Accurate Respiration Measurement Using DC-Coupled Continuous-Wave Radar Sensor for Motion-Adaptive Cancer Radiotherapy

2012 ◽  
Vol 59 (11) ◽  
pp. 3117-3123 ◽  
Author(s):  
Changzhan Gu ◽  
Ruijiang Li ◽  
Hualiang Zhang ◽  
A. Y. C. Fung ◽  
C. Torres ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Radar Sensor ◽  
Cancer Radiotherapy ◽  
Wave Radar

scholarly journals Self-Identification Respiratory Disorder Based on Continuous Wave Radar Sensor System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 40019-40026 ◽  
Author(s):  
Nguyen Thi Phuoc Van ◽  
Liqiong Tang ◽  
Amardeep Singh ◽  
Nguyen Duc Minh ◽  
Subhas Chandra Mukhopadhyay ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Sensor System ◽  
Respiratory Disorder ◽  
Radar Sensor ◽  
Wave Radar

A Double-Sideband Continuous-Wave Radar Sensor for Carotid Wall Movement Detection

2018 ◽  
Vol 18 (19) ◽  
pp. 8162-8171 ◽  
Author(s):  
Stefano Pisa ◽  
Simone Chicarella ◽  
Erika Pittella ◽  
Emanuele Piuzzi ◽  
Orlandino Testa ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Movement Detection ◽  
Radar Sensor ◽  
Wall Movement ◽  
Wave Radar ◽  
Carotid Wall

An X-Band Frequency-Modulated Continuous-Wave Radar Sensor System With a Single-Antenna Interface for Ranging Applications

2018 ◽  
Vol 66 (9) ◽  
pp. 4216-4231 ◽  
Author(s):  
Hao-Chung Chou ◽  
Yu-Hsien Kao ◽  
Chun-Chieh Peng ◽  
Yu-Jiu Wang ◽  
Ta-Shun Chu
Keyword(s):  
Continuous Wave ◽  
Sensor System ◽  
Radar Sensor ◽  
Band Frequency ◽  
Single Antenna ◽  
X Band ◽  
Wave Radar

scholarly journals Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6202
Author(s):  
Eugin Hyun ◽  
Young-Seok Jin ◽  
Jae-Hyun Park ◽  
Jong-Ryul Yang
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Continuous Wave ◽  
Rigid Motion ◽  
Doppler Spectrum ◽  
Classification Error ◽  
Test Bed ◽  
Radar Sensor ◽  
Detection Scheme ◽  
Wave Radar

In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an ‘extended degree of scattering points’ and a ‘different degree of scattering points’ to represent the characteristics of the non-rigid motion of a moving human in a vehicle. We also design one newly defined feature referred to as the ‘presence of vital signs’, which is related to extracting the Doppler frequency of chest movements due to breathing. Additionally, we use a BDT (Binary Decision Tree) for machine learning during the training and test steps with these three extracted features. We used a 2.45 GHz CW radar front-end module with a single receive antenna and a real-time data acquisition module. Moreover, we built a test-bed with a structure similar to that of an actual vehicle interior. With the test-bed, we measured radar signals in various scenarios. We then repeatedly assessed the classification accuracy and classification error rate using the proposed algorithm with the BDT. We found an average classification accuracy rate of 98.6% for a human with or without motion.

Step frequency continuous wave RADAR sensor for level measurement of molten solids

2017 ◽  
Vol 32 (3) ◽  
pp. 281-292 ◽  
Author(s):  
Yugandhara R. Yadam ◽  
Balamurugan T. Sivaprakasam ◽  
Krishnamurthy C. Venkata ◽  
Kavitha Arunachalam
Keyword(s):  
Continuous Wave ◽  
Step Frequency ◽  
Radar Sensor ◽  
Level Measurement ◽  
Wave Radar
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