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A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

Computers, Materials & Continua 2024年第7期80卷 897-912页

作者： Xu Zhang Yi He Haiwen Yi Yulu Zhang Yuan Li Shuai Ma Gui Li Zhiyuan Zhao Yue Liu Junyang Liu 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

When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to *** on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is *** algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame *** grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID *** outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.

关键词： Radio frequency identification anti-collision tags grouping dynamic framed slotted Aloha

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A Double-Timescale Reinforcement Learning Based Cloud-Edge Collaborative Framework for Decomposable Intelligent Services in Industrial Internet of Things

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China communications 2024年第10期21卷 181-199页

作者： Zhang Qiuyang Wang Ying Wang Xue School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications) Beijing 100876China

With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing *** this paper,we propose an intelligent service computing *** the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing *** the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the *** the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and *** solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more *** large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning *** simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.

关键词： computing service edge intelligence industrial internet of things(IIoT) reinforcement learning(RL)

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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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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on Measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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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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Integrating Digital Twin and Augmented Reality to Enhance Daily Inspection for Wastewater Treatment Plant 16th

Integrating Digital Twin and Augmented Reality to Enhance Da...

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16th International Conference on Computer Science and its Applications, CSA 2024

作者： Kong, Guangze Sun, Haotian Sun, Huaying Liu, Chao Miao, Sheng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China School of Environment and Municipal Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9789819656929

This paper discusses in detail an intelligent inspection system of sewage treatment plant based on Augmented Reality to solve the problems of low efficiency, slow speed and waste of human resources in the existing daily inspection process of wastewater treatment plant. The intelligent inspection system designed in this paper is realized based on Augmented Reality technology, and a Digital Twin model is built to connect with the running data of the wastewater treatment plant in real time. Hololens2 headset is used to realize human-computer interaction. The intelligent inspection system is connected to multiple sensors in the wastewater treatment plant through PLC, and the real-time data in the scene is dynamically presented in the model. Through the sensor of Hololens2, the system can quickly identify the current position of the inspectors, map the model and real-time data of the corresponding scene into the reality, and mark the data with problems, so as to help the inspectors quickly judge and deal with the problems in the operation of the wastewater treatment plant. The structure and implementation of the system are introduced in detail in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inspection equipment

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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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