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Communication and Sensing aspects of Sensor Networks, Wireless and Ad-Hoc Networks

A DYNAMIC BAYESIAN NETWORK APPROACH FOR DEVICE-FREE RADIO VISION: MODELING, LEARNING AND INFERENCE FOR BODY MOTION RECOGNITION

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Authors:
Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa
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12 March 2016 - 8:19am
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[1] Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa, "A DYNAMIC BAYESIAN NETWORK APPROACH FOR DEVICE-FREE RADIO VISION: MODELING, LEARNING AND INFERENCE FOR BODY MOTION RECOGNITION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/636. Accessed: Feb. 18, 2020.
@article{636-16,
url = {http://sigport.org/636},
author = {Stefano Savazzi; Sanaz Kianoush; Vittorio Rampa },
publisher = {IEEE SigPort},
title = {A DYNAMIC BAYESIAN NETWORK APPROACH FOR DEVICE-FREE RADIO VISION: MODELING, LEARNING AND INFERENCE FOR BODY MOTION RECOGNITION},
year = {2016} }
TY - EJOUR
T1 - A DYNAMIC BAYESIAN NETWORK APPROACH FOR DEVICE-FREE RADIO VISION: MODELING, LEARNING AND INFERENCE FOR BODY MOTION RECOGNITION
AU - Stefano Savazzi; Sanaz Kianoush; Vittorio Rampa
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/636
ER -
Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa. (2016). A DYNAMIC BAYESIAN NETWORK APPROACH FOR DEVICE-FREE RADIO VISION: MODELING, LEARNING AND INFERENCE FOR BODY MOTION RECOGNITION. IEEE SigPort. http://sigport.org/636
Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa, 2016. A DYNAMIC BAYESIAN NETWORK APPROACH FOR DEVICE-FREE RADIO VISION: MODELING, LEARNING AND INFERENCE FOR BODY MOTION RECOGNITION. Available at: http://sigport.org/636.
Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa. (2016). "A DYNAMIC BAYESIAN NETWORK APPROACH FOR DEVICE-FREE RADIO VISION: MODELING, LEARNING AND INFERENCE FOR BODY MOTION RECOGNITION." Web.
1. Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa. A DYNAMIC BAYESIAN NETWORK APPROACH FOR DEVICE-FREE RADIO VISION: MODELING, LEARNING AND INFERENCE FOR BODY MOTION RECOGNITION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/636

Directional Maximum Likelihood Self-Estimation of the Path-Loss Exponent


Directional Self-Estimation of the Path-Loss Exponent

The path-loss exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction.

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Authors:
Geert Leus
Submitted On:
11 March 2016 - 6:03pm
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[1] Geert Leus, "Directional Maximum Likelihood Self-Estimation of the Path-Loss Exponent", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/613. Accessed: Feb. 18, 2020.
@article{613-16,
url = {http://sigport.org/613},
author = {Geert Leus },
publisher = {IEEE SigPort},
title = {Directional Maximum Likelihood Self-Estimation of the Path-Loss Exponent},
year = {2016} }
TY - EJOUR
T1 - Directional Maximum Likelihood Self-Estimation of the Path-Loss Exponent
AU - Geert Leus
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/613
ER -
Geert Leus. (2016). Directional Maximum Likelihood Self-Estimation of the Path-Loss Exponent. IEEE SigPort. http://sigport.org/613
Geert Leus, 2016. Directional Maximum Likelihood Self-Estimation of the Path-Loss Exponent. Available at: http://sigport.org/613.
Geert Leus. (2016). "Directional Maximum Likelihood Self-Estimation of the Path-Loss Exponent." Web.
1. Geert Leus. Directional Maximum Likelihood Self-Estimation of the Path-Loss Exponent [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/613

Wireless Power Transfer for Distributed Estimation in Wireless Passive Sensor Networks


This work examines the charging power allocation and beam selection problem for distributed estimation in wireless passive sensor networks, where the sensors are charged over the air by RF-enabled energy sources. A two-phase replenishment and transmission cycle is considered. In the replenishment phase, each wireless charger emits power over the air through a carefully selected beam and power to replenish the wireless sensors.

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Authors:
Teng-Cheng Hsu
Submitted On:
23 February 2016 - 1:44pm
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[1] Teng-Cheng Hsu, "Wireless Power Transfer for Distributed Estimation in Wireless Passive Sensor Networks", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/455. Accessed: Feb. 18, 2020.
@article{455-15,
url = {http://sigport.org/455},
author = {Teng-Cheng Hsu },
publisher = {IEEE SigPort},
title = {Wireless Power Transfer for Distributed Estimation in Wireless Passive Sensor Networks},
year = {2015} }
TY - EJOUR
T1 - Wireless Power Transfer for Distributed Estimation in Wireless Passive Sensor Networks
AU - Teng-Cheng Hsu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/455
ER -
Teng-Cheng Hsu. (2015). Wireless Power Transfer for Distributed Estimation in Wireless Passive Sensor Networks. IEEE SigPort. http://sigport.org/455
Teng-Cheng Hsu, 2015. Wireless Power Transfer for Distributed Estimation in Wireless Passive Sensor Networks. Available at: http://sigport.org/455.
Teng-Cheng Hsu. (2015). "Wireless Power Transfer for Distributed Estimation in Wireless Passive Sensor Networks." Web.
1. Teng-Cheng Hsu. Wireless Power Transfer for Distributed Estimation in Wireless Passive Sensor Networks [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/455

Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks


Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks

This paper investigates the use of wireless power transfer (WPT) for {measurement sensing} and information transmission in a wireless sensor network (WSN) performing distributed parameter estimation using an adaptive diffusion least mean-squares (LMS) strategy. We consider a hybrid WSN consisting of common sensor nodes (CNs) and super sensor nodes (SNs) that are capable of WPT. In each diffusion iteration, all nodes sense measurements and exchange parameter estimates with their neighbors. Each SN also transfers wireless power to its neighboring CNs via beamforming.

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Authors:
Yong Liang Guan
Submitted On:
23 February 2016 - 1:44pm
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[1] Yong Liang Guan, "Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/449. Accessed: Feb. 18, 2020.
@article{449-15,
url = {http://sigport.org/449},
author = {Yong Liang Guan },
publisher = {IEEE SigPort},
title = {Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks},
year = {2015} }
TY - EJOUR
T1 - Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks
AU - Yong Liang Guan
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/449
ER -
Yong Liang Guan. (2015). Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks. IEEE SigPort. http://sigport.org/449
Yong Liang Guan, 2015. Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks. Available at: http://sigport.org/449.
Yong Liang Guan. (2015). "Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks." Web.
1. Yong Liang Guan. Optimal Wireless Power Transfer and Harvested Power Allocation for Diffusion LMS in Wireless Sensor Networks [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/449

Distortion Minimization via Adaptive Digital and Analog Transmission for Energy Harvesting-based Wireless Sensor Networks

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23 February 2016 - 1:38pm
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[1] , "Distortion Minimization via Adaptive Digital and Analog Transmission for Energy Harvesting-based Wireless Sensor Networks", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/438. Accessed: Feb. 18, 2020.
@article{438-15,
url = {http://sigport.org/438},
author = { },
publisher = {IEEE SigPort},
title = {Distortion Minimization via Adaptive Digital and Analog Transmission for Energy Harvesting-based Wireless Sensor Networks},
year = {2015} }
TY - EJOUR
T1 - Distortion Minimization via Adaptive Digital and Analog Transmission for Energy Harvesting-based Wireless Sensor Networks
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/438
ER -
. (2015). Distortion Minimization via Adaptive Digital and Analog Transmission for Energy Harvesting-based Wireless Sensor Networks. IEEE SigPort. http://sigport.org/438
, 2015. Distortion Minimization via Adaptive Digital and Analog Transmission for Energy Harvesting-based Wireless Sensor Networks. Available at: http://sigport.org/438.
. (2015). "Distortion Minimization via Adaptive Digital and Analog Transmission for Energy Harvesting-based Wireless Sensor Networks." Web.
1. . Distortion Minimization via Adaptive Digital and Analog Transmission for Energy Harvesting-based Wireless Sensor Networks [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/438

Sensor Selection in Energy Harvesting Wireless Sensor Networks


In this paper, we propose a novel energy harvesting (EH)-aware sensor selection policy. Our goal is to minimize the distortion in the reconstruction of the underlying source subject to the causality constraints imposed by the EH process at the sensor nodes. Besides, we determine the optimal power allocation for a given sensor selection (which admits a two-dimensional directional waterfilling interpretation) as the solution of an offline convex optimization problem. To that aim, we propose an iterative procedure.

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Authors:
Javier Matamoros, Carles Antón-Haro
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23 February 2016 - 1:44pm
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[1] Javier Matamoros, Carles Antón-Haro, "Sensor Selection in Energy Harvesting Wireless Sensor Networks", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/380. Accessed: Feb. 18, 2020.
@article{380-15,
url = {http://sigport.org/380},
author = {Javier Matamoros; Carles Antón-Haro },
publisher = {IEEE SigPort},
title = {Sensor Selection in Energy Harvesting Wireless Sensor Networks},
year = {2015} }
TY - EJOUR
T1 - Sensor Selection in Energy Harvesting Wireless Sensor Networks
AU - Javier Matamoros; Carles Antón-Haro
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/380
ER -
Javier Matamoros, Carles Antón-Haro. (2015). Sensor Selection in Energy Harvesting Wireless Sensor Networks. IEEE SigPort. http://sigport.org/380
Javier Matamoros, Carles Antón-Haro, 2015. Sensor Selection in Energy Harvesting Wireless Sensor Networks. Available at: http://sigport.org/380.
Javier Matamoros, Carles Antón-Haro. (2015). "Sensor Selection in Energy Harvesting Wireless Sensor Networks." Web.
1. Javier Matamoros, Carles Antón-Haro. Sensor Selection in Energy Harvesting Wireless Sensor Networks [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/380

Coding Performance for Signal Dependent Channels in Visible Light Communication System

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Authors:
Ming Yuan, Xiao Liang, Ming Jiang, Jiaheng Wang, Chunming Zhao
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23 February 2016 - 1:44pm
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[1] Ming Yuan, Xiao Liang, Ming Jiang, Jiaheng Wang, Chunming Zhao, "Coding Performance for Signal Dependent Channels in Visible Light Communication System", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/314. Accessed: Feb. 18, 2020.
@article{314-15,
url = {http://sigport.org/314},
author = {Ming Yuan; Xiao Liang; Ming Jiang; Jiaheng Wang; Chunming Zhao },
publisher = {IEEE SigPort},
title = {Coding Performance for Signal Dependent Channels in Visible Light Communication System},
year = {2015} }
TY - EJOUR
T1 - Coding Performance for Signal Dependent Channels in Visible Light Communication System
AU - Ming Yuan; Xiao Liang; Ming Jiang; Jiaheng Wang; Chunming Zhao
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/314
ER -
Ming Yuan, Xiao Liang, Ming Jiang, Jiaheng Wang, Chunming Zhao. (2015). Coding Performance for Signal Dependent Channels in Visible Light Communication System. IEEE SigPort. http://sigport.org/314
Ming Yuan, Xiao Liang, Ming Jiang, Jiaheng Wang, Chunming Zhao, 2015. Coding Performance for Signal Dependent Channels in Visible Light Communication System. Available at: http://sigport.org/314.
Ming Yuan, Xiao Liang, Ming Jiang, Jiaheng Wang, Chunming Zhao. (2015). "Coding Performance for Signal Dependent Channels in Visible Light Communication System." Web.
1. Ming Yuan, Xiao Liang, Ming Jiang, Jiaheng Wang, Chunming Zhao. Coding Performance for Signal Dependent Channels in Visible Light Communication System [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/314

Recursive Filters with Bayesian Quadratic Network Game Fusion


Distributed filter in networks mainly involves two stages, local estimation by private observation and information fusion with neighbor nodes based on the underlying topology. Since Bayesian game is a powerful tool to analyze the interaction equilibrium of multi-player with incomplete information in networks, we combine the recursive LMMSE filter with network game of quadratic utilities under the Bayesian filtering framework. In our algorithm, the nodes update their local beliefs on the unknown state by private observations and historical actions from neighbors in network.

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Authors:
Muyuan Zhai, Tao Yang, Bo Hu
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23 February 2016 - 1:38pm
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[1] Muyuan Zhai, Tao Yang, Bo Hu, "Recursive Filters with Bayesian Quadratic Network Game Fusion", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/287. Accessed: Feb. 18, 2020.
@article{287-15,
url = {http://sigport.org/287},
author = {Muyuan Zhai; Tao Yang; Bo Hu },
publisher = {IEEE SigPort},
title = {Recursive Filters with Bayesian Quadratic Network Game Fusion},
year = {2015} }
TY - EJOUR
T1 - Recursive Filters with Bayesian Quadratic Network Game Fusion
AU - Muyuan Zhai; Tao Yang; Bo Hu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/287
ER -
Muyuan Zhai, Tao Yang, Bo Hu. (2015). Recursive Filters with Bayesian Quadratic Network Game Fusion. IEEE SigPort. http://sigport.org/287
Muyuan Zhai, Tao Yang, Bo Hu, 2015. Recursive Filters with Bayesian Quadratic Network Game Fusion. Available at: http://sigport.org/287.
Muyuan Zhai, Tao Yang, Bo Hu. (2015). "Recursive Filters with Bayesian Quadratic Network Game Fusion." Web.
1. Muyuan Zhai, Tao Yang, Bo Hu. Recursive Filters with Bayesian Quadratic Network Game Fusion [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/287

Channel-Robust Compressed Sensing via Vector Pre-Quantization in Wireless Sensor Networks


This paper addresses channel-robust compressed sensing (CS) acquisition of sparse sources under complexity-constrained encoding over noisy channels in wireless sensor networks. We propose a single-sensor joint source-channel coding method based on channel-optimized vector quantization by designing a CS-aware encoder-decoder pair to minimize the end-to-end mean square error (MSE) distortion of the signal reconstruction. As our key target is to obtain tolerable encoding complexity at the resource-limited sensor, the method relies on vector pre-quantization of the measurement space.

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Authors:
Marian Codreanu, Markku Juntti
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23 February 2016 - 1:44pm
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[1] Marian Codreanu, Markku Juntti, "Channel-Robust Compressed Sensing via Vector Pre-Quantization in Wireless Sensor Networks", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/272. Accessed: Feb. 18, 2020.
@article{272-15,
url = {http://sigport.org/272},
author = {Marian Codreanu; Markku Juntti },
publisher = {IEEE SigPort},
title = {Channel-Robust Compressed Sensing via Vector Pre-Quantization in Wireless Sensor Networks},
year = {2015} }
TY - EJOUR
T1 - Channel-Robust Compressed Sensing via Vector Pre-Quantization in Wireless Sensor Networks
AU - Marian Codreanu; Markku Juntti
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/272
ER -
Marian Codreanu, Markku Juntti. (2015). Channel-Robust Compressed Sensing via Vector Pre-Quantization in Wireless Sensor Networks. IEEE SigPort. http://sigport.org/272
Marian Codreanu, Markku Juntti, 2015. Channel-Robust Compressed Sensing via Vector Pre-Quantization in Wireless Sensor Networks. Available at: http://sigport.org/272.
Marian Codreanu, Markku Juntti. (2015). "Channel-Robust Compressed Sensing via Vector Pre-Quantization in Wireless Sensor Networks." Web.
1. Marian Codreanu, Markku Juntti. Channel-Robust Compressed Sensing via Vector Pre-Quantization in Wireless Sensor Networks [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/272

Wireless Body-Area Network Time Synchronization using R Peak Reference Broadcasts


Telemonitoring of biosignals is a growing area of research due to the aging world population. Telemonitoring utilizes a wireless body-area network (WBAN) consisting of wearable biosignal sensors equipped with ultra low power radios. The measured data from each sensor on the patient is sent to a smartphone, which then sends the data to a healthcare provider via the internet. To enable real-time telemonitoring of the biosignals, it is desirable to have accurate timestamped data from the sensors in the WBAN.

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23 February 2016 - 1:43pm
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[1] , "Wireless Body-Area Network Time Synchronization using R Peak Reference Broadcasts", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/215. Accessed: Feb. 18, 2020.
@article{215-15,
url = {http://sigport.org/215},
author = { },
publisher = {IEEE SigPort},
title = {Wireless Body-Area Network Time Synchronization using R Peak Reference Broadcasts},
year = {2015} }
TY - EJOUR
T1 - Wireless Body-Area Network Time Synchronization using R Peak Reference Broadcasts
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/215
ER -
. (2015). Wireless Body-Area Network Time Synchronization using R Peak Reference Broadcasts. IEEE SigPort. http://sigport.org/215
, 2015. Wireless Body-Area Network Time Synchronization using R Peak Reference Broadcasts. Available at: http://sigport.org/215.
. (2015). "Wireless Body-Area Network Time Synchronization using R Peak Reference Broadcasts." Web.
1. . Wireless Body-Area Network Time Synchronization using R Peak Reference Broadcasts [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/215

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