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Machine Learning for Signal Processing

AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS

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14 May 2020 - 11:44am
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[1] , "AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5309. Accessed: Aug. 13, 2020.
@article{5309-20,
url = {http://sigport.org/5309},
author = { },
publisher = {IEEE SigPort},
title = {AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS},
year = {2020} }
TY - EJOUR
T1 - AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5309
ER -
. (2020). AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS. IEEE SigPort. http://sigport.org/5309
, 2020. AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS. Available at: http://sigport.org/5309.
. (2020). "AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS." Web.
1. . AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5309

Deep geometric knowledge distillation with graphs

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Authors:
Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega
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14 May 2020 - 11:17am
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[1] Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega, "Deep geometric knowledge distillation with graphs", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5306. Accessed: Aug. 13, 2020.
@article{5306-20,
url = {http://sigport.org/5306},
author = {Carlos Lassance; Myriam Bontonou; Ghouthi Boukli Hacene; Vincent Gripon; Jian Tang; Antonio Ortega },
publisher = {IEEE SigPort},
title = {Deep geometric knowledge distillation with graphs},
year = {2020} }
TY - EJOUR
T1 - Deep geometric knowledge distillation with graphs
AU - Carlos Lassance; Myriam Bontonou; Ghouthi Boukli Hacene; Vincent Gripon; Jian Tang; Antonio Ortega
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5306
ER -
Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega. (2020). Deep geometric knowledge distillation with graphs. IEEE SigPort. http://sigport.org/5306
Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega, 2020. Deep geometric knowledge distillation with graphs. Available at: http://sigport.org/5306.
Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega. (2020). "Deep geometric knowledge distillation with graphs." Web.
1. Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega. Deep geometric knowledge distillation with graphs [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5306

A NOVEL TWO-PATHWAY ENCODER-DECODER NETWORK FOR 3D FACE RECONSTRUCTION


3D Morphable Model (3DMM) is a statistical tool widely employed in reconstructing 3D face shape. Existing methods are aimed at predicting 3DMM shape parameters with a single encoder but suffer from unclear distinction of different attributes. To address this problem, Two-Pathway Encoder-Decoder Network (2PEDN) is proposed to regress the identity and expression components via global and local pathways. Specifically, each 2D face image is cropped into global face and local details as the inputs for the corresponding pathways.

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Authors:
Xianfeng Li, Zichun Weng, Juntao Liang, Lei Cai, Youjun Xiang, Yuli Fu
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14 May 2020 - 3:23am
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[1] Xianfeng Li, Zichun Weng, Juntao Liang, Lei Cai, Youjun Xiang, Yuli Fu, "A NOVEL TWO-PATHWAY ENCODER-DECODER NETWORK FOR 3D FACE RECONSTRUCTION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5246. Accessed: Aug. 13, 2020.
@article{5246-20,
url = {http://sigport.org/5246},
author = {Xianfeng Li; Zichun Weng; Juntao Liang; Lei Cai; Youjun Xiang; Yuli Fu },
publisher = {IEEE SigPort},
title = {A NOVEL TWO-PATHWAY ENCODER-DECODER NETWORK FOR 3D FACE RECONSTRUCTION},
year = {2020} }
TY - EJOUR
T1 - A NOVEL TWO-PATHWAY ENCODER-DECODER NETWORK FOR 3D FACE RECONSTRUCTION
AU - Xianfeng Li; Zichun Weng; Juntao Liang; Lei Cai; Youjun Xiang; Yuli Fu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5246
ER -
Xianfeng Li, Zichun Weng, Juntao Liang, Lei Cai, Youjun Xiang, Yuli Fu. (2020). A NOVEL TWO-PATHWAY ENCODER-DECODER NETWORK FOR 3D FACE RECONSTRUCTION. IEEE SigPort. http://sigport.org/5246
Xianfeng Li, Zichun Weng, Juntao Liang, Lei Cai, Youjun Xiang, Yuli Fu, 2020. A NOVEL TWO-PATHWAY ENCODER-DECODER NETWORK FOR 3D FACE RECONSTRUCTION. Available at: http://sigport.org/5246.
Xianfeng Li, Zichun Weng, Juntao Liang, Lei Cai, Youjun Xiang, Yuli Fu. (2020). "A NOVEL TWO-PATHWAY ENCODER-DECODER NETWORK FOR 3D FACE RECONSTRUCTION." Web.
1. Xianfeng Li, Zichun Weng, Juntao Liang, Lei Cai, Youjun Xiang, Yuli Fu. A NOVEL TWO-PATHWAY ENCODER-DECODER NETWORK FOR 3D FACE RECONSTRUCTION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5246

Training machine learning on JPEG compressed images

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Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic
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1 April 2020 - 2:44am
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[1] Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic, "Training machine learning on JPEG compressed images", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5063. Accessed: Aug. 13, 2020.
@article{5063-20,
url = {http://sigport.org/5063},
author = {Maxime Pistono; Gouenou Coatrieux; Jean-Claude Nunes; Michel Cozic },
publisher = {IEEE SigPort},
title = {Training machine learning on JPEG compressed images},
year = {2020} }
TY - EJOUR
T1 - Training machine learning on JPEG compressed images
AU - Maxime Pistono; Gouenou Coatrieux; Jean-Claude Nunes; Michel Cozic
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5063
ER -
Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic. (2020). Training machine learning on JPEG compressed images. IEEE SigPort. http://sigport.org/5063
Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic, 2020. Training machine learning on JPEG compressed images. Available at: http://sigport.org/5063.
Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic. (2020). "Training machine learning on JPEG compressed images." Web.
1. Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic. Training machine learning on JPEG compressed images [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5063

Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks

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24 April 2020 - 12:55pm
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[1] , "Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5024. Accessed: Aug. 13, 2020.
@article{5024-20,
url = {http://sigport.org/5024},
author = { },
publisher = {IEEE SigPort},
title = {Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks},
year = {2020} }
TY - EJOUR
T1 - Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5024
ER -
. (2020). Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks. IEEE SigPort. http://sigport.org/5024
, 2020. Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks. Available at: http://sigport.org/5024.
. (2020). "Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks." Web.
1. . Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5024

Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients


Segmenting a document image into text-lines and words finds applications in many research areas of DIA(Document Image Analysis) such as OCR, Word Spotting, and document retrieval. However, carrying out segmentation operation directly in the compressed document images is still an unexplored and challenging research area. Since JPEG is most widely accepted compression algorithm, this research paper attempts to segment a JPEG compressed printed text document image into text-lines and words, without fully decompressing the image.

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Authors:
Mohammed Javed, P Nagabhushan, Watanabe Osamu
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7 April 2020 - 5:04am
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DCC2020 Paper ID 181

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[1] Mohammed Javed, P Nagabhushan, Watanabe Osamu, "Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5001. Accessed: Aug. 13, 2020.
@article{5001-20,
url = {http://sigport.org/5001},
author = {Mohammed Javed; P Nagabhushan; Watanabe Osamu },
publisher = {IEEE SigPort},
title = {Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients},
year = {2020} }
TY - EJOUR
T1 - Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients
AU - Mohammed Javed; P Nagabhushan; Watanabe Osamu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5001
ER -
Mohammed Javed, P Nagabhushan, Watanabe Osamu. (2020). Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients. IEEE SigPort. http://sigport.org/5001
Mohammed Javed, P Nagabhushan, Watanabe Osamu, 2020. Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients. Available at: http://sigport.org/5001.
Mohammed Javed, P Nagabhushan, Watanabe Osamu. (2020). "Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients." Web.
1. Mohammed Javed, P Nagabhushan, Watanabe Osamu. Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5001

Improved Subspace K-Means Performance via a Randomized Matrix Decomposition


Subspace clustering algorithms provide the capability
to project a dataset onto bases that facilitate clustering.
Proposed in 2017, the subspace k-means algorithm simultaneously
performs clustering and dimensionality reduction with the goal
of finding the optimal subspace for the cluster structure; this
is accomplished by incorporating a trade-off between cluster
and noise subspaces in the objective function. In this study,
we improve subspace k-means by estimating a critical transformation

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Authors:
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley
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14 November 2019 - 7:39pm
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[1] Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley, "Improved Subspace K-Means Performance via a Randomized Matrix Decomposition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4958. Accessed: Aug. 13, 2020.
@article{4958-19,
url = {http://sigport.org/4958},
author = {Trevor Vannoy; Jacob Senecal; Veronika Strnadova-Neeley },
publisher = {IEEE SigPort},
title = {Improved Subspace K-Means Performance via a Randomized Matrix Decomposition},
year = {2019} }
TY - EJOUR
T1 - Improved Subspace K-Means Performance via a Randomized Matrix Decomposition
AU - Trevor Vannoy; Jacob Senecal; Veronika Strnadova-Neeley
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4958
ER -
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. (2019). Improved Subspace K-Means Performance via a Randomized Matrix Decomposition. IEEE SigPort. http://sigport.org/4958
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley, 2019. Improved Subspace K-Means Performance via a Randomized Matrix Decomposition. Available at: http://sigport.org/4958.
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. (2019). "Improved Subspace K-Means Performance via a Randomized Matrix Decomposition." Web.
1. Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. Improved Subspace K-Means Performance via a Randomized Matrix Decomposition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4958

Poster: Generative-Discriminative Crop Type Identification using Satellite Images


Crop type identification refers to distinguishing certain crop from other landcovers, which is an essential and crucial task in agricultural monitoring. Satellite images are good data input for identifying different crops since satellites capture relatively wider area and more spectral information. Based on prior knowledge of crop phenology, multi-temporal images are stacked to extract the growth pattern of varied crops.

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Authors:
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu
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9 November 2019 - 7:23pm
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Poster: Generative-Discriminative Crop Type Identification using Satellite Images

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[1] Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu, "Poster: Generative-Discriminative Crop Type Identification using Satellite Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4942. Accessed: Aug. 13, 2020.
@article{4942-19,
url = {http://sigport.org/4942},
author = {Nan Qiao; Yi Zhao; Ruei-Sung Lin; Bo Gong; Zhongxiang Wu; Mei Han; Jiashu Liu },
publisher = {IEEE SigPort},
title = {Poster: Generative-Discriminative Crop Type Identification using Satellite Images},
year = {2019} }
TY - EJOUR
T1 - Poster: Generative-Discriminative Crop Type Identification using Satellite Images
AU - Nan Qiao; Yi Zhao; Ruei-Sung Lin; Bo Gong; Zhongxiang Wu; Mei Han; Jiashu Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4942
ER -
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu. (2019). Poster: Generative-Discriminative Crop Type Identification using Satellite Images. IEEE SigPort. http://sigport.org/4942
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu, 2019. Poster: Generative-Discriminative Crop Type Identification using Satellite Images. Available at: http://sigport.org/4942.
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu. (2019). "Poster: Generative-Discriminative Crop Type Identification using Satellite Images." Web.
1. Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu. Poster: Generative-Discriminative Crop Type Identification using Satellite Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4942

A deep network for single-snapshot direction of arrival estimation


This paper examines a deep feedforward network for beamforming with the single--snapshot Sample Covariance Matrix (SCM). The Conventional beamforming formulation, typically quadratic in the complex weight space, is reformulated as real and linear in the weight covariance and SCM. The reformulated SCMs are used as input to a deep feed--forward neural network (FNN) for two source localization. Simulations demonstrate the effect of source incoherence and performance in a noisy tracking example.

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Authors:
Peter Gerstoft, Emma Ozanich, Haiqiang Niu
Submitted On:
28 October 2019 - 10:56am
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[1] Peter Gerstoft, Emma Ozanich, Haiqiang Niu, "A deep network for single-snapshot direction of arrival estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4898. Accessed: Aug. 13, 2020.
@article{4898-19,
url = {http://sigport.org/4898},
author = {Peter Gerstoft; Emma Ozanich; Haiqiang Niu },
publisher = {IEEE SigPort},
title = {A deep network for single-snapshot direction of arrival estimation},
year = {2019} }
TY - EJOUR
T1 - A deep network for single-snapshot direction of arrival estimation
AU - Peter Gerstoft; Emma Ozanich; Haiqiang Niu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4898
ER -
Peter Gerstoft, Emma Ozanich, Haiqiang Niu. (2019). A deep network for single-snapshot direction of arrival estimation. IEEE SigPort. http://sigport.org/4898
Peter Gerstoft, Emma Ozanich, Haiqiang Niu, 2019. A deep network for single-snapshot direction of arrival estimation. Available at: http://sigport.org/4898.
Peter Gerstoft, Emma Ozanich, Haiqiang Niu. (2019). "A deep network for single-snapshot direction of arrival estimation." Web.
1. Peter Gerstoft, Emma Ozanich, Haiqiang Niu. A deep network for single-snapshot direction of arrival estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4898

Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks


We focus on a wireless sensor network powered with an energy beacon, where sensors send their measurements to the sink using the harvested energy. The aim of the system is to estimate an unknown signal over the area of interest as accurately as possible. We investigate optimal energy beamforming at the energy beacon and optimal transmit power allocation at the sensors under non-linear energy harvesting models. We use a deep reinforcement learning (RL) based approach where multi-layer neural networks are utilized.

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Authors:
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen
Submitted On:
16 October 2019 - 8:16am
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[1] Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen, "Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4875. Accessed: Aug. 13, 2020.
@article{4875-19,
url = {http://sigport.org/4875},
author = {Ayca Ozcelikkale; Mehmet Koseoglu; Mani Srivastava; Anders Ahlen },
publisher = {IEEE SigPort},
title = {Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks},
year = {2019} }
TY - EJOUR
T1 - Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks
AU - Ayca Ozcelikkale; Mehmet Koseoglu; Mani Srivastava; Anders Ahlen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4875
ER -
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen. (2019). Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks. IEEE SigPort. http://sigport.org/4875
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen, 2019. Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks. Available at: http://sigport.org/4875.
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen. (2019). "Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks." Web.
1. Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen. Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4875

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