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2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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A prune-HRNet-based road pothole detection model adapted to suboptimal detection environments 4

A prune-HRNet-based road pothole detection model adapted to ...

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4th international conference on signal image Processing and Communication, ICSIPC 2024

作者： Li, Tao Qiu, Hanlong Liu, Yongqi Wu, Youwei Gannan University of Science and Technology Ganzhou China

ISBN: (纸本)9781510682467

Road defect detection is an important task to ensure road safety and prevent vehicle damage. However, there are limitations in identifying road potholes in situations where the detection environment is not ideal. In this paper, a prune-HRNet model is proposed to optimise the HR-Net model by pruning process to ensure high accuracy with reduced computational cost. First, 70% of the dataset is randomly selected as the training set and 30% as the test set to train and test the proposed model. Then the above experiments are repeated using LeNet and ResNet models and compared. The results of the road defect recognition experiments show that the accuracy of the test set of the proposed model in this paper is 0.1068 higher than that of the traditional model, and the recall rate is 0.1226 higher than that of the traditional model. the generalisation ability of the model is verified. Therefore, the model proposed in this paper can be used for real-time monitoring of road potholes to ensure road safety and reduce road maintenance costs, which is of guiding significance for the promotion of intelligent transport systems. © 2024 SPIE.

关键词： Motor transportation

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signal Modulation Recognition of Satellite internet based on Dual Input Convolutional Neural Network

Signal Modulation Recognition of Satellite Internet Based on...

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IEEE international conference on signal and image Processing (ICSIP)

作者： Yiyu Pan Feng Gao Southwest Institute of Communication China Electronics Technology Group Corporation Chengdu China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In order to identify the main modulation modes used in satellite internet communication systems, a communication signal modulation recognition algorithm based on double-ended convolutional neural network is proposed. The time domain waveform of the signal is transformed into the high-power spectrum and the time-frequency image, and further converted into gray images as the shallow feature expressions of the signal. The modulation recognition problem is converted into an image recognition problem, and a modulation recognition model based on dual input convolutional neural network is designed. Through the training of the network, the shallow features are extracted and mapped deeply, and the modulation recognition of the target signal is finally completed. The simulation results show that the performance of the proposed algorithm is significantly improved compared with the single feature input modulation recognition algorithm, and it has good recognition performance for satellite internet signals

关键词： Training Time-frequency analysis image recognition Satellites Target recognition Modulation Feature extraction

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Research on Urban Photoelectric Sensing Environment Sensor based on Big Data technology of Computer internet of Things

Research on Urban Photoelectric Sensing Environment Sensor B...

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2023 IEEE international conference on image Processing and Computer Applications, ICIPCA 2023

作者： Ma, Chuantao Lu, Jing School of Physics and Electronic Engineering Taishan University Tai'an271000 China

ISBN: (纸本)9798350314670

This paper designs and implements the perception layer, network layer and application layer in the framework of environmental monitoring system. To minimize the bit error rate of free space optical communication (FSO) system under the limitation of multi-mode Gaussian background optical noise field and the energy efficiency of the received optical signal, an adaptive photoelectric array signal processing algorithm for intensity modulation/direct detection (IM/DD) is proposed. Design and implement sensor data acquisition circuit, main control processor and peripheral circuit, system communication interface circuit in environmental monitoring system hardware. The data storage part mainly realizes the storage and processing of data, which involves cloud platform technology. The server platform realizes the final webpage display and large screen display, and the key technologies are communication technology. The analysis and simulation results show that the system can greatly reduce the computational complexity while the performance loss is almost negligible. © 2023 IEEE.

关键词： Big data

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RFF Recognition based on Adaptive signal Transform and Weighted Feature Fusion 16

RFF Recognition Based on Adaptive Signal Transform and Weigh...

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16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Wang, Yuqian Liu, Nan Pan, Zhiwen You, Xiaohu Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

ISBN: (纸本)9798350390643

Radio Frequency Fingerprinting (RFF) is an effective physical layer identification technique that makes internet of Things (IoT) systems more secure. It uses the deviation of the parameters of each electronic component in the transmitter from the nominal parameters to perform the authentication task. RFF recognition schemes based on deep learning (DL) are emerging due to their ability to automatically perform feature extraction and classification. To fully exploit the information in RF signals, researchers perform feature extraction from different signal representations and fuse these features for subsequent classification tasks. However, selecting the combination of signal representations is a time-consuming task. Therefore, in this paper, we propose an RFF recognition model based on Adaptive signal Transform (AST) and Weighted Feature Fusion (WFF). The AST module is able to learn a signal transform form, which can greatly improve recognition accuracy and reduce the pressure of manual selection of signal representations. In addition, we use multiple convolution layers to form the WFF module. This module weighs the RFF features and highlights the feature components that favor classification. Simulation results show the ability of these modules to improve the performance of RFF recognition and the effectiveness of the proposed AST-WFF model. © 2024 IEEE.

关键词： Deep learning

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Research on the Design and Application of Sampling Circuits for Deep Learning-based Leakage Detectors 4

Research on the Design and Application of Sampling Circuits ...

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4th international conference on Energy Engineering and Power systems, EEPS 2024

作者： Gao, Chengnan Liu, Shuo Chen, Yanjun Song, Hui Mu, Xiangyang Zhongfu Safety Technology Co. Ltd Jinan China College of Mechanical and Electronic Engineering Shandong Agricultural and Engineering University Jinan China

ISBN: (纸本)9798350366914

With the widespread use of electrical equipment, power devices operating in humid and rainy environments often suffer from insulation degradation, leading to frequent leakage incidents that pose serious threats to personal and property safety. This paper proposes an intelligent leakage detector based on deep learning, aimed at improving the accuracy and intelligence of leakage detection. a comprehensive set of leakage detection hardware circuits is created, which includes a current acquisition circuit, voltage acquisition circuit, signal conditioning circuit, and so on. And a second-order band-pass filter was designed and tested in real engineering applications. The detector achieved a leakage detection accuracy of 98% in complex electrical environments, which is more than 10% higher than traditional design methods. This study provides new insights for the development of leakage detection technology and is of great significance for enhancing electrical safety. © 2024 IEEE.

关键词： Timing circuits

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Design and Application of image Recognition Framework based on Cloud Edge Collaboration in Distribution Stations 5

Design and Application of Image Recognition Framework Based ...

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5th IEEE international conference on Power, Intelligent Computing and systems, ICPICS 2023

作者： Wu, Wenwei Zhang, Lishou Kang, Kai Liu, Juntao State Grid Shanghai Municipal Electric Power Company Construction and Operation Center Shanghai China Shanghai Yitong Information Technology Co. Ltd R and D Department Shanghai China

ISBN: (纸本)9798350333442

Targeted on the intelligent identification and cloud load reduction requirements of power system in video monitoring, a vision analysis system platform and implementation scheme for distribution station based on cloud edge system are proposed. Through multi-source data fusion analysis, optimization of defect fault location, image recognition algorithm, cloud edge threshold classification collaboration, image processing and feature extraction technologies such as YOLOv3, Mask-CRNN, etc., the high-definition real-time image is analyzed, and the main equipment status recognition and security abnormal behavior recognition functions of the distribution station are realized. The real-time and accuracy of each analysis function is verified by the on-site distribution station, and the relevant experimental results of some functions in the power grid are compared to show the effectiveness of the system scheme. © 2023 IEEE.

关键词： internet of things

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Driver physiological condition assessment system based on image-based photoplethysmography 4

Driver physiological condition assessment system based on im...

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4th international conference on Testing technology and Automation Engineering, TTAE 2024

作者： Chen, Chen Wang, Qi Li, Haochen Jin, Yu Chen, Jinhao Chen, Yujie Huang, Jiating Hu, Danfeng Soochow University School of Electronic and Information Engineering Suzhou China

ISBN: (数字)9781510686656

ISBN: (纸本)9781510686649

Automated driving is in the stage of development and industry standardization. In order to ensure the safety of autonomous driving, intervention by the driver is required at any time to address unexpected situations during the autonomous driving process. Due to the fatigue and other factors, drivers are often unable to intervene in time to respond to unexpected situations, and autonomous driving accidents occur from time to time. Some companies have already installed DMS in their vehicles, however, many physiological conditions caused by fatigue vary from person to person, and there is still a need for a more reliable and cost-effective method to monitor the driver's condition. based on the study of a large number of driver monitoring systems (DMS), we propose the use of image-based photoplethysmography (iPPG) for driver physiological condition monitoring, which can effectively solve most of the pain points of the DMS. In this paper, we present an innovative dual-camera measurement system designed to tackle measurement errors arising from environmental variability and motion artifacts. This dual-camera configuration aims to improve the accuracy of physiological signal monitoring, thereby providing more reliable assessments of the driver's condition during autonomous driving. © 2024 SPIE.

关键词： Condition monitoring

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A Self-Adaptive image Segmentation-based Dual Watermarking Technique for Medical images 8

A Self-Adaptive Image Segmentation-Based Dual Watermarking T...

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8th international conference on signal and image Processing, ICSIP 2023

作者： Meng, Bowen Yuan, Xiaochen Wang, Fan Macao Polytechnic University Faculty of Applied Sciences Macao China

ISBN: (纸本)9798350397932

The smart healthcare system plays a more critical role than ever before. It is designed to share electronic patient records (EPR) to improve medical care and facilitate research. However, protecting the security of EPR data is still a significant challenge. This paper proposes a new method for safeguarding EPR data in such systems. The method is a self-adaptive image segmentation-based dual watermarking technique that embeds both EPR data and authentication information in the wavelet domain of selected region of non-interest (RONI) for enhanced security, imperceptibility, and robustness. The experiments are based on a large dataset of medical data, The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD). The results found that the method is effective for the smart healthcare system. Compared with existing techniques, the proposed approach is better regarding security, robustness, and capacity. © 2023 IEEE.

关键词： image segmentation

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internet of Things-based Bridge Surveillance System 4th

Internet of Things-Based Bridge Surveillance System

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4th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Parashuram, N. Rohith, K. Rajesh Kumar, B. Nagsai, K. Nazeer Hussain, D. Department of CSE G. Pullaiah College of Engineering and Technology Kurnool India G. Pullaiah College of Engineering and Technology Kurnool India

ISBN: (纸本)9789819994410

The internet of Things has repeatedly demonstrated its ability to make previously unavailable solutions available. Bridge health monitoring is a system where internet of Things (BHM) solutions have proven their feasibility. The bridge is made of clementine wood and is exposed to harsh environmental conditions day and night that damage various structural components. These include but are not limited to cracks in the surface of the bridge, excessive bridge weight, and flooding around the bridge. This BHM system collects data using various sensors (water level sensors, IR sensors, etc.) and uses algorithms (rapid screening algorithms, image processing) or approaches to identify accessible areas of the bridge or can you solve the specific problem of cracks in difficult situations, such as inaccessible areas? Data can be analyzed and processed to determine if the bridge is damaged or if it is surrounded by flood-like conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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