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Power Budget Calculation and Sensitivity Enhancement for N×25 Gbps TDM-PON Configuration 3

Power Budget Calculation and Sensitivity Enhancement for N×...

3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Feituri, Ahmed B Abdullah, M.F.L. Swedan, Abdulhameed Almabrok Suliman, Hager A Universiti Tun Hussen Onn Malaysia Faculty of Electrical and Electronic Engineering Batu Pahat Malaysia The Electronic and Communication Research Center Applied Research and Development Authority Tripoli Libya University of Misurata Department of Computer Science Misurata Libya

ISBN: (纸本)9798350319897

Passive Optical Network (PON) standards relied on Time Division Multiplexing (TDM) and Wavelength Division Multiplexing (WDM) methods to maximize data rate. The latest 25GS-PON specifications were declared by Multi-Source Agreement (MSA) group in 2022. The current work stacks two and four 25-Gb/s TDM-PON downstream (DS) channels. The simulation, evaluation, and outcomes are completed using various versions of *** software. O-band wavelength plan as well as Continuous-Wave (CW) laser with Non-Return-to-Zero/On-Off-Keying (NRZ-OOK) format support the system to mitigate Chromatic Dispersion (CD) and upgrade the system capacity. The power budget of 31.1 dB is suitable for Optical Distribution Network (ODN) standards, and the standard value of receiver sensitivity (-24.1 dBm) is achieved. The transmitted power of 1, 4, and 7 dBm are needed for 32, 64, and 128 users/λ, respectively. The transmission is successful for 512 subscribers and 20 km optical reach without the use of amplifiers, Forward Error Correction (FEC), and compensation techniques. The effect of four-wave mixing (FWM) is solved by the wavelength space of 0.8 nm as well as the slight increase in transmitted power from 7 to 7.8 dBm. The open and clear eye diagrams prove the quality of received signals. The simplicity of structure is high because of utilizing basic components and Intensity Modulation/Direct Detection (IM/DD) strategy without amplification and equalization. FEC and compensation techniques are recommended for future work. © 2023 IEEE.

关键词： Passive optical networks

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IoT-Enabled Smart Manhole Management System for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Imran, Md Asif Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Anzum Mahi, Md. Arif Islam, Md Babul Haque, Rezaul Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Modeling Electronic and System Engineering Unical Dept. of Computer Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Modeling Electronic and System Engineering Unical Department of Computer Rende Italy

ISBN: (纸本)9798350375237

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring System improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city. © 2024 IEEE.

关键词： Maintenance

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Security Standards and use of Cyberinfrastructure Systems in Clinical research 23

Security Standards and use of Cyberinfrastructure Systems in...

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2023 Practice and Experience in Advanced research Computing, PEARC 2023

作者： Shankar, Anurag Hancock, David Michael, Scott Huber, Laura Butler, Joseph Henschel, Robert Stewart, Craig Center for Applied Cybersecurity Research Indiana University BloomingtonIN United States Research Technologies Indiana University BloomingtonIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9781450399852

Many areas of biomedical research, including precision health research, inherently involve the use of IT systems properly secured to store and analyze protected health information (PHI). Managing cybersecurity policies in ways that allow such storage and analysis of PHI on computing systems is involved, but so doing can facilitate important research. In 2005, Indiana University established the ability to analyze PHI (HIPAA alignment) as the minimal security level for its High Performance Computing (HPC) systems and research storage systems. Starting in 2010, REDCap was also offered as a central service, providing capabilities for conducting surveys that collect PHI and storing those data in ways that align with HIPAA. Usage data shows that providing computational and data resources aligned with HIPAA has increased the use of advanced research cyberinfrastructure over time. We explain some of our tactics in providing secure environments for handling protected health information and offer suggestions that other cyberinfrastructure centers may find useful in setting strategies for serving medical researchers. © 2023 ACM.

关键词： Clinical research

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Monitoring System of Wearable Sensor Signal in Rehabilitation Using Efficient Deep Learning Approaches 26

Monitoring System of Wearable Sensor Signal in Rehabilitatio...

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26th International computer Science and engineering Conference, ICSEC 2022

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

ISBN: (纸本)9781665491983

Recognition of human activity has utilized inputs from wearable sensors, which has significant implications for rehabilitative medicine and cognitive neuroscience. Unfortunately, some crucial dynamic data on upper-limb movements need to be included in the feature extraction procedure for wearable sensor data. The issue is that only a few rehabilitative motions can be recognized, and classification precision is readily compromised. We study several convolution neural networks to extract valuable characteristics from multichannel wearable sensor inputs automatically and precisely identify rehabilitation operations. We gathered wearable sensor signal data for six physiotherapy exercises to assess identification effectiveness using the SPARS9x standard rehabilitation dataset. Experiments showed that the PyramidNet18 model had the highest F1-score on the benchmark dataset, 99.15%. © 2022 IEEE.

关键词： Physical therapy

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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Stochastic Triangular Fuzzy Number (S-TFN) Normalization: A New Approach for Nonmonotonic Normalization 27

Stochastic Triangular Fuzzy Number (S-TFN) Normalization: A ...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Kizielewicz, Bartlomiej Dobryakova, Larisa Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Data normalization is essential in many fields, such as speech recognition, deep learning, machine learning, and optimization. Many researchers focus on developing various normalization techniques, such as min-max, score-based, maximum, vector, or sum, on processing data efficiently. However, classical normalization methods may need to be improved in Multi-Criteria Decision-Analysis (MCDA), where criteria are often non-monotonic. This paper proposes a new approach for identifying and normalizing non-monotonic criteria in MCDA. The method combines a stochastic optimization technique - a genetic algorithm - with normalization based on triangular fuzzy numbers. This paper compares the proposed approach with classical normalization techniques, such as min-max, maximum, vector, or non-linear, using the classical MCDA method - the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS). The study results showed that the proposed approach to normalizing non-monotonic criteria yields a better match between decision preferences and the actual reference model than traditional normalization methods. Optimization using the genetic algorithm helps identify the means of triangular fuzzy numbers, which generate more accurate results. Analysis of the R2 coefficient confirms a very good fit of the proposed model. Therefore, the new non-linear normalization approach in MCDA presented in this work has great potential in adjusting decision preferences to non-monotonic criteria. Future research should continue on the accuracy of this approach in identifying non-linear decision models and apply it to other MCDA methods and real-world decision problems. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decision analysis Fuzzy sets MCDA Normalization TOPSIS

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A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial Intelligence (AI) Logic 3

A Novel Methodology to Predict Flood and Landslide Disasters...

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

作者： Raj, G. Bhupal Patan, Muzeeb Khan Gupta, Ashwani Kumar Lakshmi, C. Gopinath, D. Yadav, Ajay Singh Sr University School of Agriculture Warangal India University of Technology and Applied Sciences College of Engineering and Technology Department of Eee Muscat Oman Chandigarh University Faculty of Business and Commerce Uttar Pradesh Unnao India Saveetha Engineering College Chennai India Department of Computer Science Karnataka Bengaluru India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India

ISBN: (纸本)9798331509675

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques. © 2025 IEEE.

关键词： Landslides

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Heart Disease Diagnosis by Machine Learning Techniques

Heart Disease Diagnosis by Machine Learning Techniques

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Lhamo, Tashi Arora, Geeta Emadifar, Homan Omole, Ezekiel Olaoluwa Abualigah, Laith Onu, Peter Olaleru, Grace Idowu Joshua, Ade-Omowaye Lovely Professional University School of Chemical Engineering and Physical Sciences Department of Mathematics India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Mathematics Tamil Nadu India Landmark University Department of Physical Science Mathematics Programme Omu-Aran Nigeria Landmark University SDG3 Good Health and Well-Being Research Group Omu-Aran Nigeria Research Center Tabuk1491 Saudi Arabia Al-Alhiyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University MEU Research Unit Amman11831 Jordan Omu-Aran Nigeria Landmark University Department of Electrical and Information Engineering Omu-Aran Nigeria

ISBN: (纸本)9798350358155

Disease diagnosis is a crucial component of healthcare for early disease detection and treatment. Recent machine learning techniques have improved efficiency and accuracy due to the abundance of available medical data. In this study, various algorithms for diagnosing heart disease are examined, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes (NB), Ensemble Learning and Support Vector Machine (SVM). This study's findings will advance disease diagnosis methods and build a more efficient healthcare system. Medical professionals can use machine learning to diagnose diseases, provide better patient care, and save lives. © 2024 IEEE.

关键词： Adversarial machine learning

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Set Stabilization of Large-Scale Stochastic Boolean Networks:A Distributed Control Strategy

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 806-808页

作者： Lin Lin Jinde Cao Jianquan Lu Leszek Rutkowski the Department of Mechanical Engineering the University of Hong KongHong Kong China the School of Mathematics Southeast UniversityNanjing 210096China IEEE the School of Mathematics and the Frontiers Science Center for Mobile Information Communication and Security Southeast UniversityNanjing 210096China the School of Mathematics the Southeast UniversityNanjing 210096 the School of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China the Systems Research Institute of the Polish Academy of Sciences Warsaw 01-447 the Institute of Computer Science AGH University of Science and TechnologyKraków 30-059 the Information Technology Institute University of Social SciencesŁódź90-113Poland

Dear Editor,This letter deals with the set stabilization of stochastic Boolean control networks(SBCNs)by the pinning control strategy,which is to realize the full control for systems by imposing control inputs on a fr... 详细信息

关键词： Boolean stabilization letter

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