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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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Adhesive Carbon Paste Electrode for Convenient and Long-term Electrocardiogram Monitoring: A Pilot Study

Adhesive Carbon Paste Electrode for Convenient and Long-term...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xin Wang Qiong Tian Biying Zhang Yangjie Xu Junyu Ji Yuchao He Jinzhu Huang Mingxing Zhu Lin Wang Shixiong Chen Zhiyuan Liu Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Gastroenterology Ningbo No.2 Hospital Ningbo China Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Luxembourg Department of Endocrine The First Dongguan Affiliated Hospital of Guangdong Medical University Dongguan China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

Long-term electrocardiogram (ECG) monitoring is an important and widely-used technique in the clinic that helps with the diagnosis of possible diseases that cannot be detected in a short time monitoring. However, the clinically used electrode needs conductive gel to reduce the impedance between the skin and the electrodes, which easily causes the possibility of allergy. Moreover, as the conductive gel becomes dry, the signal’s quality will decrease accordingly. In this paper, we proposed a novel adhesive Carbon Paste Electrode (CPE) to achieve convenient and long-term ECG monitoring. By comparing the time-domain waveforms, the R-R peak intervals difference, and the Signal-to-Noise Ratio (SNR) of ECG with the traditional conductive gel-based electrode (Gel) in fixed and unfixed conditions, the performance of the proposed CPE was investigated. The results showed that the CPE could achieve similar ECG monitoring both in fixed and unfixed conditions. When on Day 2, the quality acquired by Gel began to decrease while CPE was still stable, which was obvious especially in unfixed condition. The R-R peak intervals showed that on Day 2, the Gel was unreliable with some abnormal points occurring. Besides, the results of SNR and average heart rate (AHR) also confirmed that the CPE could achieve similar results as Gel on Day 1 and outperformed Gel on Day 2. It is believed that the proposed CPE opens a window of high-quality long-term ECG monitoring with more convenience.

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Development of Automated Neonatal Sleep-Wake Cycle Intelligent Detection Algorithm 17

Development of Automated Neonatal Sleep-Wake Cycle Intellige...

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17th International Conference on Sensing Technology, ICST 2024

作者： Wu, Jiaxi Wang, Xin Zhang, Ke Huang, Weimin Men, Lina Chen, Shixiong School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China Shenzhen Children' s Hospital Department of Otolaryngology Shenzhen China Shenzhen Children' s Hospital Department of Neonatology Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350374827

While there are various algorithms for automatic identification of sleep-wake cycles based on electroencephalography (EEG), there is a notable lack of approaches utilizing amplitude-integrated electroencephalography (aEEG). As EEG is increasingly replaced by aEEG in many contexts due to its large data size and challenges in information capture, it is essential to advance sleep-wake cycle (SWC) detection algorithms based on aEEG. Previous research primarily established a threshold to differentiate between quiet sleep (QS) and non-QS, but this required manual adjustment of individual thresholds for each patient' s data, leading to considerable inconvenience. Facing this challenge, the study developed an algorithm for the automatic identification of boundaries between QS and non-QS segments in the dataset. Each entry can have its own QS/non-QS boundary, eliminating the need for manual setting. The algorithm was developed under EEG recordings from seven neonates with post-menstrual ages ranging from 32 to 41 weeks. First, EEG data was converted into aEEG and subjected to smoothing. The upper and lower boundaries of the smoothed signal were utilized as the foundation for boundary assessment. The stable segment selection method was then applied to identify wide segments with limited variability, which were classified as QS periods. The analysis reveals that the detection algorithm performed well for extended QS periods (lasting over 15 minutes). In contrast, detection for shorter QS periods (less than 3 minutes) presented variability, with both false positives and false negatives observed. However, with proper adjustments, this method can effectively assist physicians in identifying suspicious QS phases and directly reviewing the corresponding video for validation. © 2024 IEEE.

关键词： Sleep research

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Evolving gaussian process based learning of knee angle and velocity

Evolving gaussian process based learning of knee angle and v...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Yang, Jiantao He, Yong He, Chen Shi, Ping Institute of Rehabilitation Engineering and Technology University of Shanghai for Science and Technology Shanghai200093 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

Transparent human-exoskeleton interaction requires accurate human joint angle and velocity learning which are regarded as human intent detection to cope with the unspecific and irregular kinematics and dynamics of the system. This paper attempts to address the limitations and deficiencies encountered by traditional methods which make it challengeable to figure out the natural relationships among the strongly coupled multi-source information from each of the human-exoskeleton subsystems. Dependent Gaussian process (DGP) based data fusion algorithm is established and serves as the mathematics foundation to explore the deep layer correlation among the human joint angle, interactive force and processed sEMG achieving satisfactory prediction results of joint angle. Gradient estimation model is then performed to obtain the human joint velocity by differential of a GP model. The statistic nature of the proposed model offers superior flexibility and encouraging human motion prediction results. And the proposed model can achieve human joint angle and velocity learning simultaneously without accessional sensors which may incur marked cost or may be impossible. Experimental works on an in-house exoskeleton which is the key step to verify the superiority of the proposed algorithms are also presented. © 2021 IEEE.

关键词： Gaussian distribution

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Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor

Analysis of Artifactual Components Rejection Threshold towar...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mojisola Grace Asogbon Yaping Huai Oluwarotimi Williams Samuel Zhengxiang Jing Yixin Ma Jingyu Liu Yibo Jiang Yunfa Fu Guanglin Li Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Information Engineering and Automation Kunming University of Science and Technology Yunnan China

Research advancement has spurred the usage of electroencephalography (EEG)-based neural oscillatory rhythms as a biomarker to complement clinical rehabilitation strategies for the recovery of motor functions in stroke survivors. However, the inevitable contamination of EEG signals with artifacts from various sources limits its utilization and effectiveness. Thus, the integration of Independent Component Analysis (ICA) and Independent Component Label (ICLabel) has been widely employed to separate neural activity from artifacts. A crucial step in the ICLabel preprocessing pipeline is the artifactual ICs rejection threshold (TH) parameter, which determines the overall signal's quality. For instance, selecting a high TH will cause many ICs to be rejected, thereby leading to signal over-cleaning, and choosing a low TH may result in under-cleaning of the signal. Toward determining the optimal TH parameter, this study investigates the effect of six different TH groups (NO-TH and TH1-TH6) on EEG signals recorded from post-stroke patients who performed four distinct motor imagery (MI) tasks including wrist and grasping movements. Utilizing the EEG-beta band signal at the brain's sensorimotor cortex, the performance of the TH groups was evaluated using three notable EEG quantifiers. Overall, the obtained result shows that the considered THs will significantly alter neural oscillatory patterns. Comparing the performance of the TH-groups, TH-3 with a confidence level of 60% showed consistently stronger signal desynchronization and lateralization. The correlation result shows that most of the electrode pairs with high correlation values are replicable across all the MI tasks. It also revealed that brain activity correlates linearly with distance, and a strong correlation between electrode pairs is independent of the different brain cortices. The study outcome may facilitate adequate therapeutic intervention for stroke *** Relevance: This study indicated that optimal s

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An Approach for EEG Data Augmentation Based on Deep Convolutional Generative Adversarial Network

An Approach for EEG Data Augmentation Based on Deep Convolut...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Yuanzhe Dong Xi Tang Fangning Tan Qingge Li Yingying Wang Huanqing Zhang Jun Xie Wenyuan Liang Guanglin Li Peng Fang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China School of Mechanical Engineering Xi‘an Jiaotong University Xi‘an China National Research Center for Rehabilitation Technical Aids Beijing China

High-quality Electroencephalogram (EEG) signals are crucial for BCI applications. In practice, processing and analysis of EEG are often hindered due to either data scarcity or data imbalance, which would lower the performance of EEG-based classification. In this work, we implemented a data augmentation approach based on Deep Convolutional Generative Adversarial Network (DCGAN), which employed a generator and a discriminator to compete with each other to gain high-quality data for augmentation. In the approach, one-dimensional (ID) convolution layers were used by the discriminator to extract time features of raw signals, and ID transposed convolutional layers were adopted by the generator to synthesize new data. The quality of synthesized data was analyzed in time domain, and as well in both frequency and time-frequency domains by using Fast Fourier Transform and Continuous Wavelet Transform. The experimental results showed that the synthesized data have very similar temporal and spectral features to the raw data, which confirmed the effectiveness of our proposed method in synthesizing useable data from limited original signals with a concise structure and fewer network parameters. This work provides a possible deep learning method for EEG data augmentation with a lot of application potentials in BCI areas.

关键词： Time-frequency analysis Wavelet domain Convolution Generative adversarial networks Feature extraction Wavelet analysis Electroencephalography

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Semantic-focused Patch Tokenizer with Multi-branch Mixer for Visual Place Recognition

Semantic-focused Patch Tokenizer with Multi-branch Mixer for...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhenyu Xu Ziliang Ren Qieshi Zhang Jie Lou Dacheng Tao Jun Cheng CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology Dongguan University of Technology Dongguan China Chinese University of Hong Kong Hong Kong China China Nuclear Power Operations Co. Ltd. Shenzhen China Nanyang Technological University Singapore

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual Place Recognition (VPR) is critical for navigation and loop closure in autonomous driving tasks, mitigating the impact of shift errors caused by dynamic changes in the environment. Due to the limited ability of backbone networks and extreme environmental changes, current methods fail to capture foundational semantic details that include the distinctive attributes for unique place identification. To address this problem, we propose a new visual token-guided VPR framework that contains a semantic-focused patch tokenizer and a multi-branch Mixer. To mitigate the inference from place-unrelated objects, the semantic-focused patch tokenizer exploits attention-based channel selection and spatial partition, which efficiently captures important semantic information within the channels and preserve spatial relationships among the backbone features. To extract abstract features with spatial structure information, the multi-branch Mixer utilizes a multi-branch structure to aggregate local and global position information, improving the robustness of global representations to environmental changes. Experimental results demonstrate that our method outperforms state-of-the-art methods, achieving 85.3% Recall@1 on the MSLS val dataset and 59.1% Recall@1 on the Nordland dataset when using ResNet18 as the backbone.

关键词： Visualization Navigation Semantics Feature extraction Robustness Data mining Task analysis

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Multiple time scale motion images for action recognition 22

Multiple time scale motion images for action recognition

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Cheng, Qin Ren, Ziliang Liu, Jianming Cheng, Jun School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728162676

This paper proposes a simple and effective approach for RGB-based action recognition using multiple streams Convolutional Neural Networks (ConvNets). We utilize a new method to represent temporal structure of RGB videos, named Motion Image (MI), which is constructed from the difference between frames with a certain time scale. Considering the different duration of different actions, multiple time scale sampling MIs can obtain more temporal information. Furthermore, we adopt multiple streams ConvNets, including MIs and RGB streams, to learn spatial-temporal features for action recognition. Our approach has been evaluated on UCF-101 and HMDB-51, and the experimental results demonstrate the effectiveness and significantly improve action recognition rate at a small computational cost. © 2021 IEEE.

关键词： Motion estimation

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A Novel Soft Exosuit Based on Biomechanical Analysis for Assisting Lower Extremity

A Novel Soft Exosuit Based on Biomechanical Analysis for Ass...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Youfu Liu Chunjie Chen Jun Lu Yida Liu Yu Zhang Zhuo Wang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology. Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China College of Mechanical &Electrical Engineering Shaanxi University of Science & Technology. Shenzhen China

ISBN: (数字)9781728162676

ISBN: (纸本)9781728162683

In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which added extra inertial to low limb and restricted wearers' movement, the soft wearable lower extremity exoskeleton minimized the impact of these factors on the human body locomotion. In this paper, a novel design of exosuit that provided assistive force for both hip extension and flexion through the variation in hip joint dynamics during strides was proposed. Based on the change of hip moment, an assistance strategy and assistance force curve were came up with. PD type iterative learning control (ILC) method was introduced to reduce the error caused by wearing position and biological characteristics to improve assistance performance. To evaluate assistance performance, the metabolic cost of four subjects wearing exosuit and walking on treadmill at 5km/h in the situations of that without assistance, assisting both hip extension and hip flexion, and assisting hip extension respectively was measured. Compared with assisting hip extension only and wearing exosuit with no assistance, results indicated that the decrease in average net metabolic cost of assisting both hip extension and flexion was 0.445W/kg and 1.027W/kg respectively, corresponding to the average net metabolic cost rate decrease was 7.45% and 15.67%.

关键词： Legged locomotion Force Exoskeletons Production Timing Hip Iterative learning control

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High-performance Zn-graphite battery based on LiPF6 single-salt electrolyte with high working voltage and long cycling life

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Journal of Energy Chemistry 2021年第7期30卷 602-609页

作者： Yong Wang Luojiang Zhang Fan Zhang Xuan Ding Kyungsoo Shin Yongbing Tang Functional Thin Films Research Center Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangdongChina University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Advanced Materials Processing&Mold Ministry of EducationZhengzhou UniversityZhengzhou 450002HenanChina Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangdongChina

Zinc-ion batteries(ZIBs) are promising alternative energy storage devices to lithium-ion batteries owing to the merits of large abundance,high theoretical capacity,and environmental ***,critical challenges including low working voltage(below 2 V),low energy density as well as dendrites formation during long cycling caused by aqueous ZIB systems still hinder their practical ***,a high-voltage Zn-graphite battery(ZGB) based on a non-zinc ion single-salt electrolyte(2.5 M LiPF6 in carbonate solvent) is ***,we surprisingly found that Zn^(2+) is dissolved in the LiPF6 single-salt electrolyte during resting and discharging processes,thus enabling reversible Zn plating/stripping mechanism on the Zn foil anode in the ZGB over the voltage window of 1.0-3.1 *** a result,the ZGB achieves long-term cycling performance with a capacity retention of ~100% for over1200 cycles at 3 C and high Coulombic efficiency of ~100% in 1.0-3.1 V with no dendrites ***,the ZGB exhibits a high working voltage of up to 2.2 V,thus contributing to both high energy density(up to 210 Wh kg^(-1)) and high power density(up to 1013 W kg^(-1)),superior than most reported ZIBs.

关键词： Zinc-ion battery Zn anode Graphite cathode LiPF6 single-salt electrolyte High voltage

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