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Brain Age Detection of Alzheimer’s Disease Magnetic Resonance Images Based on Mutual Information—Support Vector Regression

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 130-135页

作者： LIU Yuchuan LI Hao TANG Yulong LIANG Dujuan TAN Jia FU Yue LI Yongming School of Intelligent Technology and Engineering Chongqing University of Science and TechnologyChongqing401331China Communication Sergeant School Army Engineering UniversityChongqing400035China CISDI Information Technology(Chongqing)Co. Ltd.Chongqing401329China School of Microelectronics and Communication Engineering Chongqing UniversityChongqing400044China

Brain age is an effective biomarker for diagnosing Alzheimer’s disease(AD).Aimed at the issue that the existing brain age detection methods are inconsistent with the biological hypothesis that AD is the accelerated aging of the brain,a mutual information—support vector regression(MI-SVR)brain age prediction model is ***,the age deviation is introduced according to the biological hypothesis of ***,fitness function is designed based on mutual information ***,support vector regression and fitness function are used to obtain the predicted brain age and fitness value of the subjects,*** optimal age deviation is obtained by maximizing the fitness ***,the proposed method is compared with some existing brain age detection *** results show that the brain age obtained by the proposed method has better separability,can better reflect the accelerated aging of AD,and is more helpful for improving the diagnostic accuracy of AD.

关键词： brain age Alzheimer’s disease(AD) mutual information-support vector regression(MI-SVR) age deviation

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Tire Wear Assessment Using VGG16: A Computer Vision Approach 4

Tire Wear Assessment Using VGG16: A Computer Vision Approach

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Shanthi, K.G. Rukmani Devi, S. Blesslin Sheeba, T. Kalaiselvi, A. Purnananda Rao, P.G. Jaswanth, P. Harshal Venkat, K. R.M.K College of Engineering and Technology Dept of Electronics and Communication Engineering Chennai India R.M.D Engineering College Dept of Electronics and Communication Engineering Chennai India R.M.K Engineering College Dept of Electronics and Communication Engineering Chennai India Kumaraguru College of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

This paper addresses the issue of tire bursts caused by uneven pressure and wear by implementing an efficient Computer Vision-Based tire health monitoring system using VGG16 in Machine Learning. This system involves capturing tire images using a camera and processing them with a machine learning module, such as VGG 16, to obtain the tire's current state. The system can offer early warning of potential tire failures, allowing drivers to take proactive measures to prevent accidents and minimize maintenance costs. Furthermore, it can assist fleet managers in scheduling tire replacements more efficiently, resulting in improved fleet efficiency overall. The system operates in real-time, generating results almost instantly, and can be integrated with existing vehicle systems for ease of use. The proposed tire health monitoring system is an essential solution to preventing tire bursts, enhancing safety, and minimizing maintenance costs. © 2025 IEEE.

关键词： Convolutional neural networks

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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Dual circularly polarized monostatic STAR antenna with enhanced isolation

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Journal of Systems engineering and electronics 2025年第1期36卷 73-81页

作者： XIE Mingcong WEI Xizhang TANG Yanqun HU Dujuan School of Electronics and Communication Engineering Sun Yat-sen UniversityShenzhen 518107China School of Systems Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China

Separated transmit and receive antennas are employed to improve transmit-receive isolation in conventional short-range radars, which greatly increases the antenna size and misaligns of the transmit/receive radiation patterns. In this paper,a dual circularly polarized(CP) monostatic simultaneous transmit and receive(MSTAR) antenna with enhanced isolation is proposed to alleviate the problem. The proposed antenna consists of one sequentially rotating array(SRA), two beamforming networks(BFN), and a combined decoupling structure. The SRA is shared by the transmit and receive to reduce the size of the antenna and to obtain a consistent transmit and receive *** BFN achieve right-hand CP for transmit and left-hand CP for receive. By exploring the combined decoupling structure of uniplanar compact electromagnetic band gap(UC-EBG) and ringshaped defected ground structure(RS-DGS), good transmitreceive isolation is achieved. The proposed antenna prototype is fabricated and experimentally characterized. The simulated and measured results show good agreement. The demonstrate transmit/receive isolation is height than 33 dB, voltage standing wave ratio is lower than 2, axial ratio is lower than 3 dB, and consistent radiation for both transmit and receive is within4.25-4.35 GHz.

关键词： dual circularly polarization(CP) monostatic simultaneous transmit and receive(MSTAR) sequential rotation array(SRA) uniplanar compact electromagnetic band gap(UC-EBG) ring-shaped defected ground structure(RS-DGS)

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Hybrid Network Architecture and Design of Terrestrial and Satellite Networks, its Reliability for Mongolia

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Journal of Electrical engineering and technology 2025年 1-12页

作者： Yura, Otgonbaatar Shin, Kwangcheol Zundui, Buyankhishig Shagdar, Ganbold Khurelbaatar, Zagarzusem Choi, Juhee Huree University of Information and Communication Technology and School of Information and Communication Technology MUST Ulaanbaatar Mongolia Huree University of Information and Communication Technology Ulaanbaatar Mongolia School of Information and Communication Technology MUST Ulaanbaatar Mongolia Department of Smart Information Communication Engineering Sangmyung University Cheonan Korea Republic of

Mongolia is planning to launch its national satellite by 2030, with a phased approach to developing a comprehensive national policy. The International Telecommunication Union has allocated two geosynchronous orbital positions for Mongolia: 113.6°E for Fixed Satellite Service (FSS) and 74°E for Broadband Satellite Service (BSS). Therefore, we are conducting various scientific studies on connecting and integrating future national satellites with the current terrestrial networks of Mongolia. The purpose of this research work is to design a hybrid terrestrial and satellite network based on the future national satellite at the 113°E orbital position for Mongolia. First, the requirements for hybrid of satellite-terrestrial network are defined, followed by an analysis of related research work and a summary of the experiences of countries that have developed hybrid satellite and terrestrial networks. Then a detailed analysis of the current backbone network in Mongolia will be conducted, focusing on the penetration of the fiber optic network. Following that, satellite geometry parameters will be calculated for each province in Mongolia. Finally, we aim to define a new model for the satellite network in each zone and optimally design a hybrid satellite-terrestrial network based on this new model for each zone. Additionally, the reliability of the hybrid satellite-terrestrial network for Mongolia will be calculated. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2025.

关键词： Network function virtualization

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Sustainable AI Systems for Monitoring and Predicting Wildfires in Vulnerable Forest Regions

Sustainable AI Systems for Monitoring and Predicting Wildfir...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raj, T. Selvin Retna Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vikkiramapandian, D. Jothi, J. Nirmal Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331509828

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities © 2025 IEEE.

关键词： Convolutional neural networks

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Spectral efficiency enhancement and energy optimization in 5G networks via stochastic optimization-inspired by modified slime mould algorithm

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International Journal of Information technology (Singapore) 2025年第2期17卷 883-894页

作者： Anumala, Vijayasankar Shaik, Fayaz Ahamed Kalluru, Padma Vasavi Nadakuduru, Udaya Kumar Kopanaty, Sambasiva Rao Meruga, Surendra Kumar Department of Electronics and Communication Engineering V R Siddhartha Engineering College Andhra Pradesh Vijayawada 520007 India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram 534202 India Department of Electronics and Communication Engineering SRKR Engineering College Chinamiram Rural Andhra Pradesh Bhimavaram 534204 India Department of Electronics and Communication Engineering Bapatla Engineering College Andhra Pradesh Bapatla 522102 India Department of Electronics and Communication Engineering Vikas Institute of Technology Nunna Andhra Pradesh Vijayawada 521212 India

In the context of implementing Fifth Generation (5G) wireless networks, Energy Efficiency (EE), along with Spectral Efficiency (SE), are critical to consider. Simultaneously sharing the infrastructure between various operators was also observed to be a trend on wireless communication networks. Evaluating network performance is challenging, given factors like transmission time and power control. While the previous efforts have emphasized equalizing radio resources among access links and backhauls of millimetre wave (mmWave) small cells in 5G cellular systems. The focus was primarily on network throughput rather than Energy Efficiency (EE). Consequently, it has become imperative to consider both EE and SE as vital parameters to design 5G systems. This study introduces a novel approach, the Modified Slime Mould Algorithm (MSMA), which optimizes EE by maximizing SE. This optimization is achieved by fine-tuning parameters such as transmit power and bandwidth in 5G networks. The performance of the proposed network was evaluated through cost function and statistical analyses. The developed spectral efficiency enhancement model in the 5G network significantly improved SE when compared to Artificial Gorilla Troops Optimizer (AGTO), Jaya Algorithm (JA), Harris Hawks Optimization (HHO), and Slime Mould Algorithm (SMA) techniques. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Energy efficiency Energy optimization Fifth generation wireless networks Modified slime mould algorithm Spectral efficiency

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Design and analysis of series and parallel circuits based on plasmonic waveguides for high-performance computing devices

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Journal of Optical communications 2025年第1期46卷 193-200页

作者： Jain, Satyendra Choudhary, Kuldeep Kumar, Santosh Department of Electrical and Electronics & Communication Engineering DIT University Dehradun248009 India Shandong Key Laboratory of Optical Communication Science and Technology School of Physics Science and Information Technology Liaocheng University Shandong Liaocheng252059 China

Nowadays, mental-insulator-metal (MIM) waveguides are widely used in the design of optical plasmonic waveguides due to their superior ability to confine surface plasmons up to sub wavelength scale. This research proposes the design of series and parallel circuits using cascaded plasmonic Mach-Zehnder interferometers (PMZIs). Each PMZI employs a nonlinear arm to switch light across the output port via the insertion of the nonlinear Kerr material poly(2-methoxy-5-(28-ethylhexyloxy)-PPV). The proposed design is having footprint of 104 μm × 11 μm. The finite-difference time-domain method is used to study, design, and analysis all optical series and parallel circuit proposals. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Metal insulator boundaries

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Automated liver segmentation from CT images using modified ResUNet

Gastroenterology and Endoscopy

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Gastroenterology and Endoscopy 2025年第2期3卷 93-104页

作者： Manjunath, R.V. Gowda N, Yashaswini Manu, H.M. Department of Electronics & Communication Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bangalore 82 India Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore-78 India

The Segmentation of liver from computed tomography (CT) images is a critical task in medical research particularly when employing deep learning methods. Most doctors prefer to use Computed tomography images for liver disease identification hence automatic liver segmentation plays a decisive role. To perform automatic segmentation of liver popular architectures such as modified ResUNet are specifically designed to capture multi-resolution features. In this study we proposed an automatic system that utilizes convolutional layers to efficiently extract features while maintaining spatial information. A modified Residual UNet model was developed for liver segmentation using the 3Dircadb dataset, the model demonstrated excellent performance by achieving a Dice Score of 93.08 %, accuracy of 98.57 %, Jaccard Index of 87.12 %, a volumetric overlap error of 12.87 %, and a relative volume difference of 14.91 % on 128x128 size images. These results highlight the effectiveness of the proposed method. The primary goal of this research is to apply deep learning techniques for liver segmentation and assess the model's performance using segmentation metrics. © 2025 The Authors

关键词： 128x128 Computed tomography Liver segmentation Resnet ResUNet Volumetric overlapping error

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