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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications"

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Research Progress in Multi-sensor Data fusion algorithms 24

Research Progress in Multi-Sensor Data Fusion Algorithms

引用

2024 7th International conference on Computer Information Science and Artificial Intelligence, CISAI 2024

作者： Jiang, Huhai Ke, Huimeng Southwest Institute of Technical Physics Sichuan Chengdu China

ISBN: (纸本)9798400707254

With the rapid advancement of modern science and technology, the importance of multi-sensor data fusion technology in various applications has become increasingly prominent. However, traditional non-intelligent fusion methods reveal many shortcomings when dealing with complex and dynamic environments. These methods rely on preset models and algorithms, showing poor adaptability to system nonlinearities, time-varying characteristics, and uncertainties, making it challenging to achieve real-time, efficient, and accurate information fusion. Moreover, traditional methods often struggle with processing multi-source heterogeneous data, being easily affected by noise and interference, thus reducing fusion accuracy and system robustness. Intelligent multi-sensor data fusion algorithms, by incorporating advanced technologies such as machine learning, deep learning, neural networks, and reinforcement learning, can dynamically learn and adapt to environmental changes, automatically adjusting fusion strategies and overcoming the limitations of traditional methods. This paper presents a multi-sensor data fusion algorithm based on an improved BP neural network, enhanced by particle swarm optimization (PSO) to improve network performance. This method prevents premature convergence of the network, speeds up convergence, and thus enhances data fusion accuracy. The study of intelligent multi-sensor information fusion algorithms holds significant practical significance and broad application prospects. By systematically summarizing and analyzing recent research progress in intelligent fusion algorithms, this paper reveals the unique advantages of intelligent fusion technologies in improving fusion accuracy, enhancing system robustness, and adaptability. It also explores their potential applications in various fields and identifies future development directions and research priorities. © 2024 Copyright held by the owner/author(s).

关键词： Information fusion

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AFTer-SAM: Adapting SAM with Axial fusion Transformer for Medical Imaging Segmentation

AFTer-SAM: Adapting SAM with Axial Fusion Transformer for Me...

引用

IEEE/CVF Winter conference on applications of Computer Vision (WACV)

作者： Yan, Xiangyi Sun, Shanlin Han, Kun Le, Thanh-Tung Ma, Haoyu You, Chenyu Xie, Xiaohui Univ Calif Irvine Irvine CA 92717 USA Yale Univ New Haven CT USA

ISBN: (纸本)9798350318920;9798350318937

The Segmentation Anything Model (SAM) has demonstrated effectiveness in various segmentation tasks. However, its application to 3D medical data has posed challenges due to its inherent design for both 2D and natural images. While there have been attempts to apply SAM to medical images on a slice-by-slice basis, the outcomes have been less than optimal. In this study, we introduce AFTer-SAM, an adaptation of SAM designed for volumetric medical image segmentation. By incorporating an Axial fusion Transformer, AFTer-SAM is capable of capturing both intra-slice details and inter-slice contextual information, essential for accurate medical image segmentation. Given the potential computational challenges of training this enhanced model, we utilize Low-Rank Adaptation (LoRA) to efficiently finetune the weights of the Axial fusion Transformer. This ensures a streamlined training process without compromising on performance. Our results indicate that AFTer-SAM offers significant improvements in volumetric medical image segmentation, suggesting a promising direction for the application of large pre-trained models in medical imaging.

关键词： algorithms and algorithms applications Biomedical / healthcare / medicine formulations Machine learning architectures

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Research Progress and Application of Multi-sensor Data fusion Technology in AGVs 5

Research Progress and Application of Multi-Sensor Data Fusio...

引用

5th International conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Huang, Chuan Ma, Zhiyong Huzhou Univ Sch Engn Dept Elect Engn Huzhou Zhejiang Peoples R China

ISBN: (纸本)9798350363272;9798350363265

Multi-sensor data fusion technology, which utilizes information from different sensors, combines algorithms and models to improve system performance and accuracy, has been widely applied in various fields. However, Automated Guided Vehicles (AGVs) often rely on multiple sensors to achieve autonomous navigation, positioning, and obstacle avoidance functions. Multi-sensor data fusion technology can assist AGVs in perceiving the surrounding environment more accurately, achieving precise navigation, and avoiding obstacles. Such compound applications can enhance the autonomy and reliability of AGVs, making them more suitable for industrial automation and logistics. In this regard, by combining the performance of single-sensor technology, AGV technology, and multi-sensor data fusion technology in AGVs, this paper comprehensively summarizes the relevant research and application achievements of scholars in the field of AGV multi-sensor data fusion technology in recent years. It also briefly expounds on the future development and prospects of this technology, providing an important theoretical basis for the application research of multi-sensor data fusion technology in AGVs.

关键词： Multi-sensor Data fusion AGV Environment sensing Obstacle avoidance

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Enhanced Detection of Brain Tumors using fusion-Based Feature Optimization and Stacking Ensemble Algorithm

Enhanced Detection of Brain Tumors using Fusion-Based Featur...

引用

2024 International conference on Artificial Intelligence and Quantum Computation-Based sensor applications, ICAIQSA 2024

作者： Raut, Sweta Thakur, Uma Shelke, Nilesh Borkar, Pradnya Motghare, Manish Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India Pune India G.H.Raisoni College of Engineering and Management Department of Artificial Intelligence Nagpur India

ISBN: (纸本)9798331517953

The variation of the appearance of brain tumors is a challenging detection task-the interpretation of cells due to abnormal growth in dimension decreases the accuracy of the detection algorithm. Recently, several algorithms have been applied based on transform-based and machine learning to detect brain tumors. The mapping of retrieved brain tumor features in the classification algorithms is the main drawback of these techniques. This paper deployed a feature of a fusion-based approach for detecting brain tumours-the feature of a fusion approach using transform-based function and a heuristic-based algorithm. The fused feature passes through the proposed machine learning algorithm for detection-the proposed detection using a multi-kernel. © 2024 IEEE.

关键词： Heuristic algorithms

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Hyperspectral VNIR-SWIR Image fusion on Cultural Heritage and Remote Sensing Datasets using Image Sharpening Techniques 30

Hyperspectral VNIR-SWIR Image Fusion on Cultural Heritage an...

引用

conference on algorithms, Technologies, and applications for Multispectral and Hyperspectral Imaging XXX

作者： Macalintal, Jose N. G. M. Hassanzadeh, Amir Messinger, David Rochester Inst Technol One Lomb Mem Rochester NY 14623 USA

ISBN: (纸本)9781510673816;9781510673809

Hyperspectral imaging is one of the prevailing tools for the analysis of material composition and feature identification in images. The spectral features contained within image pixels are studied to better understand a pixel's material composition. However, a major limitation of using a single hyperspectral image sensor is that not all distinguishing spectral features of an image are captured within the conventional operating wavelength bands of a single hyperspectral sensor. While a straightforward solution of using a single hyperspectral sensor with larger wavelength ranges can address such an issue, this is often met with external limiting factors such as sensor availability or costs of operations. Therefore, in order to overcome the limitations of a single-sensor approach, there is motivation to integrate information from multiple sensors instead to create a more useful and accurate representation of a real-world scene. A particular challenge is that different sensors typically produce images of the same scene but at different spatial and spectral resolutions. In this study, we attempt to fuse VNIR and SWIR hyperspectral images and evaluate the fused images' performance as applied to Cultural Heritage. Specifically, image fusion is performed on a medieval manuscript referred to as the Italian Leaf provided by the University of Durham.

关键词： hyperspectral image fusion cultural heritage

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Efficient Centralised and Decentralised Gaussian Process Approaches for Online Tracking within Stone Soup 27

Efficient Centralised and Decentralised Gaussian Process App...

引用

27th International conference on Information fusion (fusion)

作者： Lyu, Chenyi Liu, Xingchi Wright, James Barra, Jordi Hunter, Alasdair Mihaylova, Lyudmila Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Rutherford Appleton Lab Sci Comp Dept Sci & Technol Facil Council Didcot Oxon England Def Sci & Technol Lab Salisbury Wilts England

ISBN: (纸本)9798350371420;9781737749769

This paper explores the application of centralised and distributed Gaussian process algorithms to real-time target tracking and compares their performance. By embedding the algorithms into the Stone Soup, the focus is on the innovative implementation of Gaussian process methods with learning hyperparameters and implementation with a factorised variance of the Gaussian kernel. The performance of the methods with different kernels was evaluated, not only with the Gaussian kernel. Extensive experiments with various kernel configurations demonstrate their importance in enhancing prediction accuracy and efficiency, especially in real-time tracking. The case studies with manoeuvring targets show significant advancements in tracking capabilities, particularly in wireless sensor networks, using optimised Gaussian process methods. This work advances Stone Soup's capabilities and lays the groundwork for future investigations into adaptive Gaussian Process applications in tracking and sensor data analysis.

关键词： Learning Gaussian process methods Distributed Gaussian process sensor networks covariance matrix tracking Stone Soup online tracking

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Learned sensor fusion For Robust Human Activity Recognition in Challenging Environments

Learned Sensor Fusion For Robust Human Activity Recognition ...

引用

2024 International conference on Intelligent Robots and Systems

作者： Conway, Max Reily, Brian Reardon, Christopher Univ Denver Dept Comp Sci Denver CO 80210 USA DEVCOM US Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350377712;9798350377705

Human activity recognition is a vital area of robotics with significant real-world applications, from enhancing security and surveillance to improving healthcare and human-robot interaction. A critical challenge lies in bridging the gap between research models, which often assume ideal conditions, and the complexities of real-world environments. In practice, conditions can be far from perfect, including scenarios with poor lighting, adverse weather, or blurred views. In this paper, we present an innovative approach for robust activity recognition through learned sensor fusion, in which our recognition framework identifies a latent weighted combination of input modalities, enabling classifiers to capitalize on advantages provided by various sensors. In support of our work, we have released a dataset of human activities across multiple modalities with environmental degradation factors such as darkness, fog, and thermal blur. Our proposed approach identifies a weighted combination of modality representations derived from existing architectures. We show that our approach is able to achieve 24% higher classification performance than existing single-modality approaches. Our approach also attains comparable performance to modality fusion approaches in significantly reduced classification time. In real-world robotics applications, particularly those occurring in dangerous, degraded environments, this speed is critical.

关键词： Human robot interaction

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A Modular sensor fusion and Quality-Driven sensor Selection Algorithm for UAV Navigation 20th

A Modular Sensor Fusion and Quality-Driven Sensor Selection ...

引用

20th International conference on Intelligent Computing (ICIC)

作者： Chen, Hainuo Ni, Xuebin Gao, Ming Ma, Hui Zhang, Chen Yunsheng Intelligent Technol Co Ltd Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol Tianjin 300457 Peoples R China

ISBN: (纸本)9789819755936;9789819755943

Unmanned Aerial Vehicles (UAVs) are increasingly integrated in various applications. The urgent need for accurate navigation algorithms is emphasized in various applications. In this paper, we propose an integrated framework focusing on robust attitude and position/velocity estimation for UAV navigation using Kalman filtering employing Kalman filtering (KF) technique and chi-square test based sensor fusion quality assessment. Our approach integrates sensor data through a filter-based approach, and Kalman filter handles attitude estimation and position/velocity estimation. The modal architecture enhances robustness and facilitates seamless integration of multiple sensors while prioritizing reduced attitude estimation and position/velocity estimation. Accurate attitude estimation is also prioritized to reduce the risk in case of deviation. Accurate attitude estimation is also prioritized to reduce the risk of deviation in case of higher order state estimators. Experimental validation on the "SHENG PROUAV" model proves the effectiveness of our framework and demonstrates accurate attitude estimation results.

关键词： Integrated Navigation Systems Modular sensor fusion Kalman Filters Fault Detection

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Evaluation of Occupancy Detection with Distributed Environmental sensors for IoT applications 20

Evaluation of Occupancy Detection with Distributed Environme...

引用

20th Annual International conference on Distributed Computing in Smart sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Bovo, Matteo Agyeman, Rockson Arif, Muhammad Rinner, Bernhard Univ Udine Dipartimento Matemat Informat & Fis Udine Italy Univ Klagenfurt Inst Networked & Embedded Syst Klagenfurt Austria AAU SAL USE Lab Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350369458;9798350369441

Occupancy refers to the presence of people in rooms and buildings. It is an essential input for IoT applications, including controlling lighting, heating, access, and monitoring space limitation policies. Occupancy information can also be used to improve users' comfort and to reduce energy waste in buildings. This paper evaluates the performance and resource consumption of recent machine learning techniques for occupancy detection and measurement by exploiting data from distributed environmental sensors. This evaluation is founded on a dataset captured by our dedicated sensor network for indoor monitoring, comprising temperature, humidity, and carbon dioxide (CO2) sensors. Using different sensor modalities and spatio-temporal data selections, we compare eight classification algorithms based on the accuracy achieved and the required runtimes. Binary classification for occupancy detection (OD) achieves accuracies over 90% for individual modalities and close to 100% for modality combinations. Multi-class classification for occupancy measurements (OM) shows as clear ranking of the sensor modalities, and gradient boosting algorithms are superior when combining sensor modalities and fusing data from multiple sensors.

关键词： sensor network indoor environments multiclass classification sensor fusion smart rooms

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Innovation-trigger-based sequential fusion Kalman filter for CPSs with multiplicative noises against deception attacks 3

Innovation-trigger-based sequential fusion Kalman filter for...

引用

3rd conference on Fully Actuated System Theory and applications (FASTA)

作者： Zheng, Baifu Zhang, Xuenan Gao, Yuan Liu, Jinfang Heilongjiang Univ Elect Engn Coll Harbin 150086 Peoples R China Key Lab Informat Fus Estimat & Detect Harbin 150086 Peoples R China Lingnan Normal Univ Sch Comp Sci & Intelligence Educ Zhanjiang 524000 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

For the CPSs with multiplicative noises against deception attacks, the sequential fusion filters under the innovation-triggered mechanism are presented. The random deception attacks occur in the transmitted measurements, the innovation-triggered mechanism is introduced to ease off the network transmission pressure, the fusion center makes two-sensor fusion estimation under three different fusion algorithms according to the arriving order of the local estimators. The results indicate that three sequential fusion algorithms can effectively ease off random attack effection and computational pressure, and the accuracy of sequential state fusion filter is higher than those of SCI fusion filter and SICI fusion filter. A simulation example verifies the effectiveness of above sequential fusion algorithms.

关键词： Sequential fusion CPSs Innovation-triggered mechanism Multiplicative noises Deception attacks

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