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共 10630 条记录，以下是101-110 订阅

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A Non-Intrusive Load Monitoring Method Based on Relative Position Matrix and Residual Network

A Non-Intrusive Load Monitoring Method Based on Relative Pos...

引用

International conference on intelligent Control and information processing (ICICIP)

作者： Jin-Yang Huang Chun-Hua Chen Yi Jiang Junwei Yan Jun Zhang Zhi-Hui Zhan School of Software Engineering South China University of Technology Guangzhou China Department of Electrical and Electronic Engineering Hanyang University Ansan South Korea School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Non-Intrusive Load Monitoring (NILM) technology identifies various appliances’ types and operational status by analyzing electrical data collected from sensors in residential or commercial buildings, offering significant practical value. However, due to the limitations of existing methods for constructing appliance features, the identification performance of NILM models needs improvement. Addressing this, we propose a method for constructing appliance features based on a relative position matrix (RPM), which more accurately captures the temporal characteristics of electrical data compared to other methods. Additionally, a deep learning model based on the improved ResNet18 with multi-scale fusion is proposed for the load identification task. In this paper, comparative experiments were conducted on the PLAID dataset. We compared the RPM with three appliance feature construction methods: voltage-current trajectory (VI), markov transition field (MTF), and gramian angular field (GAF). We also compared our results with advanced methods reported in other literature. The results show that this method outperforms existing methods in terms of precision, recall, and F1 score across various evaluation metrics.

关键词： Measurement Load monitoring Buildings information processing Feature extraction sensors Trajectory Load modeling Residual neural networks intelligent control

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ISSNIP 2008 - Proceedings of the 2008 International conference on Intellingent sensors, sensor networks and information processing

ISSNIP 2008 - Proceedings of the 2008 International Conferen...

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2008 International conference on intelligent sensors, sensor networks and information processing, ISSNIP 2008

ISBN: (纸本)9781424429578

The proceedings contain 107 papers. The topics discussed include: automatic recognition of obstructive sleep apnoea syndrome using power spectral analysis of electrocardiogram and hidden Markov models;Legendre polynomials based biometric authentication using QRS complex of ECG;frequency analysis of inertial sensor data for measuring toe clearance;correlation analysis of seizure detection features;Fourier analysis of tibia acceleration in subjects with knee osteoarthritis: preliminary results;ECG compression using multilevel thresholding of wavelet coefficients;a supervised ICA algorithm for fetal ECG extraction;evaluating bouncing gait in idiopathic toe-walkers using accelerometer;finite element analysis of a tactile sensor for a robotic hand;evaluation of human gait through observing body movement;and establishing normative data for peripheral arterial disease using pulse wave analysis.

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IoT Based Water Management and Monitoring System

IoT Based Water Management and Monitoring System

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Muthulakshimi S Malathy Sathyamoorthy Akash S Charumathi P Sri Suvathi N M Department of Information Technology KPR Institute Of Engineering And Technology Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

IoT (Internet of Things) has revolutionized various industries, and water management is no exception. This abstract introduces an IoT-based water management system designed to address the challenges of water scarcity, pollution, and inefficient distribution. Leveraging IoT devices like sensors, actuators, and communication modules, the system creates a networked infrastructure for real-time monitoring and management of water resources. sensors deployed throughout the water distribution network collect data on flow, pressure, quality, and consumption patterns, which is then transmitted to a central server or cloud platform. The system instantly detects pipe leakage and promptly halts the water flow, even in the absence of human presence. This automated response helps prevent water scarcity. The system supports remote monitoring and control via web-based dashboards or mobile applications, facilitating proactive maintenance and troubleshooting. Additionally, it provides data analytics and reporting functionalities, empowering stakeholders to make informed decisions and promote water conservation initiatives. Overall, this IoT-based water management system offers a comprehensive solution to enhance sustainability, efficiency, and resilience in water distribution networks.

关键词： Water conservation Water pollution sensor systems Real-time systems Maintenance Internet of Things Stakeholders Sustainable development Water resources intelligent sensors

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Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions

Deep Learning Enabled Secure IoT Module for Smart Agricultur...

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International conference on Communication Systems and Network Technologies (CSNT)

作者： Shaharyar Alam Ansari Salman Ali Mohammad Luqman Shahnawaz Ahmad School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Aligrah Muslim University Aligarh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The Internet of Things (IoT) stands as a revolutionary technology which impacts smart agriculture and different sectors beyond it. The research describes an intelligent IoT-enabled monitoring system which uses sensors to bring improved agricultural performance through environmental real-time observations combined with artificial intelligence decision systems. The proposed system completes its operation by executing sensor data acquisition followed by deep learning-based analysis and secure data transmission procedures. The initial stage of IoT-enabled operation utilizes environment-sensing devices which collect essential parameters for precision farming. A Recurrent Neural Network (RNN) processing system applies classification methods to collected data to generate useful insights. Secure cloud data transmission takes place through an encryption system which combines Substitution-Caesar cipher with Improved Elliptic Curve Cryptography (IECC). The system demonstrates 99.64% accuracy, and the proposed encryption method proves efficiency through its average execution times of encryption at 0.3904 ms and decryption at 0.1102 ms which exceeds current models in speed and security performance.

关键词： Smart agriculture Ciphers Recurrent neural networks Elliptic curve cryptography sensor systems Encryption Internet of Things Data communication intelligent sensors Monitoring

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Enhanced Security-Aware Beamforming and Trajectory Design for UAV-Assisted Integrated Sensing and Communication

Enhanced Security-Aware Beamforming and Trajectory Design fo...

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sensors and information Technology (ICSI), International conference on

作者： Zeyu Yang School of Information Engineering Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Integrated Sensing and Communication (ISAC) en-ables simultaneous sensing and communication functions and is regarded as a cutting-edge technology for future mobile networks. In this paper, we propose a UAV-enabled joint sensing and communication (ISAC) system, where the UAV serves as a base station. In the presence of eavesdroppers, the system aims to ensure that multiple sensing targets are effectively illuminated while maximizing the sum of achievable secrecy rates for multiple communication users under secure communication constraints. To achieve this, we develop a solution based on alternating optimization (AO) and successive convex approximation (SCA). Simulation results validate the effectiveness of the proposed method, demonstrating its ability to balance communication and sensing requirements while significantly enhancing communication security.

关键词： Array signal processing Simulation Radar Physical layer security Integrated sensing and communication Autonomous aerial vehicles Vectors Security Trajectory optimization sensor arrays

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An Optimized Edge Platform for 3D Object Detection in Autonomous Driving

An Optimized Edge Platform for 3D Object Detection in Autono...

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International conference on Electronics, information and Communications (ICEIC)

作者： Kwonseung Bok Yumi Kim Aeri Kim Sang-Seol Lee Kyungho Kim Intelligent Image Processing Research Center Korea Electronics Technology Institute Gyeonggi-do South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Autonomous driving technology has been developed to enhance the safety of vehicles and drivers by recognizing surrounding objects and making appropriate situational judgments using sophisticated sensors. In particular, 3D object detection networks utilizing LiDAR within deep learning models have advanced to achieve accurate object detection. However, performing 3D object detection using LiDAR and advanced sensors requires processing a massive amount of data, making it challenging to handle directly within the vehicle. While external computational resources such as cloud servers can be employed, this approach introduces difficulties in guaranteeing low data exchange latency and ensuring data stability and security. In this paper, we propose a method to process 3D object detection on an edge platform, enabling it to be handled locally for autonomous driving applications. The proposed edge platform consists of a hardware architecture optimized for processing large volumes of point cloud data and a quantized, lightweight network. This enables the edge platform to handle the entire process of acquiring point cloud data via LiDAR, performing 3D object detection, and executing image post-processing tasks directly on the device.

关键词： Point cloud compression Solid modeling Three-dimensional displays Laser radar Image edge detection Computational modeling Object detection sensors Object recognition Autonomous vehicles

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Spectrum Sensing and Management in Cognitive Radio networks using Genetic Algorithm based Soft Decision Fusion

Spectrum Sensing and Management in Cognitive Radio Networks ...

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intelligent Systems and Computational networks (ICISCN), International conference on

作者： Rana Veer Samara Sihman Brahmatej Rupavath Suganya. E Saef Obidhusin Payal Soni Gupta R. Anitha Indiana Wesleyan University Marion USA Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering and Technology India Department of Information Technology New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent years, the advancements of wireless technologies have led to a significant demand in radio frequency spectrum. Cognitive Radio networks (CRN) are used among the intelligent wireless communication systems that enhanced spectrum efficiency. However, CRN with their help in enhancing the spectrum but possesses various challenges such as noise, dynamic environments that complicate the process of sensing. Therefore, in this research Genetic Algorithm based Soft Decision Fusion (GASDF) model is proposed for an effective Spectrum Sensing and Management in CRN. Primarily, the input data is collected through radio network signals, then the signals are preprocessed using the Z-score normalization to reduce the noise from the signals. Further, the relevant features are extracted using Continuous Wavelet Transform (CWT). Then, spectrum allocation is done using Differential Evolution (DE) and the spectrum sensing is done through GASDF. From the results, proposed model attained better outcomes compared to existing Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) with 2.45% Probability of false alarm (Pf) rate.

关键词： Wireless sensor networks Time-frequency analysis Continuous wavelet transforms Noise Feature extraction sensors Cognitive radio Resource management Convolutional neural networks Genetic algorithms

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Optimized Hybrid Deep Learning Approach for Detecting DoS Attacks Using Feature Selection in Wireless sensor networks

Optimized Hybrid Deep Learning Approach for Detecting DoS At...

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Computational, Communication and information Technology (ICCCIT), International conference on

作者： S Sasson Taffwin Moses L Abraham David G S R Emil Selvan M P Ramkumar Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai Tamil Nadu India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The rapid growth of smart cities, healthcare monitoring, and environmental sensing relies heavily on the real-time data processing capabilities of Wireless sensor networks (WSNs). However, these networks face significant security challenges, particularly from malicious activities like Denial of Service(DoS) attacks, which can disrupt critical operations. A hybrid intrusion detection model that combines Convolutional Neural networks (CNN) and Deep Neural networks (DNN) to improve the detection of such threats has been proposed. The preprocessing phase includes the Synthetic Minority Over-sampling Technique (SMOTE) which mitigates class imbalances ensuring robust performance across diverse attack scenarios. The model incorporates advanced feature selection techniques including SelectKBest and Grey Wolf Optimization (GWO) to identify the most critical features for enhanced detection accuracy. The CNN component effectively captures local traffic patterns, which are then refined by the DNN for accurate classification. This hybrid model's effectiveness in detecting diverse DoS attack scenarios is evaluated using WSN-DS dataset, ensuring robust security for WSNs. By achieving an impressive 98% detection accuracy, this hybrid model proves to be effective in intrusion detection. Its applications can significantly strengthen the security and reliability of WSNs, ensuring uninterrupted operations in key sectors such as smart cities, healthcare, and environmental monitoring.

关键词： Wireless sensor networks Accuracy Smart cities Intrusion detection Medical services Traffic control Feature extraction sensors Convolutional neural networks Reliability

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Design and Implementation of a Flexible Wireless sensor Network for Environmental Monitoring with in Digital Cities Framework

Design and Implementation of a Flexible Wireless Sensor Netw...

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intelligent Systems and Computational networks (ICISCN), International conference on

作者： B. Sai Manogna R Gowri Shankar Rao K Ravi Chythanya Navamani C K. S. Raghuram J. Samson Isaac Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad India Department of Physics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computer Science and Engineering Nandha Engineering College (Autonomous) Erode India Department of Mechanical Engineering Vignan's Institute of Information Technology (A) Visakhapatnam India Department of Biomedical Engineering School of Engineering and Technology Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The paper highlights a Wireless sensor Network (WSN) implementation for environmental monitoring, emphasizing cost-effectiveness and adaptability. APIs designed to handle values instead of voltage levels increase system resilience, while open standards promote security, cost efficiency, and auditability. The data shows a diurnal temperature range from 20°C (night) to 38°C (day), stable humidity at 38%, and UV radiation peaking at 6 (day) and dropping to 1.5 (night). Smoke levels are consistently high at 490 μg/m 3 , while toxic gas concentrations slightly rise to 0.5 ppm during the day from 0.45 ppm at night. Fire metrics remain low and stable. These findings highlight the need for UV protection, air quality controls, and consistent fire safety measures. The system achieves effective environmental monitoring while remaining adaptable for future needs, underscoring its value in scalable, cost-efficient, and sustainable public management solutions.

关键词： Temperature sensors Wireless sensor networks Temperature distribution Technological innovation Zigbee User interfaces Environmental monitoring Security Sustainable development Standards

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A Study on Smart Assistive Stick for Visually Impaired Individuals

A Study on Smart Assistive Stick for Visually Impaired Indiv...

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Visual Analytics and Data Visualization (ICVADV), International conference on

作者： K.Y Megajai. P Nithish Kumar. K Lakshmi Prabha. Department of Advanced Communication Technology Chennai Institute Of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Vision impairment heavily affects mobility, information access, employment, and independence for millions of people across the globe. Assistive technology for blind and visually impaired persons has grown from conventional white canes to high-tech smart devices with ultrasonic sensors, GPS, GSM modules, and artificial intelligence (AI). The current review compares development in smart assistive devices, such as smart blind sticks, wearable navigation systems, and IoT-based solutions. New technology like AI-based obstacle detection, image processing, and sensor fusion provide higher accuracy and realtime feedback. Despite this, prohibitive costs, short detection ranges, and issues in dynamic environments prevent its broad application. This research highlights major advancements, their drawbacks, and directions for future enhancement of accessibility, safety, and autonomy for the visually impaired. The suggested smart assistive stick prototype combines multi-sensor technology with AI-based capabilities to improve mobility and independence, overcoming current limitations in assistive navigation devices.

关键词： GSM Accuracy Navigation Visual impairment Real-time systems Acoustics sensor systems Artificial intelligence intelligent sensors Global Positioning System

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