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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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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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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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Pollution source intelligent location algorithm in water quality sensor networks

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NEURAL COMPUTING & APPLICATIONS 2021年第1期33卷 209-222页

作者： Yan, Xuesong Gong, Jingyu Wu, Qinghua China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Peoples R China

Water is the source of human life and water pollution is becoming more and more serious with the development of cities. The supervision and treatment of water resources have become a big problem of urban development. Water quality monitoring is not timely, flood warning is not timely is directly related to the livelihood of the people. And the development of smart water utilities can solve problems timely and accurately. By placing water quality sensors in the urban water supply network, real-time monitoring of water quality can be performed to prevent incidents of drinking water pollution. After an incident of drinking water pollution occurs, reverse locating the pollution source through the information detected by the water quality sensors represents a challenging problem because in the actual water supply network, the direction and speed of the water flow will change with the water demand of the residents, thus leading to uncertainty in this problem. In conventional studies of pollution source location problems, it is often assumed that the water demand is fixed. However, due to the variability of the water demand of residents, this problem is actually a dynamic change problem and thus can be considered as a dynamic optimization problem. In this study, a Poisson distribution model was used to simulate the change of water demand among urban residents. On this basis, we proposed an improved genetic algorithm to solve the pollution source location problem and implemented two different water supply networks to perform the simulation experiments, which could accurately locate the pollution sources. The simulation results were compared with the standard genetic algorithm to verify the accuracy and robustness of the proposed algorithm.

关键词： Pollution source location Water quality monitoring sensor networks Poisson distribution Simulation optimization Genetic algorithm

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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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Target Detection and Re-Identification Techniques in Cross-Domain Multidimensional Environments

Target Detection and Re-Identification Techniques in Cross-D...

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

作者： Yahan Wang Jin Zhu Xudong Liu Zifeng Qiu Huihui Bai Gang Wang The 54th Research Institute of CETC Shijiazhuang China Beijing Jiaotong University Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Target detection and re-identification technology in cross-domain multi-dimensional environments aims to detect and re-identify pedestrians and vehicles for videos captured by UAVs and surveillance cameras in multiple dimensions. Pedestrians and vehicles captured by UAVs have lower resolution and more partial view, and can utilize all the target's feature information; pedestrians and vehicles captured by surveillance cameras have higher resolution and can utilize the target's license plate and face information. Firstly, we use an anchor-free target detection algorithm for multi-scale weak features to locate the area where pedestrians and vehicles are located, next, we use the re-identification model to transform the input image area into feature vectors and use the distance of feature vectors to measure the similarity of appearance of different targets. Through multi-task learning and information fusion-based target re-identification and localization, multiple sensors are fused using Bayesian networks to finally complete the accurate representation of target re-identification and localization information.

关键词： Location awareness Pedestrians Surveillance Signal processing algorithms Object detection sensor fusion Feature extraction Multitasking Cameras Vectors

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Application LSTM Neural networks for Biological Signal Classification

Application LSTM Neural Networks for Biological Signal Class...

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2022 IEEE International Multi-conference on Engineering, Computer and information Sciences, SIBIRCON 2022

作者： Vasiliev, Anton V. Melnikov, Alexey O. Institute of Cybersecurity and Digital Technologies Moscow Russia

ISBN: (纸本)9781665464802

Biological signals can be used to build an intelligent interface. Currently, there is an increased interest in research in the field of gesture recognition systems for building intelligent human-machine interfaces. Electromyography (EMG) can be used to extract information about hand movement and recognize the target gesture. Many sensors are required to create a human-machine interface based on EMG for people with amputated hands. This study uses the recurrent neural network long short-term memory (LSTM) to process EMG signals for gesture interaction. A paper proposes a new neural network architecture for gesture classification based on electromyography signals. It is shown that neural networks based on LSTM and ultra-precise layers can be used in the problem of EMG signal classification. A comparative analysis of the developed hybrid network model with classical ultra-precise networks was carried out. The classification accuracy of the developed model was obtained at 94.59% on test data. Another advantage of using the proposed hybrid neural network architecture is reducing the number of trainable parameters, which speeds up the learning process. © 2022 IEEE.

关键词： Digital signal processing

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Classifying Activities of Electrical Line Workers Based on Deep Learning Approaches Using Wrist-Worn sensor 20

Classifying Activities of Electrical Line Workers Based on D...

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20th International Joint conference on Computer Science and Software Engineering, JCSSE 2023

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

ISBN: (纸本)9798350300505

The field o f human activity recognition (HAR) is a priority for cutting-edge study because of its potential to revolutionize the way we understand and improve our everyday lives. A large variety of ordinary, everyday tasks has been classified using H AR. Nevertheless, inc ontrast to basic human actions, the increasing demands of numerous real-world applications have attracted the interest of the HAR area of study. Electrical line workers (ELWs) face a variety of challenges, including long hours, working in isolated locations, and performing particularly hazardous tasks. Wearable sensor-based HAR allows for unobtrusive tracking of ELW efficiency and security. This study explores deep learning strategies for automatically categorizing ELWs' complicated actions through sensor data collected through a wrist-worn device. We propose ResNeXt, a deep residual neural network, and evaluate it with other deep learning networks for their ability to categorize ELW activities effectively. We employ a publicly available benchmark dataset that includes 10 ELW tasks. The results of the experiment demonstrate that the proposed ResNeXt achieved the highest accuracy (98.74%) and F1-score (98.81%) compared to other deep learning networks studied. © 2023 IEEE.

关键词： Wearable sensors

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Micro intelligent Vehicles in Multi-Dimensional Space Drive AI Agricultural Construction

Micro Intelligent Vehicles in Multi-Dimensional Space Drive ...

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IEEE Advanced information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Chuan Zeng Anhui University of Finance and Economics Anhui China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

In the context of scientific and technological development of agriculture, this paper actively explores the road of agricultural modernisation development, and designs a driving AI agricultural construction scheme based on a micro intelligent vehicle in multi-dimensional space, aiming to improve the efficiency of agricultural production, reduce the cost, and realise intelligent agricultural management. The design idea is to use STM32 microcontroller as the core, control a variety of sensors to obtain environmental information such as temperature, humidity, smoke, etc.; wireless communication through the ESP8266 network module, the acquired environmental data information is transmitted to the cloud platform; the path information is detected through the OV7725 camera, and finally, the microprocessor carries out the decision-making control and outputs the PWM control signal. The design combines artificial intelligence technology with agricultural production to achieve intelligent agricultural production and management, which brings new development opportunities for the agricultural field.

关键词： Temperature sensors Space vehicles Wireless communication Wireless sensor networks Temperature distribution intelligent vehicles Production

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