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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications VII"

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GIIDS: Generalized Intelligent Intrusion Detection System for Heterogeneous UAVs in UAM 27

GIIDS: Generalized Intelligent Intrusion Detection System fo...

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27th International conference on advanced Communications Technology, ICACT 2025

作者： Kabir, Fahmina Mowla, Nishat I. Doh, Inshil Division of Artificial Intelligence and Software Ewha Womans University Seoul Korea Republic of Department of Industrial Systems RISE Research Institutes of Sweden Sundsvall Sweden Department of Cyber Security Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9791188428137

Unmanned Aerial Vehicles (UAVs) are increasingly integral in various sectors, simultaneously encountering rising security threats as UAV and Urban Air Mobility (UAM) networks continue to expand. This paper addresses the challenge of securing UAM networks while also emphasizing generalizability of the security solution to protect heterogeneous UAVs against threats that compromise their stability, reliability and can cause catastrophic failures such as a crash landing. The deployment of traditional machine learning (ML) based intrusion detection systems (IDSs) is often hampered in real-world applications due to a lack of generalizability of the security solution. As a result, the system fails to provide adequate security across the varying models and platforms of UAVs, each with its unique statistical properties and data distributions. To address these challenges, we focus on employing a comprehensive set of UAV sensor parameters, tailored feature engineering and selection to develop multi-stage cross-validated ensemble learning systems to facilitate generalized detection of attack and non-attack cases. For additional analysis, we cross-validate the models using two different cross-validation techniques. The proposed stacking ensemble systems provide the overall best performance, with AUC within the range of 92% to 100% across different crossvalidations. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Lora-Enabled System for Smart Farming Management and Oversight, Utilizing Wireless sensor Networks 14

Lora-Enabled System for Smart Farming Management and Oversig...

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14th IEEE International conference on Communication systems and Network Technologies, CSNT 2025

作者： Elumalai, J. Kumuthapriya, K. Hema, R. Sundaram, P. Sakthi Shunmuga Department of Electronics and Communication Engineering S.K.R Engineering College Ponamallee Chennai600123 India Department of Electronics and Communication Engineering Tagore Engineering College Rathinamangalam Kelambakkam - Vandalur Rd Tamil Nadu Chennai600127 India

ISBN: (纸本)9798331531935

The purpose of this study is to create a LoRa-based smart farming management and surveillance system. Utilizing Wireless sensor Networks (WSNs) in rural settings, with the aim of replacing the existing technology in agricultural monitoring systems. A dedicated private network server is established and connected to a gateway, which gathers information or signals from the nodes at the end and sends this data to the cloud without the need for routers. This data can then be utilized for various applications by the end users. The system overcomes issues related to communication failures and energy-efficient data transmission. This advanced farming platform enhances the effectiveness of farming methods. The proposed methodology is the application of resources including as water, fertilizers, and pesticides should be optimized in order to achieve maximum efficiency while simultaneously minimizing waste. Farming productivity and sustainability are both significantly improved because of its contribution, notably in terms of the efficient management of resources. And intelligent cities used to improve agriculture-comparing the planned weather station and the numerous developed patents. The technique here is like a cutting-edge, reasonably priced choice for local weather monitoring. © 2025 IEEE.

关键词： Agricultural robots

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Planar Rogowski Coil-Based Switch Current Measurement for a 1.2 kV SiC MOSFET Embedded Die PCB 14

Planar Rogowski Coil-Based Switch Current Measurement for a ...

引用

14th Annual IEEE Applied Power Electronics conference and Exposition, APEC 2025

作者： Spieler, Matthias Chang, Che-Wei El-Refaie, Ayman M. Dong, Dong Burgos, Rolando Virginia Tech Center for Power Electronics Systems BlacksburgVA United States Marquette University Opus College of Engineering MilwaukeeWI United States

ISBN: (纸本)9798331516116

This paper introduces the integration of a small footprint planar Rogowski coil into an 1.2 kV embedded die PCB for the switch transient current measurement thereof. Due to the high-density integration of embedded die PCBs, measuring switch transient currents has been challenging. The planar Rogowski coil enables the current measurement with a small impact on the commutation loop design and parasitic stray inductance. The geometry of the 106 mm2 small coil increases the stray inductance in the commutation loop by 298 pH. The 187 MHz bandwidth current sensor allows measuring the fast switch transient currents of the 1.2 kV SiC MOSFETs. The planar Rogowski coil is first compared to alternative high-bandwidth current measurement technologies. Double pulse tests are conducted at a dc-bus voltage of 600 V and a drain current of 150 A. The overshoot voltage during the turn-off switch transient for embedded die PCBs with and without the planar Rogowski coil at a current slope of -48.2 A/ns are 646.1 V and 657.6 V, respectively. The small overshoot voltage highlights the minimal added insertion stray inductance of the current sensor into the commutation loop. © 2025 IEEE.

关键词： Electric current measurement

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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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Quality and Trust Indicators of Digital Road Infrastructure Data Are Essential to Improve Its Usability: An Intelligent Speed Assist (ISA) Study 11

Quality and Trust Indicators of Digital Road Infrastructure ...

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11th International conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2025

作者： van de Sluis, Jacco Altgassen, Daniel Schackmann, Peter-Paul Networks Department TNO - ICT The Hague Netherlands Integrated Vehicle Safety Department TNO - Mobility & Build Environment Helmond Netherlands

ISBN: (纸本)9789897587450

The promise of a Digital Road Infrastructure (DRI) is to improve both road and vehicle safety. ADAS/ADS with DRI support, can help vehicles in overcoming certain sensor limitations, handle more complex operational situations and offer additional situational awareness. An effective DRI must be trusted and must offer the required data quality, both are currently lacking. Intelligent Speed Assist (ISA) is an interesting show case for the added value of DRI. In our approach camera-based traffic sign detections and map-based speed limit information, both occasionally wrong, are augmented with actual speed limit and road layout information coming from DRI. A Misbehaviour Detection and Reporting (MBD&R) concept tailored to the ISA sources is deployed in the vehicle to detect and report ISA related misbehaviour. Trust and quality indicators are calculated for data coming from camera, map and DRI, which are used to verify and compare theses sources and make improved ISA speed limit decisions. The vehicle implementation is tested under real-life traffic conditions. Our work is a first step in realizing a trusted DRI. The long-term goal is collaboration among all stakeholders to implement mechanisms that improve trust and the quality of shared data sources for use in traffic safety applications. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

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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sensor-Agnostic Graph-Aware Kalman Filter for Multi-Modal Multi-Object Tracking 27th

Sensor-Agnostic Graph-Aware Kalman Filter for Multi-Modal Mu...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Sani, Depanshu Iyer, Anirudh Rai, Prakhar Anand, Saket Srivastava, Anuj Kalyanaraman, Kaushik IIIT-Delhi New Delhi India Florida State University Tallahassee United States

ISBN: (纸本)9783031784439

Recent progress in open-source object detection techniques has significantly advanced Multi-Object Tracking (MOT) methodologies, primarily under the tracking-by-detection paradigm. To enhance the robustness and reliability of MOT systems, recent research has proposed integrating information gathered from diverse sensors. However, many Kalman filter-based MOT approaches assume the independence of object trajectories, overlooking potential inter-object relationships. While some efforts have been made to incorporate these relationships, they often concentrate on learning feature representations to facilitate better association. Moreover, the existing filter-based method for estimating graphs from noisy data is unsuitable for online MOT applications. To alleviate these problems, we introduce a sensor Agnostic Graph-Aware (SAGA) Kalman filter, which is the first online state estimation technique designed to fuse multi-modal graphs derived from noisy multi-sensor data. We validate the effectiveness of our proposed framework through extensive experiments conducted on both synthetic and real-world driving dataset (nuScenes). Our results showcase an improvement in MOTA and a reduction in estimated position errors (MOTP) and identity switches (IDS) for tracked objects using the SAGA-KF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Kalman filters

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SwarmCatcher: An AI-Powered Anti-Drone Surveillance System for Public Safety Over 5G and Beyond Ecosystems 28

SwarmCatcher: An AI-Powered Anti-Drone Surveillance System f...

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28th conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Kalafatidis, Sarantis Tsiktsiris, Dimitrios Trakos, Dimitrios Lalas, Antonios Votis, Konstantinos Tzovaras, Dimitrios Centre for Research and Technology Hellas Information Technologies Institute Greece

ISBN: (纸本)9798331542399

The proliferation of unmanned aerial vehicles (UAVs) presents significant challenges to public safety and critical infrastructure protection. This paper introduces SwarmCatcher, an innovative AI-powered anti-drone and surveillance system designed to detect and classify UAV threats in real-time. SwarmCatcher integrates multi-sensor technologies, including optical, thermal, RF, and acoustic systems, with advanced AI algorithms to provide thorough situational awareness. Utilizing the low-latency, high-throughput capabilities of cloud-native architecture and 5G networks, the system supports scalable deployment across diverse environments, offering rapid threat mitigation. Real trials were conducted, utilizing 5G infrastructures in the FIDAL and CERTH testbeds, evaluating and demonstrating SwarmCatcher’s potential to address the requirements of public safety agencies and pave the way for integration with 6G networks. The paper investigates SwarmCatcher’s architecture, key performance metrics, and its role in advancing next-generation vertical-specific applications for public safety. © 2025 IEEE.

关键词： 5G mobile communication systems

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A Smart Robotic Device for Automated Environmental Monitoring: An Integrated Approach for Real-Time Data Collection and Analysis

A Smart Robotic Device for Automated Environmental Monitorin...

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Computational Intelligence Approaches and applications (ICCIAA), International conference on

作者： Amjad Aldweesh College of Computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Smart robotic devices have emerged as a promising solution for automated environmental monitoring that addresses the limitations of traditional monitoring methods. We present a novel smart robotic device that integrates advanced mobility, energy management, data processing, and sensor modularity. The device features a robust chassis structure that supports a terrain-adaptive mobility assembly, a 360-degree rotational modular sensor arm, an onboard data processing unit, and a power management system with a rechargeable battery and a solar panel assembly. This integrated approach enables continuous, real-time data collection and analysis in diverse and challenging environments. The modular design of the device facilitates rapid customization and upgrades to meet specific monitoring needs, ensuring versatility and extended service life. We discuss the key components of the device, their interactions, and the advantages of this integrated approach for environmental monitoring applications.

关键词： Smart cities Power system management Data collection Robot sensing systems Smart systems Real-time systems Environmental monitoring Solar panels Resource management Assembly

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Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection and Early Warning Control System 3

Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection ...

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3rd International conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Li, Wenshan Hunan Institute of Traffic Engineering Hunan Hengyang China

ISBN: (纸本)9798331508456

Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveraging millimeter-wave radar technology to monitor vital signs in real time within the vehicle, providing timely alerts when necessary. This approach not only enhances the accuracy of readings but also ensures a swift response that is crucial during vehicle operation. By tapping into the unique abilities of millimeter-wave radar to detect subtle human signs, we propose a novel method that processes radar signals to monitor vital signs like heart rate and respiratory rate. This technique is highly effective in meeting the stringent demands of real-time accuracy and speed, especially while driving. Our system includes three main components: radar sensors, a signal processing unit, and an early-warning control module, working together seamlessly to ensure optimal monitoring. The radar sensor continuously transmits and receives millimeter-wave signals, allowing it to track vital signs without interruption. A specially designed feature extraction algorithm filters out noise and extracts critical information, including heart and respiratory rates. Then, advanced algorithms analyze this data in real time, spotting any abnormalities and triggering alerts through the control module. What sets this system apart is its rigorous testing under various vehicle speeds and environmental conditions, confirming its stability, reliability, and fast response times. In real-world applications, the system responds to a high heart rate in 3.7 to 4.0 seconds and to a rapid breathing rate in 4.1 to 4.3 seconds. This millimeter-wave radar-based system offers a fresh approach to vehicle safety, combining practicality with promising future potential. © 2025 IEEE.

关键词： Electronic guidance systems

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