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Underwater visible light communication: recent advancements and channel modeling

Optical and Quantum Electronics 2024年第10期56卷 1-45页

作者： Elfikky, Abdelrahman Boghdady, Ayman I. Mumtaz, Sajid Elsayed, Ebrahim E. Singh, Mehtab Abd El-Mottaleb, Somia A. Mohsan, Syed Agha Hassnain Aly, Moustafa H. Electrical and Computer Engineering Department Baskin School of Engineering University of California Santa Cruz United States Arab Academy for Science Technology and Maritime Transport Alexandria1029 Egypt Institute of Nano Optoelectronics Research and Technology Universiti Sains Malaysia Pulau Malaysia Department of Electronics and Communications Engineering Faculty of Engineering Mansoura University Mansoura Egypt Department of Electronics and Communication Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Alexandria Higher Institute of Engineering and Technology Alexandria Egypt Ocean College Zhejiang University Hangzhou China

Underwater Visible Light Communication (UVLC) is a promising technology for high-speed data transmission in aquatic environments. However, the performance and reliability of UVLC systems are often challenged by factors such as absorption, scattering, and oceanic turbulence. In this paper, we explore various UVLC modulation techniques, analyzing both Single Carrier Modulation and Multi-Carrier Modulation schemes, including their respective advantages and limitations. Through simulations and experimental evaluations, we investigate the effectiveness of modulation strategies such as Non-Return-to-Zero On-Off Keying, Pulse Position Modulation, and Quadrature Amplitude Modulation-Carrierless Amplitude Phase Modulation in addressing the inherent challenges of UVLC. Additionally, we examine advanced techniques like Orthogonal Frequency Division Multiplexing and space-domain index modulation, highlighting their potential to achieve higher data rates and improved reliability in UVLC systems. Our findings underscore the importance of selecting optimal modulation schemes to enhance spectral efficiency, reduce interference, and improve Bit Error Rate performance. Recent experimental advancements have showcased remarkable achievements, such as data rates exceeding 30 Gbps over distances up to 21 m, demonstrating significant progress in system reliability and transmission quality. This paper also identifies potential challenges and open research directions in UVLC, such as the need for efficient channel estimation and system configuration algorithms, contributing to the design and optimization of robust and scalable underwater communication systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Orthogonal frequency division multiplexing

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Performance Enhancement of Praseodymium Doped Fiber Amplifiers

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computers, Materials & Continua 2022年第12期73卷 5411-5422页

作者： Abdullah G.Alharbi Jawad Mirza Mehak Raza Salman Ghafoor Department of Electrical Engineering Faculty of EngineeringJouf UniversitySakaka42421Saudi Arabia SEECS Photonics Research Group Islamabad44000Pakistan Institute of Space Technology Islamabad44000Pakistan School of Electrical Engineering and Computer Science(SEECS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan

In this paper,we report a simulation study on the performance enhancement of Praseodymium doped silica fiber amplifiers(PDFAs)in O-band(1270-1350 nm)in terms of small signal gain,power conversion efficiency(PCE),and output optical power by employing bidirectional *** PDFA performance is examined by optimizing the length of Praseodymium doped silica fiber(PDF),its mode-field diameter(MFD)and the concentration of Pr^(3+).A small-signal peak gain of 56.4 dB,power conversion efficiency(PCE)of 47%,and output optical power of around 1.6 W(32 dBm)is observed at optimized parameters for input signal wavelength of 1310 *** noise figure(NF)of 4.1 dB is observed at input signal wavelength of 1310 ***,the effect of varying the pump wavelength and pump power on output optical power of the amplifier and amplified spontaneous emission(ASE)noise is also investigated,***,the impact of ion-ion interaction(up-conversion effect)on small-signal gain of the amplifier is also studied by considering different values of up-conversion coefficient.

关键词： Praseodymium-doped fiber doped fiber amplifiers optical amplification mode field diameter noise figure

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Neuromorphic-Driven Agentic AI for Autonomous Decision-Making Systems 4

Neuromorphic-Driven Agentic AI for Autonomous Decision-Makin...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Manjunath Kamath, K. Samata Mehta, S. Akshaya, H.L. Shilpashree, N. Jadhav, Girish Mitra, Abhijit Department of Cyber Security Karkala India Bengaluru India Sarada Vilas College Department of Computer Science Mysuru India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department Of Electrical Engineering Post Limda India Pune India

ISBN: (纸本)9798350352931

Agentic AI represents a paradigm shift in the development of intelligent systems capable of adaptive and proactive interactions in dynamic and complex environments. By integrating reinforcement learning (RL) with cognitive frameworks, Agentic AI goes beyond traditional rule-based and reactive models, enabling autonomous systems to make informed decisions, anticipate future states, and learn from experience. This paper explores the theoretical foundations and practical applications of Agentic AI, highlighting its potential to transform a variety of fields, including robotics, autonomous driving, finance, and healthcare. Through a detailed review of state-of-the-art research, we illustrate how cognitive architectures such as ACT-R and Soar, combined with advanced RL techniques like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), contribute to the development of AI agents with human-like reasoning and decision-making capabilities. Experimental results demonstrate that Agentic AI significantly outperforms conventional AI approaches in terms of adaptability, learning efficiency, and decision accuracy. The findings suggest that Agentic AI offers a robust framework for creating intelligent systems capable of complex problem-solving, long-term planning, and proactive behavior, paving the way for the next generation of AI-driven applications. © 2024 IEEE.

关键词： Intelligent systems

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Image Acquisition and Deep Convolutional Neural Network Incorporated Cost-Effective Automated Nursery Disease Detection Method for the Tea Industry 10

Image Acquisition and Deep Convolutional Neural Network Inco...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Weerasekara, Dinithi Bhagya Kahatapitiya, Nipun Shantha Sanjeewa, Nuwan Wathudura, Vimukthi Madushan Prasanna, Nuwan Kalupahana, Deshan Wijenayake, Udaya Silva, Bhagya Nathali Wijethunge, Akila Wijesinghe, Ruchire Eranga University of Sri Jayewardenepura Faculty of Technology Department of Materials and Mechanical Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka IOT Solutions Pvt Ltd Homagama Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331529048

Tea cultivation, a globally significant industry across over 60 countries, requires effective management of tea nurseries for robust plant growth and disease control. Implementing automated disease detection in tea nurseries using advanced technologies enhances efficiency, precision, and sustainability compared to manual inspection methods. An image acquisition system incorporating a Deep Convolutional Neural Network (DCNN) was developed as a cost-effective automated solution for nursery disease detection. First, images of Blister Blight (BB) disease in tea plants were collected primarily from various estates. These images were then preprocessed, labeled, and augmented to create a robust dataset. Two models based on the YOLOv5 architecture were trained, with the second model benefiting from dataset augmentation and enhanced labeling. The Improved Model achieved a mean Average Precision (mAP) of 80.6% at a 0.5 threshold, demonstrating its high accuracy in BB detection in tea nurseries. It was integrated with the image acquisition system to enable continuous monitoring of tea leaves in nursery plantations and facilitate the immediate detection of BB diseases. This system holds significant potential for further enhancement in disease management across the tea industry, promoting healthier crops, reducing costs, and minimizing environmental impact. © 2024 IEEE.

关键词： Convolutional neural networks

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A Bagging MLP-based Autoencoder for Detection of False Data Injection Attack in Smart Grid

A Bagging MLP-based Autoencoder for Detection of False Data ...

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2022 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2022

作者： Paul, Shuva Kundu, Ripan Kumar Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States University of Rostock Faculty of Computer Science & Electrical Engineering Rostock Germany

ISBN: (纸本)9781665437752

The accelerated move toward adopting the Smart Grid paradigm has resulted in numerous drawbacks as far as security is concerned. Traditional power grids are becoming more vulnerable to cyberattacks as all the control decisions are generated based on the data the Smart Grid generates during its operation. This data can be tampered with or attacked in communication lines to mislead the control room in decision-making. The false data injection attack (FDIA) is one of the most severe cyberattacks on today's cyber-physical power system, as it has the potential to cause significant physical and financial damage. However, detecting cyberattacks are incredibly challenging since they have no known patterns. In this paper, we launch a random FDIA on IEEE-39 bus system. Later, we propose a Bagging MLP-based autoencoder to detect the FDIAs in the power system and compare the result with a single ML model. The Bagging MLP-based autoencoder outperforms the Isolation forest while detecting FDIAs. © 2022 IEEE.

关键词： Smart power grids

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Creation of a Smart Energy Management System in the RTU Student Campus in Kipsala 65

Creation of a Smart Energy Management System in the RTU Stud...

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65th IEEE Annual International Scientific Conference on Power and electrical engineering of Riga Technical University, RTUCON 2024

作者： Dzelzitis, Egils Apse-Apsitis, Peteris Lungis, Edijs Institute of Heat Gas and Water Technology RTU Riga Latvia Institute of Industrial Electronics Electrical Engineering and Energy RTU Riga Latvia RTU Faculty Of Computer Science Information Tehnology And Energy Riga Latvia

The task of the energy management system is to create conditions for maximizing the efficiency of electricity and heat consumption in RTU buildings, while ensuring a comfortable indoor climate and enabling continuous ...

ISBN: (纸本)9798350365771

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Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

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Neural Computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

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Digital Image Watermarking Method Based on LSB and DWT Hybrid Technique 2

Digital Image Watermarking Method Based on LSB and DWT Hybri...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Ghrare, Seddeq. E. Adim Mohamad Alamari, A. Emhemed, Hajer Abdulhkim University of Gharyan Faculty of Engineering Department of Electrical and Computer Engineering Gharyan Libya Higher Institute of Engineering Technology Department of Computer Engineering Gharyan Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The watermarking technique can be considered as an important mechanism to protect the Intellectual Property Rights (IPR) of multimedia contents. Currently, most of multimedia contents are exchanged online in a digital form, which exposes the content to be illegal alteration easily without recognize theoffenders. Digital watermarking is used to embed a watermark into the digital media in such away that can not be discovered from the original watermarked data. Consequently, the watermark can be removed and used to verify the copyright ownership of that digital media. In this paper, a hybrid watermarking technique based on Least Significant Bit (LSB) and Discrete Wavelet Transform (DWT) is introduced and explained.. In order to investigate the effectiveness of the proposed hybrid technique in terms of robustness and imperceptibility, a comparison between the three techniques is also presented. This comparison was carried out using Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM). The SSIM results were very close to 1, which means that the recovered watermarks from watermarked image were not distorted in the three cases. The PSNR and MSE values show that the quality of the watermarked images using the proposed technique is better compared to that watermarked images using standalone LSB and DWT. Moreover, the results prove that the proposed hybrid technique was more effective in terms of robustness and imperceptibility compared to LSB and DWT techniques © 2022 IEEE.

关键词： Discrete wavelet transforms

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & Sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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Network Traffic Prediction with Decomposition and Multi-Scale Autocorrelation in Large-Scale Cloud Data Centers 21

Network Traffic Prediction with Decomposition and Multi-Scal...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Meijia Yuan, Haitao Kuang, Zhenwei Ma, Hanbo Bi, Jing Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350365221

As the Internet and big data technologies advance, a tremendous amount of data is generated daily. Efficient network operations are essential for handling such data. Accurately predicting future network traffic in real-time enables prompt response from cloud infrastructure and efficient traffic scheduling and allocation, ultimately reducing costs, preventing economic losses, and optimizing the performance of downstream facilities. However, predicting network traffic in large-scale data centers is challenging due to the multidimensional, nonlinear, and high-volatility nature of the time series. Traditional prediction methods, e.g., regression algorithms, struggle to capture non-linear features effectively. Many deep learning models face issues such as gradient explosion or vanishing during their training. Current commonly used prediction methods fail to fully uncover vital information about the frequency domain features in the time series. To do so, this work proposes a novel network traffic prediction model that combines a Savitzky Golay filter, sequence decomposition, multi-scale attention, a temporal convolutional network, an autocorrelation mechanism, and bidirectional long short-term memory. It can accurately predict future network traffic by adaptively extracting important features without requiring excessive feature engineering of the original data. It performs end-to-end sequence prediction, feature selection, and automatic learning of data features and temporal dependencies to achieve accurate time series prediction and avoid redundant data processing. Experiments involving ablation studies and comparisons with advanced prediction models are performed with the Google cluster trace. Experimental results show that the proposed model improves the prediction accuracy by at least 38.52% over the state-of-the-art models. © 2024 IEEE.

关键词： Autocorrelation

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