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1st International Symposium on Systems Modelling and Simulation, SMS 2024

作者： Mugunthan, Sivaguru Abdul Rahman, Juwairiyyah Abu Hassan, Hasliza Muslim, Norliana Ab Rahman, Mohammad Syuhaimi Department of Engineering Faculty of Engineering and Life Sciences Universiti Selangor Selangor Bestari Jaya45600 Malaysia Department of Computer and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar31900 Malaysia Department of Electrical Electronic and System Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (纸本)9789819646128

Modeling Fiber To The Home (FTTH) networks involves several technical aspects and design considerations to enable effective and high-quality service delivery. This research seeks to boost the received signal quality for end-users inside a FTTH network. In order to create suitable and high-quality FTTH networks, this study will simulate FTTH networks utilizing network feasibility criteria, with an emphasis on BER and Q-factor. The signal distribution inside access networks is improved using Wavelength Division Multiplexing (WDM) and 10G-Passive Optical Network (PON) technology is used since it can deliver a fourfold improvement in downstream data transfer rates per user. Simulation result using OptiSystem 21 software, by setting transmitter wavelengths of 1490 nm and 1550 nm with a loss of 0 dB for a maximum of 2 users for total access based on the Q-factor calculation, which must be more than 6, while the BER must be less than 1 × e−9. System performance parameters that demonstrate Q-factor and Bit Error Rate (BER) give good quality and good performance is also shown by eye pattern in optical simulation. For 1490 nm, the highest Q-factor for 2 users is 35.8226, with a minimum BER of 2.4360 × 10−281. For 1550 nm, the highest Q-factor is 15.3668 (in phase) and 14.8087 (in quadrature), with minimal BER values of 1.3674 × e−53 (in phase) and 6.4291 × e−50 (in quadrature). Good performance is also shown by eye pattern in optical simulation that displays amplitude signals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Q factor measurement

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Periodicity and Bifurcation of Single Pulse and Soliton Molecules

Periodicity and Bifurcation of Single Pulse and Soliton Mole...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xiaoqiang Ban Xiaohui Li Qian Li Ming Zhong School of Electronic and Computer Engineering Peking University Shenzhen China School of Physics and Information Technology Shaanxi Normal University Xi'an China Faculty of Education Shenzhen University Shenzhen China

Period bifurcation of single pulse and soliton molecule are studied in fiber lasers, where period-timing bifurcation reveals the abundant nonlinear pulse dynamics and may be applied to optical switch.

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Optical switches Power lasers Asia Solitons Bifurcation Optical fiber communication Nonlinear dynamical systems Laser pulses Photonics Fiber lasers

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Tri-band Minkowski Island Split Ring Resonator Patch Antenna for WLAN and 5G Applications

Tri-band Minkowski Island Split Ring Resonator Patch Antenna...

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International Symposium on Antennas and Propagation (ISAP)

作者： Nornikman Hassan Badrul Hisham Ahmad Amier Hafizun Ab Rashid Saidatul Norlyna Azemi Centre of Telecommunication Research and Innovation Faculty of Electronics and Computer Technology and Engineering Universiti Teknikal Malaysia Melaka (UTeM) Durian Tunggal Melaka Malaysia Kolej Komuniti Segamat Johor Segamat Malaysia Advanced Communication Engineering Center of Excellence Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Arau Perlis Malaysia

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

This research investigates the effects of the Minkowski Island split ring resonator (MI-SRR) on the patch antenna. Initially, a Minkowski Island patch antenna design, Antenna A, was created in the CST Microwave Studio. Incorporating different diameters of the MI-SRR on the substrate resulted in three distinct resonant frequencies, shown as in Antenna B and Antenna C. This tri-band antenna performs best at 2.40 GHz, 3.52 GHz, and 5.22 GHz of resonant frequencies, with return losses of -21.945 dB, -17.154 dB, and -16.536 dB, respectively.

关键词： Microwave antennas Wireless LAN Split ring resonators 5G mobile communication Patch antennas Resonant frequency Substrates Gain Antenna radiation patterns

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Construction of PCOS Prediction Model Based on BP Neural Network

Construction of PCOS Prediction Model Based on BP Neural Net...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Xianqing Hu Amit Yadav Asif Khan Abhishek Pratap Sah Sami Azam School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP N eural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients.

关键词： Training Adaptation models Accuracy Biological system modeling Computational modeling Neural networks Predictive models Data models Robustness Tuning

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Game-Theory-Guided Reinforcement Learning for Autonomous Vehicles in Highway Scenarios 8

Game-Theory-Guided Reinforcement Learning for Autonomous Veh...

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8th IEEE Information technology and Mechatronics engineering Conference, ITOEC 2025

作者： Wang, Jiayang Sun, Wanlu Xiao, Pengbo Li, Qian Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia XC-AS/EDR3-CN Bosch Automotive Products (Suzhou) Co. Ltd. Suzhou China Hangzhou University of Electronic Science and Technology College of Mechanical Engineering Hangzhou China Tianjin University State Key Laboratory of Engines Tianjin China

ISBN: (纸本)9798331517915

Autonomous vehicles have become a popular topic with the development of technology. In the early stages of an automated driving system, the key to correctly guiding the vehicle's behavior lies in the effective collection and application of expert driving experience. This study introduces a game-theoretic guided reinforcement learning (GTG-RL) framework that merges game-theoretic expert models with reinforcement learning to enhance decision-making efficiency for autonomous vehicles in complex motorway scenarios. Validation confirms the framework's effectiveness in improving performance and adaptability under dynamic traffic conditions. The results underscore the potential of integrating game-theoretic expertise with reinforcement learning to tackle autonomous driving challenges. © 2025 IEEE.

关键词： Autonomous Vehicles Decision-Making Efficiency Dynamic Traffic Conditions Expert Driving Experience Game-Theoretic Guided Reinforcement Learning

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An Advanced NLP Framework for Automated Medical Diagnosis with DeBERTa and Dynamic Contextual Positional Gating

arXiv

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arXiv 2025年

作者： Khaniki, Mohammad Ali Labbaf Saadati, Sahabeh Manthouri, Mohammad Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran Department of Computer Engineering North Tehran Branch Islamic Azad University Tehran Iran Department Department of Electrical and Electronic Engineering Shahed University Tehran Iran

This paper presents a novel Natural Language Processing (NLP) framework for enhancing medical diagnosis through the integration of advanced techniques in data augmentation, feature extraction, and classification. The proposed approach employs back-translation to generate diverse paraphrased datasets, improving robustness and mitigating overfitting in classification tasks. Leveraging Decoding-enhanced BERT with Disentangled Attention (DeBERTa) with Dynamic Contextual Positional Gating (DCPG), the model captures fine-grained contextual and positional relationships, dynamically adjusting the influence of positional information based on semantic context to produce high-quality text embeddings. For classification, an Attention-Based Feedforward Neural Network (ABFNN) is utilized, effectively focusing on the most relevant features to improve decision-making accuracy. Applied to the classification of symptoms, clinical notes, and other medical texts, this architecture demonstrates its ability to address the complexities of medical data. The combination of data augmentation, contextual embedding generation, and advanced classification mechanisms offers a robust and accurate diagnostic tool, with potential applications in automated medical diagnosis and clinical decision support. This method demonstrates the effectiveness of the proposed NLP framework for medical diagnosis, achieving remarkable results with an accuracy of 99.78%, recall of 99.72%, precision of 99.79%, and an F1-score of 99.75%. These metrics not only underscore the model's robust performance in classifying medical texts with exceptional precision and reliability but also highlight its superiority over existing methods, making it a highly promising tool for automated diagnostic systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Real-Time Disease Classification in Coffee Plants with Coral Developer Board Mini

Real-Time Disease Classification in Coffee Plants with Coral...

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2024 IEEE International Conference on Agrosystem engineering, technology and Applications, AGRETA 2024

作者： Warsi, Mohammad Hamzah Nugroho, Hermawan Almurib, Haider Abbas Siang, Tay Fei Eswaran, Sivaraman University of Nottingham Malaysia Department of Electrical and Electronic Engineering Semenyih Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Campus Sarawak Malaysia Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350373660

Coffee significantly contributes to the socioeconomic development of numerous countries. However, coffee production faces several challenges, including the detrimental effects of coffee leaf diseases, which impact crop yield and quality. Traditional methods for diagnosing coffee leaf diseases are time-consuming and prone to human error. This paper presents an automated system for coffee leaf disease classification with edge computing using the Coral Developer Board Mini, featuring Google's Edge TPU. The system leverages advanced convolutional neural network (CNN) models, including MobileNetV2, VGG-16, VGG19, and DenseNet121, to achieve high accuracy in disease identification. The MobileNetV2 model achieved the highest training accuracy of 98.9%. Inference time comparisons revealed that the Coral Developer Board Mini, with an inference time of 10 milliseconds, outperformed a traditional GPU-based workstation, which had an inference time of 40 milliseconds. Results highlight the efficiency and suitability of the Coral Developer Board Mini for real-time, on-device machine learning applications in agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Twenty years of e-learning in health science: A bibliometric

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Procedia computer Science 2023年 216卷 604-612页

作者： Fairuz Iqbal Maulana M. Aldiki Febriantono Dwi Risza Budi Raharja Khaeruddin Ridho Herasmara Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health Sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health Sciences field, which Scopus index.

关键词： e-learning health science bibliometric vosviewer education

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Ternary Spike-Based Neuromorphic Signal Processing System

SSRN

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SSRN 2024年

作者： Wang, Shuai Zhang, Dehao Belatreche, Ammar Xiao, Yichen Qing, Hongyu Wei, Wenjie Zhang, Malu Yang, Yang Department of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom

Deep Neural Networks (DNNs) have been successfully implemented across various signal processing fields, resulting in significant enhancements in performance. However, DNNs generally require substantial computational resources, leading to significant economic costs and posing challenges for their deployment on resource-constrained edge *** this study, we take advantage of spiking neural networks (SNNs) and quantization technologies to develop an energy-efficient and lightweight neuromorphic signal processing system. Our system is characterized by two principal innovations: a threshold-adaptive encoding (TAE) method and a quantized ternary SNN (QT-SNN). The TAE method can efficiently encode time-varying analog signals into sparse ternary spike trains, thereby reducing energy and memory demands for signal processing. QT-SNN, compatible with ternary spike trains from the TAE method, quantifies both membrane potentials and synaptic weights to reduce memory requirements while maintaining performance. Extensive experiments are conducted on two typical signal-processing tasks: speech and electroencephalogram recognition. The results demonstrate that our neuromorphic signal processing system achieves state-of-the-art (SOTA) performance with a 94% reduced memory requirement. Furthermore, through theoretical energy consumption analysis, our system shows 7.5× energy saving compared to other SNN works. The efficiency and efficacy of the proposed system highlight its potential as a promising avenue for energy-efficient signal processing. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Real-Time Disease Classification in Coffee Plants with Coral Developer Board Mini

Real-Time Disease Classification in Coffee Plants with Coral...

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Agrosystem engineering, technology & Applications (AGRETA), IEEE International Conference on

作者： Mohammad Hamzah Warsi Hermawan Nugroho Haider Abbas AlMurib Tay Fei Siang Sivaraman Eswaran Department of Electrical and Electronic Engineering University of Nottingham Malaysia Semenyih Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

关键词： Training Accuracy Computational modeling Scalability Real-time systems Workstations Convolutional neural networks Socioeconomics Diseases Edge computing

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