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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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EMG-based Wrong Posture Detection System using Raspberry Pi and Cloud-based Visualization

EMG-based Wrong Posture Detection System using Raspberry Pi ...

引用

Computing and Data Science (ICCDS), International conference on

作者： C.S. Manikandababu J.K. Ishvarya M Hiroshith JSP Arun Aakarshan Department of ECE Sri Ramakrishna Engineering College Coimbatore Tamil Nadu

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

Human-computer interaction experts investigate posture detection with cutting-edge methodologies and electromyography (EMG) signals. This study presents an innovative system that provides users with real-time feedback via cloud visualization, electromyography (EMG) sensors, and a Raspberry Pi. To obtain more comprehensive data, sensor fusion methods integrate gyroscopes and accelerometers, while state-of-the-art machine learning algorithms are employed to improve the precision of posture detection. The proposed approach provides a precise and nonintrusive substitute for incorrect posture recognition to avoid the shortcomings of current technologies. The report emphasizes sensor integration, pattern recognition, and classification algorithms as critical elements in enhancing the system's dependability. In addition to strategically placed accelerometers, the methodology incorporates a deep learning framework of LSTM. Subsequent investigations should concentrate on developing real-time processing algorithms and exploring alternative sensor modalities to obtain more comprehensive data, given the promising practical applications of posture detection systems.

关键词： Accelerometers Cloud computing Machine learning algorithms Data visualization sensor fusion Electromyography sensor systems

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Acuity: Creating Realistic Digital Twins Through Multi-resolution Pointcloud Processing and Audiovisual sensor fusion 23

Acuity: Creating Realistic Digital Twins Through Multi-resol...

引用

8th ACM/IEEE International conference on Internet of Things Design and Implementation (IoTDI)

作者： Wu, Jason Wang, Ziqi Sarker, Ankur Srivastava, Mani Univ Calif Los Angeles Elect & Comp Engn Dept Los Angeles CA 90095 USA

ISBN: (纸本)9798400700378

As augmented and virtual reality (AR/VR) technology matures, a method is desired to represent real-world persons visually and aurally in a virtual scene with high fidelity to craft an immersive and realistic user experience. Current technologies leverage camera and depth sensors to render visual representations of subjects through avatars, and microphone arrays are employed to localize and separate high-quality subject audio through beamforming. However, challenges remain in both realms. In the visual domain, avatars can only map key features (e.g., pose, expression) to a predetermined model, rendering them incapable of capturing the subjects' full details. Alternatively, high-resolution point clouds can be utilized to represent human subjects. However, such three-dimensional data is computationally expensive to process. In the realm of audio, sound source separation requires prior knowledge of the subjects' locations. However, it may take unacceptably long for sound source localization algorithms to provide this knowledge, which can still be error-prone, especially with moving objects. These challenges make it difficult for AR systems to produce real-time, high-fidelity representations of human subjects for applications such as AR/VR conferencing that mandate negligible system latency. We present Acuity, a real-time system capable of creating high-fidelity representations of human subjects in a virtual scene both visually and aurally. Acuity isolates subjects from high-resolution input point clouds. It reduces the processing overhead by performing background subtraction at a coarse resolution, then applying the detected bounding boxes to fine-grained point clouds. Meanwhile, Acuity leverages an audiovisual sensor fusion approach to expedite sound source separation. The estimated object location in the visual domain guides the acoustic pipeline to isolate the subjects' voices without running sound source localization. Our results demonstrate that Acuity can isolate

关键词： Real time systems

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Robust sensor fusion algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International conference on

作者： Sakthitharan Subramanian Gandrakoti Lokeshwari S. Jagadeesan S. Leela V. G Pratheep Murugesan Venkatasudhahar Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology School of Computing Chennai Tamil Nadu India Department of Cybersecurity Geethanjali College of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Nandha Engineering college(Autonomous) Erode Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Mechatronics Engineering Kongu Engineering College Perundurai Tamil Nadu India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques.

关键词： Deep learning Uncertainty Smart cities sensor fusion Inference algorithms Bayes methods Internet of Things Reliability Kalman filters Streams

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Model-fusion-based sustainable trust assessment method 23

Model-fusion-based sustainable trust assessment method

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data Systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Liang Fang, Yuan Sheng, Jianqiao Xu, Jingxuan Ding, Xin Center of Cloud Platform Operation and Network Security State Grid Anhui Electric Power Co. Ltd. Information and Communication Branch Hefei China

ISBN: (纸本)9781665494571

Power mobile Internet plays an important role in the development of smartcity. There is a large amount of interactive information in power mobile applications, and the trust in individuals can be judged by analyzing these behaviors and texts. Traditional trust evaluation methods cannot achieve accurate and reliable trust evaluation. This paper proposes a sustainable trust evaluation method under the zero-trust architecture. First, according to the data generated by the user's operation in the mobile application, the sentiment analysis of the user's text interaction information is carried out to establish a more reasonable trust evaluation model. Secondly, classic clustering algorithms and residual networks are used to learn the collected data. When the learning accuracy is stable, the algorithm can predict the trustworthiness of subsequent behaviors. Finally, when predicting future data, loading the previously trained model can achieve sustainability. Experimental simulation shows that the trust evaluation model and algorithm can effectively judge whether the user's operation is trustworthy within a period, and achieve a more reasonable and efficient sustainable trust evaluation. © 2021 IEEE.

关键词： Training Analytical models Sentiment analysis Clustering algorithms Prediction algorithms Feature extraction Mobile applications

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International conference on Power Engineering and Intelligent Systems, PEIS 2023

International Conference on Power Engineering and Intelligen...

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International conference on Power Engineering and Intelligent Systems, PEIS 2023

ISBN: (纸本)9789819972159

The proceedings contain 32 papers. The special focus in this conference is on Power Engineering and Intelligent Systems. The topics include: Design and Implementation of Seven-Level Reduced Switch Count Multilevel Inverter for Electric Vehicle applications;ultrasound Image Classification and Follicle Segmentation for the Diagnosis of Polycystic Ovary Syndrome;Twitter Data Analysis Using BERT and Graph-Based Convolution Neural Network;a Topology for Reactive Power Compensation in Grid System Using a Low-Cost Thyristor Switched Capacitor Scheme;An Automated Two-Stage Brain Tumour Diagnosis System Using SVM and Geodesic Distance-Based Colour Segmentation;assessment of Online Teaching Using Statistical and Unsupervised Learning Methods;an Image-Based Automated Model for Plant Disease Detection Using Wavelet;Computer Vision Assisted Bird–Eye Chilli Classification Framework Using YOLO V5 Object Detection Model;an Effective Grid Connected Multi Level Inverter Based Hybrid Wind and Solar Energy;detecting Obfuscated Malware Using Graph Neural Networks;a Comprehensive Analysis of Autism Spectrum Disorder Using Machine Learning algorithms: Survey;energy-Efficient Cluster Head Election and Data Aggregation Ensemble Machine Learning Algorithm;Multi-sensor Data fusion for Early Fire Estimation Using ML Techniques;design and Analysis of Solar Cell Coplanar Antenna for Wireless applications;open Permissioned Blockchain Solution for Private Equity Funding Using a Global, Cross-Cloud Network Blockchain Platform;the Challenge of Recognizing Artificial Intelligence as Legal Inventor: Implications and Analysis of Patent Laws;comparative Analysis of Imbalanced Malware Byteplot Image Classification Using Transfer Learning;recommender Systems for Personalized Business Marketing: Employing Artificial Intelligence and Business Intelligence in Machine Learning Techniques;blockchain Technology for Secure Smart Grid Access Control.

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International conference on Power Engineering and Intelligent Systems, PEIS 2023

International Conference on Power Engineering and Intelligen...

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International conference on Power Engineering and Intelligent Systems, PEIS 2023

ISBN: (纸本)9789819973828

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Hybrid Intelligent fusion-Based Perspectives for WSN-IOT

Hybrid Intelligent Fusion-Based Perspectives for WSN-IOT

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2023 IEEE International conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Rao, Manjula Gururaj Kumar Reddy, K Hemant Kumar, ChSree Priyanka, H. Pawar, Sumathi Nmam Institute of Technology Nitte Karkala India Vit Ap University Department of Computer Science and Engineering Karkala India Department of Computer Science Nist Berhampur India Computer Science and Engineering Pesu Bengaluru India

ISBN: (纸本)9798350300826

Currently, numerous applications in modern life incorporate IoT (Internet of Things). The fundamental principle of IoT involves connecting various devices to the internet to access information. Among these devices are sensor nodes (SN) that utilize Wireless sensor Networks (WSN) for internet connectivity. The SN's battery life will steadily decrease as it is communicating with the BS (Base Station). Several methods are suggested to lengthen the lifespan of the SNs. By using a hybrid technique that separates the entire WSN network into clusters and leverages the cluster head (CH) to cut down on communication between the IOT devices. The suggested hybrid model focuses on managing the WSN-IOT energy efficiently. The system enhances its energy efficiency during communication, which includes inter-cluster communication, intra-cluster communication, and communication with the BS. The proposed model utilizes M-LEACH, M-CS, and M-PSO algorithms in distinct stages. The network is divided into three levels: The first level concentrates on inter-cluster formation and CH selection using the M-LEACH. In the second stage, M-CS facilitates communication between multiple CHs and the BS. Finally, the M-PSO algorithm is employed based on the threshold of CH nodes for improved outcomes. © 2023 IEEE.

关键词： sensor nodes

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Evaluation of a Wireless sensor Network for the Detection of Forest Fires 2

Evaluation of a Wireless Sensor Network for the Detection of...

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2nd International conference on Edge Computing and applications, ICECAA 2023

作者： Anuradha, T. Suganya, A. Muthalagu, R. Narender, M. Jose Anand, A. Kcg College of Technology Department of Electrical and Electronics Engineering Chennai600097 India Tagore Engineering College Department of It Kancheepuram District Tamilnadu Rathinamangalam600127 India Agni College of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai600130 India Tkr College of Engineering & Technology Department of Cse Hyderabad India Kcg College of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai600097 India

ISBN: (纸本)9798350347579

The objective of the study is to assess the effectiveness and reliability of the WSN in detecting forest fires accurately and providing timely alerts to the authorities. Field experiments in a forested area are conducted with controlled fire scenarios to simulate real-world fire conditions. The WSN sensors were deployed, and data from the sensors were collected and analyzed. Proposed result demonstrate that the WSN is capable of detecting forest fires with high accuracy and providing timely alerts. The sensors effectively captured changes in environmental parameters associated with fire events. Through data fusion and intelligent algorithms, the WSN system accurately differentiated between normal environmental variations and fire-related anomalies. The wireless communication capability of the network enabled real-time transmission of data to a central monitoring station, allowing for immediate analysis and decision-making. Additionally, the WSN system demonstrated robustness and fault tolerance, as it could continue functioning even if individual sensors were damaged or lost. The evaluation of the WSN for forest fire detection showed promising results, highlighting its potential as an effective tool for early fire detection and prevention. The use of such a wireless sensor network can enhance the efficiency of fire management strategies, enabling timely responses, reducing response times, and minimizing the impact of forest fires on ecosystems and communities. © 2023 IEEE.

关键词： Fires

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ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE sensor BIAS

ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR...

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IET International Radar conference 2023, IRC 2023

作者： Ni, Na Jiang, Qi Mao, Huafeng Zhang, Jichuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Radar observation of group targets has recently received much attention in the flight mechanism research and many other applications. Group targets are usually closely spaced with lots of missed detections and false alarms, making it difficult for spatial reconstruction. Multisensor systems make use of data from multiple radars to provide more accurate measurements and robust tracks than a single radar, playing an important role in the group targets observation. In the data fusion processes, multisensor measurements are associated after transformed into a global coordinate system. However, large sensor bias makes it difficult to associate measurements of closely spaced targets given outliers, and nonideal association further increase the sensor bias estimation error. In this paper, we proposed a robust joint association and registration method to simultaneously acquire sensor bias estimates and association results. A nonlinear least median of squares estimator is used to achieve better accuracy and robustness of sensor bias estimation. Simulation results show the better bias estimation and association performance of the proposed method compared to other algorithms. © The Institution of Engineering & Technology 2023.

关键词： Radar measurement

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