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This study analyzes the basic principles and structural models of multi-sensor data fusion, and emphasizes the importance of effective fusion algorithms. The proposed improved weighted information fusion algorithm combines the Jackknife method and the adaptive weighting method, while the improved Kalman filter fusion algorithm combines the weighting factor, the state transfer matrix, the measurement transfer matrix and the process noise distribution matrix. The effectiveness of the proposed algorithm is verified through extensive simulations and practical applications, and the estimation accuracy is improved by 15% and the error rate is reduced by 20% compared with the traditional methods. The improved algorithm shows stronger robustness in dealing with the uncertainty and inconsistency of sensor data, and the adaptive weighting mechanism effectively solves the problem of sensor reliability variation. The computational complexity and scalability of the proposed algorithms are analyzed, and it is shown that they can be implemented in large-scale sensor networks in real time. This study also discusses the challenges and opportunities of multi-sensor data fusion, including the integration of deep learning techniques, the extension to heterogeneous sensor scenarios, and the need for broader experimental validation in real-world precision measurement applications. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Kalman filters

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Roadside sensor Systems for vulnerable Road User Protection: A Review of Methods and applications

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IEEE ACCESS 2025年 13卷 62717-62738页

作者： Zhang, Tianya Cheng, Lei Bang, Tam Guo, Lihao Hajij, Mustafa Cao, Siyang Harris, Austin Sartipi, Mina Univ Tennessee Chattanooga Ctr Urban Informat & Progress CUIP UTC Res Inst Chattanooga TN 37405 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA Univ San Francisco Elect Engn Dept San Francisco CA 94117 USA

This review surveyed infrastructure-based sensor technologies for protecting vulnerable road users (vRUs) in traffic safety systems, focusing on four key components of the roadside system: calibration methods, sensor fusion approaches, trajectory prediction, and risk analysis frameworks. The paper analyzes different sensor types, discussing their characteristics, advantages, and limitations. Key challenges in sensor calibration for roadside deployment are addressed, alongside various fusion strategies at data, feature, and decision levels. The review covers trajectory prediction methods, from classical approaches to deep learning architectures, examining their applications in vRU behavior analysis. A comprehensive evaluation of safety assessment methodologies using surrogate safety measures is provided, considering both single and multi-sensor implementations. Technical challenges including scalability, real-time processing, and sensor synchronization are discussed, while identifying opportunities in emerging technologies such as advanced AI and vehicle-to-everything (v2X) integration. The paper concludes by addressing future research directions and ethical implications for improving urban vRU safety.

关键词： Safety Roads Surveys vehicle-to-everything Calibration Protection Pedestrians Real-time systems Accidents Trajectory vulnerable road users adaptive calibration sensor fusion prediction and safety analysis

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AiCareGaitRehabilitation: Multi-modalities sensor data fusion for AI-IoT enabled realtime electrical stimulation device for pre-Fog and post-Fog to person with Parkinson's Disease

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INFORMATION fusion 2025年 122卷

作者： Ghayvat, Hemant Awais, Muhammad Geddam, Rebakah Quasim, Mohammad Tabrez Khowaja, Sunder Ali Dev, Kapal Linnaeus Univ Fac Technol Comp Sci & Media Technol Dept PG Vejdes Vag 351 95 S-35234 Vaxjo Sweden Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10065 USA Karnavati Univ Unitedworld Inst Technol Gandhinagar 382422 Gujarat India Univ Bisha Coll Comp & Informat Technol Dept Comp Sci & Artificial Intelligence Bisha 551 Saudi Arabia Dublin City Univ ADAPT Ctr Dublin D09 V209 Ireland Dublin City Univ Sch Comp Dublin D09 V209 Ireland Munster Technol Univ ADAPT Ctr Bishopstown T12 P928 Cork Ireland Munster Technol Univ Dept Comp Sci Bishopstown T12 P928 Cork Ireland Univ Johannesburg Dept Inst intelligent Syst T12 P928 Auckland Pk Johannesburg South Africa Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

The neuromuscular dysfunction known as Freezing of Gait (FoG), which is more prevalent in individuals suffering from Parkinson's Disease (PD), significantly reduces the quality of life and increases their risk of falling. Wearable FoG sensing technologies provide timely biofeedback cues to assist people regain control over their gait. However, the devices being bulky, intrusive, and annoyance of current FoG detection algorithms limit their usability in real-world applications. This study proposes a more efficient approach by integrating the FoG detection fusion algorithm into a Functional Electrical Stimulation (FES) module. The design leverages features with low computational requirements and specialized hardware to minimize the use of physical space and memory. The Convolutional Neural Networks (CNN) approach with SvM output was deployed to classify FoG and non-FoG periods in real-time. Additionally, the study uses CNN algorithms in fusion with data from a triaxial accelerometer, strain sensors, and piezoelectric plantar sensors to test shank-worn FoG detection devices. The study demonstrates that electrical stimulation-based cueing strategies significantly improve gait control and mitigate FoG episodes in people with Parkinson's disease. The AiCareGaitRehabilitation system employs a multi-modal sensor fusion strategy to improve the efficacy of the FES device. Data from various sensors-such as strain sensor, 18 plantar sensors, and four quadriceps sensors-the system provides a holistic view of both pre-freezing of Gait (pre-FOG) and post-freezing of Gait (post-FOG) scenarios. This research aims to improve mobility, reduce fall risks, and eventually improve the quality of life for individuals with Parkinson's disease.

关键词： sensor data fusion Parkinson's disease Freezing of gait Gait analysis Electrical stimulation Deep learning Transformer model

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Simulating effects of sensor-to-segment alignment errors on IMU-based estimates of lower limb joint angles during running

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SPORTS ENGINEERING 2025年第1期28卷 1-11页

作者： Potter, Michael v. Francis Marion Univ Dept Phys & Engn Florence SC 29506 USA

Wearable inertial measurement units offer opportunities to monitor and study running kinematics in relatively unconstrained environments. However, there remain many challenges for accurately estimating joint angles from inertial measurement unit sensor data. One important challenge involves determining the sensor-to-segment alignment parameters which specify the relative positions and orientations between the sensor and anatomical coordinate frames. Errors in these parameters can lead to errors in joint angle estimates, so it is important for practitioners, researchers, and algorithm developers to understand the required accuracy of sensor-to-segment alignment parameters for different applications. In this study, 480,000 simulations were used to investigate the effects of varying levels of simultaneous sensor-to-segment alignment errors on the accuracy of joint angle estimates from an inertial measurement unit-based method for running. The results demonstrate that accurate lower limb joint angle estimates are obtainable with this method when sensor-to-segment alignment errors are low, but these estimates rapidly degrade as errors in the relative orientations between frames grow. The results give guidance on how accurate sensor-to-segment alignment parameters must be for different applications. The methods used in this paper may also provide a valuable framework for assessing the impact of simultaneous sensor-to-segment alignment errors for other inertial measurement unit based algorithms and activities.

关键词： Wearables Kinematics sensor fusion Inertial motion capture

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A novel road attribute detection system for autonomous vehicles using sensor fusion

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International Journal of Information Technology (Singapore) 2025年第1期17卷 161-168页

作者： Thomas, Anoop Antony, Jobin K. Isaac, Ashish v. Aromal, M.S. verghese, Sam Department of Electronics and Communication Engineering Rajagiri School of Engineering and Technology Kochi India Affiliated to APJ Abdul Kalam Technological University Thiruvananthapuram India

The development of Society of Automotive Engineers (SAE) Level 5 Autonomous vehicles (Avs), which are capable of navigating a variety of roads and weather situations on their own, is examined in this study. Through the application of sophisticated computational algorithms, such as Improved You Only Look Once (YOLO) v5, Single Shot multibox Detector (SSD), Mask- Region based Convolutional Neural Networks (RCNN), and Nanodet, which are based on Convolutional Neural Networks (CNN), the research aims to enhance perception, prediction, and decision-making for secure and efficient autonomous navigation. The focus is on detecting road attributes like humps and potholes. Comparative analysis is carried out on both standard and custom datasets, leading to the selection of algorithms for real-time implementation. The proposed system employs a high-resolution camera mounted on a vehicle, connected to Graphics Processing Unit (GPU) accelerated embedded board, and implemented on the Robot Operating System (ROS) based software platform. Data collected on a specific route serves as valuable input for autonomous navigation. Additionally, the paper delves into the fusion of camera and Light Detection and Ranging (LiDAR) sensor data, introducing novel software architecture to seamlessly integrate road attribute detection into existing Av navigation pipelines on the ROS platform. © Bharati vidyapeeth's Institute of Computer applications and Management 2024.

关键词： Camera COCO dataset LiDAR Mask-RCNN Nanodet ROS SAE SSD YOLO

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A Comprehensive Survey on Traffic Light Detection sensors for vehicle Safety and Automatic Emergency Braking Systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Gowri, B. Shyamala Priya, G. vishnu Anjana, S. Arjun, v. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with Advanced Emergency Braking Systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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Reconstruction of the lost Saraswati river course and its associated palaeochannels using multi-resolution SAR and MSS images in the northwestern desertic plains of Rajasthan, India

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REMOTE SENSING applications-SOCIETY AND ENvIRONMENT 2025年 38卷

作者： Biswas, Raja Rathore, v. S. Krishna, A. P. Das, Anup Kumar Bhattacharya, Avik Porwal, Alok Birla Inst Technol Dept Remote Sensing Ranchi India Space Applicat Ctr Dept Space Ahmadabad India Indian Inst Technol Ctr Studies Resources Engn Mumbai India

The lost Saraswati River offers a significant challenge for geomorphologists and archaeologists. It is believed that the river used to flow through the present-day Thar Desert during the vedic age. It desiccated between similar to 7000 and similar to 1200 BC due to tectonic and climatic changes, leaving behind palaeochannels and playas in northwestern Rajasthan. This study aims to delineate the ancient Saraswati River and its associated palaeochannels using multi-sensor satellite data, including SAR (Sentinel-1A, ALOS PALSAR), multispectral (Sentinel-2A), and DEM. Multiple fusion algorithms (IHS, GS, PCA, Wavelet, and Ehlers) were used to fuse SAR and optical data, enhancing the visibility of the river course and palaeochannels. various image indices assessing surface moisture and vegetation patterns further helped in palaeochannel detection. Among the fused images, the IHS, GS, and PCA techniques, combining Sentinel-2 and ALOS PALSAR data, were found to be the most effective in highlighting palaeochannels. Further, image indices such as NDvI, NDWI and NDMI led to confirm palaeochannels and the old river course by showing linearly oriented vegetation and soil moisture. The study successfully traced two major palaeo-courses of the Saraswati River, originating from the Ghaggar River near Anupgarh and flowing through Beriyawali, Bahla, Tanot, and Jaisalmer before emptying into the Great Rann of Kutch. Additionally, three major palaeo-drainage systems of the Saraswati River could be delineated. Moreover, the association of the Harappan archaeological sites distribution along with the delineated Saraswati River course and its paleochannels, evidence from the historical maps, and bore-well drilling data (groundwater levels and lithologs) also confirm the results of this study.

关键词： Saraswati Palaeochannels Image-fusion SAR Remote Sensing

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Advances in multi-sensor fusion for body sensor networks: algorithms, architectures, and applications

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INFORMATION fusion 2019年 45卷 150-152页

作者： Fortino, Giancarlo Gravina, Raffaele Ghasemzadeh, Hassan Liu, Peter X. Poon, Carmen C. Y. Wang, Zhelong Univ Calabria Commenda Di Rende Italy Washington State Univ Pullman WA 99164 USA Carleton Univ Ottawa ON Canada Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Dalian Univ Technol Dalian Peoples R China

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