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IEEE Workshop on applications of Computer Vision (WACV)

作者： Zi-Xiang Xia Sudeep Fadadu Yi Shi Louis Foucard Aurora Innovation Inc.

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Advances in machine learning algorithms for sensor fusion have significantly improved the detection and prediction of other road users, thereby enhancing safety. However, even a small angular displacement in the sensor's placement can cause significant degradation in output, especially at long range. In this paper, we demonstrate a simple yet generic and efficient multi-task learning approach that not only detects misalignment between different sensor modalities but is also robust against them for long-range perception. Along with the amount of misalignment, our method also predicts calibrated uncertainty, which can be useful for filtering and fusing predicted misalignment values over time. In addition, we show that the predicted misalignment parameters can be used for self-correcting input sensor data, further improving the perception performance under sensor misalignment.

关键词： sensor placement Uncertainty Machine learning algorithms Roads sensor fusion Multitasking Safety Proposals Autonomous vehicles Synthetic data

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Proceedings of SPIE: sensor fusion: architectures, algorithms, and applications II

Proceedings of SPIE: Sensor Fusion: Architectures, Algorithm...

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sensor fusion: architectures, algorithms and applications II

This Volume 3376 of the conference proceedings contains 22 papers. Topics discussed include sensor fusion, sensor fusion architectures, feature and decision level fusion, data and image level fusion and sensor fusion ... 详细信息

关键词： sensor data fusion

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Path Optimization Method Under UAV Charging Scheduling Network 24th

Path Optimization Method Under UAV Charging Scheduling Netwo...

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24th International conference on algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Yang, Tingting Wang, Yiqian Zhu, Jie Chang, Shuyu Huang, Haiping Nanjing University of Posts and Telecommunications Nanjing210003 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9789819615278

Unmanned Aerial Vehicles (UAVs) have emerged as integral components in logistics systems, where their potential for efficient delivery services is being explored. However, the limited battery capacity of UAVs poses a significant challenge for long-distance delivery applications. In this paper, we investigate a hybrid problem involving UAV trajectory planning and charging scheduling for long-distance logistics systems with a charging network. In the problem under study, multiple UAVs are assigned long-distance delivery tasks. A UAV can stop at any charging station in the charging network to charge if it is in a low-energy state. An Ant Colony Optimization-based UAV Trajectory Planning and Charging Scheduling (ACO-TPCS) algorithm is proposed to minimize task completion time by optimizing UAV trajectory and charging plans. The main framework of the ACO-TPCS algorithm consists of several key components, including reachable graph construction, candidate flight path generation, pheromone matrix construction, ant generation, feasible solution generation, and pheromone update method. All these components are delicately designed. Through extensive experimentation and comparison with baseline algorithms, we demonstrate the effectiveness of the ACO-TPCS algorithm in addressing the problem under study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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sensor fusion: architectures, algorithms, and applications VI

Sensor fusion: Architectures, algorithms, and applications V...

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sensor fusion: architectures, algorithms, and applications VI

The proceedings contains 31 papers from the conference on sensor fusion: architectures, algorithms, and applications VI. Topics discussed include: adaptive sequential Bayesian classification using Page's test;ontology-based multiagent approach to data fusion;unified approach to the fusion of imperfect data;fusion approach to stereo vision uncertainity;robust fusion with reliabilities weights;distributed air-to-ground targeting;and fuser design for thick film pH sensor electrodes using emperical data.

关键词： sensor data fusion

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sensor fusion Techniques for Accurate Indoor Tracking in IoT-Based Smart Environments

Sensor Fusion Techniques for Accurate Indoor Tracking in IoT...

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Automation and Computation (AUTOCOM), International conference on

作者： S. Krishnakumar Kalyan Acharjya Shivam Khurana Rakesh Ranjan Swain Sourav Rampal Manjunath C Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Centre for Cyber Security Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

sensor fusion integrates data from multiple sensors to provide a more accurate, reliable, and contextual sense of the surrounding environment. So, it became a key method for correct monitoring in battery-dependent Internet of Things clever environments, especially Indoor tracking. There is only one dedicated sensor. To benefit the most from it and surround these weaknesses in complex indoor environments with wrongness. The sensor fusion methods for accurate indoor tracking in IoT-based Smart Environments are proposed to solve the problems above by integrating data from multiple sources, including Wi-Fi APs and BLE beacons, RFID readers, and IMUs. This results in a more complete and detailed view of the environment crucial for location-based applications such as asset tracking, localization, or navigation within smart buildings. There are many algorithms to fuse sensor data. The algorithms process the sensor data and give a fused result that outperforms all of these individual components. sensor fusion in IoT-based smart environments improves indoor tracking accuracy, robustness, and scalability. It also permits real-time tracking localization of objects and people; it is the key to optimally managing smart environments. The sensor fusion method generally improves indoor tracking properties in the rapidly growing IoT-based smart environments.

关键词： Location awareness Accuracy Smart buildings Tracking Fuses sensor fusion Indoor environment Internet of Things Wireless fidelity Smart phones

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UAV Target Tracking with Bandit-Based Data fusion 17th

UAV Target Tracking with Bandit-Based Data Fusion

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17th International conference on Combinatorial Optimization and applications, COCOA 2024

作者： Lv, Yang Fan, Guochao Li, Mengzhen Liu, Xiongjun Liu, Pengqing Zhang, Yapu Beijing Jinghang Computation and Communication Research Institute Beijing 100074 China Institute of Operations Research and Information Engineering Beijing University of Technology Beijing 100124 China

ISBN: (纸本)9789819644445

Unmanned Aerial Vehicles have emerged as the optimal solution for target tracking due to their low cost and high maneuverability. We study the problem of target tracking in partially observable adversarial environments. Building on previous research, we develop a data fusion algorithm to optimize target tracking. To enable a single UAV to track a hard-to-observe target with limited sensor capabilities, we construct a target environment tracking model along with its corresponding reward function. In the algorithm design, we incorporate the Exp4-IX algorithm from the framework of an adversarial multi-armed bandit with advice, and we prove that the regret bound of this algorithm exhibits sub-linear growth. In numerical experiments, the Exp4-IX algorithm integrates the Previous Position algorithm, Particle Filtering algorithm, and Trajectory Fitting algorithm, and is benchmarked against the Average fusion algorithm. The results demonstrate its effectiveness in online fusion for smooth trajectory scenarios. This integration allows the UAV to predict the target’s position with greater accuracy compared to other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data fusion Exp4-IX algorithm Multi-armed bandit Target tracking Unmanned Aerial Vehicles

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Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach

Uncertainty-Aware Online Extrinsic Calibration: A Conformal ...

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IEEE Workshop on applications of Computer Vision (WACV)

作者： Mathieu Cocheteux Julien Moreau Franck Davoine Universite de technologie de Compiegne CNRS Heudiasyc France CNRS INSA Lyon UCBL LIRIS UMR5205 France

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Accurate sensor calibration is crucial for autonomous systems, yet its uncertainty quantification remains under-explored. We present the first approach to integrate un-certainty awareness into online extrinsic calibration, com-bining Monte Carlo Dropout with Conformal Prediction to generate prediction intervals with a guaranteed level of coverage. Our method proposes a framework to en-hance existing calibration models with uncertainty quan-tification, compatible with various network architectures. Validated on KITTI (RGB Camera-LiDAR) and DSEC (Event Camera-LiDAR) datasets, we demonstrate effective-ness across different visual sensor types, measuring performance with adapted metrics to evaluate the efficiency and reliability of the intervals. By providing calibration pa-rameters with quantifiable confidence measures, we offer insights into the reliability of calibration estimates, which can greatly improve the robustness of sensor fusion in dy-namic environments and usefully serve the Computer Vision community.

关键词： Computer vision Visualization Uncertainty Monte Carlo methods Autonomous systems Computer network reliability sensor fusion Probabilistic logic Robustness Calibration

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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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Cluster Head Selection Algorithm using Machine Learning

Cluster Head Selection Algorithm using Machine Learning

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： M.S. Nidhya E. Gurumoorthi M. Babu A.C. Charumathi Mohd Naved Balajee Maram School of Computer Applications Dayananda Sagar University Bangalore School of Engineering Malla Reddy University Dept of AI & DS Chennai Institute of Technology college Chennai Dept of Information Technology Sona College of technology Dept of Business Analytics Jaipuria Institute of Management Noida School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Warangal

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Wireless sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge with the limited lifetime of sensor node batteries. The conventional heuristic-based cluster head (CH) selection techniques tend not to adapt dynamically to network changes, resulting in inefficient energy utilization and decreased network lifetime. This research discusses Machine Learning (ML)-oriented CH selection approaches to improve energy efficiency, network lifetime, and data fusion. Using supervised and unsupervised learning methods, ML algorithms are able to learn optimal CHs dynamically based on residual energy, network structure, and data traffic. The comparative analysis show that ML-based CH selection enhances network stability by 95% and lowers energy consumption by 50% compared to traditional methods. The research points to the promise of ML in WSN performance optimization and opens the doors to intelligent, adaptive clustering technologies.

关键词： Wireless sensor networks Machine learning algorithms Smart cities Heuristic algorithms Supervised learning Clustering algorithms Magnetic heads Energy efficiency Stability analysis Unsupervised learning

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Research on the fusion of Autonomous Navigation algorithms and Environmental Perception in Intelligent Ship Technology

Research on the Fusion of Autonomous Navigation Algorithms a...

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Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Zhijie Zhang Yonggang Zong Ocean Department Binzhou Polytechnic Binzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The research focus of this paper is on the organic combination of autonomous navigation algorithm and environmental perception technology, with the aim of enhancing the autonomous navigation capability of intelligent ships in complex marine environments. Autonomous navigation relies on the close coordination of multiple perception systems and algorithms, among which the simultaneous localization and mapping (SLAM) algorithm is particularly critical. The SLAM algorithm generates high-precision environmental maps in real time and provides accurate ship positioning by fusing multi-sensor information. This paper proposes and designs an autonomous navigation system that integrates environmental perception and SLAM technology. Its system architecture includes sensor data processing, path planning and control decision modules. The effectiveness of the proposed algorithm is verified through a series of model simulations, and experimental evaluation is carried out in multiple test scenarios. The simulation results show that compared with traditional navigation methods, the algorithm significantly improves the navigation accuracy and safety of ships in complex marine environments, and the positioning error rate is reduced by more than 15%. The research results of this paper provide a solid theoretical basis and technical support for the future research and development of autonomous navigation systems for intelligent ships.

关键词： Simultaneous localization and mapping Accuracy Navigation Heuristic algorithms Simulation Systems architecture Real-time systems Safety Marine vehicles Autonomous robots

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