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A resonant high-pressure microsensor based on a composite pressure-sensitive mechanism of diaphragm bending and volume compression

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Microsystems & Nanoengineering 2024年第2期10卷 57-66页

作者： Pan Qian Zongze Yu Jie Yu Yulan Lu Bo Xie Jian Chen Deyong Chen Junbo Wang The State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing100190China The School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing100049China

In this paper,a composite pressure-sensitive mechanism combining diaphragm bending and volume compression was developed for resonant pressure microsensors to achieve high-pressure measurements with excellent *** composite mechanism was explained,and the sensor structure was designed based on theoretical analysis and finite element *** all-silicon resonant high-pressure microsensor with multiple miniaturized cavities and dual resonators was developed,where dual resonators positioned in two resonant cavities with suitably different widths are used to perform opposite characteristics in pressure and the same characteristics at different temperatures,which can improve pressure sensitivities and realize temperature self-compensation by differential frequency *** microsensor was fabricated by microfabrication,and the experimental results showed that the sensor had an accuracy of±0.015%full scale(FS)in a pressure range of 0.1~100 MPa and a temperature range of−10~50℃.The pressure sensitivity of the differential frequency was 261.10 Hz/MPa(~2523 ppm/MPa)at a temperature of 20℃,and the temperature sensitivities of the dual resonators were−1.54 Hz/℃(~−14.5 ppm/℃)and−1.57 Hz/℃(~−15.6 ppm/℃)at a pressure of 2 *** differential output had an outstanding stability within±0.02 Hz under constant temperature and ***,this research provides a convenient solution for high-pressure measurements because of its advantages,namely,large range,excellent accuracy and stability.

关键词： stability resonant bending

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Passive IoT Localization technology Based on SD-PDOA in NLOS and Multi-Path Environments

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Computers, Materials & Continua 2024年第7期80卷 913-930页

作者： Junyang Liu Yuan Li Yulu Zhang Shuai Ma Gui Li Yi He Haiwen Yi Yue Liu Xiaotao Xu Xu Zhang Jinyao He Guangjun Wen Jian Li School of Information and Communication Engineering/Yibin Institute University of Electronic Science and Technology of ChinaChengdu611731China Department of IoT Technology Research China Mobile Research InstituteBeijing100053China The 10th Research Institute of China Electronics Technology Group Corporation Chengdu611731China College of Information and Communication National University of Defense TechnologyWuhan430019China

Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID *** efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 *** findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 *** complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 *** compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath *** research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.

关键词： Indoor localization multipath and NLoS environments combined SD-PDOA and RSSI technology

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An Efficient Cluster-Based Asynchronous Activity Detection Algorithm for Cell-Free Massive MIMO Systems 16

An Efficient Cluster-Based Asynchronous Activity Detection A...

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16th International Conference on Wireless communications and Signal Processing, WCSP 2024

作者： You, Chenhao Li, Ziheng Wang, Tong Gao, Lin Jiang, Yufei School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China

ISBN: (纸本)9798350390643

In this paper, we propose an efficient cluster-based activity detection algorithm for asynchronous grant-free random access (GFRA) cell-free massive multiple-input multiple-output (CF-mMIMO) systems. To address the uncertain delay challenge in the asynchronous transmission scenario, we aim to enhance the accuracy of active user devices detection and meanwhile reducing the computational complexity of the detection process by utilizing an efficient cluster-based block coordinate descent (BCD) activity detection algorithm. This challenge is heightened in the context of ultra-reliable low-latency communications (URLLC), where precise and timely detection of active users is crucial. To enhance the efficiency of the detection process, we investigate an active set strategy to reduce the number of potential active user devices requiring identification. The accuracy of active user device detection for asynchronous GFRA in CF-mMIMO systems is significantly enhanced by the implementation of the proposed cluster-based BCD algorithm with active set. Simulation results demonstrate that the proposed algorithm achieves superior detection performance with lower computational complexity compared to the traditional algorithms. © 2024 IEEE.

关键词： Clustering algorithms

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Deep brain implantable microelectrode arrays for detection and functional localization of the subthalamic nucleus in rats with Parkinson’s disease

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Bio-Design and Manufacturing 2024年第4期7卷 439-452页

作者： Luyi Jing Zhaojie Xu Penghui Fan Botao Lu Fan Mo Ruilin Hu Wei Xu Jin Shan Qianli Jia Yuxin Zhu Yiming Duan Mixia Wang Yirong Wu Xinxia Cai State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

The subthalamic nucleus(STN)is considered the best target for deep brain stimulation treatments of Parkinson’s disease(PD).It is difficult to localize the STN due to its small size and deep *** microelectrode arrays(MEAs)can rapidly and precisely locate the STN,which is important for precise *** this paper,16-channel MEAs modified with multiwalled carbon nanotube/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(MWCNT/PEDOT:PSS)nanocomposites were designed and fabricated,and the accurate and rapid identification of the STN in PD rats was performed using detection sites distributed at different brain *** results showed that nuclei in 6-hydroxydopamine hydrobromide(6-OHDA)-lesioned brains discharged more intensely than those in unlesioned *** addition,the MEA simultaneously acquired neural signals from both the STN and the upper or lower boundary nuclei of the ***,higher values of spike firing rate,spike amplitude,local field potential(LFP)power,and beta oscillations were detected in the STN of the 6-OHDA-lesioned brain,and may therefore be biomarkers of STN *** with the STNs of unlesioned brains,the power spectral density of spikes and LFPs synchronously decreased in the delta band and increased in the beta band of 6-OHDA-lesioned *** may be a cause of sleep and motor disorders associated with ***,this work describes a new cellular-level localization and detection method and provides a tool for future studies of deep brain nuclei.

关键词： Functional localization Implantable microelectrode arrays Parkinson’s disease Subthalamic nucleus

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HKB-YOLO: transmission line fire detection method based on hierarchical feature fusion

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International Journal of Security and Networks 2024年第4期19卷 188-198页

作者： Ying, Zhilei Meng, Yanan Chen, Ruoxi Lou, Jianlou School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin 132022 China Department of Automation Jilin Institute of Chemical Technology Jilin 132022 China School of Computer Engineering Northeast Electric Power University Jilin 132012 China

Timely transmission line fire inspections are vital for power system safety. Although deep learning models are widely used for flame detection, struggle with small target recognition due to background interference and are vulnerable to input data perturbations, posing security risks. This study introduces a hierarchical feature fusion method based on the you only look once Version 8 (YOLOV8) framework. It employs high-performance GPU network version 2 (HGNetV2) to enhance small-target feature extraction while reducing computational complexity. A spatial pyramid pooling-fast module with large separable kernel attention is designed to highlight key features and suppress background noise. The proposed bidirectional slim neck structure (BiSlimneck) reduces feature loss. Adversarial training enhances the model’s robustness. Experimental results show a 1.4% accuracy improvement, with a reduction in parameters and complexity by 18.3% and 21.6%, respectively. After adversarial training, the accuracy of the network in the face of attacks increased by 10% compared to the pre-training. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Premixed flames

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Research on Multi-Source Combined Supply System 6

Research on Multi-Source Combined Supply System

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6th IEEE International Conference on Civil Aviation Safety and information technology, ICCASIT 2024

作者： Huang, Yanan Zheng, Haojie Wang, Yulin Liaoning University of Engineering and Technology School of Electrical and Control Engineering Huludao China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350389418

In order to reduce the cost of the power generation system, reduce the rate of system wind and light abandonment, and build a cleaner, safer and more efficient power system, this paper establishes a PV-wind-battery optimal scheduling model with the objective of minimizing the system operating cost, and the system power balance, and the battery SOC range as constraints. The multi-source joint power supply system containing wind power, photovoltaic, energy storage system, and main grid is analyzed in the arithmetic example, and the daily power supply system cost is at least 0.173 million yuan when there is energy storage system participation, and the daily power supply cost is at least 0. 1 7 9 million yuan when there is no energy storage system participation, and the penalty cost of both of them for wind and light abandonment is 0.098 million yuan and 0.115 million yuan. It is proved that the system containing energy storage can effectively reduce the system power supply cost and reduce the rate of wind and light abandonment. © 2024 IEEE.

关键词： multivariate combined power supply system Photovoltaic-wind-battery optimal scheduling model wind and light discard rate

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New Delay Doppler communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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Exploring Invariance Matters for Domain Generalization

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IEEE Transactions on Image Processing 2025年 34卷 3336-3351页

作者： Wang, Shanshan He, Houmeng Yang, Xun Liu, Zhipu Zhong, Yuanhong Zhang, Xingyi Wang, Meng Anhui University National Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology Institutes of Physical Science and Information Technology Hefei230601 China University of Science and Technology of China School of Information Science and Technology Department of Electronic Engineering and Information Science Hefei230026 China Chongqing University of Technology School of Computer Science and Engineering Chongqing400054 China Chongqing University School of Microelectronics and Communication Engineering Chongqing400030 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Domain generalization (DG) aims to solve the problem of significant performance degradation when target domain data collected from the Out-Of-Distribution (O.O.D). Previous efforts try to exploit invariant features in the source domain through CNN networks. However, inspired by causal mechanisms, we find that the complex spurious-invariant information is still hidden in this view invariant features, and the impact of domain and class discrepancies on extracting invariance has not been effectively mitigated. To alleviate these issues, we propose a self-weighted multi-view mining invariance domain generalization framework (SMIDG). On the one hand, to make up for the insufficiency of traditional single-view convolutional feature extraction networks, we propose to mine features from another frequency view and use the self-adaptive adversarial masks to eliminate some spurious correlations, ensuring causal invariance in the coarse-grained generalization. However, due to inconsistencies in discriminative information between interdomain and intra-domain samples, as well as inter-class and intra-class samples, the coarse-grained elimination of spurious associations does not fully resolve this issue. On the other hand, we also consider the fine-grained generalization from two aspects. Firstly, to better tackle the domain discrepancies, we propose a novel progressive contrastive learning strategy that learns the underlying specific features of samples while gradually mitigating domain discrepancies, thereby ensuring domain invariance in fine-grained generalization. Secondly, due to the issue of feature inconsistency, we adopt a self-adaptive hard sample mining method with information gain to ensure that the model pays more attention on hard disentangled samples, thus maintaining feature invariance. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art approaches. © 1992-2012 IEEE.

关键词： Contrastive learning Feature extraction Frequency-domain analysis Training Data models Data mining Correlation Data augmentation Convolutional neural networks Computer science

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Automated object recognition in high-resolution optical remote sensing imagery

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National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

关键词：

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Efficient Dual-Mode Generalized Spatial Modulation Detection with Enhanced DNN Architecture

Efficient Dual-Mode Generalized Spatial Modulation Detection...

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2024 International Conference on Electronics, information, and communication, ICEIC 2024

作者： Wang, Zihui Jiang, Xue-Qin Yu, Jinming Wen, Miaowen Li, Jun Hai, Han School of Information Science and Technology Donghua University Shanghai China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Electronics and Communication Engineering Guangzhou University Guangzhou China

ISBN: (纸本)9798350371888

Dual-mode generalized spatial modulation (DM-GSM) enhances spectral efficiency in GSM systems using two modes across transmit antennas. However, interference between antennas poses a challenge for signal detection. For this, a deep learning detector, the dual-mode deep neural network (DM-DNN), is proposed. The DM-DNN enables simultaneous detection of the antenna mode and modulation symbol through its network structure and label generation. A loss function is proposed to train the DM-DNN, approximating optimal bit error rate (BER) performance. Simulation results demonstrate that the DM-DNN achieves BER performance close to the maximum likelihood (ML) detector while significantly reducing complexity. © 2024 IEEE.

关键词： Deep neural networks

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