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Machine Learning for Signal Processing

Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization


The development of children’s cognitive and perceptual skills depends heavily on object exploration and manipulative experiences. New types of robotic assistive technologies that enable children with disabilities to interact with their environment, which prove to be beneficial for their cognitive and perceptual skills development, have emerged in recent years. In this study, a human-robot interface that uses Event-Related Desynchronization (ERD) brain response during movement was developed.

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12 November 2017 - 10:57pm
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2017 GlobalSIP slide.pdf

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[1] , "Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2317. Accessed: Oct. 19, 2018.
@article{2317-17,
url = {http://sigport.org/2317},
author = { },
publisher = {IEEE SigPort},
title = {Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization},
year = {2017} }
TY - EJOUR
T1 - Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2317
ER -
. (2017). Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization. IEEE SigPort. http://sigport.org/2317
, 2017. Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization. Available at: http://sigport.org/2317.
. (2017). "Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization." Web.
1. . Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2317

End-To-End Chinese Text Recognition

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Authors:
Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang
Submitted On:
11 November 2017 - 12:19am
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presentation at GlobalSIP

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[1] Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang, "End-To-End Chinese Text Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2304. Accessed: Oct. 19, 2018.
@article{2304-17,
url = {http://sigport.org/2304},
author = {Jie Hu; Tszhang Guo; Ji Cao; Changshui Zhang },
publisher = {IEEE SigPort},
title = {End-To-End Chinese Text Recognition},
year = {2017} }
TY - EJOUR
T1 - End-To-End Chinese Text Recognition
AU - Jie Hu; Tszhang Guo; Ji Cao; Changshui Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2304
ER -
Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang. (2017). End-To-End Chinese Text Recognition. IEEE SigPort. http://sigport.org/2304
Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang, 2017. End-To-End Chinese Text Recognition. Available at: http://sigport.org/2304.
Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang. (2017). "End-To-End Chinese Text Recognition." Web.
1. Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang. End-To-End Chinese Text Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2304

Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues

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Authors:
QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG
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9 November 2017 - 10:15pm
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poster.pdf

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[1] QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG, "Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2280. Accessed: Oct. 19, 2018.
@article{2280-17,
url = {http://sigport.org/2280},
author = {QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG },
publisher = {IEEE SigPort},
title = {Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues},
year = {2017} }
TY - EJOUR
T1 - Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues
AU - QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2280
ER -
QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG. (2017). Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues. IEEE SigPort. http://sigport.org/2280
QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG, 2017. Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues. Available at: http://sigport.org/2280.
QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG. (2017). "Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues." Web.
1. QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG. Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2280

Sparse Modeling in Image Processing and Deep Learning (Keynote Talk)


Sparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk we start by presenting this model and its features, and then turn to describe two special cases of it – the convolutional sparse coding (CSC) and its multi-layered version (ML-CSC). Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to … deep-learning.

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Authors:
Michael Elad
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23 January 2018 - 7:06pm
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ICIP_KeyNote_Talk.pdf

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[1] Michael Elad, "Sparse Modeling in Image Processing and Deep Learning (Keynote Talk)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2259. Accessed: Oct. 19, 2018.
@article{2259-17,
url = {http://sigport.org/2259},
author = {Michael Elad },
publisher = {IEEE SigPort},
title = {Sparse Modeling in Image Processing and Deep Learning (Keynote Talk)},
year = {2017} }
TY - EJOUR
T1 - Sparse Modeling in Image Processing and Deep Learning (Keynote Talk)
AU - Michael Elad
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2259
ER -
Michael Elad. (2017). Sparse Modeling in Image Processing and Deep Learning (Keynote Talk). IEEE SigPort. http://sigport.org/2259
Michael Elad, 2017. Sparse Modeling in Image Processing and Deep Learning (Keynote Talk). Available at: http://sigport.org/2259.
Michael Elad. (2017). "Sparse Modeling in Image Processing and Deep Learning (Keynote Talk)." Web.
1. Michael Elad. Sparse Modeling in Image Processing and Deep Learning (Keynote Talk) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2259

TOWARDS 3D CONVOLUTIONAL NEURAL NETWORKS WITH MESHES


Voxels are an effective approach to 3D mesh and point cloud classification because they build upon mature Convolutional Neural Network concepts. We show however that their cubic increase in dimensionality is unsuitable for more challenging problems such as object detection in a complex point cloud scene. We observe that 3D meshes are analogous to graph data and can thus be treated with graph signal processing techniques.

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Authors:
Felipe Petroski Such, Shagan Sah, Raymond Ptucha
Submitted On:
19 September 2017 - 11:34am
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ICIPPoster2017MiguelDominguez.pdf

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[1] Felipe Petroski Such, Shagan Sah, Raymond Ptucha, "TOWARDS 3D CONVOLUTIONAL NEURAL NETWORKS WITH MESHES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2234. Accessed: Oct. 19, 2018.
@article{2234-17,
url = {http://sigport.org/2234},
author = {Felipe Petroski Such; Shagan Sah; Raymond Ptucha },
publisher = {IEEE SigPort},
title = {TOWARDS 3D CONVOLUTIONAL NEURAL NETWORKS WITH MESHES},
year = {2017} }
TY - EJOUR
T1 - TOWARDS 3D CONVOLUTIONAL NEURAL NETWORKS WITH MESHES
AU - Felipe Petroski Such; Shagan Sah; Raymond Ptucha
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2234
ER -
Felipe Petroski Such, Shagan Sah, Raymond Ptucha. (2017). TOWARDS 3D CONVOLUTIONAL NEURAL NETWORKS WITH MESHES. IEEE SigPort. http://sigport.org/2234
Felipe Petroski Such, Shagan Sah, Raymond Ptucha, 2017. TOWARDS 3D CONVOLUTIONAL NEURAL NETWORKS WITH MESHES. Available at: http://sigport.org/2234.
Felipe Petroski Such, Shagan Sah, Raymond Ptucha. (2017). "TOWARDS 3D CONVOLUTIONAL NEURAL NETWORKS WITH MESHES." Web.
1. Felipe Petroski Such, Shagan Sah, Raymond Ptucha. TOWARDS 3D CONVOLUTIONAL NEURAL NETWORKS WITH MESHES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2234

Greedy Deep Transform Learning


We introduce deep transform learning – a new
tool for deep learning. Deeper representation is learnt by
stacking one transform after another. The learning proceeds in
a greedy way. The first layer learns the transform and features
from the input training samples. Subsequent layers use the
features (after activation) from the previous layers as training
input. Experiments have been carried out with other deep
representation learning tools – deep dictionary learning,
stacked denoising autoencoder, deep belief network and PCANet

Paper Details

Authors:
Jyoti Maggu, Angshul Majumdar
Submitted On:
18 September 2017 - 1:57pm
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ICIP_greedyDTL.pdf

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[1] Jyoti Maggu, Angshul Majumdar, "Greedy Deep Transform Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2180. Accessed: Oct. 19, 2018.
@article{2180-17,
url = {http://sigport.org/2180},
author = {Jyoti Maggu; Angshul Majumdar },
publisher = {IEEE SigPort},
title = {Greedy Deep Transform Learning},
year = {2017} }
TY - EJOUR
T1 - Greedy Deep Transform Learning
AU - Jyoti Maggu; Angshul Majumdar
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2180
ER -
Jyoti Maggu, Angshul Majumdar. (2017). Greedy Deep Transform Learning. IEEE SigPort. http://sigport.org/2180
Jyoti Maggu, Angshul Majumdar, 2017. Greedy Deep Transform Learning. Available at: http://sigport.org/2180.
Jyoti Maggu, Angshul Majumdar. (2017). "Greedy Deep Transform Learning." Web.
1. Jyoti Maggu, Angshul Majumdar. Greedy Deep Transform Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2180

AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY

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Authors:
Xiuyan Li, Yang Lu,Jianming Wang,Xin Dang,Qi Wang,Xiaojie Duan,Yukuan Sun
Submitted On:
15 September 2017 - 12:21am
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Poster for paper 3030-ICIP2017.pdf

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[1] Xiuyan Li, Yang Lu,Jianming Wang,Xin Dang,Qi Wang,Xiaojie Duan,Yukuan Sun, "AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2078. Accessed: Oct. 19, 2018.
@article{2078-17,
url = {http://sigport.org/2078},
author = {Xiuyan Li; Yang Lu,Jianming Wang,Xin Dang,Qi Wang,Xiaojie Duan,Yukuan Sun },
publisher = {IEEE SigPort},
title = {AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY},
year = {2017} }
TY - EJOUR
T1 - AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY
AU - Xiuyan Li; Yang Lu,Jianming Wang,Xin Dang,Qi Wang,Xiaojie Duan,Yukuan Sun
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2078
ER -
Xiuyan Li, Yang Lu,Jianming Wang,Xin Dang,Qi Wang,Xiaojie Duan,Yukuan Sun. (2017). AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY. IEEE SigPort. http://sigport.org/2078
Xiuyan Li, Yang Lu,Jianming Wang,Xin Dang,Qi Wang,Xiaojie Duan,Yukuan Sun, 2017. AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY. Available at: http://sigport.org/2078.
Xiuyan Li, Yang Lu,Jianming Wang,Xin Dang,Qi Wang,Xiaojie Duan,Yukuan Sun. (2017). "AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY." Web.
1. Xiuyan Li, Yang Lu,Jianming Wang,Xin Dang,Qi Wang,Xiaojie Duan,Yukuan Sun. AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2078

FAST DE-STREAKING METHOD USING PLAIN NEURAL NETWORK

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Authors:
Yuxiang Li, Bo Zhang, Raoul Florent
Submitted On:
14 September 2017 - 6:40am
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presentation_icip2017b.pdf

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[1] Yuxiang Li, Bo Zhang, Raoul Florent, "FAST DE-STREAKING METHOD USING PLAIN NEURAL NETWORK", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2010. Accessed: Oct. 19, 2018.
@article{2010-17,
url = {http://sigport.org/2010},
author = {Yuxiang Li; Bo Zhang; Raoul Florent },
publisher = {IEEE SigPort},
title = {FAST DE-STREAKING METHOD USING PLAIN NEURAL NETWORK},
year = {2017} }
TY - EJOUR
T1 - FAST DE-STREAKING METHOD USING PLAIN NEURAL NETWORK
AU - Yuxiang Li; Bo Zhang; Raoul Florent
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2010
ER -
Yuxiang Li, Bo Zhang, Raoul Florent. (2017). FAST DE-STREAKING METHOD USING PLAIN NEURAL NETWORK. IEEE SigPort. http://sigport.org/2010
Yuxiang Li, Bo Zhang, Raoul Florent, 2017. FAST DE-STREAKING METHOD USING PLAIN NEURAL NETWORK. Available at: http://sigport.org/2010.
Yuxiang Li, Bo Zhang, Raoul Florent. (2017). "FAST DE-STREAKING METHOD USING PLAIN NEURAL NETWORK." Web.
1. Yuxiang Li, Bo Zhang, Raoul Florent. FAST DE-STREAKING METHOD USING PLAIN NEURAL NETWORK [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2010

TAD16K: An Enhanced Benchmark for Autonomous Driving


Although promising results have been achieved in the areas of object detection and classification, few works have provided an end-to-end solution to the perception problems in the autonomous driving field. In this paper, we make two contributions. Firstly, we fully enhanced our previously released TT100K benchmark and provide 16,817 elaborately labeled Tencent Street View panoramas.

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Authors:
Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su
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14 September 2017 - 6:10am
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ICIP2017_poster.pdf

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[1] Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su, "TAD16K: An Enhanced Benchmark for Autonomous Driving", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2007. Accessed: Oct. 19, 2018.
@article{2007-17,
url = {http://sigport.org/2007},
author = {Yuming Li; Jue Wang; Tengfei Xing; Tianlu Liu; Chengjun Li; Kuifeng Su },
publisher = {IEEE SigPort},
title = {TAD16K: An Enhanced Benchmark for Autonomous Driving},
year = {2017} }
TY - EJOUR
T1 - TAD16K: An Enhanced Benchmark for Autonomous Driving
AU - Yuming Li; Jue Wang; Tengfei Xing; Tianlu Liu; Chengjun Li; Kuifeng Su
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2007
ER -
Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su. (2017). TAD16K: An Enhanced Benchmark for Autonomous Driving. IEEE SigPort. http://sigport.org/2007
Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su, 2017. TAD16K: An Enhanced Benchmark for Autonomous Driving. Available at: http://sigport.org/2007.
Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su. (2017). "TAD16K: An Enhanced Benchmark for Autonomous Driving." Web.
1. Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su. TAD16K: An Enhanced Benchmark for Autonomous Driving [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2007

Phase Retrieval via Coordinate Descent


Phase retrieval refers to recovery of a signal-of-interest given only the intensity measurement samples and has wide applicability including important areas of astronomy, computational biology, crystallography, digital communications, electron microscopy, neutron radiography and optical imaging. The classical problem formulation is to restore the time-domain signal from its power spectrum observations, although the Fourier transform can be generalized to any linear mappings.

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28 June 2017 - 11:19pm
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[1] , "Phase Retrieval via Coordinate Descent", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1799. Accessed: Oct. 19, 2018.
@article{1799-17,
url = {http://sigport.org/1799},
author = { },
publisher = {IEEE SigPort},
title = {Phase Retrieval via Coordinate Descent},
year = {2017} }
TY - EJOUR
T1 - Phase Retrieval via Coordinate Descent
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1799
ER -
. (2017). Phase Retrieval via Coordinate Descent. IEEE SigPort. http://sigport.org/1799
, 2017. Phase Retrieval via Coordinate Descent. Available at: http://sigport.org/1799.
. (2017). "Phase Retrieval via Coordinate Descent." Web.
1. . Phase Retrieval via Coordinate Descent [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1799

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