Article
Nanoscience & Nanotechnology
Li Yang, Guanghao Zheng, Yaoqian Cao, Chuizhou Meng, Yuhang Li, Huadong Ji, Xue Chen, Guangyu Niu, Jiayi Yan, Ye Xue, Huanyu Cheng
Summary: This study presents the design and demonstration of a moisture-resistant, stretchable NOx gas sensor based on laser-induced graphene (LIG). The gas sensor exhibits a large response, an ultralow detection limit, fast response/recovery, and excellent selectivity. It can be stretched by 30% and has been demonstrated to monitor the personal local environment and analyze human breath samples for disease diagnostics.
MICROSYSTEMS & NANOENGINEERING
(2022)
Review
Chemistry, Analytical
Alishba T. John, Krishnan Murugappan, David R. Nisbet, Antonio Tricoli
Summary: Electonic noses, relying on an array of chemical gas sensors, are used for identifying various compounds, particularly in the food industry and environmental monitoring. Advances in nanofabrication, sensor design, neural networks, and system integration have improved their efficacy. Commercial and custom Enoses face challenges in wider adoption and use across different applications.
Article
Chemistry, Analytical
Shen Liu, Wenqi Yan, Junlan Zhong, Tao Zou, Min Zhou, Peijing Chen, Hang Xiao, Bonan Liu, Zhiyong Bai, Yiping Wang
Summary: This article introduces an all fiber-optic breath sensor based on E-HIPFG, which has ultrafast response and recovery time, insensitivity to external influences, and does not require additional coating materials or fiber architectures, making it suitable for continuous and long-term breath monitoring.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Review
Chemistry, Physical
Wenwen Hu, Weiwei Wu, Yingying Jian, Hossam Haick, Guangjian Zhang, Yun Qian, Miaomiao Yuan, Mingshui Yao
Summary: This review article critically considers and summarizes the volatolomics in healthcare, clarifying the relationship between the volatolome and specific diseases and introducing analytical instruments and advanced detection technologies.
Article
Chemistry, Analytical
Weijia Bao, Fengyi Chen, Huailei Lai, Shen Liu, Yiping Wang
Summary: The study introduces an all fiber-optic flexible humidity sensor for wearable breath monitoring, showing fast response and high flexibility. The sensor can recognize different breathing patterns and extract breathing frequency. It exhibits excellent reproducibility, high sensitivity, and self-compensation capability.
SENSORS AND ACTUATORS B-CHEMICAL
(2021)
Article
Engineering, Electrical & Electronic
Sara Boumali, Mohamed Taoufik Benhabiles, Ahmed Bouziane, Fouad Kerrour, Khalifa Aguir
Summary: This study successfully developed an electronic nose for non-invasive blood glucose monitoring, utilizing homemade gas sensors exposed to acetone and ethanol. Through feature extraction, selection, and classification, the system achieved 100% accuracy in estimating gas concentrations with a root mean square error of 0.2236 and 0.6639 for acetone and ethanol, respectively.
SEMICONDUCTOR SCIENCE AND TECHNOLOGY
(2021)
Review
Chemistry, Analytical
Kaushiki Dixit, Somayeh Fardindoost, Adithya Ravishankara, Nishat Tasnim, Mina Hoorfar
Summary: Researching the application of exhaled breath analysis in diabetes monitoring is crucial yet challenging. A comprehensive evaluation of current technologies and sensing methods can help understand the shortcomings of blood glucose monitoring and further drive the development of non-invasive diabetes monitoring devices. It is important to focus on studying breath biomarker clusters and incorporating novel sensing materials and transduction mechanisms to realize breath analysis as an effective healthcare approach.
Article
Chemistry, Analytical
Nicholas Kostikis, George Rigas, Spyridon Konitsiotis, Dimitrios Fotiadis
Summary: Sensor placement identification in body sensor networks plays a crucial role in improving system robustness and transparency, as well as user convenience for long-term data collection. This study discusses an offline, fixed class method for sensor placement identification in PDMonitor(R), achieving an overall average accuracy of 99.1% based on evaluations with 88 subjects.
Article
Materials Science, Textiles
Liyun Ma, Ronghui Wu, Hao Miao, Xuwei Fan, Lingqing Kong, Aniruddha Patil, Xiang Yang Liu, Jun Wang
Summary: The article introduces a fibrous capacitive humidity sensor prepared using domestic winding fabrication facility and sputtering technique, exhibiting good repeatability and responsiveness performance.
TEXTILE RESEARCH JOURNAL
(2021)
Article
Chemistry, Analytical
Dionisio V. Del Orbe, Hyung Ju Park, Myung-Joon Kwack, Hyung-Kun Lee, Do Yeob Kim, Jung Gweon Lim, Inkyu Park, Minji Sohn, Soo Lim, Dae-Sik Lee
Summary: Obesity increases the risk of chronic diseases, and it is important to develop portable devices to monitor exercise-induced fat burn in real time for weight management. In this study, a compact breath analyzer was developed for convenient and noninvasive estimation of fat burning, which showed a high level of accuracy using a machine learning algorithm.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Materials Science, Multidisciplinary
Xiaoyin Chen, Shuxiang Mei, Wei Zhao, Yuancheng Zhang, Xiaomeng Zhang, Zhe Cui, Peng Fu, Xinchang Pang, Minying Liu, Yong Ye
Summary: A flexible wearable device for humidity detection and breath monitoring was fabricated using a molecular designable thermoplastic polyamide elastomer. The device showed negative humidity sensitivity, wide humidity monitoring range, and stability against bending deformation.
MATERIALS & DESIGN
(2023)
Article
Engineering, Multidisciplinary
Mariam El Gharbi, Raul Fernandez-Garcia, Ignacio Gil
Summary: This paper presents the design and validation of a new embroidered meander dipole antenna-based sensor integrated into a commercially available T-shirt for real-time breathing monitoring. The sensor operates at 2.4 GHz and measures the resonant frequency shift induced by chest expansion and lung air volume displacement during breathing. The measurements were carried out to monitor breathing patterns in different positions, and the textile sensor showed significant surface consumption reduction compared to other reported wearable sensors.
Article
Polymer Science
Yin-Hsuan Chang, Ting-Hung Hsieh, Kai-Chi Hsiao, Ting-Han Lin, Kai-Hsiang Hsu, Ming-Chung Wu
Summary: Human breath contains water, oxygen, carbon dioxide, and gases related to metabolism. A highly sensitive sensing material (WO3/SnO2/Ag/PMMA) fabricated by electrospinning is proposed to detect breath acetone. The material exhibits a sensing limit of 20 ppm and shows specificity for acetone even in ambient humidity.
Review
Green & Sustainable Science & Technology
Angelo Milone, Anna Grazia Monteduro, Silvia Rizzato, Angelo Leo, Corrado Di Natale, Sang Sub Kim, Giuseppe Maruccio
Summary: Gas sensing research has experienced a worldwide revival in recent years. The emergence of novel sensing materials and increasing opportunities for applications have greatly contributed to the progress in this field. This review focuses on emerging approaches, recent breakthroughs, and applications in various sectors such as environmental monitoring, food aroma identification, and healthcare.
ADVANCED SUSTAINABLE SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Martina Corsi, Alessandro Paghi, Stefano Mariani, Giulia Golinelli, Aline Debrassi, Gabriella Egri, Giuseppina Leo, Eleonora Vandini, Antonietta Vilella, Lars Daehne, Daniela Giuliani, Giuseppe Barillaro
Summary: This study presents a bioresorbable nanostructured pH sensor that can measure local pH levels in real time. The sensor is composed of a micrometer-thick porous silica membrane coated with a nanometer-thick multilayer stack of polyelectrolytes labeled with a pH-insensitive fluorophore. It has high stability, reproducibility, and accuracy, and can continuously operate for over 100 hours. In vivo experiments on mice confirmed the real-time monitoring of pH levels through the skin. The sensor degrades completely within one week and exhibits good biocompatibility after 2 months.
Article
Computer Science, Artificial Intelligence
Hao Zhang, Yajie Zou, Xiaoxue Yang, Hang Yang
Summary: This study adopts a novel architecture called TFT to predict freeway speed, which can capture short-term and long-term temporal dependence and improve prediction accuracy by incorporating various types of inputs.
Article
Computer Science, Artificial Intelligence
Xindi Yang, Hao Zhang, Zhuping Wang
Summary: This article introduces a data-based distributed control algorithm to address the consensus control problem in multiagent systems, successfully overcoming the challenges of asynchronous learning. By incorporating an actor-critic structure and neural networks, the algorithm achieves convergence and optimality in both synchronous and asynchronous cases.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zhichen Li, Yu Zhao, Huaicheng Yan, Hao Zhang, Lu Zeng, Xiaolei Wang
Summary: This paper investigates the time-varying formation tracking control problem for multi-agent systems (MASs). The main objective is to achieve asymptotic convergence of formation tracking error despite nonparametric and nonvanishing uncertainties. A fuzzy extended state observer (FESO) based on event-triggered mechanism is proposed to estimate unmodeled dynamics and external disturbances. Furthermore, a distributed control law is developed using neighborhood formation tracking errors, and total disturbance compensation is introduced to attenuate uncertainty influence in real time. The effectiveness of the proposed control protocol is demonstrated using a numerical example on unmanned aerial vehicle swarm system.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Computer Science, Artificial Intelligence
Hang Wang, Youtian Du, Yabin Zhang, Shuai Li, Lei Zhang
Summary: This paper proposes a novel one-stage approach called VRR-TAMP, which formulates the task of VRR as an end-to-end bounding box regression problem by effectively integrating Transformers and an adaptive message passing mechanism. Experimental results demonstrate that our approach significantly outperforms its one-stage competitors and achieves competitive results with the state-of-the-art multi-stage methods.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Automation & Control Systems
Min Xue, Huaicheng Yan, Hao Zhang, Xisheng Zhan, Kaibo Shi
Summary: This article discusses compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. Utilizing single exponential smoothing as a compensation scheme, an asynchronous output feedback controller is designed with stochastic stability and strict dissipativity. Novel sufficient conditions for controller existence based on mode-dependent Lyapunov function are derived, along with an algorithm for determining the optimal smoothing parameter. Simulation results demonstrate the validity and advantages of the design approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Bo Qin, Huaicheng Yan, Hao Zhang, Yueying Wang, Simon X. Yang
Summary: This paper proposes a control method based on an enhanced reduced-order extended state observer for precise motion control in mobile robot systems. The method reduces energy consumption by estimating unknown state error and negative disturbance and uses a simple state-feedback-feedforward controller to track the reference signal and compensate for negative disturbance.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xindi Yang, Hao Zhang, Zhuping Wang, Huaicheng Yan, Changzhu Zhang
Summary: This article presents a model-free predictive control algorithm for real-time systems that improves system performance through data-driven multi-step policy gradient reinforcement learning. By learning from offline and real-time data, the algorithm avoids the need for knowledge of system dynamics in its design and application. Cooperative games are used to model predictive control as multi-agent optimization problems and ensure the optimal predictive control policy. Neural networks are employed to approximate the action-state value function and predictive control policy, with weights determined using weighted residual methods. Numerical results demonstrate the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhichen Li, Huaicheng Yan, Hao Zhang, Simon X. Yang, Mengshen Chen
Summary: This article investigates the design problem of an extended state observer (ESO) for uncertain nonlinear systems subject to limited network bandwidth. A dynamic event-triggered communication protocol is proposed for rational information exchange scheduling, achieving a desirable tradeoff between observation performance and communication resource efficiency. A novel paradigm of event-triggered Takagi-Sugeno fuzzy ESO is introduced, and the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics under the dynamic event-triggered mechanism. The effectiveness of the proposed method is verified through numerical examples, expanding the application scope of ESO with improved event-triggered strategies.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoming Li, Shiguang Zhang, Shangchen Zhou, Lei Zhang, Wangmeng Zuo
Summary: This paper proposes a blind face restoration method that explicitly memorizes generic and specific features through dual dictionaries to improve the performance of blind face restoration. By learning generic and specific dictionaries and combining the dictionary transform module and multi-scale dictionaries, the restoration results are improved.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jianqi Ma, Shi Guo, Lei Zhang
Summary: This paper proposes a method to improve the resolution and visual quality of scene text images by embedding text recognition prior into the super-resolution model, which also boosts the performance of text recognition. Experimental results show that this method effectively improves the visual quality of scene text images and significantly enhances the text recognition accuracy.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Qi Liu, Hao Qian, Biao Xiang, Qing Cui, Jun Zhou, Enhong Chen
Summary: This paper proposes a novel model called EATN for accurately classifying sentiment polarities towards aspects in multiple domains in sentiment analysis tasks. The model incorporates a Domain Adaptation Module (DAM) to learn common features and uses multiple-kernel selection method to reduce feature discrepancy among domains. Additionally, EATN includes an aspect-oriented multi-head attention mechanism to capture the direct associations between aspects and contextual sentiment words. Extensive experiments on six public datasets demonstrate the effectiveness and universality of the proposed method compared to current state-of-the-art methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Xiaoming Li, Chaofeng Chen, Xianhui Lin, Wangmeng Zuo, Lei Zhang
Summary: Designing proper training pairs is crucial for superresolving real-world low-quality images. Previous methods focus on modeling degradation with limited improvement. This study uses real-world low-quality face images to model complex degradation and transfers it to natural images to synthesize realistic low-quality counterparts. The results show that this method can effectively learn the real degradation process and improve the quality of non-facial areas.
COMPUTER VISION - ECCV 2022, PT XVIII
(2022)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, Radu Timofte
Summary: Recent works have shown that using a denoiser as the image prior can improve the performance of plug-and-play image restoration methods. However, existing methods are limited by the lack of suitable denoiser priors. In this study, we propose a deep denoiser prior that significantly outperforms other state-of-the-art model-based and learning-based methods for various image restoration tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yunkai Lv, Hao Zhang, Zhuping Wang, Huaicheng Yan
Summary: This article focuses on the real-time localization problem in dynamic multi-agent systems with measurement and communication noises under directed graphs. It introduces barycentric coordinates to describe the relative position between agents and proposes a novel robust distributed localization estimation algorithm based on iterative learning. The algorithm uses a relative-distance unbiased estimator constructed from historical iterative information to suppress measurement noise, and a designed stochastic approximation method with two iterative-varying gains to inhibit communication noise. The asymptotic convergence of the proposed methods is derived under certain conditions of zero-mean and independent distribution of measurement and communication noises. Numerical simulations and robot experiments are conducted to test and verify the effectiveness and practicability of the proposed methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Binghui Chen, Weihong Deng, Biao Wang, Lei Zhang
Summary: This article emphasizes the importance of generalization ability in deep metric learning for zero-shot image retrieval and clustering tasks. It proposes a confusion-based metric learning framework that uses energy confusion and diversity confusion regularization terms to optimize a robust metric. The framework confuses the learned model in an adversarial manner and serves as an efficient regularization for deep metric learning. Experimental results demonstrate the significance of learning an embedding/metric with good generalization.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
