scholarly journals Correction: Park, J.-H. et al. 915-MHz Continuous-Wave Doppler Radar Sensor for Detection of Vital Signs. Electronics 2019, 8, 561

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 855 ◽  
Author(s):  
Park ◽  
Jeong ◽  
Lee ◽  
Oh ◽  
Yang
Keyword(s):  
Continuous Wave ◽  
Doppler Radar ◽  
Vital Signs ◽  
Radar Sensor ◽  
Continuous Wave Doppler ◽  
Published Paper

The authors wish to make the following corrections to the published paper [...]

Continuous-Wave Doppler Radar for Human Gait Classification

2017 ◽  
pp. 95-119
Author(s):  
Fok Hing Chi Tivive ◽  
Abdesselam Bouzerdoum ◽  
Bijan G. Mobasseri
Keyword(s):  
Continuous Wave ◽  
Doppler Radar ◽  
Human Gait ◽  
Continuous Wave Doppler ◽  
Gait Classification

scholarly journals Neural Networks Application for Processing of the Data from the FMICW Radars

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1308 ◽  
Author(s):  
Lubos Rejfek ◽  
Tan N. Nguyen ◽  
Pavel Chmelar ◽  
Ladislav Beran ◽  
Phuong T. Tran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Processing ◽  
Continuous Wave ◽  
Doppler Radar ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Target Area ◽  
The Neural Network ◽  
Machine Learning Applications

In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted Continuous Wave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processing steps. These steps are 2D spectrum filtration, thresholding, unification of the target, target area transforming to the rectangular shape (optional step), and target board line detection. The proposed neural network was tested with sets of four cases (100 tests for every case). This neural network provides image processing of the 2D spectrum. The results obtained from this new system are much better than the results of our previous algorithm.

Wireless Hand Gesture Recognition Based on Continuous-Wave Doppler Radar Sensors

2016 ◽  
Vol 64 (11) ◽  
pp. 4012-4020 ◽  
Author(s):  
Tenglong Fan ◽  
Chao Ma ◽  
Zhitao Gu ◽  
Qinyi Lv ◽  
Jialong Chen ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Continuous Wave ◽  
Doppler Radar ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Radar Sensors ◽  
Continuous Wave Doppler
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