Deep-Learning-Based Deconvolution of Mechanical Stimuli with Ti3C2Tx MXene Electromagnetic Shield Architecture via Dual-Mode Wireless Signal Variation Mechanism

ACS Nano ◽  
2020 ◽  
Vol 14 (9) ◽  
pp. 11962-11972 ◽  
Author(s):  
Gun-Hee Lee ◽  
Gang San Lee ◽  
Junyoung Byun ◽  
Jun Chang Yang ◽  
Chorom Jang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mechanical Stimuli ◽  
Dual Mode ◽  
Signal Variation ◽  
Wireless Signal ◽  
Electromagnetic Shield ◽  
Variation Mechanism

scholarly journals A Survey on Deep Learning Techniques in Wireless Signal Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaofan Li ◽  
Fangwei Dong ◽  
Sha Zhang ◽  
Weibin Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Future Trends ◽  
Signal Recognition ◽  
Wireless Technology ◽  
Modulation Recognition ◽  
Spectrum Monitoring ◽  
Learning Techniques ◽  
Wireless Signal ◽  
New Challenges

Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

scholarly journals 5G-enabled contactless multi-user presence and activity detection for independent assisted living

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aboajeila Milad Ashleibta ◽  
Ahmad Taha ◽  
Muhammad Aurangzeb Khan ◽  
William Taylor ◽  
Ahsen Tahir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Well Being ◽  
Home Health ◽  
Activity Monitoring ◽  
Single Subject ◽  
Channel State ◽  
Average Accuracy ◽  
Wireless Signal ◽  
Simultaneous Identification

AbstractWireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.

Transmitter-Oriented Dual-Mode SWIPT With Deep-Learning-Based Adaptive Mode Switching for IoT Sensor Networks

2020 ◽  
Vol 7 (9) ◽  
pp. 8979-8992 ◽  
Author(s):  
Jong Jin Park ◽  
Jong Ho Moon ◽  
Kang-Yoon Lee ◽  
Dong In Kim
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Mode Switching ◽  
Dual Mode

scholarly journals On the nature of wireless signal variations. —II

1927 ◽  
Vol 115 (771) ◽  
pp. 305-317 ◽  
Keyword(s):  
Signal Intensity ◽  
Intensity Variation ◽  
Signal Variation ◽  
Wireless Signal ◽  
Signal Fading ◽  
Theoretical Considerations ◽  
Direct Investigation ◽  
Signal Intensity Variation

The present paper continues the account of investigations on the nature of signal fading given in paper I of similar title. As the argument is essentially continuous from paper I to paper II the numbering of equations, tables and figures has been made to run continuously from the former to the latter paper. 2. Direct Investigation of the Downcoming Waves by suppression of the Ground Waves . ( a ) Theoretical Considerations . —The ordinary curves of signal intensity variation obtained either with a vertical aerial or with a loop in the plane of propagation do not give us directly the variations in amplitude of the downcoming waves because we are unable to deduce from them whether a particular signal variation is due to a change of phase or a change of amplitude. We can, however, investigate the changes in intensity of the downcoming waves if we suppress the ground waves. This may be done by using the particular combination of loop and vertical aerial discussed below.

scholarly journals Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging

2020 ◽  
Vol 39 (10) ◽  
pp. 3079-3088 ◽  
Author(s):  
Dongwoon Hyun ◽  
Lotfi Abou-Elkacem ◽  
Rakesh Bam ◽  
Leandra L. Brickson ◽  
Carl D. Herickhoff ◽  
...  
Keyword(s):  
Deep Learning ◽  
Molecular Imaging ◽  
Real Time ◽  
Dual Mode ◽  
Nondestructive Detection ◽  
Targeted Microbubbles ◽  
Ultrasound Molecular Imaging
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