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MARL-DCF: A Dual-Channel fusion Routing Algorithm Based on Multi-agent Reinforcement Learning 24th

MARL-DCF: A Dual-Channel Fusion Routing Algorithm Based on ...

引用

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

作者： Li, Zhenyu Sun, Runyuan Liang, Zhifeng Liu, Bo Sun, Zhenxiang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9789819615414

Emerging applications have imposed stringent demands on Internet quality of service (QoS). To ensure that various data streams within different Internet services receive the appropriate QoS, advancements in flow classification technology, software-defined networking (SDN), and programmable network devices have enabled the network to identify user demands and precisely control fine-grained traffic routing quickly. Although significant progress has been made with these technologies, optimizing the transmission paths of business flows to meet diverse QoS requirements in real-time remains a challenge. To address this issue, this paper proposes a dual-channel fusion routing algorithm based on multi-agent reinforcement learning (MARL-DCF). Implemented within an SDN architecture, the algorithm was experimentally validated in a Mininet environment using real network topologies and traffic trajectories. The results demonstrate that MARL-DCF can optimize routing paths according to the network environment, meet various QoS demands, and exhibit strong adaptability and reliability in complex scenarios such as link failures and traffic fluctuations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Multimodal perception for obstacle detection for flying boats - Unmanned Surface Vehicle (USV)

Multimodal perception for obstacle detection for flying boat...

引用

OCEANS conference

作者： Douguet, Ronan Heller, Dominique Laurent, Johann CNRS Lab STICC UMR 6285 Lorient France

ISBN: (纸本)9798350332261

Unmanned surface vehicles (USVs) are increasingly used in bathymetric survey, maritime surveillance, and maintenance applications. However, detecting obstacles in the maritime environment poses significant challenges due to sea clutter, background variability, and other factors. Although various sensors such as radar, AIS, LiDAR, and camera have been used for obstacle detection, they have limitations in terms of range and effectiveness, especially at different USV speeds. In this paper, we present a real-time obstacle detection system for USVs in the maritime environment by explaining the used sensors, the architecture and the implemented algorithms. We focus on evaluating the performance of the YOLOV7 network, which forms the basis of our fusion algorithm. Our results demonstrate that our system can effectively detect maritime objects in real-time, providing improved safety and efficiency for USV operations. Additionally, we provide an open solution for visualizing the detected targets on a chart plotter navigation.

关键词： sensor fusion obstacle detection USV YOLOv7 ROS

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RGB-X Object Detection via Scene-Specific fusion Modules

RGB-X Object Detection via Scene-Specific Fusion Modules

引用

IEEE Workshop on applications of Computer Vision (WACV)

作者： Sri Aditya Deevi Connor Lee Lu Gan Sushruth Nagesh Gaurav Pandey Soon-Jo Chung California Institute of Technology Ford Motor Company

Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing deep sensor fusion methods usually employ convoluted architectures with intermingled multimodal features, requiring large coregistered multimodal datasets for training. In this work, we present an efficient and modular RGB-X fusion network that can leverage and fuse pre-trained single-modal models via scene-specific fusion modules, thereby enabling joint input-adaptive network architectures to be created using small, coregistered multimodal datasets. Our experiments demonstrate the superiority of our method compared to existing works on RGB-thermal and RGB-gated datasets, performing fusion using only a small amount of additional parameters. Our code is available at https://***/dsriaditya999/RGBXfusion.

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Streamlining River Flood Prevention with an Integrated AIoT Framework 20

Streamlining River Flood Prevention with an Integrated AIoT ...

引用

20th ACS/IEEE International conference on Computer Systems and applications (AICCSA)

作者： Boulouard, Zakaria Ouaissa, Mariya Ouaissa, Mariyam Krichen, Moez Almutiq, Mutiq Algarni, Mohammad Hassan II Univ LIM Casablanca Morocco Cadi Ayyad Univ Comp Syst Engn Lab Marrakech Morocco Chouaib Doukkali Univ Lab Informat Technol El Jadida Morocco Al Baha Univ Dept Informat Technol Al Baha Saudi Arabia Qassim Univ Dept Management Informat Syst & Prod Management Coll Business & EconPOB 6640 Buraydah 51452 Saudi Arabia Al Baha Univ Comp Sci Dept Al Baha 65431 Saudi Arabia

ISBN: (纸本)9798350319439

River floods stand among the natural disasters with far-reaching impacts, affecting human lives, the economy, infrastructure, agriculture, and more. Substantial investments by organizations are directed toward innovative strategies for flood prevention. The concept of Artificial Intelligence of Things (AIoT), a fusion of Artificial Intelligence and Internet of Things technologies, has showcased its prowess across various domains. In this paper, we introduce an AIoT framework where river flood sensors, located in every region, transmit their data using LoRaWAN technology to local broadcast centers in proximity. These broadcast centers subsequently forward the data via 4G/5G networks to a centralized cloud server. The server employs efficient AI algorithms to analyze the data and predict river conditions nationwide, contributing to proactive flood prevention. This approach has demonstrated effectiveness on multiple fronts. LoRaWAN-based communication between sensor nodes and broadcast centers offers reduced energy consumption and expanded coverage, while AI-driven data analysis enhances the accuracy of river flood predictions.

关键词： Predictive River Flooding sensor Devices LoRaWAN Technology AI (Artificial Intelligence) IoT (Internet of Things) AIoT (Artificial Intelligence of Things)

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Multi sensor Signal-based Multi-level Association strategy for Real-time Track Association and fusion 9

Multi Sensor Signal-based Multi-level Association strategy f...

引用

9th International conference on Big Data and Information Analytics, BigDIA 2023

作者： Guo, Jiaxin Fang, Zhi Yu, Zengwen Zhang, Hang Zheng, Yifan Zhang, Hong Beijing Institute of Computer Technology and Application Beijing China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9798350330076

The real-time track association and fusion task(the AF task) aims to integrate the tracks of maritime targets collected by sensors into a coherent track. In real-world scenarios, track data collected by a single sensor often lacks the required stability, falling short of meeting the demands of various applications. While adopting multi-sensor-based AF task methods is prevalent, existing approaches have overlooked critical aspects, including temporal consistency. This paper tackles crucial challenges in the multi-sensor AF tasks, focusing on problems such as asynchronous radar scanning and real-time matching across multiple sensors. Utilizing multi-level association and integrated learning algorithms, we enhance matching accuracy and efficiency while ensuring track continuity. Leveraging the dataset provided by the 2023 National Big Data and Computational Intelligence Challenge 1, our approach significantly outperforms traditional methods in three key aspects: the AF task of radar point trace , the AF task of ESM bearing line, and the AF task of rader and ESM. In comparison with conventional methods, three values are reduced by 1.1325, 0.0870, and 0.2637 on the difference metrics between the generated tracks and the actual tracks, respectively. These results further validate the significant improvements of our method in enhancing the effectiveness of the AF task. © 2023 IEEE.

关键词： multiple sensors Real-time track association and fusion

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Data fusion in Wireless sensor Network: An Overview

Data Fusion in Wireless Sensor Network: An Overview

引用

CHILEAN conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

作者： Daniel Fernando Quintero Bernal Héctor Kaschel John Kern Departamento de Ingeniería Eléctrica Universidad de Santiago de Chile Santiago Chile

Due to the massive increase in data sets in contemporary applications and the difficulty of their processing in various approaches, data fusion remains a predominant way to obtain outstanding results, in terms of reliability, efficiency, and precision. The following document presents an overview of data fusion in wireless sensor networks; in which, various concepts to applications are considered to generate a base document for future research. This article gives a comprehensive and current view of models or architectures, techniques, methodologies or algorithms, and a current review of applications in different areas.

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Vehicle Target Detection Based on LIDAR and Camera Information fusion 3

Vehicle Target Detection Based on LIDAR and Camera Informati...

引用

3rd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2023

作者： Geng, Linwen Ding, Hongchang Chongqing Research Institute of Changchun University of Science and Technology Chongqing400000 China

ISBN: (纸本)9781643684444

With the continuous growth of intelligent driving applications, the accuracy of vehicle target detection is of critical importance. The limitations of a single sensor cannot meet the stringent requirements for vehicle-level detection accuracy at present. Therefore, this research is committed to solving this problem, mainly through the following steps: first, a unified spatiotemporal fusion strategy is adopted, associating the detection results of multiple sensors, while deep preprocessing of point cloud data is performed to ensure high-quality data and improve processing efficiency. In addition, the YOLOv4 algorithm is optimized to improve vehicle detection accuracy in occluded environments. On this basis, this paper proposes a multi-sensor fusion strategy. By setting a lower visual confidence threshold, the omission rate of vehicle targets is reduced. During the correlation matching stage, LIDAR is used to 'review' the visual detection results to further enhance the confidence of visual detection results. The fused results are output with a high confidence threshold, significantly improving the detection accuracy of target vehicles. To verify the performance of the target detection system designed in this paper, a real-vehicle verification was conducted in an outdoor road environment. The experimental results show that the detection accuracy of vehicle targets can be effectively improved by an average of about 4.5%. © 2023 The Authors.

关键词： Optical radar

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An iToF/Triangulation Depth sensor for Mixed Reality applications 6

An iToF/Triangulation Depth Sensor for Mixed Reality Applica...

引用

6th conference on Optical architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality-AR, VR, MR

作者： Godbaz, John P. Zhu, Ling Mogallapu, Vishali Poostchi, Mahdieh Gullapalli, Jay Oh, Jerry Li Minseok Santos, Jonathan Karicherla, Annapurna Mehta, Swati Morales, Lucas Perry, Travis Rendon, Kyle Roland, Brock Sutherland, Reid Microsoft Corp 1045 Avenida St Mountain View CA 94043 USA

ISBN: (纸本)9781510686069;9781510686076

This paper presents a prototype two-in-one, dense-plus-sparse depth module designed for Mixed Reality headsets that uses a novel approach to sparse depth imaging by combining indirect Time-of-Flight (iToF) and triangulation modalities to provide improved range and accuracy. The module uses an ADI ADSD3030 VGA iToF sensor and reduces module size from 62 x 24 x 13 mm in HoloLens 2 to 33.5 x 15 x 7.2 mm. Sparse frames have temporal noise of <1% of distance at 3% reflectivity, 3 klx sunlight equivalent spectrum and can image 95% reflectivity targets out to the ambiguity distance of 21 m while consuming 14.7 mJ per-frame. New low-compute algorithms that combine iToF and triangulation are presented and are demonstrated to mitigate systematic errors such as multipath and subsurface diffusion as well as extend dynamic range under full-sunlight and high-signal conditions. sensor applications include scene understanding, object capture and hand-tracking.

关键词： Depth Mixed Reality Time of Flight Indirect Time of Flight iToF Lidar Triangulation Structured Light

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A Distributed Adaptive Algorithm for Node-Specific Signal fusion Problems in Wireless sensor Networks 31

A Distributed Adaptive Algorithm for Node-Specific Signal Fu...

引用

31st European Signal Processing conference, EUSIPCO 2023

作者： Musluoglu, Cem Ates Bertrand, Alexander STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

ISBN: (纸本)9789464593600

The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve spatial filtering and signal fusion problems in a distributed fashion over a wireless sensor network, reducing the communication and energy costs compared to a centralized approach. The DASF framework assumes that there is a common goal across the nodes, i.e., all nodes collaborate to optimize the same network-wide objective function. However, some applications require node-specific objectives, which are linked via a common latent target signal subspace. In this work, we propose the distributed adaptive node-specific signal fusion (DANSF) algorithm which builds upon the DASF framework, and extends it to allow for such node-specific spatial filtering problems. © 2023 European Signal Processing conference, EUSIPCO. All rights reserved.

关键词： Adaptive algorithms

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Research on Magnetic Field Sensing and Data fusion Technology in Power System

Research on Magnetic Field Sensing and Data Fusion Technolog...

引用

2024 International conference on Intelligent Communication, Sensing and Electromagnetics, ICSE 2024

作者： Cai, Xiong Dai, Shuzhi Yu, Fan Miao, Wulin Song, Mengtian State Grid Zhejiang Electric Power Co. Ltd. Daishan Power Supply Branch Zhejiang Zhoushan316299 China State Grid Zhejiang Electric Power Co. Ltd. Zhoushan Power Supply Branch Zhejiang Zhoushan316021 China

ISBN: (纸本)9798350354935

The research on magnetic field sensing and data fusion technology in the power system aims to enhance the monitoring and management capabilities of the power system. With the continuous growth of electricity demand, traditional monitoring methods are no longer able to meet the requirements of real-Time and accuracy. In response to this challenge, magnetic field sensing technology has received widespread attention due to its high sensitivity and non-contact measurement advantages. By combining data fusion technology, multiple sources of information can be effectively integrated to achieve comprehensive monitoring and fault prediction of power equipment status. Research has shown that optimizing sensor layout and data processing algorithms can not only improve the reliability and safety of the system, but also provide solid technical support for the intelligent development of the power system. The combination of magnetic field sensing and data fusion has opened up new directions for innovative applications in the field of power, and has important academic value and practical significance. © 2024 IEEE.

关键词： big data analysis Fiber optic sensing Low frequency electromagnetic signals

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