Search

Your search keyword '"Liang, Fulai"' showing total 48 results
Start Over Author "Liang, Fulai"
48 results on '"Liang, Fulai"'

1. BERT: Accelerating Vital Signs Measurement for Bioradar with An Efficient Recursive Technique

Author

Tang, Chengyao, Dai, Yongpeng, Li, Zhi, Song, Yongping, Liang, Fulai, and Jin, Tian

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Recent years have witnessed the great advance of bioradar system in smart sensing of vital signs (VS) for human healthcare monitoring. As an important part of VS sensing process, VS measurement aims to capture the chest wall micromotion induced by the human respiratory and cardiac activities. Unfortunately, the existing VS measurement methods using bioradar have encountered bottlenecks in making a trade-off between time cost and measurement accuracy. To break this bottleneck, this letter proposes an efficient recursive technique (BERT) heuristically, based on the observation that the features of bioradar VS meet the conditions of Markov model. Extensive experimental results validate that BERT measurement yields lower time costs, competitive estimates of heart rate, breathing rate, and heart rate variability. Our BERT method is promising us a new and superior option to measure VS for bioradar. This work seeks not only to solve the current issue of how to accelerate VS measurement with an acceptable accuracy, but also to inspire creative new ideas that spur further advances in this promising field in the future., Comment: 4pages, 8 figures, submitted to the IEEE for possible publication

Published
2024

5. UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario

Author

Li Zhao, Ma Yangyang, Zhang Yang, Liang Fulai, Yu Xiao, Qi Fugui, Lv Hao, Lu Guohua, and Wang Jianqi

Subjects
Correlation coefficient, distinguishing between human and animal, multipath effect, post-disaster rescue, ultra-wideband (UWB) radar, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.

Published
2021
Full Text
View/download PDF

8. Imaging of Multiple Stationary Humans Using a UWB MIMO Bio-radar

Author

Liang Fulai, Li Haonan, Qi Fugui, An Qiang, and Wang Jianqi

Subjects
Bioradar, Ultra-Wide Band (UWB), Multiple-Input Multiple-Output (MIMO), Imaging, Electricity and magnetism, QC501-766
Abstract

Because of the mutual interference between multiple stationary humans, distinguishing individuals via the range profile of a single-channel bioradar is difficult. To solve this problem, we used an Ultra-WideBand Multiple-Input Multiple-Output (UWB MIMO) bioradar to compose high-resolution two-dimensional images. These images allow multiple stationary humans to be separated from space. Then, vital signs are enhanced on the basis of the UWB MIMO image sequence, thereby leading to effective suppression of interference. The experimental results demonstrate that the proposed method can compose high-resolution images of multiple stationary humans. Thus, high-performance detection and localization of multiple stationary humans can be expected.

Published
2016
Full Text
View/download PDF

9. UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario

Author

Yu Xiao, Qi Fugui, Li Zhao, Liang Fulai, Zhang Yang, Lu Guohua, Lv Hao, Wang Jianqi, and Ma Yangyang

Subjects
General Computer Science, Computer science, General Engineering, post-disaster rescue, multipath effect, TK1-9971, law.invention, Correlation coefficient, law, General Materials Science, Electrical engineering. Electronics. Nuclear engineering, Radar, ultra-wideband (UWB) radar, distinguishing between human and animal, Post disaster, Remote sensing
Abstract

Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.

Published
2021
Full Text
View/download PDF

27. An Adaptive-MSSA-Based Algorithm for Detection of Trapped Victims Using UWB Radar

Author

Miao Liu, Xijing Jing, Hao Lv, Yang Zhang, Liang Fulai, Qiang An, Jianqi Wang, and Teng Jiao

Subjects
Computer science, Pulse-Doppler radar, Geotechnical Engineering and Engineering Geology, Radar lock-on, Ultra wideband radar, law.invention, Continuous-wave radar, Noise, Radar engineering details, law, Clutter, Algorithm design, Electrical and Electronic Engineering, Radar, Algorithm
Abstract

Postdisaster search and rescue is an important application of ultrawideband (UWB) radar systems, which mainly detect trapped victims by their respiratory-motion response. The development of a respiratory-motion detection (RMD) algorithm that can eliminate nonstationary clutter and noise is a challenging task for the application. A new algorithm is proposed to deal with the task in this letter. It uses the multichannel singular spectrum analysis (MSSA) technique to reconstruct the respiratory-motion response detected by a UWB radar. During the reconstruction, the periodicity and range interrelation characteristics of the response are exploited to adaptively identify signal subspaces. The performance of the algorithm is verified both by simulated and real data. The results show its improved performance over the reference algorithms, e.g., a singular-value-decomposition-based algorithm. The adaptive-MSSA-based RMD algorithm has great promise not only in practical use but also for future research of UWB-radar-based human being remote sensing.

Published
2015
Full Text
View/download PDF

47. An Accurate Method to Distinguish Between Stationary Human and Dog Targets Under Through-Wall Condition Using UWB Radar.

Author

Ma, Yangyang, Liang, Fulai, Wang, Pengfei, Lv, Hao, Yu, Xiao, Zhang, Yang, and Wang, Jianqi

Subjects
*ULTRA-wideband radar, *SUPPORT vector machines, *SERVICE animals, *DOGS, *BEAGLE (Dog breed), *RADAR
Abstract

Research work on distinguishing humans from animals can help provide priority orders and optimize the distribution of resources in earthquake- or mining-related rescue missions. However, the existing solutions are few and their stability and accuracy of classification are less. This study proposes an accurate method for distinguishing stationary human targets from dog targets under through-wall condition based on ultra-wideband (UWB) radar. Eight humans and five beagles were used to collect 130 samples of through-wall signals using the UWB radar. Twelve corresponding features belonging to four categories were combined using the support vector machine (SVM) method. A recursive feature elimination (RFE) method determined an optimal feature subset from the twelve features to overcome overfitting and poor generalization. The results after ten-fold cross-validation showed that the area under the receiver operator characteristic (ROC) curve can reach 0.9993, which indicates that the two subjects can be distinguished under through-wall condition. The study also compared the ability of the proposed features of four categories when used independently in a classifier. Comparison results indicated that wavelet entropy-corresponding features among them have the best performance. The method and results are envisioned to be applied in various practical situations, such as post-disaster searching, hostage rescues, and intelligent homecare. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results