Ultra Wideband Transceivers Based on Chaotic Pulses and their Application to Wireless Body Area Networks

2014 ◽  
Vol 2 ◽  
pp. 221-224
Author(s):  
Yuri V. Andreyev ◽  
Alexander S. Dmitriev ◽  
Elena V. Efremova ◽  
Vadim A. Lazarev
Keyword(s):  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Ultra Wideband ◽  
Body Area ◽  
Ultra Wideband Transceivers

scholarly journals Erratum for: A wearable ultra-wideband antenna for wireless body area networks

2016 ◽  
Vol 58 (9) ◽  
pp. 2285-2285 ◽  
Author(s):  
Kinza Shafique ◽  
Bilal A. Khawaja ◽  
Munir A. Tarar ◽  
Bilal M. Khan ◽  
Muhammad Mustaqim ◽  
...  
Keyword(s):  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Ultra Wideband ◽  
Body Area ◽  
Wideband Antenna

scholarly journals ON-OFF Body Ultra-Wideband (UWB) Antenna for Wireless Body Area Networks (WBAN): A Review

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 150844-150863
Author(s):  
Sarmad Nozad Mahmood ◽  
Asnor Juraiza Ishak ◽  
Alyani Ismail ◽  
Azura Che Soh ◽  
Zahriladha Zakaria ◽  
...  
Keyword(s):  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Ultra Wideband ◽  
Uwb Antenna ◽  
Body Area

scholarly journals Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4229 ◽  
Author(s):  
Krzysztof K. Cwalina ◽  
Piotr Rajchowski ◽  
Olga Blaszkiewicz ◽  
Alicja Olejniczak ◽  
Jaroslaw Sadowski
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Impulse Response ◽  
Indoor Environment ◽  
Measurement Data ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Feedforward Neural Network ◽  
Ultra Wideband ◽  
Body Area

In this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on the basis of the measurement data for dynamic scenarios in an indoor environment. The obtained results clearly prove the validity of the proposed DL approach in the UWB WBANs and high (over 98.6% for most cases) efficiency for LOS and NLOS conditions classification.

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