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IEEE Electron Device Letters 2025年

作者： Liu, Yijia Qiao, Lijun Tang, Xin Liang, Song Zhang, Mingjiang Taiyuan University of Technology College of Physics and Optoelectronics Taiyuan030024 China Ministry of Education Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Shanxi Taiyuan030024 China Chinese Academy of Sciences Key Laboratory of Optoelectronic Materials and Devices Beijing100083 China University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering Beijing100049 China

We propose and numerically investigate a new step-ridge-waveguide InGaAlAs/InP multi-quantum well (MQW) transistor laser (TL). The carrier nonradiative recombination centers caused by the exposed deep-ridge waveguide sidewall significantly degrade the optoelectronic performance of the TLs. The step-ridge-waveguide structure guides carriers through the center portion of the ridge waveguide, thereby effectively mitigating the effect of the nonradiative recombination defects. Simulation results show that the optoelectronic characteristics of the step-ridge TL are far better than those of the conventional deep-ridge TL, and the current amplification factor of the step-ridge TL is not affected by the laser excitation. This structure provides a new option for the simultaneous enhancement of the optoelectronic performance of TLs. © 1980-2012 IEEE.

关键词： Laser excitation

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A Mechanical Biosensor Based on Magnetic-Mediated Sensitization for 5-Ht Detection

SSRN

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SSRN 2024年

作者： Zhao, Dong Yao, Penglong Yan, Zhaosheng Jian, Aoqun Bai, Jinsuo Wang, Haoyu Guo, Xing Sang, Shengbo Zhou, Chuangang Dong, Xiushan Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information And Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Taiyuan University of Technology China Shanxi Academy of Medical Sciences China

The real-time detection of neurotransmitters had attracted more attention due to the potential to provide vital information for pathology research, disease diagnosis, and clinical efficacy evaluation. Here, a magnetic-mediated biosensor was developed to improve sensitivity for detection of serotonin (5-HT). The PDMS membrane doped with magnetic particles serves as a conversion element, enhancing the deformation of the thin film caused by surface stress under the action of a magnetic field and amplifying the resistance response signal of the biosensor. The 5-HT detection associated with neurotransmitter diseases serve as a proof of concept revealing the magnetic sensitization. The detection limit of the 5-HT was decreased to 3.21 nM in the linear range of 5-500 nM, which was lower than that of the biosensor without magnetic field. The clinical 5-HT samples were also been accurately detected, indicating a positive application potential of the as-prepared magnetic-mediated biosensor. All these results reveal the high sensitivity and practicality of the as-prepared magnetic-mediated biosensor and provide a new method for neurotransmitter diseases diagnosis. © 2024, The Authors. All rights reserved.

关键词： Biosensors

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Defect Detection in Solar Panels Based on PV-Flow

Defect Detection in Solar Panels Based on PV-Flow

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WRC Symposium on advanced Robotics and Automation (WRC SARA)

作者： Minghao Niu Jiaqi Li Mengyu Li Yifan Zhao Ying Liu Shuhuan Wen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Yingli Energy Development Co. Baoding China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

With technological advancements, resource utilization has increased, making renewable energy sources like solar power essential. Effective defect detection for solar panels is crucial. However, manual inspection is currently inefficient and machine vision lacks accuracy. In order to improve defect detection in high-resolution images and overcome the limitations of existing methods, we propose an unsupervised deep learning model called PV-Flow, which combines a hybrid attentional mechanism and a multiscale feature extraction module of the flow model. Before defect detection we need to categorize the defects and make a dataset, after observing the features we classify the defects into black spots, broken grids, hidden cracks and fragmentations. The performance of the model while performing defect detection is evaluated by metrics of reconstructed images and visual comparisons. Experiments on real solar panel datasets show that our proposed PV-Flow model is able to accurately identify the location of broken gate defects and show more details, with even the smallest defects clearly labeled and magnified. This suggests that our proposed model is able to detect potential defects earlier, thus reducing production risks and costs. We also conducted comparative experiments to further validate the reliability of the model.

关键词： Deep learning Visualization Accuracy Costs Sensitivity Production Feature extraction

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Optical encryption using structural uncorrelated characteristics of biological scattering media

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Optica Acta: International Journal of Optics 2023年第1期70卷 69-76页

作者： Aiping Zhai Qing Han Teng Zhang Wenjing Zhao Dong Wang a College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan People’s Republic of China a College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan People’s Republic of Chinab Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan University of Technology Taiyuan People’s Republic of China

ABSTRACTABSTRACTInspired by the unique internal structures with inherent anisotropy in biological tissues and their dynamic scattering characteristic, a simple and easy-to-fabricate optical encryption technique using structural uncorrelated characteristics of biological tissues was proposed. It can generate unique keys with the random characteristics of the tissues and transforms multiple image plaintexts into speckle-like ciphertexts. The optical encryptions of grayscale images are demonstrated utilizing the uncorrelated characteristics of shallot and chicken breast respectively. Ciphertext-only-attack is resisted by phase retrieval due to the inherent dynamic randomness of biological tissues and the complexity of the plaintexts. Besides, once the unique PSF keys is recorded by the optical setup, the encryption can be accomplished, either online using the setup, or offline through performing convolutions on the plaintexts with pre-recorded uncorrelated PSFs to yield ciphertexts. Therefore, it is very secure, simple, and flexible to guarantee the promising potentials for information encryption of grayscale images and/or videos.

关键词： Optical encryption structural uncorrelated characteristics biological tissues grayscale images

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CPL-SLAM: Centralized Collaborative Multi-Robot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center The Ministry of Education for Intelligent Control System and Intelligent Equipment Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering China Carleton University Canada

Traditional visual-inertial SLAM (Simultaneous Localization and Mapping) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intra-robot and inter-robot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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A Monitoring Method for the Ice Shape and the Freeze-Thaw Process of Ice Accretion on Transmission Lines Based on Circular FBG Plane Principal Strain Sensor

arXiv

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arXiv 2025年

作者： Qin, Zhuoke Jia, Bin Shen, Xiahui Zhang, Lizhen Lu, Honggang Du, Chao Cui, Liqin Zhang, Li Deng, Xiao The College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan030024 China The Scientific Research Office of Shanxi Electric Power Technical College Taiyuan030021 China The College of Physics and Optoelectronics The Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education The Shanxi Key Laboratory of Precision Measurement Physics Taiyuan University of Technology Taiyuan030024 China

As a key infrastructure for China's "West-to-East Power Transmission" project, transmission lines (TL) face the threat of ice accretion under complex microclimatic conditions. This study proposes a plane principal strain sensing method based on a fiber Bragg grating circular array, achieving synchronous monitoring of 6 strains (ranging from -2000 to 2000 μΕ) across the TL cross-section. Through finite element simulation experiments, a mapping relationship between the bending of TL and the plane principal strain has been established. After completing the sensor calibration, an experimental platform for the freeze-thaw process of ice accretion on the TL was built. The relationships between ice mass and bending strain, as well as the ice shape on the TL cross-section (C-shaped and circular ice) and plane principal strain, were studied. Furthermore, a BP neural network model was developed to determine the 4 states of the icing process (no ice/freeze/stable/thaw), achieving an accuracy of 91.23%. This study provides effective monitoring of the freeze-thaw process of ice accretion on the TL, offering important technical support for the prevention and control of ice accretion in power grid. Copyright © 2025, The Authors. All rights reserved.

关键词： Thawing

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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High Sensitivity Submicron Scale Temperature Sensor Based on Perovskite Nanoplatelet Lasers

SSRN

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SSRN 2024年

作者： Zhao, Ruofan Tao, Jianxun Xi, Yuying Chen, Jiangzhao Ji, Ting Wang, Wenyan Wen, Rong Cui, Yanxia Chen, Junsheng Lia, Guohui College of Physics and Optoelectronics Key Laboratory of Interface Science and Engineering in Advanced Materials Key lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Taiyuan030024 China School of Materials Science and Engineering Kunming University of Science and Technology Kunming650093 China Shanxi-Zheda institute of advanced materials and chemical engineering China Nano-Science Center Department of Chemistry University of Copenhagen Universitetsparken 5 CopenhagenDK-2100 Denmark

Submicron scale temperature sensors are crucial for a range of applications, particularly in micro and nanoscale environments. One promising solution involves the use of active whispering gallery mode (WGM) microresonators. These resonators can be remotely excited and read out using free-space structures, simplifying the process of sensing. In this study, we present a submicron-scale temperature sensor with a remarkable sensitivity up to 190 pm/℃ based on a triangular CH3NH3PbI3 nanoplatelet (NPL) laser. Notably, as temperature changes, the peak wavelength of the laser line shifts linearly. This unique characteristic allows for precise temperature sensing by tracking the peak wavelength of the NPL laser. The optical modes are confined within the perovskite NPL, which measures just 85 nanometers in height, due to total internal reflection. Our NPL laser boasts several key features, including a high Q-factor of ~ 2610 and a low laser threshold of about 19.8 µJ cm−2. The combination of exceptional sensitivity and ultra-small size makes our WGM device an ideal candidate for integration into systems that demand compact temperature sensors. This advancement paves the way for significant progress in the development of ultrasmall temperature sensors, opening new possibilities across various fields. © 2024, The Authors. All rights reserved.

关键词： Perovskite

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A Novel Magnetoelastic Biosensor Based on Pdms/Fesib/Qds Composite Film for African Swine Fever Virus Detections

SSRN

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SSRN 2024年

作者： Liu, Yuanhang Sang, Shengbo Zhao, Dong Ge, Yang Xue, Juanjuan Duan, Qianqian Guo, Xing Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of The Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China

African swine fever (ASF) is a highly contagious and severe hemorrhagic disease caused by the African swine fever virus (ASFV). The continuous spread of ASFV affects the safety of global meat supply, so the establishment of sensitive and specific detection methods for ASFV has become an important hot spot in food safety. Herein, we developed a flexible magnetoelastic (ME) biosensor based on PDMS/FeSiB/QDs composite films for the detection of ASFV. Based on the high luminescence performance of CsPbBr3 quantum dots and the excellent magnetoelastic effect of FeSiB, flexible ME biosensors convert stress signals generated by antibody-antigen-specific binding into optical and electromagnetic signals. The nanostructures covalently linked by quantum dots and PDMS provide biomodification sites for ASFV antibodies, simplifying the functionalization modification process compared to conventional biosensors. The deformation of the PDMS film is amplified and conversion of surface stress signal to electrical signal is enhanced by exposing the biosensor to a uniform magnetic field. The experimental results proved that the flexible ME biosensor has wide linear range of 10 ng/mL - 100 μg/mL, and the detection limit is as low as 0.7933 ng/mL. Moreover, the flexible ME biosensor have also shown to good stability, sensitivity and specificity. © 2024, The Authors. All rights reserved.

关键词： Biosensors

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Self-supervised Multi-frame Monocular Depth Estimation with Pseudo-LiDAR Pose Enhancement

Self-supervised Multi-frame Monocular Depth Estimation with ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wenhua Wu Guangming Wang Jiquan Zhong Hesheng Wang Zhe Liu MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Insititute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Depth estimation is one of the most important tasks in scene understanding. In the existing joint self-supervised learning approaches of depth-pose estimation, depth estimation and pose estimation networks are independent of each other. They only use the adjacent image frames for pose estimation and lack the use of the estimated geometric information. To enhance the depth-pose association, we propose a monocular multi-frame unsupervised depth estimation framework, named PLPE-Depth. There are a depth estimation network and two pose estimation networks with image input and pseudo-LiDAR input. The main idea of our approach is to use the pseudo-LiDAR reconstructed from the depth map to estimate the pose of adjacent frames. We propose depth re-estimation with a better pose between the image pose and the pseudo-LiDAR pose to improve the accuracy of estimation. Besides, we improve the reconstruction loss and design a pseudo-LiDAR pose enhancement loss to facilitate the joint learning. Our approach enhances the use of the estimated depth information and strengthens the coupling between depth estimation and pose estimation. Experiments on the KITTI dataset show that our depth estimation achieves state-of-the-art performance at low resolution. Our source codes will be released on https://***/IRMVlabIPLPE-Depth.

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