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Machine Learning for Signal Processing

Stable and Symmetric Filter Convolutional Neural Network

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Authors:
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do
Submitted On:
18 March 2016 - 10:27pm
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icassp2016.pdf

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[1] Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do, "Stable and Symmetric Filter Convolutional Neural Network", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/781. Accessed: May. 30, 2017.
@article{781-16,
url = {http://sigport.org/781},
author = {Raymond Yeh; Mark Hasegawa-Johnson; Minh N. Do },
publisher = {IEEE SigPort},
title = {Stable and Symmetric Filter Convolutional Neural Network},
year = {2016} }
TY - EJOUR
T1 - Stable and Symmetric Filter Convolutional Neural Network
AU - Raymond Yeh; Mark Hasegawa-Johnson; Minh N. Do
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/781
ER -
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do. (2016). Stable and Symmetric Filter Convolutional Neural Network. IEEE SigPort. http://sigport.org/781
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do, 2016. Stable and Symmetric Filter Convolutional Neural Network. Available at: http://sigport.org/781.
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do. (2016). "Stable and Symmetric Filter Convolutional Neural Network." Web.
1. Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do. Stable and Symmetric Filter Convolutional Neural Network [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/781

Deep Learning: Propelling Recent Rapid Advances in AI

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23 February 2016 - 1:44pm
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GlobalSIP-Plenary-LiDeng-Dec16-2015 - reduced.pptx

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[1] , "Deep Learning: Propelling Recent Rapid Advances in AI", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/600. Accessed: May. 30, 2017.
@article{600-16,
url = {http://sigport.org/600},
author = { },
publisher = {IEEE SigPort},
title = {Deep Learning: Propelling Recent Rapid Advances in AI},
year = {2016} }
TY - EJOUR
T1 - Deep Learning: Propelling Recent Rapid Advances in AI
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/600
ER -
. (2016). Deep Learning: Propelling Recent Rapid Advances in AI. IEEE SigPort. http://sigport.org/600
, 2016. Deep Learning: Propelling Recent Rapid Advances in AI. Available at: http://sigport.org/600.
. (2016). "Deep Learning: Propelling Recent Rapid Advances in AI." Web.
1. . Deep Learning: Propelling Recent Rapid Advances in AI [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/600

A feasibility study of automated plug-load identification from high-frequency measurements


Plug-meters benefit many grid and building-level energy management applications like automated load control and load scheduling. However, installing and maintaining large and/orlong term deployments of such meters requires assignment and updating of the identity (labels) of electrical loads connected to them. Although the literature on electricity disaggregation and appliance identification is extensive, there is no consensus on the generalizability of the proposed solutions, especially with respect to the features that are extracted from voltage and current measurements.

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Authors:
Emre Can Kara, Suman Giri, Mario Berges
Submitted On:
23 February 2016 - 1:44pm
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A feasibility study of automated plug-load identification from high-frequency measurements.pdf

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[1] Emre Can Kara, Suman Giri, Mario Berges, "A feasibility study of automated plug-load identification from high-frequency measurements", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/334. Accessed: May. 30, 2017.
@article{334-15,
url = {http://sigport.org/334},
author = {Emre Can Kara; Suman Giri; Mario Berges },
publisher = {IEEE SigPort},
title = {A feasibility study of automated plug-load identification from high-frequency measurements},
year = {2015} }
TY - EJOUR
T1 - A feasibility study of automated plug-load identification from high-frequency measurements
AU - Emre Can Kara; Suman Giri; Mario Berges
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/334
ER -
Emre Can Kara, Suman Giri, Mario Berges. (2015). A feasibility study of automated plug-load identification from high-frequency measurements. IEEE SigPort. http://sigport.org/334
Emre Can Kara, Suman Giri, Mario Berges, 2015. A feasibility study of automated plug-load identification from high-frequency measurements. Available at: http://sigport.org/334.
Emre Can Kara, Suman Giri, Mario Berges. (2015). "A feasibility study of automated plug-load identification from high-frequency measurements." Web.
1. Emre Can Kara, Suman Giri, Mario Berges. A feasibility study of automated plug-load identification from high-frequency measurements [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/334

Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation


Greedy pursuit is one of the standard approaches for sparse approximation. Since the derivation of the conventional greedy pursuit schemes, including matching pursuit (MP) and orthogonal MP (OMP), is based on the inner product space, they may not work properly in the presence of impulsive noise. In this work, we devise a new definition of correlation in lp-space with p>0, called lp-correlation, and introduce the concept of orthogonality in lp-space.

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23 February 2016 - 1:43pm
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outlier-robust_greedy_pursuit.pdf

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[1] , "Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/236. Accessed: May. 30, 2017.
@article{236-15,
url = {http://sigport.org/236},
author = { },
publisher = {IEEE SigPort},
title = {Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation},
year = {2015} }
TY - EJOUR
T1 - Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/236
ER -
. (2015). Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation. IEEE SigPort. http://sigport.org/236
, 2015. Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation. Available at: http://sigport.org/236.
. (2015). "Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation." Web.
1. . Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/236

Computer Vision and Image Processing for Automated Surveillance


Presentation slides covering:

- robust foreground detection / background subtraction via patch-based analysis
- person re-identification based on representations on Riemannian manifolds
- robust object tracking via Grassmann manifolds
- adapting the lessons from big data to computer vision
- future paradigm shifts: computer vision based on networks of neurosynaptic cores

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Authors:
Submitted On:
23 February 2016 - 1:43pm
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Sanderson_Computer_Vision_and_Image_Processing_for_Automated_Surveillance.pdf

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[1] , "Computer Vision and Image Processing for Automated Surveillance", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/202. Accessed: May. 30, 2017.
@article{202-15,
url = {http://sigport.org/202},
author = { },
publisher = {IEEE SigPort},
title = {Computer Vision and Image Processing for Automated Surveillance},
year = {2015} }
TY - EJOUR
T1 - Computer Vision and Image Processing for Automated Surveillance
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/202
ER -
. (2015). Computer Vision and Image Processing for Automated Surveillance. IEEE SigPort. http://sigport.org/202
, 2015. Computer Vision and Image Processing for Automated Surveillance. Available at: http://sigport.org/202.
. (2015). "Computer Vision and Image Processing for Automated Surveillance." Web.
1. . Computer Vision and Image Processing for Automated Surveillance [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/202

Object Tracking and Person Re-Identification on Manifolds


person re-identification examples

Slides from the Tutorial on Riemannian Geometry in Computer Vision, presented at the Asian Conference on Computer Vision (ACCV), Singapore, 2014.

The slides show (1) how objects can be interpreted as points on Riemannian and Grassmann manifolds, and (2) various distance measures on manifolds. Demonstrates usefulness of manifold techniques in applications such as object tracking and person re-identification.

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23 February 2016 - 1:43pm
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sanderson_riemannian_geometry_tutorial_slides_accv_2014.pdf

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[1] , "Object Tracking and Person Re-Identification on Manifolds", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/198. Accessed: May. 30, 2017.
@article{198-15,
url = {http://sigport.org/198},
author = { },
publisher = {IEEE SigPort},
title = {Object Tracking and Person Re-Identification on Manifolds},
year = {2015} }
TY - EJOUR
T1 - Object Tracking and Person Re-Identification on Manifolds
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/198
ER -
. (2015). Object Tracking and Person Re-Identification on Manifolds. IEEE SigPort. http://sigport.org/198
, 2015. Object Tracking and Person Re-Identification on Manifolds. Available at: http://sigport.org/198.
. (2015). "Object Tracking and Person Re-Identification on Manifolds." Web.
1. . Object Tracking and Person Re-Identification on Manifolds [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/198

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