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学者姓名：陈良琴
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Abstract ：
This article presents a novel millimeter-wave (mm-wave) radar-based continuous motion detection approach called mmCMD for the long-term care of the elderly. The proposed mmCMD visualizes the micro-Doppler signatures of continuous motions generated from millimeter radar as images, which are then analyzed by an object detection network for continuous motion detection. To improve the imaging quality of micro-Doppler signatures, a dynamic feature visualization (DFV) method is proposed by selectively mapping the micro-Doppler matrix (MDM) elements with significant values, highlighting human motion to enhance subsequent detection network's accuracy in capturing the details of the motion. Furthermore, a novel detection network is designed for the visualized micro-Doppler images by combining the specially designed fusion squeeze-and-excitation (FSE) module with the coordinate attention (CA) into the YOLOv5 architecture, which is distinct from prior works that overlook global contextual information. Experimental results demonstrate that the proposed mmCMD achieves a mean average precision (mAP) of 93% at the intersection over union (IoU) thresholds from 0.5 to 0.95 and an F1 score of 99% for 12 actions, which makes it a promising solution for remotely monitoring and detecting elderly individuals' activities to enhance safety and risk prevention capabilities.
Keyword ：
Continuous human motion detection Continuous human motion detection frequency-modulated continuous-wave (FMCW) radar frequency-modulated continuous-wave (FMCW) radar micro-Doppler effect micro-Doppler effect object detection object detection YOLOv5 YOLOv5
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| GB/T 7714 | Xu, Zhimeng , Ding, Junyin , Zhang, Shanshan et al. mmCMD: Continuous Motion Detection From Visualized Radar Micro-Doppler Signatures Using Visual Object Detection Techniques [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (3) : 3394-3405 . |
| MLA | Xu, Zhimeng et al. "mmCMD: Continuous Motion Detection From Visualized Radar Micro-Doppler Signatures Using Visual Object Detection Techniques" . | IEEE SENSORS JOURNAL 24 . 3 (2024) : 3394-3405 . |
| APA | Xu, Zhimeng , Ding, Junyin , Zhang, Shanshan , Gao, Yueming , Chen, Liangqin , Vasic, Zeljka Lucev et al. mmCMD: Continuous Motion Detection From Visualized Radar Micro-Doppler Signatures Using Visual Object Detection Techniques . | IEEE SENSORS JOURNAL , 2024 , 24 (3) , 3394-3405 . |
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Abstract ：
In this paper, a context-based human target detection and position estimation algorithm, as well as a position calibration algorithm based on radar irradiation angle are proposed to improve the positioning accuracy, which is limited by the sparse and easily submerged characteristics of the point cloud generated by millimeter-wave radar, which leads to difficulty in achieving high-precision positioning. Furthermore, an indoor target positioning and tracking system is built using 77 GHz millimeter-wave radar to verify the proposed algorithms. The experimental results indicate that the proposed algorithms can improve the positioning accuracy both in single-person and multi-person positioning scenarios, with median positioning errors 8.7 cm (36.7% decrease) and 12.95 cm (average) respectively. Therefore, the proposed sensing method is considered as a very promising technique for designing a high precision human trajectory tracking and positioning radar system.
Keyword ：
millimeter-wave radar millimeter-wave radar point cloud point cloud target positioning target positioning
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| GB/T 7714 | Xu, Zhimeng , Wu, Zhenbin , Li, Dan et al. Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2023 , 34 (2) . |
| MLA | Xu, Zhimeng et al. "Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar" . | MEASUREMENT SCIENCE AND TECHNOLOGY 34 . 2 (2023) . |
| APA | Xu, Zhimeng , Wu, Zhenbin , Li, Dan , Chen, Liangqin , Zhang, Shanshan , Chen, Zhizhang (David) . Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2023 , 34 (2) . |
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Like its outdoor counterpart (e.g., GPS), an indoor tracking system can bring about disruptive changes in how we live and work. This paper proposes a location and tracking system using a single WiFi link based on channel state information. The system can realize real-time, decimeter-level localization and tracking. In this system, phase calibration and static path elimination are realized by multiplying the conjugate signals of different antennas. Then, a three-dimensional MUSIC algorithm is employed to estimate the angle of arrival (AOA), the time of flight (TOF), and the velocity of a target. A scheme is then developed to adjust the MUSIC search range and reduce the computation time from about ten hours to tens of seconds. The Widar2.0 data set from Tsinghua University are used for the experiments; the proposed system is found to have an average tracking error of 0.68 m in the three environments of classroom, office, and corridor, which is better than the existing single link localization and tracking system.
Keyword ：
angle of arrival (AOA) angle of arrival (AOA) channel state information (CSI) channel state information (CSI) Doppler Doppler indoor tracking indoor tracking Kalman filter Kalman filter MUSIC MUSIC time of flight (TOF) time of flight (TOF) tracking tracking velocity velocity WiFi WiFi
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| GB/T 7714 | Tian, Li-Ping , Chen, Liang-Qin , Xu, Zhi-Meng et al. A Localization and Tracking System Using Single WiFi Link [J]. | REMOTE SENSING , 2023 , 15 (9) . |
| MLA | Tian, Li-Ping et al. "A Localization and Tracking System Using Single WiFi Link" . | REMOTE SENSING 15 . 9 (2023) . |
| APA | Tian, Li-Ping , Chen, Liang-Qin , Xu, Zhi-Meng , Chen, Zhizhang (David) . A Localization and Tracking System Using Single WiFi Link . | REMOTE SENSING , 2023 , 15 (9) . |
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People easily get distracted or tired after long-duration actions such as driving and online classes, which can lead to accidents or poor efficiency. To detect such human behaviors,a head motion detection method based on low-resolution infrared array sensors is proposed with the protection of personal privacy. First,promi. nent areas of the human body are extracted based on image processing techniques. Then a 3D image fusion algo. rithm is developed to extract the change information in the spatiotemporal domain. Finally,an improved residual network is developed to achieve head motion classification. Ten head movements are designed for driving and on. line classroom scenarios. Experimental results show that in the detection range of 50 cm to 100 cm,our average recognition rate is 96. 76%,and the processing speed is 9 frames per second,which is better than theexisting state-of-the-art algorithms. The accuracy of the system is 93. 7% when it is applied to the vehicle experiment.
Keyword ：
3D image fusion 3D image fusion distraction behavior distraction behavior head motion detection head motion detection infrared array sensor infrared array sensor
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| GB/T 7714 | Chen Liang-Qin , Zeng Ming-Xuan , Xu Zhi-Meng et al. Head motion detection based on low resolution infrared array sensor [J]. | JOURNAL OF INFRARED AND MILLIMETER WAVES , 2023 , 42 (2) : 276-284 . |
| MLA | Chen Liang-Qin et al. "Head motion detection based on low resolution infrared array sensor" . | JOURNAL OF INFRARED AND MILLIMETER WAVES 42 . 2 (2023) : 276-284 . |
| APA | Chen Liang-Qin , Zeng Ming-Xuan , Xu Zhi-Meng , Chen Zhi-Zhang . Head motion detection based on low resolution infrared array sensor . | JOURNAL OF INFRARED AND MILLIMETER WAVES , 2023 , 42 (2) , 276-284 . |
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Abstract ：
在驾驶和在线课堂这类持续时间较长的行为中，人们容易出现分心而导致事故发生或上课效率差。为检测这类分心行为，提出了一种基于低分辨率红外阵列传感器的头部运动检测方法，它在实现行为监测的同时也保护了个人隐私。首先，基于图像处理方法提取了人体的显著区域；然后设计了一种三维图像融合算法来提取时空域的变化信息；最后，设计了一个改进的残差网络来实现头部运动分类。面向驾驶和在线课堂应用场景设计了10种头部运动。实验结果表明，在50 cm到100 cm的检测范围内，平均识别率为96.76%，处理速度为9帧/s，优于现有算法。将该系统应用于车内实测，也达到了93.7%的准确率。
Keyword ：
三维图像融合 三维图像融合 分心行为 分心行为 头部运动检测 头部运动检测 红外阵列传感器 红外阵列传感器
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| GB/T 7714 | 陈良琴 , 曾铭萱 , 许志猛 et al. 基于低分辨率红外阵列传感器的头部运动检测（英文） [J]. | 红外与毫米波学报 , 2023 , 42 (02) : 276-284 . |
| MLA | 陈良琴 et al. "基于低分辨率红外阵列传感器的头部运动检测（英文）" . | 红外与毫米波学报 42 . 02 (2023) : 276-284 . |
| APA | 陈良琴 , 曾铭萱 , 许志猛 , 陈志璋 . 基于低分辨率红外阵列传感器的头部运动检测（英文） . | 红外与毫米波学报 , 2023 , 42 (02) , 276-284 . |
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基于Wi-Fi的手势识别技术在智慧医疗、智能家居、工业生产、游戏交互等领域中有着广阔的应用前景,然而在实际应用中,一个在原用户(源域)数据集上训练得到的手势识别模型应用于新的用户(目标域)时准确率会显著下降.为了解决这一问题,提出了一种应用于Wi-Fi的域自适应手势识别方案.首先,使用一种新的轻量级卷积神经网络对源域数据预训练；然后,设计一种新的域自适应网络进行无监督迁移学习,引入了相关对齐损失将源和目标域深度特征的二阶统计量对齐,并使用中心损失提高特征的可判别性,使类内聚合、类间分散.实验证明提出的方案用于识别新用户手势动作具有很好的效果.在用户变化的情况下,所提方案将手势识别平均准确率从62.7%提升至90.2%,可以显著提升用户变化时Wi-Fi手势识别的鲁棒性.
Keyword ：
信道状态信息 信道状态信息 手势识别 手势识别 深度域适应 深度域适应 深度学习 深度学习
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| GB/T 7714 | 孙北晨 , 许志猛 , 陈良琴 et al. 基于域自适应的Wi-Fi手势识别方案 [J]. | 微电子学与计算机 , 2023 , (10) : 38-47 . |
| MLA | 孙北晨 et al. "基于域自适应的Wi-Fi手势识别方案" . | 微电子学与计算机 10 (2023) : 38-47 . |
| APA | 孙北晨 , 许志猛 , 陈良琴 , 郑勤 . 基于域自适应的Wi-Fi手势识别方案 . | 微电子学与计算机 , 2023 , (10) , 38-47 . |
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在驾驶和在线课堂这类持续时间较长的行为中,人们容易出现分心而导致事故发生或上课效率差.为检测这类分心行为,提出了一种基于低分辨率红外阵列传感器的头部运动检测方法,它在实现行为监测的同时也保护了个人隐私.首先,基于图像处理方法提取了人体的显著区域;然后设计了一种三维图像融合算法来提取时空域的变化信息;最后,设计了一个改进的残差网络来实现头部运动分类.面向驾驶和在线课堂应用场景设计了10种头部运动.实验结果表明,在50 cm到100 cm的检测范围内,平均识别率为96.76％,处理速度为9帧/s,优于现有算法.将该系统应用于车内实测,也达到了93.7％的准确率.
Keyword ：
三维图像融合 三维图像融合 分心行为 分心行为 头部运动检测 头部运动检测 红外阵列传感器 红外阵列传感器
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| GB/T 7714 | 陈良琴 , 曾铭萱 , 许志猛 et al. 基于低分辨率红外阵列传感器的头部运动检测 [J]. | 红外与毫米波学报 , 2023 , 42 (2) : 276-284 . |
| MLA | 陈良琴 et al. "基于低分辨率红外阵列传感器的头部运动检测" . | 红外与毫米波学报 42 . 2 (2023) : 276-284 . |
| APA | 陈良琴 , 曾铭萱 , 许志猛 , 陈志璋 . 基于低分辨率红外阵列传感器的头部运动检测 . | 红外与毫米波学报 , 2023 , 42 (2) , 276-284 . |
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Face recognition for surveillance remains a complex challenge due to the disparity between low-resolution (LR) face images captured by surveillance cameras and the typically high-resolution (HR) face images in databases. To address this cross-resolution face recognition problem, we propose a two-stage dual-resolution face network to learn more robust resolution-invariant representations. In the first stage, we pre-train the proposed dual-resolution face network using solely HR images. Our network utilizes a two-branch structure and introduces bilateral connections to fuse the high- and low-resolution features extracted by two branches, respectively. In the second stage, we introduce the triplet loss as the fine-tuning loss function and design a training strategy that combines the triplet loss with competence-based curriculum learning. According to the competence function, the pre-trained model can train first from easy sample sets and gradually progress to more challenging ones. Our method achieves a remarkable face verification accuracy of 99.25% on the native cross-quality dataset SCFace and 99.71% on the high-quality dataset LFW. Moreover, our method also enhances the face verification accuracy on the native low-quality dataset.
Keyword ：
Convolutional neural network Convolutional neural network Cross-resolution face recognition Cross-resolution face recognition Curriculum learning Curriculum learning Multi-resolution feature fusion Multi-resolution feature fusion Surveillance systems Surveillance systems
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| GB/T 7714 | Chen, Liangqin , Chen, Jiwang , Xu, Zhimeng et al. Two-stage dual-resolution face network for cross-resolution face recognition in surveillance systems [J]. | VISUAL COMPUTER , 2023 , 40 (8) : 5545-5556 . |
| MLA | Chen, Liangqin et al. "Two-stage dual-resolution face network for cross-resolution face recognition in surveillance systems" . | VISUAL COMPUTER 40 . 8 (2023) : 5545-5556 . |
| APA | Chen, Liangqin , Chen, Jiwang , Xu, Zhimeng , Liao, Yipeng , Chen, Zhizhang . Two-stage dual-resolution face network for cross-resolution face recognition in surveillance systems . | VISUAL COMPUTER , 2023 , 40 (8) , 5545-5556 . |
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提出一种非下采样轮廓波变换(non-subsampled contourlet transform, NSCT)和分数阶微分相结合的图像去雾算法.该算法首先通过对低质有雾图像进行NSCT分解，得到一个低频子带与多尺度多方向的多个高频子带；然后采用分数阶微分算子对图像的低频子带进行增强，同时通过对各子带的高频系数进行非线性处理，实现高频子带的增强；最后进行NSCT重构，得到增强后的图像.对不同低质有雾图像进行实验比较，结果表明：本算法增强了主观视觉效果，使图像变清晰的同时，具有较高的对比度增益、清晰度增益、信息熵和平均梯度.
Keyword ：
分数阶微分 分数阶微分 图像去雾 图像去雾 非下采样轮廓波变换 非下采样轮廓波变换 非线性变换 非线性变换
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| GB/T 7714 | 曾铭萱 , 李娟 , 许志猛 et al. 结合多尺度和分数阶微分的单幅图像去雾算法 [J]. | 福州大学学报(自然科学版) , 2022 , 50 (03) : 330-336 . |
| MLA | 曾铭萱 et al. "结合多尺度和分数阶微分的单幅图像去雾算法" . | 福州大学学报(自然科学版) 50 . 03 (2022) : 330-336 . |
| APA | 曾铭萱 , 李娟 , 许志猛 , 陈良琴 . 结合多尺度和分数阶微分的单幅图像去雾算法 . | 福州大学学报(自然科学版) , 2022 , 50 (03) , 330-336 . |
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An angle estimation algorithm for tracking indoor moving targets with WiFi is proposed. First, phase calibration and static path elimination are proposed and performed on the collected channel state information signals from different antennas. Then, the angle of arrival information is obtained with the joint estimation algorithm of the angle of arrival (AOA) and time of flight (TOF). To deal with the multipath effects, we adopt the DBscan spatiotemporal clustering algorithm with adaptive parameters. In addition, the time-continuous angle of arrival information is obtained by interpolating and supplementing points to extract the dynamic signal paths better. Finally, the least-squares method is used for linear fitting to obtain the final angle information of a moving target. Experiments are conducted with the tracking data set presented with Tsinghua's Widar 2.0. The results show that the average angle estimation error with the proposed algorithm is smaller than Widar2.0. The average angle error is about 7.18 degrees in the classroom environment, 3.62 degrees in the corridor environment, and 12.16 degrees in the office environment; they are smaller than the errors of the existing system.
Keyword ：
angle estimation angle estimation AOA AOA channel state information (CSI) channel state information (CSI) DBscan DBscan least-squares method least-squares method WiFi WiFi
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| GB/T 7714 | Tian, Liping , Chen, Liangqin , Xu, Zhimeng et al. An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering [J]. | SENSORS , 2022 , 22 (1) . |
| MLA | Tian, Liping et al. "An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering" . | SENSORS 22 . 1 (2022) . |
| APA | Tian, Liping , Chen, Liangqin , Xu, Zhimeng , Chen, Zhizhang . An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering . | SENSORS , 2022 , 22 (1) . |
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