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Audio and Acoustic Signal Processing

AUDIO-VISUAL PERSON RECOGNITION IN MULTIMEDIA DATA FROM THE IARPA JANUS PROGRAM

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Authors:
Gregory Sell, Kevin Duh, David Snyder, Dave Etter, Daniel Garcia-Romero
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16 April 2018 - 8:48am
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ICASSP18_Janus_slides.pptx

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[1] Gregory Sell, Kevin Duh, David Snyder, Dave Etter, Daniel Garcia-Romero, "AUDIO-VISUAL PERSON RECOGNITION IN MULTIMEDIA DATA FROM THE IARPA JANUS PROGRAM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2913. Accessed: May. 23, 2018.
@article{2913-18,
url = {http://sigport.org/2913},
author = {Gregory Sell; Kevin Duh; David Snyder; Dave Etter; Daniel Garcia-Romero },
publisher = {IEEE SigPort},
title = {AUDIO-VISUAL PERSON RECOGNITION IN MULTIMEDIA DATA FROM THE IARPA JANUS PROGRAM},
year = {2018} }
TY - EJOUR
T1 - AUDIO-VISUAL PERSON RECOGNITION IN MULTIMEDIA DATA FROM THE IARPA JANUS PROGRAM
AU - Gregory Sell; Kevin Duh; David Snyder; Dave Etter; Daniel Garcia-Romero
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2913
ER -
Gregory Sell, Kevin Duh, David Snyder, Dave Etter, Daniel Garcia-Romero. (2018). AUDIO-VISUAL PERSON RECOGNITION IN MULTIMEDIA DATA FROM THE IARPA JANUS PROGRAM. IEEE SigPort. http://sigport.org/2913
Gregory Sell, Kevin Duh, David Snyder, Dave Etter, Daniel Garcia-Romero, 2018. AUDIO-VISUAL PERSON RECOGNITION IN MULTIMEDIA DATA FROM THE IARPA JANUS PROGRAM. Available at: http://sigport.org/2913.
Gregory Sell, Kevin Duh, David Snyder, Dave Etter, Daniel Garcia-Romero. (2018). "AUDIO-VISUAL PERSON RECOGNITION IN MULTIMEDIA DATA FROM THE IARPA JANUS PROGRAM." Web.
1. Gregory Sell, Kevin Duh, David Snyder, Dave Etter, Daniel Garcia-Romero. AUDIO-VISUAL PERSON RECOGNITION IN MULTIMEDIA DATA FROM THE IARPA JANUS PROGRAM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2913

ImageFusion Using Belief Propagation

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Authors:
Dave Bull
Submitted On:
15 April 2018 - 6:47pm
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Poster for ICASSP 2018

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[1] Dave Bull, "ImageFusion Using Belief Propagation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2902. Accessed: May. 23, 2018.
@article{2902-18,
url = {http://sigport.org/2902},
author = {Dave Bull },
publisher = {IEEE SigPort},
title = {ImageFusion Using Belief Propagation},
year = {2018} }
TY - EJOUR
T1 - ImageFusion Using Belief Propagation
AU - Dave Bull
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2902
ER -
Dave Bull. (2018). ImageFusion Using Belief Propagation. IEEE SigPort. http://sigport.org/2902
Dave Bull, 2018. ImageFusion Using Belief Propagation. Available at: http://sigport.org/2902.
Dave Bull. (2018). "ImageFusion Using Belief Propagation." Web.
1. Dave Bull. ImageFusion Using Belief Propagation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2902

A Novel Thresholding Technique for the Denoising of Multicomponent Signals


This paper addresses the issues of the denoising and retrieval of the components of multicomponent signals from their short-time Fourier transform (STFT). After having recalled the hard-thresholding technique, in the STFT context, we develop a new thresholding technique by exploiting some limitations of the former. Numerical experiments illustrating the benefits of the proposed method to retrieve the modes of noisy multicomponent signals conclude the paper.

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Authors:
Sylvain Meignen
Submitted On:
15 April 2018 - 8:50am
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presentation_cor_Sylvain.pdf

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[1] Sylvain Meignen, "A Novel Thresholding Technique for the Denoising of Multicomponent Signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2893. Accessed: May. 23, 2018.
@article{2893-18,
url = {http://sigport.org/2893},
author = {Sylvain Meignen },
publisher = {IEEE SigPort},
title = {A Novel Thresholding Technique for the Denoising of Multicomponent Signals},
year = {2018} }
TY - EJOUR
T1 - A Novel Thresholding Technique for the Denoising of Multicomponent Signals
AU - Sylvain Meignen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2893
ER -
Sylvain Meignen. (2018). A Novel Thresholding Technique for the Denoising of Multicomponent Signals. IEEE SigPort. http://sigport.org/2893
Sylvain Meignen, 2018. A Novel Thresholding Technique for the Denoising of Multicomponent Signals. Available at: http://sigport.org/2893.
Sylvain Meignen. (2018). "A Novel Thresholding Technique for the Denoising of Multicomponent Signals." Web.
1. Sylvain Meignen. A Novel Thresholding Technique for the Denoising of Multicomponent Signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2893

Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation

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Authors:
Christopher Tegho, Pawel Budzianowski, Milica Gasic
Submitted On:
19 April 2018 - 12:50pm
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ICASSP Presentation (1).pdf

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[1] Christopher Tegho, Pawel Budzianowski, Milica Gasic, "Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2891. Accessed: May. 23, 2018.
@article{2891-18,
url = {http://sigport.org/2891},
author = {Christopher Tegho; Pawel Budzianowski; Milica Gasic },
publisher = {IEEE SigPort},
title = {Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation},
year = {2018} }
TY - EJOUR
T1 - Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation
AU - Christopher Tegho; Pawel Budzianowski; Milica Gasic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2891
ER -
Christopher Tegho, Pawel Budzianowski, Milica Gasic. (2018). Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation. IEEE SigPort. http://sigport.org/2891
Christopher Tegho, Pawel Budzianowski, Milica Gasic, 2018. Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation. Available at: http://sigport.org/2891.
Christopher Tegho, Pawel Budzianowski, Milica Gasic. (2018). "Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation." Web.
1. Christopher Tegho, Pawel Budzianowski, Milica Gasic. Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2891

SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS


Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying to modify the sentiment of the output sequences were reported. In this paper, we propose five models to scale or adjust the sentiment of the chatbot response: persona-based model, reinforcement learning, plug and play model, sentiment transformation network and cycleGAN, all based on the conventional seq2seq model.

lee.pdf

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Authors:
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee
Submitted On:
15 April 2018 - 4:05am
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lee.pdf

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[1] Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee, "SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2887. Accessed: May. 23, 2018.
@article{2887-18,
url = {http://sigport.org/2887},
author = {Chih-Wei Lee; Yau-Shian Wang; Tsung-Yuan Hsu; Kuan-Yu Chen; Hung-Yi Lee; Lin-shan Lee },
publisher = {IEEE SigPort},
title = {SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS
AU - Chih-Wei Lee; Yau-Shian Wang; Tsung-Yuan Hsu; Kuan-Yu Chen; Hung-Yi Lee; Lin-shan Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2887
ER -
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee. (2018). SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS. IEEE SigPort. http://sigport.org/2887
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee, 2018. SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS. Available at: http://sigport.org/2887.
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee. (2018). "SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS." Web.
1. Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee. SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2887

Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation

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Authors:
Amarjot Singh, Nick Kingsbury
Submitted On:
14 April 2018 - 3:41pm
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Poster-G-SHDL.pdf

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[1] Amarjot Singh, Nick Kingsbury, "Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2850. Accessed: May. 23, 2018.
@article{2850-18,
url = {http://sigport.org/2850},
author = {Amarjot Singh; Nick Kingsbury },
publisher = {IEEE SigPort},
title = {Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation},
year = {2018} }
TY - EJOUR
T1 - Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation
AU - Amarjot Singh; Nick Kingsbury
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2850
ER -
Amarjot Singh, Nick Kingsbury. (2018). Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation. IEEE SigPort. http://sigport.org/2850
Amarjot Singh, Nick Kingsbury, 2018. Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation. Available at: http://sigport.org/2850.
Amarjot Singh, Nick Kingsbury. (2018). "Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation." Web.
1. Amarjot Singh, Nick Kingsbury. Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2850

High Accuracy Acoustic Estimation of Multiple Targets


This paper presents a new adaptation of a Gaussian echo model (GEM) to estimate the distances to multiple targets using acoustic signals. The proposed algorithm utilizes m-sequences and opens the door for applying other modulations and signal designs for acoustic estimation in a similar way. The proposed algorithm estimates the system impulse response and uses the GEM to limit the effect of noise before applying deconvolution to estimate the time of arrival (TOA) to multiple targets with high accuracy.

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Authors:
Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri
Submitted On:
14 April 2018 - 12:07pm
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High Accuracy Acoustic Estimation of Multiple Targets_Poster.pdf

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[1] Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri, "High Accuracy Acoustic Estimation of Multiple Targets", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2839. Accessed: May. 23, 2018.
@article{2839-18,
url = {http://sigport.org/2839},
author = {Mohammed H. AlSharif; Mohamed Saad; Mohamed Siala; Hatem Boujemaa; Tarig Ballal; Tareq Y. Al-Naffouri },
publisher = {IEEE SigPort},
title = {High Accuracy Acoustic Estimation of Multiple Targets},
year = {2018} }
TY - EJOUR
T1 - High Accuracy Acoustic Estimation of Multiple Targets
AU - Mohammed H. AlSharif; Mohamed Saad; Mohamed Siala; Hatem Boujemaa; Tarig Ballal; Tareq Y. Al-Naffouri
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2839
ER -
Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri. (2018). High Accuracy Acoustic Estimation of Multiple Targets. IEEE SigPort. http://sigport.org/2839
Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri, 2018. High Accuracy Acoustic Estimation of Multiple Targets. Available at: http://sigport.org/2839.
Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri. (2018). "High Accuracy Acoustic Estimation of Multiple Targets." Web.
1. Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri. High Accuracy Acoustic Estimation of Multiple Targets [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2839

NEURAL ADAPTIVE IMAGE DENOISER


We propose a novel neural network-based adaptive image denoiser, dubbased as Neural AIDE. Unlike other neural network-based denoisers, which typically apply supervised training to learn a mapping from a noisy patch to a clean patch, we formulate to train a neural network to learn context- based affine mappings that get applied to each noisy pixel. Our formulation enables using SURE (Stein’s Unbiased Risk Estimator)-like estimated losses of those mappings as empirical risks to minimize.

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Authors:
Sungmin Cha, Taesup Moon
Submitted On:
14 April 2018 - 8:37am
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NAIDE_Poster_ICASSP2018

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[1] Sungmin Cha, Taesup Moon, "NEURAL ADAPTIVE IMAGE DENOISER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2825. Accessed: May. 23, 2018.
@article{2825-18,
url = {http://sigport.org/2825},
author = {Sungmin Cha; Taesup Moon },
publisher = {IEEE SigPort},
title = {NEURAL ADAPTIVE IMAGE DENOISER},
year = {2018} }
TY - EJOUR
T1 - NEURAL ADAPTIVE IMAGE DENOISER
AU - Sungmin Cha; Taesup Moon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2825
ER -
Sungmin Cha, Taesup Moon. (2018). NEURAL ADAPTIVE IMAGE DENOISER. IEEE SigPort. http://sigport.org/2825
Sungmin Cha, Taesup Moon, 2018. NEURAL ADAPTIVE IMAGE DENOISER. Available at: http://sigport.org/2825.
Sungmin Cha, Taesup Moon. (2018). "NEURAL ADAPTIVE IMAGE DENOISER." Web.
1. Sungmin Cha, Taesup Moon. NEURAL ADAPTIVE IMAGE DENOISER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2825

SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS


Motivation:
Speech signal is an important information carrier in many social applications such as WeChat and GoogleTalk;
Modern digital technologies have put the security of speech at risk.
Solution: Watermarking is a promising solution to protect the speech signals by embedding digital data into them [1, 2].
Problem:
Many existing methods cannot satisfy the requirements of watermarking, e.g., inaudibility and robustness, simultaneously;

Paper Details

Authors:
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI
Submitted On:
14 April 2018 - 6:16am
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ICASSP2018_Poster.pdf

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[1] Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI, "SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2814. Accessed: May. 23, 2018.
@article{2814-18,
url = {http://sigport.org/2814},
author = {Shengbei WANG; Weitao YUAN; Jianming WANG; Masashi UNOKI },
publisher = {IEEE SigPort},
title = {SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS},
year = {2018} }
TY - EJOUR
T1 - SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS
AU - Shengbei WANG; Weitao YUAN; Jianming WANG; Masashi UNOKI
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2814
ER -
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. (2018). SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS. IEEE SigPort. http://sigport.org/2814
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI, 2018. SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS. Available at: http://sigport.org/2814.
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. (2018). "SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS." Web.
1. Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2814

A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios

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Authors:
Tobias Weber, Anja Klein
Submitted On:
14 April 2018 - 3:14am
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BC_ICASSP.pdf

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[1] Tobias Weber, Anja Klein, "A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2807. Accessed: May. 23, 2018.
@article{2807-18,
url = {http://sigport.org/2807},
author = {Tobias Weber; Anja Klein },
publisher = {IEEE SigPort},
title = {A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios},
year = {2018} }
TY - EJOUR
T1 - A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios
AU - Tobias Weber; Anja Klein
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2807
ER -
Tobias Weber, Anja Klein. (2018). A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios. IEEE SigPort. http://sigport.org/2807
Tobias Weber, Anja Klein, 2018. A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios. Available at: http://sigport.org/2807.
Tobias Weber, Anja Klein. (2018). "A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios." Web.
1. Tobias Weber, Anja Klein. A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2807

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