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Contactless Triboelectric Sensing for Real-Time 3D Motion Recognition in Human-Computer Interaction

advanced ELECTRONIC MATERIALS 2025年第0期

作者： xu, Qinghao Gong, Junhao Chen, Jiayi Zhang, Yimeng Zhao, Hongfa Yin, Jiaqi Zhao, Runze Lyu, Chuqiao Ding, Wenbo Wu, Changsheng Tsinghua Univ Inst Data & Informat Shenzhen Int Grad Sch Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen 518055 Peoples R China Harbin Inst Technol Sch Future Technol Harbin 150001 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Natl Univ Singapore Dept Mat Sci & Engn Singapore 117575 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore Natl Univ Singapore Inst Hlth Innovat & Technol Singapore 117599 Singapore Natl Univ Singapore Inst Hlth 1 Singapore 117456 Singapore

In the rapidly advancing fields of artificial intelligence and the Internet of Things, there is a growing need for human-computer interaction (HCI) solutions that are not only intuitive but also efficient and easy to use. Triboelectric nanogenerators present a promising approach to developing wireless human-machine interfaces, offering advantages such as simple operating principles and flexible, adaptable designs. This study introduces an HCI system that leverages a contactless triboelectric detector (CTD) to classify complex motion patterns in 3D space. The CTD system consists of a contactless sensing panel, a silicone rubber finger sleeve, a signal acquisition circuit, and a mobile terminal, which together enable the seamless acquisition and classification of weak wireless signals. One of the key benefits of the system is its lack of active energy consumption, making it highly energy-efficient. Additionally, its simple structure and ease of deployment make it an attractive option for various applications. Experimental results illustrate that the proposed system has significant potential for interactive perception in industrial environments. With a motion recognition accuracy of 99.33%-including for intricate motions such as spiral curves-this system demonstrates its potential as a next-generation solution for wireless HCI systems.

关键词： triboelectric sensor motion recognition contactless sensing

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User capacity analysis of high-orbit multi-beam satellite system 4

User capacity analysis of high-orbit multi-beam satellite sy...

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4th International conference on advanced Manufacturing Technology and Electronic Information, AMTEI 2024

作者： Wang, xin Yao, Yanjun Cheng, xinying Thirty-eighth Research Institute of China Electronics Technology Group Corporation Hefei230088 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

High-orbit satellites, because of their wide-area coverage, are often used in data relay back applications of various types of remote sensing, surveying and mapping satellites in low and medium orbits. At present, the point-to-point method of beam alignment is commonly used to realize the space-based data relay back, which usually needs to adopt the reservation method for service planning, with low timeliness and system capacity. The introduction of multi-access technology allows high orbit satellites to form a wide-area, static, full-coverage multi-beam on a global scale, realizing full-time visible and on-demand access to users within the irradiation range, which greatly improves the timely response capability of the system. Based on the high-orbit multi-beam satellite system, this paper simulates and analyzes the user capacity of the system under the full consideration of the impact of conflict, multiple-access interference and spatial coverage overlap, so as to provide a reference basis for further exerting the effectiveness of the use of the high-orbit satellite system. © 2025 SPIE.

关键词： Satellite relay systems

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Convolutional Neural Network-Enabled Optical Fiber SPR sensors for RI Prediction

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IEEE sensorS JOURNAL 2025年第4期25卷 6371-6379页

作者： Liao, xiao-xiao Yang, Hong Wu, Qiang Liu, Juan Hu, Yingying Zhang, Yue Liu, Wei-Qing Fu, Yue Pike, Andrew R. Liu, Bin Nanchang Hangkong Univ Key Lab Optoelect Informat Percept & Instrumentat Nanchang 330063 Peoples R China Newcastle Univ Sch Nat & Environm Sci Dept Chem Newcastle Upon Tyne NE1 7RU England

The advancement of artificial intelligence technology has led to the widespread adoption of deep learning techniques within spectral analysis over recent years. In this study, we introduce an advanced demodulation approach utilizing a 1-D convolutional neural network (1D-CNN) for feature extraction and the analysis of spectral signals from surface plasmon resonance (SPR) fiber refractive index (RI) sensors featuring a multimode-no-core-multimode (MNM) structure while simultaneously forecasting changes in RI due to environmental factors. Through segmentation-based predictive training on spectral signals, our approach achieves an average prediction accuracy exceeding 98%, even at low resolutions. Experimental findings demonstrate superior demodulation performance using our intelligent demodulation technique based on 1D-CNN compared to conventional methods. Furthermore, our method is adaptable across diverse and intricate structures enabling observation of parameter correlations spanning their entire range, thereby enhancing measurement capabilities within SPR sensing systems with significant potential applications.

关键词： Optical fiber sensors sensors Surface plasmon resonance Optical fibers Optical fiber networks Feature extraction Convolution Convolutional neural networks Neural networks sensor phenomena and characterization 1-D convolutional neural network (1D-CNN) Multimode-core-less multimode structure refractive index (RI) sensing surface plasmonic resonance

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applications of Simulation Modeling and Computer Visualizations for Studying Structured Crystals for Implementation in Technical Devices 20th

Applications of Simulation Modeling and Computer Visualizati...

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20th EAI International conference on Computer Science and Education in Computer Science, CSECS 2024

作者： Ivanova, Yoana New Bulgarian University 21 Montevideo Street Sofia1618 Bulgaria

ISBN: (纸本)9783031843112

This paper aims to present an experimental study of structured crystals, which has been realized by the integrated use of software products for assessment and analysis of crystal lattices, as well as the building of their three-dimensional color profiles based on the author’s microscopic images with a view to subsequently importing them into agent-based simulation environment models for visualization of crystal growth. The generated results can contribute to extending previous author’s research, united in a methodology for the optimization of structured crystals for telecommunication and scientific-technological applications, in which the object of the microscopic and simulation studies are the quartz crystals and the possibilities of their implementation in technical devices such as quartz filters and resonators. In the present research the focus is placed on simulation methods for the study of ruby crystals after their preliminary optimization. This approach is justified by their possible applications in laser systems, temperature sensor components of modern 3D printers, and medical devices, which aim to increase the efficiency of industrial processes. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Ruby

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A 77GHz Hybrid TDMA-MIMO Phased-Array Radar with 186m Detection Range and 3cm Range Resolution 72

A 77GHz Hybrid TDMA-MIMO Phased-Array Radar with 186m Detect...

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72nd IEEE International Solid-State Circuits conference, ISSCC 2025

作者： Zhang, Zitona Lu, Yuri xu, Wentao Chenge Hu, Bo Cui. Zhang, Zhiluo Sun, Sheng Ren, Zihao Zhang, Cong Wang, Ziyao Chen, Guangsheng Shi, Chunqi Huang, Leilei xu, Long Zhang, Runxi East China Normal University Shanghai China

ISBN: (纸本)9798331541019

Millimeter-wave radar has been a crucial component in advanced driver assistance systems (ADAS), overcoming the limitations of cameras and LiDARs under adverse weather conditions [1], [2]. As ADAS applications advance to higher levels of autonomous driving, there are more stringent requirements for radar sensors in terms of maximum detection range, range resolution and angular resolution. Long-range radar (LRR) needs a longer detection range within a narrow beam, while medium-range radar (MRR) and short-range radar (SRR) require a broader field of view (FoV) and more precise range and angle resolution capabilities. Current MIMO and RF phased-array architectures shown in Fig. 10.1.1 (top) have difficulty achieving optimal performance over the above three modes, simultaneously. For instance, [3] achieves a long-range detection of 250m, while its 15cm range-resolution limits SRR applications. [4] adopts a MIMO architecture, achieving a range resolution of 2.1 cm and a maximum detection range of 38.5m, which is only suitable for SRR applications. This paper presents a fully integrated 77GHz 8Tx/2x4Rx hybrid TDMA-MIMO phased-array FMCW radar, which supports all three detection modes of LRR/MRR/SRR using different chirp profiles. © 2025 IEEE.

关键词： advanced driver assistance systems

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A Machine Learning (ML)-Inspired Method for Intrusion Detection in IoT Devices Networks 5th

A Machine Learning (ML)-Inspired Method for Intrusion Detect...

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5th International conference on Computing Science, Communication and Security

作者： Venkatesh, Veeramuthu Raj, Pethuru Nedium, Roshitha Edara, Jahnavi Reddy, Kalluru Amarnath Anushiadevi, R. SASTRA Deemed Univ Sch Comp Thanjavur 613401 Tamilnadu India Reliance Jio Cloud Serv JCS Bangalore 560025 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

The Internet of Things (IoT) and Machine Learning (ML) represent dynamic research fields with significant growth. IoT applications have gained popularity among technology researchers and developers, and the increasing deployment of IoT devices in critical infrastructures improves efficiency and reliability and raises concerns about cyber-attacks. Enterprises have effectively implemented IoT services, enabling automated production through remote and intelligent control. However, this adoption has concurrently introduced novel security vulnerabilities. Addressing the security and privacy challenges in IoT, especially considering energy limitations and scalability issues, remains a critical focus in computer security. This paper aims to prevent multiple attacks targeting sensor nodes' data manipulation. It encompasses a range of threats, such as sensing layer attacks, malfunctions, tamper attacks, false data injection, base node and clone attacks. The proposed approach involves a threat model and a pairing algorithm that utilizes machine learning to associate each sensor node with its corresponding node. To accomplish the goal, the performance of the proposed solution is compared with various machine learning models, including Decision Tree, Linear Regression, k-nearest neighbors (KNN), Random Forest, and AdaBoost. The evaluation utilized two openly accessible real-world datasets, with metrics such as accuracy in attack detection, training time, and testing time being considered. By incorporating machine learning algorithms to improve attack detection in IoT environments, the proposed approach represents a significant advancement in enhancing security and privacy. The results highlight the importance of adopting advanced techniques to safeguard IoT systems from potential threats. The proposed method achieved an impressive 96.75% accuracy rate in detecting attacks, surpassing existing solutions with nearly twice the speed in training and testing times. As the IoT landsc

关键词： sensor nodes Security attacks Intrusion Detection System Critical infrastructure Machine learning Internet of Things (IoT)

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Construction of drive mode control function model for electric vehicle controller 4

Construction of drive mode control function model for electr...

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4th International conference on advanced Manufacturing Technology and Electronic Information, AMTEI 2024

作者： Liu, Chunhui Wei, Daili Shandong Huayu University of Technology Shandong Dezhou China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

With the rapid development of electric vehicle technology, the vehicle controller, as the core of the vehicle system, plays a crucial role in achieving efficient, safe, and comfortable driving experience through its drive mode control function. Firstly, the requirements for driving mode control functions were clarified, including multiple modes such as normal driving, braking energy recovery, and fault protection. The driving modes of pure electric vehicles can be divided into neutral mode, driving mode, parking mode, limp driving mode, and anti-skid mode. Among them, the driving mode is subdivided into forward driving mode and reverse driving mode. Each driving mode is divided into Drike mode, Break mode, and energy recovery mode. In the sensor and signal acquisition stage, key data such as accelerator pedal position, brake pedal force, vehicle speed, motor speed, battery status, etc. are obtained in real time through sensors arranged in various key parts of the vehicle, providing a basis for the formulation of subsequent control strategies. In the control strategy design section, based on knowledge from multiple fields such as vehicle dynamics, battery management, and motor control, reasonable control algorithms have been developed to ensure that the vehicle can maintain optimal driving performance under different operating conditions. © 2025 SPIE.

关键词： Battery management systems

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2021 IEEE 23rd International conference on High Performance Computing and Communications, 7th International conference on Data Science and systems, 19th International conference on Smart City and 7th International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

2021 IEEE 23rd International Conference on High Performance ...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

ISBN: (纸本)9781665494571

The proceedings contain 343 papers. The topics discussed include: three-level compact caching for search engines based on solid state drives;a fine-grained volley gesture recognition method with direction independence;universal adversarial attack against 3d object tracking;efficient hardware redo logging for secure persistent memory;a cost-efficient metadata scheme for high-performance deduplication systems;high speed true random number generator controlled by logistic map;characterization and implication of edge WebAssembly runtimes;frend for edge servers: reduce server number! keeping service quality!;on-the-fly servers placement for online multiplayer games in the fog;advanced architecture design of high-radix router based on chiplet integration and IP reusability;embrace the conflicts: exploring the integration of single port memory in systolic array-based accelerators;visual sensitivity aware rate adaptation for video streaming via deep reinforcement learning;semi-supervised federated learning with non-IID data: algorithm and system design;on-demand intelligent routing algorithms for the deterministic networks;and distributed service placement in ultra-dense edge computing: a game-theoretical approach.

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Tactile Image Processing and Shape Detection Method of Array Tactile sensor for Surgical Robot Based on Deep Learning 17th

Tactile Image Processing and Shape Detection Method of Array...

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17th International conference on Intelligent Robotics and applications, ICIRA 2024

作者： Niu, xinyao Ju, Feng Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819607761

The rapid evolution of modern medical technology has propelled surgical robots to the forefront of surgical innovation. These robots offer unprecedented accuracy and safety in procedures, empowering surgeons with advanced technical capabilities to tackle complex medical challenges. In tumor treatment, surgical robots excel in precise tumor tissue resection, minimizing damage to healthy tissue and reducing surgical risks. However, accurately identifying tumor boundaries and depth remains challenging due to limitations of traditional palpation methods. This study aims to develop a deep learning model for real-time tumor boundary detection within surgical robot systems. By processing array tactile sensor image data, this model will enable precise boundary and depth detection, enhancing surgical robot recognition and operational capabilities. This advancement promises improved surgical success rates, patient survival rates, and reduced risks of complications, ultimately enhancing patient treatment outcomes and quality of life. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Intelligent IoT-Driven advanced Predictive Maintenance System for Industrial applications

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SN Computer Science 2025年第2期6卷 1-24页

作者： Dhinakaran, D. Edwin Raja, S. Velselvi, R. Purushotham, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of CSE School of Computing Mohan Babu University Tirupati India

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industrial systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM approaches, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-Driven advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (xAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" Dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM approaches—Traditional PdM, Decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior performance of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decreases the Mean Absolute Error (MAE) by 36.7% relative to DSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust performance in real-world industrial scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies. © The Author(s), under exclusive licence to S

关键词： Explainable AI IoT Machine learning Predictive maintenance Supervised and reinforcement learning

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