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Au NUs@SiO2 sensors for sensitive and selective optical sensing of volatile compounds

Talanta 2025年 294卷

作者： Chen, Bin Mou, Qirui Liu, Chuanjun Hayashi, Kenshi Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Department of Electronics of Information Science and Electrical Engineering Kyushu University Fukuoka 819-0395 Japan

Plasmonic sensors has been widely used to detect the refractive index change in liquids, while it is challenging to apply the plasmonic sensors to the selective detection of organic vapors that adsorb non-specifically and induce extremely low refractive index change. Herein, silica shell was directly grown on Au nano-urchins (Au NUs) through sol-gel hydrolysis method to improve its vapor molecules anchoring capability and generate the selectivity to different vapors. According to the vapor sensing experiments to ethanol, acetone and hexane, the refractive index sensitivity (RIS) of Au NUs@SiO2 sensors was four times higher than that of bare Au NUs. In addition, Au NUs@SiO2 sensors demonstrate different vapor sensing performance to different organic vapors. The sensing characteristics is attributed to the large contacting area and the affinity between Au NUs@SiO2 sensors and the target molecules. These findings provide a basis for the development of novel plasmonic sensors and the RIS enhancement, which abroad their application as contactless sensors. © 2025

关键词： Sol-gels

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Blood pressure monitoring via double sandwich-structured triboelectric sensors and deep learning models

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Nano Research 2022年第6期15卷 5500-5509页

作者： Xu Ran Fangyuan Luo Zhiming Lin Zhiyuan Zhu Chuanjun Liu Bin Chen Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China Research Laboratory U.S.E.Co.Ltd.Shibuya-kuTokyo 150-0013Japan

Real-time blood pressure monitoring is essential for the timely diagnosis and treatment of cardiovascular *** traditional prediction methods estimate blood pressure by measuring multiple sets of physiological signals with energyconsuming ***,a continuous,cuffless and self-powered blood pressure monitoring system was developed based on a new double sandwich-structured triboelectric sensor and a novel blood pressure method estimation.A pyramid-patterned sensor based on the double sandwich structure realizes a sensitivity of 0.89 V/kPa in a linear range of 0–35 kPa,which is more than twice of the conventional single electrode *** sensor processes a low pressure detection limit of 1 g andfast response time of 32 ***,it can easily capture the pulse signal at the radial ***,a novel method for estimating blood pressure using pulse waves accompanied by the user’s background information was *** method measures only one set of pulse signals and is portable.A deep learning model with multi-network structures was developed to improve the estimation *** mean absolute error and standard deviation of error for systolic and diastolic blood pressure(SBP and DBP)estimations were 3.79±5.27 and 3.86±5.18 mmHg,*** work reveals a new sensing structure of triboelectric sensors and offers a novel method for blood pressure estimation.

关键词： triboelectric nanogenerator self-powered sensor blood pressure monitoring deep learning

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StO2 Stress Detection Based on Fewer Wavelengths through linear Prediction Algorithm 6

StO2 Stress Detection Based on Fewer Wavelengths through Lin...

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6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021

作者： Xiao, Xiao Liu, Xinyu Peng, Dairong Chen, Tong Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9780738126043

In recent years, tissue oxygen saturation (StO2) based on hyperspectral imaging (HSI) technology has been deeply studied in the field of affective computing. HSI StO2 is a kind of non-contact physiological signal, which can reveal people's potential emotions. However, at this stage, the number of bands to generate StO2 is too many to achieve real-time emotion detection. Therefore, based on the publicly available HSI stress database, we used a linear Prediction (LP) algorithm to select only 8 characteristic bands from the original 106 bands to generate StO2 and performed the task of identifying psychological stress and physical stress. The experimental results showed that the recognition rate of StO2 generated based on the selected 8 bands in stress detection is very close to (even higher) that of the original 106 bands, and reaches 84.44% when using the Bayes classifier. © 2021 IEEE.

关键词： Hyperspectral imaging

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A Fusion Shallow and Deep Features Network for Facial StO2 Stress Classification 6

A Fusion Shallow and Deep Features Network for Facial StO2 S...

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6th IEEE information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Xia, Haolin Sun, Sirui Liu, Xinyu Zhou, Ju Wang, Hanpu Chen, Tong School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Non-Linear Circuit and Intelligent Information Processing of Chongqing Chongqing China Institute of Legal Psychology and Intelligent Computing Chongqing China

ISBN: (纸本)9781665460033

Stress is an emotional state that affects our lives, so it is vital to detect it accurately. Numerous studies have shown that tissue oxygen saturation (StO2) is an effective physiological signal for identifying different stress states. However, previous StO2 feature extraction methods relied too heavily on baselines to be realized in practice. In this paper, a two-stream network combining shallow and deep features, called CRNet, is proposed for baseline-independent StO2 stress classification. In CRNet, shallow features are extracted using a CNN network and deep features are extracted using a ResNet network. For the extracted high-dimensional deep features, MLP module is employed for dimensionality reduction, which can effectively combine the two levels of features and improve the feature extraction capability of the network. Experimental results show that CRNet can significantly improve the performance of StO2 stress classification to the state-of-the-art (SOTA) even when using only raw imbalanced data. © 2023 IEEE.

关键词： Feature extraction

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Infant Facial Expression Analysis towards Defecation Detection 4

Infant Facial Expression Analysis towards Defecation Detecti...

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4th IEEE International Conference on Electronics Technology, ICET 2021

作者： Liu, Shihan Liu, Xinyu Chen, Tong Jiang, Ziyu Southwest University School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Chongqing China Southwest University School of Electronic and Information Engineering Chongqing China

ISBN: (纸本)9781728176734

Infants need constant care from parents or caregivers, and cannot express themselves verbally. Therefore, automatic infant facial expression recognition system plays an important role in reducing the care burden. As a non-negligible part of baby care, automatic defecation detection based on infant facial expressions has not been investigated yet. This detection is especially important for young parents with little caring experience. In this paper, we built an Infant Expression Database of 465 images from 419 infants, including four categories: happiness, pain, defecation and sleep. Based on this database, we proposed a shallow network structure containing only three convolutional layers for infant facial expression recognition, which achieved an average accuracy of 84.93% for these four categories. Moreover, the recognition results showed that the network has a strong generalization ability, with 81.82% detection accuracy of infant defecation. © 2021 IEEE.

关键词： Face recognition

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Facial StO2: A New Promising Biometric Identity

Facial StO2: A New Promising Biometric Identity

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Dairong Sun, Sirui Liu, Xinyu Zhou, Ju Chen, Tong Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing400715 China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Chongqing400715 China Southwest University School of Electronic and Information Engineering Chongqing400715 China

ISBN: (纸本)9781665468190

In this paper, we introduce a new biometric identity, facial tissue oxygen saturation (StO2). StO2 is an index of blood oxygen content in tissues and is related to blood vessel distribution pattern and metabolic rate. Experimental results show that classification accuracy can reach 83.33% in 42 participants with different stress states by using StO2 as the only input to the ResNet-50 model. We also proposed a module called StO2Net to eliminate the effects of stress on classification. The highest accuracy can reach up to 90.48% when the module is used. This pilot study shows that facial StO2 can be a promising biometric feature for identity recognition. © 2022 IEEE.

关键词： Biometrics

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Long Video Micro-expression Spotting Based On OCC Theory

Long Video Micro-expression Spotting Based On OCC Theory

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, BingTong Zhang, ZheYuan Zhou, Ju Wang, HanPu Chen, Tong Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing Chongqing400715 China Southwest University School of Electronic and Information Engineering Chongqing400715 China

ISBN: (纸本)9798350337488

The generation of micro-expression (ME) is unconscious and unaffected by human subjective consciousness. It can reveal hidden emotions in humans, which has important applications in the domains of psychological diagnosis, lie detection, and so on. ME detection, which aims to locate ME apex frames in long videos, is the foundation of ME recognition. In this paper, we propose a ME detection method based on One-Class Classification (OCC) theory for long videos. The method is the first to use neutral expression frames to detect MEs, which addresses the problem of insufficient training samples in ME detection. We design a geometric motion feature based on the prior knowledge from the FACS and utilize it in conjunction with the features extracted by CNN to train separate one-class classifiers for ME detection. Finally, we evaluate the proposed method on publicly available datasets CASME I, CASME II, SAMM, and a mixed database comprising data from the three datasets, to verify the feasibility of the proposed method in both single-database and cross-database ME detection. The experimental results show that our method outperforms three other ME detection methods, demonstrating its ability to more accurately locate ME apex frames in long videos. © 2023 IEEE.

关键词： Micro-expression Detection OCC One-Class Classifier

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Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks 57

Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-B...

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57th Annual Conference on information Sciences and Systems, CISS 2023

作者： Wang, Fei Zhang, Xi Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

ISBN: (纸本)9781665451819

We propose the novel scheme to solve a multi-objective optimization problem over an unmanned aerial vehicle (UAV) communications system to jointly minimize the energy consumption of the UAV and ground users (GUs). In particular, the UAV communicates with multiple GUs using active intelligent reflecting surfaces (IRSs), which can actively amplify and thus significantly enhance the strengths of reflected signals. We develop an energy minimization scheme based on the multi-objective hierarchical deep reinforcement learning (DRL), by decomposing the formulated optimization problem into two-layered subproblems. By solving the upper-level subproblem, we derive the optimal UAV trajectory and GUs scheduling strategies to minimize the UAV's energy consumption. By solving the lower-level subproblem, we obtain the IRS's phase shifts and amplification factors and GUs' transmit/receive beamforming to minimize the GUs' energy consumption. Finally, we validate and evaluate the proposed schemes through simulations, which show that the UAV's and GUs' energy consumption can be significantly reduced by using the active IRS, when the thermal noise powers at the IRS are much smaller than those at the UAV and GUs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G THz Mobile Wireless Networks

IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G ...

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2023 IEEE International Conference on Communications, ICC 2023

作者： Wang, Fei Zhang, Xi Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&m University College StationTX77843 United States

ISBN: (纸本)9781538674628

intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Stress recognition based on graph structure representation of facial StO2

Stress recognition based on graph structure representation o...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Jiaxiu Liu, Xinyu Jia, Yingjuan Chen, Tong Huang, Jingru Zhang, Zheyuan Southwest University College of Electronic and Information Engineering Chongqing400715 China Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China Southwest University Chongqing Key Laboratory of Non-Linear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9798350337488

Stress is an integral state that affects physical and mental health of individuals. Tissue Oxygen Saturation (StO2) is an emerging physiological signal and can reflect different stress states. Previous studies have simply extracted features of StO2 from specific regions of the face without exploring potential associations between them. In this paper, we analyze the facial Region of Interest (ROI) based on StO2, so as to explore the deep relationship between ROIs. Building upon this, we construct, for the first time, a StO2-based facial graph structure for stress recognition by using ROIs as nodes. In addition, we propose a new graph pooling method called FTPool which allows to measure node significance in terms of both node characteristics and graph topology. Finally, we further propose a dual-stream network (GCNet) combining GNN and CNN for the baseline-independent stress recognition. The experimental results show that the proposed GCNet can achieve state-of-the-art (SOTA) results with an accuracy of 78.57% on the original unbalanced database. © 2023 IEEE.

关键词： Graph pooling Graph structure Regions of Interest Stress recognition Tissue oxygen saturation

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