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学者姓名：高跃明
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Abstract ：
Person re-identification (Re-ID) plays a crucial role in the domains of security surveillance and pedestrian behavior analysis, as it aims to retrieve specific individuals captured by different cameras. However, the task of Re-ID remains immensely challenging in the field of computer vision, primarily due to the extensive intra-class variations exhibited by individuals across cameras. These variations include occlusions, illuminations, viewpoints, and poses. In this paper, we present a novel Re-ID framework that addresses the inherent issues related to intra-class variations. Our proposed approach incorporates both auxiliary-domain classification (ADC) and layered semi-second-order information bottleneck (LyrS2IB) techniques. By incorporating ADC as an auxiliary task, we leverage coarse-grained essential features that effectively distinguish individuals from the background. This enables the development of both coarse- and fine-grained feature representations for Re-ID. Furthermore, our framework integrates LyrS2IB to handle redundancy, irrelevance, and noise present in Re-ID features resulting from intra-class variations. This integration allows us to compress and optimize these features without incurring additional computation overhead during inference. Extensive experiments validate the efficacy of our proposed method, demonstrating a significant reduction in the neural network output variance of intra-class person images, firmly establishing the superior performance of our approach in the field of Re-ID.
Keyword ：
Auxiliary Domain Classification Auxiliary Domain Classification Information Bottleneck Information Bottleneck Layered Semi-Second-Order Information Bottleneck Layered Semi-Second-Order Information Bottleneck Person Re-Identification Person Re-Identification
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| GB/T 7714 | Zhang, Anguo , Wu, Junyi , Gao, Yueming et al. Layered Semi-Second-Order Information Bottleneck and Auxiliary Domain Classification for Person Re-Identification [J]. | INTERNATIONAL JOURNAL OF COMPUTER VISION , 2025 . |
| MLA | Zhang, Anguo et al. "Layered Semi-Second-Order Information Bottleneck and Auxiliary Domain Classification for Person Re-Identification" . | INTERNATIONAL JOURNAL OF COMPUTER VISION (2025) . |
| APA | Zhang, Anguo , Wu, Junyi , Gao, Yueming , Gao, Min , Chen, Zhen , Song, Yongduan et al. Layered Semi-Second-Order Information Bottleneck and Auxiliary Domain Classification for Person Re-Identification . | INTERNATIONAL JOURNAL OF COMPUTER VISION , 2025 . |
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Leadless pacemakers (LCPs) have emerged as a viable solution to mitigate the side effects associated with traditional pacing leads. Nevertheless, the absence of suitable intracardiac communication methods restricts most LCPs to single-chamber pacing. Galvanic conductive communication (GCC) offers a promising approach for achieving intracardiac communication in multichamber LCPs. However, impedance mismatch between the receiver and myocardium during the cardiac cycle can lead to a reduction in path gain, thereby affecting communication stability. This article proposed a dynamic compensation method to reduce impedance mismatch and enhance communication stability. We developed a simplified LCP system with dynamic path gain compensation and validated it on an ex vivo porcine heart dynamic experimental platform. The results demonstrated a path gain improvement of 9.23 dB and a reduction in path gain variation from 3.95 to 0.3 dB. Additionally, the bit error rate (BER) decreased by over 50% across various transmission rates, with the most significant improvement observed at 5 kbps, where it decreased by 82.7%. These findings provide a high-reliability solution for intracardiac communication and establish a foundation for future multichamber sequential pacing applications in LCPs.
Keyword ：
Dynamic compensation Dynamic compensation galvanic conductive communications (GCCs) galvanic conductive communications (GCCs) impedance matching impedance matching leadless pacemakers (LCPs) leadless pacemakers (LCPs)
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| GB/T 7714 | Li, Dongming , Wang, Zhijiong , Wang, Han et al. Dynamic Path Gain Compensation for Enhancing Intracardiac Communication in Leadless Pacemakers [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Li, Dongming et al. "Dynamic Path Gain Compensation for Enhancing Intracardiac Communication in Leadless Pacemakers" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Li, Dongming , Wang, Zhijiong , Wang, Han , Hang Pun, Sio , Un Mak, Peng , Zhang, Anguo et al. Dynamic Path Gain Compensation for Enhancing Intracardiac Communication in Leadless Pacemakers . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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Conductive intracardiac communication (CIC) is an essential approach for achieving multichamber pacing in leadless pacemakers (LCPs), significantly enhancing the therapeutic outcomes for conditions, such as bradycardia. However, the characteristics of the intracardiac channel are profoundly affected by the heart's rhythmic contractions. Accurately understanding the dynamic transmission mechanisms and channel parameters under various cardiac pathological states is crucial for enhancing the multichamber pacing functionality of LCPs. In this article, the relationship between cardiac chamber volume and channel impedance is mapped based on the electrocardiogram (ECG) data. This mapping enables precise, real-time adjustments to variable impedance, simulating the impedance changes occurring with each heartbeat. Through this approach, a time-frequency equivalent circuit phantom is proposed to accurately simulate channel characteristics for various pacemaker indications (PIs). Utilizing a quasi-dual-pump structural analogy to the heart, we designed a dynamic experimental measurement platform capable of simulating the cardiac beating process under various PIs, which is employed to validate the accuracy of the circuit phantom. The results demonstrate that the correlation coefficients in the frequency and time domains are greater than 0.9432 and 0.9150, respectively, with a time-domain consistency coefficient of less than 3.25. Through cross validation in both frequency and time domains, the circuit effectively simulates the channel characteristics of normal and PI hearts. The empirical formula established based on the time-domain measurement results can be utilized for the rapid estimation of the right atrium (RA)-right ventricle (RV) channel characteristics. The proposed phantom offers a highly accurate and reproducible experimental method for the design of intracardiac communication transceivers, advancing the development and validation of leadless multichamber pacemaker systems.
Keyword ：
Conductive intracardiac communication (CIC) Conductive intracardiac communication (CIC) intracardiac circuit phantom intracardiac circuit phantom leadless pacemakers (LCPs) leadless pacemakers (LCPs) pacemaker indications (PIs) pacemaker indications (PIs)
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| GB/T 7714 | Li, Dongming , Wang, Jiamei , Wang, Han et al. A Dynamic High-Fidelity Equivalent Circuit Phantom for Intracardiac Communication in Pacemaker Indications [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Li, Dongming et al. "A Dynamic High-Fidelity Equivalent Circuit Phantom for Intracardiac Communication in Pacemaker Indications" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Li, Dongming , Wang, Jiamei , Wang, Han , Huang, Xiaojiang , Yang, Jiejie , Gao, Yueming et al. A Dynamic High-Fidelity Equivalent Circuit Phantom for Intracardiac Communication in Pacemaker Indications . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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People's increasing demand for high-quality network services has prompted the continuous attention and development of network traffic classification (NTC). In recent years, deep flow inspection (DFI) is considered to be the most effective and promising method to solve the NTC. However, DFI still cannot effectively address the problem of changes in flow characteristics of complex packet flows and the discovery of new traffic categories. In this paper, we propose a metric learning based deep learning solution with feature compressor, named deep flow embedding (DFE). The feature compressor is used to compress the feature information transmitted layer by layer in DL backbone while maintaining the computational accuracy, so that the backbone can remove as much noise, redundancy, and other irrelevant information from the input data as possible, and achieve more robust feature extraction of network traffic flow. The deep learning (DL) backbone generates an embedding vector for each network packet flow. Then the embedding vector is compared with the vector template preset for each traffic type in the template library to determine the category of the packet flow. Experimental results verify that our method is more effective than the traditional DFI methods in overcoming the problems of flow characteristics variation and new category discovery.
Keyword ：
deep flow embedding deep flow embedding deep learning deep learning Deep learning Deep learning feature compression feature compression Feature extraction Feature extraction Inspection Inspection Libraries Libraries Measurement Measurement metric learning metric learning Network traffic classification Network traffic classification Noise Noise Protocols Protocols Robustness Robustness Telecommunication traffic Telecommunication traffic Vectors Vectors
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| GB/T 7714 | Wang, Zhijiong , Zhang, Anguo , Li, Hung Chun et al. DFE: Deep Flow Embedding for Robust Network Traffic Classification [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2025 , 12 (3) : 1597-1612 . |
| MLA | Wang, Zhijiong et al. "DFE: Deep Flow Embedding for Robust Network Traffic Classification" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 12 . 3 (2025) : 1597-1612 . |
| APA | Wang, Zhijiong , Zhang, Anguo , Li, Hung Chun , Yin, Yadong , Chen, Wei , Lam, Chan Tong et al. DFE: Deep Flow Embedding for Robust Network Traffic Classification . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2025 , 12 (3) , 1597-1612 . |
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Heart rate variability (HRV), indicating the variation in intervals between consecutive heartbeats, is a crucial physiological indicator of human health. However, detecting HRV using frequency-modulated continuous-wave (FMCW) radar is highly susceptible to interference from respiration, minor body movements, and environmental noise, especially in multitarget scenarios. To address these challenges, we propose the Health-Radar system, which comprises three functional modules. In the target detection module, the system accurately identifies the number and locations of targets. In the phase extraction module, the signal undergoes dc offset calibration to extract the chest displacement signals. In the heartbeat signal extraction module, we introduce Health-VMD, an adaptive parameter variational mode decomposition (VMD) method. This method optimizes the VMD parameters using an improved grasshopper optimization algorithm (GOA) and accurately extracts vital sign signals from chest displacement signals to estimate HRV. In addition, we propose a novel objective function, composed of permutation entropy, mutual information, and energy loss rate (PME), specifically designed for vital sign extraction. Experiments with multiple participants in various scenarios demonstrated that the designed system can accurately identify different targets and detect HRV with high precision. The root-mean-square error (RMSE) of the detected interbeat intervals (IBIs) is 29.72 ms, the RMSE of the standard deviation of NN intervals (SDNN) is 4.1 ms, and the RMSE of the root mean square of successive differences (RMSSD) is 18.61 ms, outperforming existing methods.
Keyword ：
Accuracy Accuracy Biomedical monitoring Biomedical monitoring Estimation Estimation Frequency-modulated continuous-wave (FMCW) radar Frequency-modulated continuous-wave (FMCW) radar Harmonic analysis Harmonic analysis Heart beat Heart beat Heart rate variability Heart rate variability heart rate variability (HRV) heart rate variability (HRV) multitarget vital signs detection multitarget vital signs detection noncontact detection noncontact detection Optimization Optimization Radar Radar Radar detection Radar detection Sensors Sensors variational mode decomposition (VMD) variational mode decomposition (VMD)
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| GB/T 7714 | Xu, Zhimeng , Ye, Tao , Chen, Liangqin et al. Health-Radar: Noncontact Multitarget Heart Rate Variability Detection Using FMCW Radar [J]. | IEEE SENSORS JOURNAL , 2025 , 25 (1) : 405-418 . |
| MLA | Xu, Zhimeng et al. "Health-Radar: Noncontact Multitarget Heart Rate Variability Detection Using FMCW Radar" . | IEEE SENSORS JOURNAL 25 . 1 (2025) : 405-418 . |
| APA | Xu, Zhimeng , Ye, Tao , Chen, Liangqin , Gao, Yueming , Chen, Zhizhang . Health-Radar: Noncontact Multitarget Heart Rate Variability Detection Using FMCW Radar . | IEEE SENSORS JOURNAL , 2025 , 25 (1) , 405-418 . |
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Real -time emotion recognition via wearable devices is a pivotal component of health monitoring and human- computer interaction. To realize this objective, a spiking feed-forward neural networks (SFNNs) model was developed, which leverages six physiological signals from the psychophysiology of positive and negative emotions (POPANE) dataset to construct feature vectors. By converting well-trained artificial neural networks (ANNs) to spiking neural networks (SNNs) and employing weight normalization techniques, the SFNNs with data-based normalization achieved a maximum classification accuracy of 88.17% at a maximum input firing rate of 1000 Hz. In comparison to existing models, the SFNNs model integrates multimodal physiological signals to classify six discrete emotions, demonstrating high classification performance and rapid convergence speed, rendering it ideal for real -time emotion recognition. This work has potential applications in psychological diagnosis and medical rehabilitation through the use of wearable wristbands.
Keyword ：
Emotion recognition Emotion recognition Feature extraction Feature extraction Multimodal physiological signals Multimodal physiological signals Spiking feed-forward neural networks Spiking feed-forward neural networks Time series Time series Wearable wristbands Wearable wristbands
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| GB/T 7714 | Yang, Xudong , Yan, Hongli , Zhang, Anguo et al. Emotion recognition based on multimodal physiological signals using spiking feed-forward neural networks [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 91 . |
| MLA | Yang, Xudong et al. "Emotion recognition based on multimodal physiological signals using spiking feed-forward neural networks" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 91 (2024) . |
| APA | Yang, Xudong , Yan, Hongli , Zhang, Anguo , Xu, Pan , Pan, Sio Hang , Vai, Mang I. et al. Emotion recognition based on multimodal physiological signals using spiking feed-forward neural networks . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 91 . |
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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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Transcutaneous auricular vagus nerve stimulation (taVNS), in addition to its application in treating specific disorders such as epilepsy, has shown promise in aiding the functional rehabilitation of the upper limb. Most patients with upper limb dysfunction have brain lesions and structural damage to brain networks. By conducting structural analysis of brain networks through electroencephalogram (EEG), we can investigate the effects of taVNS on the cortex directly from the source of the disease, offering a unique, bioelectrical perspective. To investigate the impact of taVNS on the neuromodulated upstream brain structures and to analyze the correlation between taVNS and the functional rehabilitation of the upper limbs, we proposed an analytical approach incorporating the network structure analysis. We conducted power spectra and phase lag index (PLI) calculations on experimentally collected EEG data and further analyzed network topology using graph theory. The results showed that compared with prestimulation, the relative power decreased in low frequencies and increased in high frequencies ( p < 0.05 ). In the alpha frequency band, the PLI showed an increasing trend ( p = 0.03), the minimum spanning tree (MST) analysis showed that the network topology became more integrated, and there were no regular changes observed in the control data ( p > 0.05). In this work, we found that taVNS activates cortical motor areas and leads to stable changes in PLI and network structure in the alpha frequency band. The mechanisms through which taVNS modulates the power spectrum, alters connectivity, and enhances network structure integration in rehabilitation therapy have been revealed as having a positive impact on motor function recovery, providing valuable implications for the clinical application of taVNS in upper limb functional rehabilitation.
Keyword ：
Electroencephalogram (EEG) Electroencephalogram (EEG) Electroencephalography Electroencephalography Epilepsy Epilepsy minimum spanning tree (MST) minimum spanning tree (MST) Network topology Network topology Pain Pain phase lag index (PLI) phase lag index (PLI) Sensors Sensors Spectral analysis Spectral analysis Stroke (medical condition) Stroke (medical condition) transcutaneous auricular vagus nerve stimulation (taVNS) transcutaneous auricular vagus nerve stimulation (taVNS)
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| GB/T 7714 | Ma, Wei , Xu, Peitao , Xu, Pan et al. Exploring the Physiological Effect of taVNS on Upper Limb Functional Rehabilitation [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (7) : 10691-10699 . |
| MLA | Ma, Wei et al. "Exploring the Physiological Effect of taVNS on Upper Limb Functional Rehabilitation" . | IEEE SENSORS JOURNAL 24 . 7 (2024) : 10691-10699 . |
| APA | Ma, Wei , Xu, Peitao , Xu, Pan , Zhou, Junwei , Vasic, Zeljka Lucev , Cifrek, Mario et al. Exploring the Physiological Effect of taVNS on Upper Limb Functional Rehabilitation . | IEEE SENSORS JOURNAL , 2024 , 24 (7) , 10691-10699 . |
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As an emerging method of intracardiac wireless communication, conductive intracardiac communication (CIC) has the advantages of low power consumption and high security of information transmission, and is suitable for synchronization between multiple leadless pacemakers (LCPs). However, the complicated intracardiac environment increases the measurement uncertainty of intracardiac channel parameters, and current research on intracardiac communication does not take into account impedance mismatch. These may lead to unreliable communication between LCPs. In this paper, an equivalent circuit model of the intracardiac channel is constructed based on the electrical characteristics of cardiac tissue. The intracardiac channel uses an L-type impedance matching circuit to improve the channel gain. The results show that the channel gain increases by more than 7 dB after adding the impedance matching circuit. Especially, the most significant increase is 8.33 dB at 10MHz. This proves the feasibility of the impedance matching method and provides an effective solution for the future design of intracardiac communication transceivers.
Keyword ：
Conductive Intracardiac Conummication(CIC) Conductive Intracardiac Conummication(CIC) impedance matching impedance matching leadless pacemakers (LCPs) leadless pacemakers (LCPs)
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| GB/T 7714 | Wang, Jiamei , Li, Dongming , Wang, Han et al. Research on Impedance Matching for Improving Gain in Conductive Intracardiac Communication [J]. | 2024 CROSS STRAIT RADIO SCIENCE AND WIRELESS TECHNOLOGY CONFERENCE, CSRSWTC 2024 , 2024 : 52-54 . |
| MLA | Wang, Jiamei et al. "Research on Impedance Matching for Improving Gain in Conductive Intracardiac Communication" . | 2024 CROSS STRAIT RADIO SCIENCE AND WIRELESS TECHNOLOGY CONFERENCE, CSRSWTC 2024 (2024) : 52-54 . |
| APA | Wang, Jiamei , Li, Dongming , Wang, Han , Yang, Jiejie , Pun, Sio Tang , Vai, Mang I. et al. Research on Impedance Matching for Improving Gain in Conductive Intracardiac Communication . | 2024 CROSS STRAIT RADIO SCIENCE AND WIRELESS TECHNOLOGY CONFERENCE, CSRSWTC 2024 , 2024 , 52-54 . |
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In clinical treatment, accurate differentiation of pathological symptoms of stroke is crucial for the development of rehabilitation treatment programs. In this study, we proposed a method based on bioimpedance to assess muscle tissue after stroke, and calculated two optimal solutions of muscle impedance circle maps: the center of the muscle impedance circle and the short axis of the muscle impedance by an optimization algorithm, so as to map the muscle impedance circle maps (MICM) of different muscle tissues. The methods were validated by clinical experiments. The results showed that muscle impedance circle mapping can be used to assess the electrical properties of muscle tissue and to differentiate between the two pathologic states of spastic and myasthenic stroke. Therefore, the method proposed in this paper has the potential to assess the rehabilitation status of muscle tissues after stroke, providing new insights for clinical assessment and development of therapeutic regimens.
Keyword ：
bioimpedancemiuscle impedance circle maps bioimpedancemiuscle impedance circle maps stroke stroke
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| GB/T 7714 | Zhou, Junwei , Xu, Pan , Xu, Peitao et al. Muscle Impedance Circle Mapping for Muscle Tissue Assessment after Stroke [J]. | 2024 CROSS STRAIT RADIO SCIENCE AND WIRELESS TECHNOLOGY CONFERENCE, CSRSWTC 2024 , 2024 : 388-390 . |
| MLA | Zhou, Junwei et al. "Muscle Impedance Circle Mapping for Muscle Tissue Assessment after Stroke" . | 2024 CROSS STRAIT RADIO SCIENCE AND WIRELESS TECHNOLOGY CONFERENCE, CSRSWTC 2024 (2024) : 388-390 . |
| APA | Zhou, Junwei , Xu, Pan , Xu, Peitao , Li, Xinyu , Wang, Weihan , Wei, Wei et al. Muscle Impedance Circle Mapping for Muscle Tissue Assessment after Stroke . | 2024 CROSS STRAIT RADIO SCIENCE AND WIRELESS TECHNOLOGY CONFERENCE, CSRSWTC 2024 , 2024 , 388-390 . |
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