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8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Yajuan Chen, Jie Gong, Zhijia College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The rapid growth of urbanization has led to an increasing need for efficient energy management systems to optimize energy consumption and reduce environmental impacts. Wireless sensor technology emerges as a pivotal solution, providing advanced capabilities in real-time monitoring, control, and management of urban energy systems. This paper explores the practical applications of wireless sensor networks (WSN) in urban energy management, highlighting their role in smart grids, intelligent building systems, smart street lighting, public transportation, water management, and renewable energy integration. Additionally, it addresses the prominent challenges such as data security, sensor durability, network scalability, data management, energy consumption, and infrastructure integration. [1-2] The paper further discusses optimization pathways to enhance the effectiveness and sustainability of WSNs in urban energy management. By overcoming these challenges and leveraging the potential of wireless sensor technology, cities can achieve significant energy savings, operational efficiencies, and contribute to broader environmental sustainability goals. © 2024 Copyright held by the owner/author(s).

关键词： Green buildings

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A Comparison Study of Person Identification Using IR Array sensors and LiDAR

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sensorS 2025年第1期25卷 271-271页

作者： Liu, Kai Bouazizi, Mondher xing, Zelin Ohtsuki, Tomoaki Keio Univ Grad Sch Sci & Technol Yokohama 2238522 Japan Keio Univ Fac Sci & Technol Yokohama 2238522 Japan

Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities-RGB, thermal, and depth-using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 x 12 to 640 x 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation. Results show a similar identification performance between the three modalities, in particular in high resolution (i.e., 640 x 480), with RGB image classification reaching 100.0%, depth images reaching 99.54% and thermal images reaching 97.93%. However, upon deeper investigation, thermal images show more robustness and generalizability by maintaining focus on subject-specific features even at low resolutions. In contrast, RGB data performs well at high resolutions but exhibits reliance on background features as resolution decreases. Depth data shows significant degradation at lower resolutions, suffering from scattered attention and artifacts. These findings highlight the importance of modality selection, with thermal imaging emerging as the most reliable. Future work will explore multi-modal integration, advanced preprocessing, and hybrid architectures to enhance model adaptability and address current limitations. This study highlights the potential of thermal imaging and the need for modality-specific strategies in designing robust person identification systems.

关键词： person identification deep learning IR array sensor LiDAR

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Microwave-Assisted Synthesis and Characterization of Iron Oxide Nanoparticles for advanced Biomedical Sensing applications

IEEE OPEN JOURNAL OF NANOTECHNOLOGY

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IEEE OPEN JOURNAL OF NANOTECHNOLOGY 2025年 6卷 10-15页

作者： Singh, Vivek Pratap Singh, Chandra Prakash Kumar, Santosh Pandey, Saurabh Kumar Punetha, Deepak IIT Dept Elect Engn Sensors & Optoelect Res Grp SORG Patna 801103 India Koneru Lakshmaiah Educ Fdn Ctr Excellence Nanotechnol Dept Elect & Commun Engn Guntur 522302 India Motilal Nehru Natl Inst Technol Dept Elect & Commun Engn Prayagraj 211004 India

This study focuses on the synthesis and characterization of Superparamagnetic Iron Oxide Nanoparticles (IONPs) with potential biomedical and sensing applications. These nanoparticles are in high demand for their biocompatibility, biodegradability, and superparamagnetic properties. In contrast to traditional high-temperature synthesis methods, microwave-assisted co-precipitation provides notable benefits, such as improved superparamagnetic characteristics, a high surface-to-volume ratio, large surface area, and simplified separation processes. The synthesis process utilized microwave-assisted co-precipitation, and a range of characterization techniques, including xRD, FESEM, VSM, FTIR, and UV-spectroscopy, were employed to assess the properties of the iron oxide nanoparticles. Analysis of the xRD, FTIR, and UV-spectroscopy results confirmed the formation of IONPs, predominantly comprising magnetite (Fe3O4). The microwave-synthesized IONPs exhibited superparamagnetic behavior, featuring an average crystallite size of 9 nm and robust saturation magnetization values (up to 68 emu/g). These attributes render them highly suitable for applications such as MRI contrast agents, thermal mediators in hyperthermia, drug delivery systems, and advanced sensor technologies, including magnetic sensing and biosensing applications, where their high magnetic responsiveness and surface functionalization capabilities can be effectively leveraged.

关键词： Iron Nanoparticles Microwave theory and techniques Magnetic resonance imaging Microwave ovens Microwave imaging x-ray scattering Saturation magnetization Nanotechnology Magnetic separation Microwave assisted co-precipitation magnetization MRI hyperthermia biomedical sensing iron oxide

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An In-Depth Review of Molecularly Imprinted Electrochemical sensors as an Innovative Analytical Tool in Water Quality Monitoring: Architecture, Principles, Fabrication, and applications

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MICROMACHINES 2025年第3期16卷 251-251页

作者： Mukendi, Mbuyamba Divin Salami, Oluseyi Sikiru Mketo, Nomvano Univ South Africa Coll Sci Engn & Technol CSET Dept Chem Florida PkCorner Christian Wet & Pioneer Ave ZA-1709 Florida South Africa

Molecularly imprinted electrochemical sensors (MI-ECSs) are a significant advancement in analytical techniques, especially for water quality monitoring (WQM). These sensors utilize molecular imprinting to create polymer matrices that exhibit high specificity and affinity for target analytes. MI-ECSs integrate molecularly imprinted polymers (MIPs) with electrochemical transducers (ECTs), enabling the selective recognition and quantification of contaminants. Their design features template-shaped cavities in the polymer that mimic the functional groups, shapes, and sizes of target analytes, resulting in enhanced binding interactions and improved sensor performance in complex water environments. The fabrication of MI-ECSs involves selecting suitable monomeric units (monomers) and crosslinkers, using a target analyte as a template, polymerizing, and then removing the template to expose the imprinted sites. advanced methodologies, such as electropolymerization and surface imprinting, are used to enhance their sensitivity and reproducibility. MI-ECSs offer considerable benefits, including high selectivity, low detection limits, rapid response times, and the potential for miniaturization and portability. They effectively assess and detect contaminants, like (toxic) heavy metals (HMs), pesticides, pharmaceuticals, and pathogens, in water systems. Their ability for real-time monitoring makes them essential for ensuring water safety and adhering to regulations. This paper reviews the architecture, principles, and fabrication processes of MI-ECSs as innovative strategies in WQM and their application in detecting emerging contaminants and toxicants (ECs and Ts) across various matrices. These ECs and Ts include organic, inorganic, and biological contaminants, which are mainly anthropogenic in origin and have the potential to pollute water systems. Regarding this, ongoing advancements in MI-ECS technology are expected to further enhance the analytical capabilities and performances

关键词： MIPs electrochemical sensors analytical tool WQM architecture principles fabrication applications

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Research on fault detection method of aerial sensor based on neural network

Research on fault detection method of aerial sensor based on...

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2024 International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Fan, xianming Li, xiaolei xue, Weijian Li, Siyuan Wei, Zhe State Grid Jinan Power Supply Company Shandong Jinan250000 China State Grid Shandong Electric Extrahigh Voltage Company Shandong Jinan250000 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

This research addresses the critical need for reliable sensor systems in the aviation industry and the challenges associated with timely and accurate fault detection. We propose a novel method for enhancing fault detection in aviation sensors using advanced neural network architectures. Our methodology involves a comprehensive exploration of neural network models tailored to the characteristics of aviation sensor data. Experiments, conducted with a meticulously designed dataset, quantitatively evaluate the proposed approach using established performance metrics. The results unequivocally demonstrate the effectiveness of our neural network-based method in detecting and diagnosing sensor faults, showcasing its potential to significantly improve the reliability of aviation systems. In conclusion, this study contributes to aviation safety by presenting an innovative method for sensor fault detection, underscoring the applicability and effectiveness of neural networks in addressing challenges associated with sensor reliability. Future prospects involve refining the model, exploring real-time implementation possibilities, and extending the methodology to diverse sensor types, providing valuable insights for advancing the state-of-the-art in aviation sensor fault detection and enhancing the safety and performance of aircraft systems. © 2025 SPIE.

关键词： Aircraft detection

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Design, Fabrication and Calibration of an Embroidery Textile Tactile sensor Array 25th

Design, Fabrication and Calibration of an Embroidery Text...

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25th Annual conference on Towards Autonomous Robotic systems, TAROS 2024

作者： Hou, Ningzhe Pontin, Marco Costi, Leone Maiolino, Perla Oxford Robotics Institute University of Oxford Oxford United Kingdom University of Genoa Liguria Genoa Italy

ISBN: (纸本)9783031720581

This paper presents the development and evaluation of an embroidery textile tactile sensor, designed for advanced pressure mapping applications. Utilizing a unique embroidery technique with conductive threads and incorporating a Velostat piezoresistive layer, the sensor array is configured in an 8×8 grid, offering multiple contact detection over large areas. The sensor’s performance is validated through several experiments, including quasi-static loading and pressure mapping with various weights and hand gestures. This research proposes a flexible, scalable solution for tactile sensing that is both cost-effective and high-performing. The findings indicate significant potential for the sensor in real-world applications, promising advancements in how tactile information is captured and utilized. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mapping

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A Highly Sensitive, Conductive, and Flexible Hydrogel Sponge as a Discriminable Multimodal sensor for Deep-Learning-Assisted Gesture Language Recognition

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advanced FUNCTIONAL MATERIALS 2025年第10期35卷

作者： Fu, Yu Yang, Chen Zhang, Boqiang Wan, Zhenshuai Wang, Shuangkun Zhang, Kun Yang, Liuhua Wei, Ronghan Henan Univ Technol Sch Mech & Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Mech & Safety Engn Zhengzhou 450001 Peoples R China Henan Polytech Univ Sch Civil Engn Jiaozuo 454000 Peoples R China

Flexible multimodal sensors have gained increasing popularity for applications in healthcare and extreme environment operations owing to their all-around environmental perception and data acquisition capabilities. However, fabricating a magnetism-mechanics-humidity multimodal sensor that possesses high sensitivity without signal overlapping while in a facile methodology remains a great challenge. Herein, a highly sensitive, conductive, and flexible hydrogel sponge sensor with discriminable magnetism, mechanics, and humidity sensing capability is proposed, which shows stable pore size (19.30 mu m) and satisfactory mechanical properties based on the synergistic hydrogen bonding among sodium alginate, poly(vinyl alcohol) and glycerol. The proposed sensors can not only display favorable humidity sensing ability with rapid response/recovery time (2.5/4 s) but also possess enhanced sensitivities (a gauge factor of 0.46 T-1 for magnetic field, -1.16 kPa-1 for pressure), superior stability and durability (over 8000 cycles). Benefiting from the separated capacitive and resistive response signals, the sensors can precisely distinguish the magnetic, mechanical, and humidity stimuli without cross-talk. Further, the sensor arrays assisted by the deep learning algorithm are developed to realize gesture language recognition with a high accuracy of 99.17%. It can be believed that this high-performance sensor will have good prospects in future soft electronics and human-machine interaction systems. A flexible hydrogel sponge sensor with discriminable multimodal sensing capability is fabricated by a multilayer assembly strategy, displaying favorable sensitivities, stability, and durability. Benefiting from the separated capacitive and resistive responses, it can precisely distinguish the magnetic, mechanical, and humidity stimuli without cross-talk. The sensor arrays assisted by deep learning algorithms are developed to realize gesture language recognition. image

关键词： deep learning algorithm discriminable magnetism-mechanics-humidity sensing gesture language recognition hydrogel sponge multimodal sensor

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Deep-Learning-Assisted Triboelectric Whisker sensor Array for Real-Time Motion Sensing of Unmanned Underwater Vehicle

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advanced MATERIALS TECHNOLOGIES 2025年第3期10卷

作者： Liu, Bo Dong, Bowen Jin, Hao Zhu, Peng Mu, Zhaoyang Li, Yuanzheng Liu, Jianhua Meng, Zhaochen Zhou, xinyue xu, Peng xu, Minyi Dalian Maritime Univ Marine Engn Coll Dalian Key Lab Marine Micro Nano Energy & Selfpowered Syst Dalian 116026 Peoples R China Peking Univ Coll Engn Intelligent Biomimet Design Lab Beijing 100871 Peoples R China

Aquatic animals can perceive their surrounding flow fields through highly evolved sensory systems. For instance, a seal whisker array understands the hydrodynamic field that allows seals to forage and navigate in dark environments. In this work, a deep learning-assisted underwater triboelectric whisker sensor array (TWSA) is designed for the 3D motion estimation and near-field perception of unmanned underwater vehicles. Each sensor comprises a high aspect ratio elliptical whisker shaft, four sensing units at the root of the elliptical whisker shaft, and a flexible corrugated joint simulating the skin on the cheek surface of aquatic animals. The TWSA effectively identifies flow velocity and direction in the 3D underwater environments and exhibits a rapid response time of 19 ms, a high sensitivity of 0.2V/ms-1, and a signal-to-noise ratio of 58 dB. The device also locks onto the frequency of the upstream wake vortex, achieving a minimal detection accuracy of 81.2%. Moreover, when integrated with an unmanned underwater vehicle, the TWSA can estimate 3D trajectories assisted by a trained deep learning model, with a root mean square error of approximate to 0.02. Thus, the TWSA-based assisted perception holds immense potential for enhancing unmanned underwater vehicle near-field perception and navigation capabilities across a wide range of applications. This work introduces a deep learning-assisted triboelectric whisker sensor array (TWSA) for 3D motion estimation and near-field perception in unmanned underwater vehicles. The TWSA detects flow velocity and direction. It achieves 81.2% accuracy in wake vortex detection and enables precise 3D trajectory estimation with a root mean square error of 0.02, significantly enhancing underwater vehicle navigation capabilities. image

关键词： triboelectric nanogenerators underwater perception whisker sensor array

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Optical Mobius transformation module for expanding beam-steering dimensions

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OPTICS AND LASER TECHNOLOGY 2025年 187卷

作者： Yang, Dong He, Qiang Wu, Zituo Wu, Lixun Yu, Siyuan Zhang, Yanfeng Sun Yat Sen Univ Sch Elect & Informat Technol State Key Lab Optoelect Mat & Technol Guangzhou 510275 Peoples R China

Beam-steering dimension expansion can map a one-dimensional (1D) array with N2 points to a 2D array with N x N points. The scanning mode of existing expansion systems is limited to raster scan, therefore a new beamsteering dimension expansion module with advanced scanning mode is high desirable for easy integration with light detection and ranging (LiDAR) system. We propose an optical Mobius transformation (OMT) module that can enable any single 1D scanning to 2D scanning. We experimentally demonstrate the transformation of a 1D static strip into 2D linear and radial fields utilizing specified OMT phase plates. We further realize the dynamic process of converting 1D steering spots into 2D steering array. The OMT module can achieve 2D beamsteering over a 15 degrees field of view with an angular resolution of 0.5 degrees combined with a Fourier transform lens. A LiDAR system that is capable of 3D imaging (with a resolution of 30 x 30 pixels) have been implemented by combing our OMT module with a simple Time-of-Flight (TOF) sensor. This scheme provides a simple and costeffective solution to generate 2D static and dynamic structured light fields from a 1D strip or scanning spots, which can facilitate applications such as LiDAR, laser processing, 2D display and illumination.

关键词： Optical Mobius transformation Beam-steering LiDAR Time-of-flight sensor 3D imaging

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Miniature Optical Fiber Photoacoustic Gas sensor Based on a 3D Micro-Printed Ferrule-Top Optomechanical Resonator

Miniature Optical Fiber Photoacoustic Gas Sensor Based on a ...

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2024 advanced Fiber Laser conference, AFL 2024

作者： Li, Taige Zhao, Pengcheng Wang, Peng Krishnaiah, Kummara Venkata Jin, Wei Zhang, A. Ping Photonics Research Institute Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Kowloon Hong Kong State Key Laboratory of Ultraprecision Machining Technology The Hong Kong Polytechnic University Kowloon Hong Kong

ISBN: (数字)9781510688889

ISBN: (纸本)9781510688872

We present a small-size high-sensitivity optical fiber photoacoustic gas sensor based on a sub-millimeter optomechanical resonator (OMR) that is directly 3D micro-printed on the end-face of a fiber-optic ferrule. The 3D micro-printed OMR and the end-face of the optical fiber form a low-finesse Fabry-Pérot (FP) microcavity for interferometric acoustic detection, achieving an acoustic sensitivity of 296 mV/Pa. Together with a pump laser beam, an optical fiber OMR-based photoacoustic gas sensor is demonstrated to detect acetylene gas at the concentration level of 137 parts per billion. This highly sensitive and compact optical fiber gas sensor offers a promising solution for trace-gas detection in diverse applications ranging from biomedical diagnostics to in-reactor monitoring. © 2025 SPIE.

关键词： Laser beams

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