
author = {Keith W. Forsythe },
publisher = {IEEE SigPort},
title = {A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation},
year = {2015} }
TY - EJOUR
T1 - A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation
AU - Keith W. Forsythe
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/213
ER -
Keith W. Forsythe. (2015). A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation. IEEE SigPort. http://sigport.org/213
Keith W. Forsythe, 2015. A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation. Available at: http://sigport.org/213.
Keith W. Forsythe. (2015). "A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation." Web.
1. Keith W. Forsythe. A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/213