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6th International conference on Mechatronics and intelligent Robotics, ICMIR 2022

作者： Jia, Siyuan Zhang, Yijie Guo, Huiru Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9781510656635

In order to realize the visualization of various index data parameters of college student formula racing, this paper designs a hardware design scheme of formula racing sensor based on AT89C52, in which a variety of sensors are used to distribute the temperature in the engine coolant of the racing car and the torque of each driving wheel, so as to accurately measure the speed of the racing car in various states and ensure the stability of the racing car in the running process,It is convenient for the driver to adjust, correct and *** this paper, the signal processing of the sensor, including filtering, noise reduction and other steps, and then the analog signals are ad converted and stored through serial *** addition, this paper also designs the upper computer window of LabVIEW for driver information exchange, and displays and processes the parameters in combination with temperature sensor and speed sensor. The sensor data is processed through AT89C52 to correct the parameters of LabVIEW upper computer. © 2022 SPIE.

关键词： Signal processing

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Enhancing Energy Efficiency and Scalability in Wireless sensor networks Using a Slime Mould Algorithm

Enhancing Energy Efficiency and Scalability in Wireless Sens...

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International conference on Signal processing, Communication, Power and Embedded System (SCOPES)

作者： N. Dharmaraj S. Ramalingam S.Harish Ganapathy S. Abirami S. Rakesh Penumalli Venkata Damsh Reddy Department of ECE Rajalakshmi Institute of Technology Thandalam Department of ECE Sri eashwar college of engineering Coimbatore Department of CCE Rajalakshmi Institute of Technology Thandalam Department of EE(VLSI) Rajalakshmi Institute of Technology Thandalam

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Wireless sensor networks are vital regarding an assortment of tasks were acquiring information from disseminated sensors is crucial. Nonetheless, maintaining energy conservation while increasing data collecting efficiency is still an exhausting challenge. A promising bio-inspired solution to these challenges is provided by the Slime Mould Algorithm, which emphasizes inspiration via the chemical reactions of slime moulds. To optimize routing circuits for accumulating data from sensor nodes to a central sink node, slime mould algorithm is explicitly implemented. The decentralized algorithm empowers nodes to autonomously modify and rebuild their routes in anticipation of local info and external influences, notably network topology changes or node failures. As demonstrated by our investigations, Slime mould algorithm-based technologies may substantially boost the scalability and robustness of wireless sensor networks, making them suited to dynamic and resource- constrained situations.

关键词： Wireless sensor networks Chemical sensors Heuristic algorithms Scalability Signal processing algorithms Clustering algorithms Quality of service Routing Robustness sensors

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Deep Leaarning in Collaborative Spectrum Sensing: An Application Overview

Deep Leaarning in Collaborative Spectrum Sensing: An Applica...

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6th International conference on intelligent Computing and Signal processing (ICSP)

作者： Siyi Liu Donghui Xu Dingyin Liu Ziyu Chen High-Tech Institute of Xi'an Xi'an Shaanxi China High-Tech Institute of 66389 Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Advancements in wireless communication have made spectrum resources scarce, requiring efficient usage and minimized interference. Cooperative spectrum sensing, which uses multiple sensors or radio nodes, improves sensing accuracy and reliability. Integrating deep learning has significantly enhanced its precision and real-time performance, sparking substantial research interest. This paper explores deep learning-based cooperative spectrum sensing, covering its evolution, principles, technologies, applications, and future prospects. It emphasizes the role of deep learning in boosting efficiency and accuracy and addresses challenges and future directions.

关键词： Military communication Deep learning Wireless sensor networks Accuracy Cooperative communication Signal processing Real-time systems sensors Reliability Radio spectrum management

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5G-Based Body sensor Network for Real-Time Feedback in Running 12

5G-Based Body Sensor Network for Real-Time Feedback in Runni...

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Proceedings of the 12th International conference on Sport Sciences Research and Technology Support, icSPORTS 2024

作者： Mayr, Sebastian Rieser, Harald Human Motion Analytics Salzburg Research Forschungsgesellschaft MbH Salzburg Austria

ISBN: (纸本)9789897587191

Immediate feedback is vital in sports and motor learning. Traditionally, coaches provide this feedback. However, recent developments in wearable sensing and feedback devices, state-of-the-art processing algorithms and sensor data integration allow for new applications and insights. While this allows for more direct feedback, it also introduces new demands on feedback systems in terms of network transmission speed, energy consumption and needed calculation power. The 5G networking standard is a promising technology to address these issues. It promises reduced energy usage, high availability, latency in the low millisecond range and high data rates up to 10 Gbps. Despite these characteristics, there has not been much research on the use of 5G in sports biofeedback systems. Therefore, this work aims to assess the usability of 5G for a real-time feedback system in the sports context. We did this by developing a distributed feedback system for the assessment of running gait symmetry. The system utilizes internal smartphone IMU as sensors and employs a 5G infrastructure for data transmission to an offsite server for the calculation of running metrics and generation of the feedback signal. Pilot tests, using the feedback system, showed mean roundtrip times of 140.14ms (sd = 14.47ms) for outdoor use and 128.92ms (sd = 25.83ms) for indoor use. These results indicate that promised low latencies by the 5G standard are currently not reachable outside of controlled testing environments. Nonetheless, as these times are below average reaction times in recreational athletes (150ms) it is still promising for real-time feedback applications in running. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Body sensor networks

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A Real TimeVehicle Detection Based on Modified Convolutional Neural networks in Different Climate

A Real TimeVehicle Detection Based on Modified Convolutional...

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2023 IEEE International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Johnson, Sandra Laxmi, B Prathusha R.M.K. Engineering College Department of Artificial Intelligence and Data Science Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665462631

In order to effectively manage and monitor traffic on the roads, intelligent technologies must be able to detect and tally the number of cars on the road. The technology may be used for controlling and monitoring traffic in both urban areas and on highways, especially in times of heavy congestion or bad weather. It has traditionally been accomplished using sensor data and the standard toolkit for image processing. Recently however, with the help of deep learning-based smart computer vision systems, this process has become highly scalable and dependable. For intelligent traffic monitoring and management of issues like traffic congestion, the information gathered from various surveillance cameras may be utilized to train models that can recognize and track on-road cars, even in low-light and blurry situations brought on by bad weather. Although they all aim to solve the same problem, vehicle identification algorithms all have to deal with one or two very distinct cases. Vehicle detection under haze, dust, sandstorms, snow, and rain, as well as daytime and nocturnal conditions, is the focus of this study. The suggested design is based on the CNN architecture as a starting point, but it adds a spatial pyramid pooling layer and removes several of the Batch Normalization levels. During training, the system achieved an average accuracy of 81% and was able to identify even the tiniest vehicles in the scene. © 2023 IEEE.

关键词： Traffic congestion

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CSRP Protocol for High Accuracy Performance in Wireless sensor networks

CSRP Protocol for High Accuracy Performance in Wireless Sens...

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Computing and information Technology (ICCIT-1441), 2020 International conference on

作者： Ahmed Subhi Abdalkafor Salah A. Aliesawi College of Computer Science and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In recent years, Wireless sensor Network (WSN) has played a broad role in many vital applications that are used to monitor environmental and physical events. One of the major issues in WSN is the overlapping region of sensing bands, which occurs due to densely and randomly deployed nodes in the target environment. The data aggregation technique is used as a solution that processes overlapping regions. This technique manages and redirects these data to the sink node through a multi-hop network. Therefore, the data transmission number and the duration toward the sink node are reduced, which fixes the overlapping region issue. However, using data aggregation techniques limited network performance due to energy consumption. The objective of this paper is to address this limitation by employing intelligent algorithms to enhance the operation strategies of data aggregation techniques. A protocol based on the Hierarchical Agglomerative Clustering (HAC), Self-Organized Map (SOM), and Enhanced Radial Basis Function (ERBF) neural network is proposed in order to accomplish this goal. The clustered SOM and RBF-based intelligent Protocol (CSRP) is the name of this proposed protocol. When compared to pertinent recent studies, the suggested protocol produced the best results in terms of the accuracy of the data arriving at the base station.

关键词： Training Wireless sensor networks Base stations Protocols Accuracy Artificial neural networks Spread spectrum communication Radial basis function networks Data aggregation sensors

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Weighted Average Consensus-Based Unscented Kalman Filtering with Cross-Covariance 4

Weighted Average Consensus-Based Unscented Kalman Filtering ...

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4th International conference on Communications, information System and Computer Engineering, CISCE 2022

作者： Li, Ze Su, Hongtao National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

This paper is concerned with the consensus-based distributed state estimation problem for nonlinear sensor networks. Most existing approaches assume the estimation errors between two connected sensors are independent, and the cross-covariance is neglected in consensus-based distributed unscented Kalman filter (DUKF) frameworks. However, the estimation errors are related considering the joint prior estimates and process noise of the connected nodes. So we propose an improved algorithm to address the problem. At first, the cross-covariances between local estimation error are considered in consensus algorithm. Then, the cross-covariances update in the local unscented Kalman filter (UKF) based on the unscented transformation (UT). Finally, a stochastic nonlinear system is provided to illustrate the advantages of the proposed algorithm. © 2022 IEEE.

关键词： sensor networks

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Energy Optimization of Wireless sensor Embedded Cloud Computing Data Monitoring System in 6G Environment

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sensors 2023年第2期23卷 1013-1013页

作者： Yang, Huaiyuan Zhou, Hua Liu, Zhenyu Deng, Xiaofan Southwest Forestry Univ Coll Big Data & Intelligent Engn Kunming 650224 Peoples R China Jiangxi Univ Finance & Econ Sch Software & Internet Things Engn Nanchang 330000 Peoples R China

With the construction and development of modern and smart cities, people's lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand. This paper provides a data governance solution based on a 6G environment. The shortcomings of critical technologies in wireless sensor networks are addressed through ZigBee energy optimization to address the shortage of energy supply and high energy consumption in the practical application of wireless sensor networks. At the same time, this improved routing algorithm is combined with embedded cloud computing to optimize the monitoring system and achieve efficient data processing. The ZigBee-optimized wireless sensor network consumes less energy in practice and also increases the service life of the network, as proven by research and experiments. This optimized data monitoring system ensures data security and reliability.

关键词： ZigBee technology energy optimization wireless sensor network embedded cloud computing monitoring system 6G environment

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A Convolutional Neural Network for Ultra-Wideband Radar-Based Hand Gesture Recognition

A Convolutional Neural Network for Ultra-Wideband Radar-Base...

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2023 International Technical conference on Circuits/Systems, Computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

New developments in sensing technology have enabled the creation of improved assistive devices that enhance daily eldercare routines and offer personalized care to users. Wearable or ambient sensors can now detect a person's behavior, but the need for constant recharging due to their high energy consumption makes wearing such devices around the clock difficult. In this paper, a new sensing technique based on deep learning (DL) approaches for hand gesture recognition (HGR) using an ultra-wideband (UWB) radar sensor is introduced. The study examines the recognition performance of a convolutional neural network (CNN) for HGR and utilizes the UWB-gestures dataset to train and test the DL networks in various experiments with different scenarios. The experimental results demonstrate that the CNN achieved the highest F1-score of 97.19%. © 2023 IEEE.

关键词： Convolutional neural networks

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A Novel Heart Disease Monitoring and Prediction Using Machine Learning Algorithm 8th

A Novel Heart Disease Monitoring and Prediction Using Machin...

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8th International conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Senbagavalli, M. Karpagalakshmi, R.C. Sumathi, D. Lenin, J. Prasad, G.R.K. Manikandan, A. Department of Computer Science and Information Technology Alliance College of Engineering and Design Alliance University Bangalore India Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Kattankulathur Chennai603203 India

ISBN: (纸本)9789819757855

Background: Continuous monitoring of patient health statistics becomes a difficult task in hospitals. Manually, it is difficult to monitor the health of the patients in the hospital continuously. Older and unconscious older people in particular need to be monitored regularly, and their relatives want to be informed about their health at every time. We, therefore, propose a revolutionary system that easily computerizes this task. Our device provides an intelligent device for monitoring patient status, which uses sensors to track hospital patients’ health status and informs their relatives in the event of a problem via the Internet. Our system uses temperature, glucose levels and heart rate detection to track health. This task has been proposed to improve the monitoring system by using the Internet of Things (IoT) for hospital applications. Result: The suggested system was developed by MAX30100, LM35, ultrasonic sensor and nodeMCU connected to the Internet. BLYNK IoT Android app has already been used to send the notification via Android Application. The remote healthcare monitoring system is also proposed with cloud service and data analytics as to the aiding features. The readings are captured by mobile phone, which acts as the graphical user interface to get the status of the patient’s health. The implemented hardware results illustrate that the suggested model can continuously observe the physiologic parameters and save lives promptly. Conclusion: ML classification algorithms like Regression tree, SVM, and RF analysis for prediction accuracy of patient health data. The proposed RF algorithm’s simulation results provide minimum prediction error and high accuracy compared with the existing Regression tree and SVM algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultrasonic sensors

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