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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 34-48页

作者： Hammadi, Ahmed Al Bariah, Lina Muhaidat, Sami Al-Qutayri, Mahmoud Sofotasios, Paschalis C. Debbah, Merouane Technology Innovation Institute Abu Dhabi United Arab Emirates Khalifa University Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaK1S 5B6 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere33014 Finland Khalifa University KU 6G Center Abu Dhabi United Arab Emirates

Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of both the overall SE and Jain's Fair Index, achieved at a fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance. © 2023 The Authors.

关键词： Spectrum efficiency

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Accuracy in Wireless Communication: Improved Arrival Direction Estimation in Adaptive Antennas via Expanded MUSIC Beamforming

Accuracy in Wireless Communication: Improved Arrival Directi...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Kumar, Kapil Pandey, Priyank Khari, Manju Jawaharlal Nehru University School of Computer & Systems Sciences New Delhi India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350382723

Smart antennas are now widely expended in transmission systems due to their vital plusses. To boost the performance of smart antenna operation, it is crucial to design an efficient beamforming pattern based on specific antenna parameters. Due to their numerous benefits, smart antennas are now often utilized in communication systems. It is essential to create a successful beamforming pattern based on certain antenna parameters with the objective of improvement in the performance of smart antenna operation. Beamforming strategies utilizing a combination of soft computing techniques were proposed in earlier works. These methods, however, did not take signal Direction of Arrival (DOA) precision into account. While determining the beamforming pattern for smart antennas, this research addresses the DOA issue. The researchers use a variety of techniques and conduct a comparison analysis to determine an exact DOA estimate. The authors proposed a method that shows an optimal result after resolving the existing problems for estimating the DOA with precision. In comparison to the traditional DOA pattern, this innovation produces a more accurate DOA pattern with reduced undesired side lobes. The researchers derive the antenna beamforming pattern in accordance with the necessary direction of angles using the predicted DOA pattern. The experimental results demonstrated that the proposed beamforming technology performs better than earlier techniques. In this work, advancement in beamforming precision can lead to better communication system performance and overall efficiency of smart antennas. © 2024 IEEE.

关键词： Beamforming

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Integrated Leaf Disease Recognition Across Diverse Crops through Transfer Learning 5

Integrated Leaf Disease Recognition Across Diverse Crops thr...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Tambe, Utkarsh Yashwant Shanthini, A. Hsiung, Pao-Ann Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan

Global food security is seriously threatened by plant diseases and can cause severe economic losses to farmers. Automated detection of plant diseases using computer vision and machine learning techniques has become a popular research area due to its potential to provide faster and more accurate results than traditional methods. In this research, we propose a plant disease detection model based on transfer learning using the MobileNetV2. We evaluate the suggested method on a dataset consisting of 38 various kinds of diseases across 14 different plants. Our experimental findings indicate that the proposed method has a typical accuracy of 91.98% and outperforms additional cutting-edge CNN models for plant disease detection. The experimental findings support the suggested approach's validity and show that it effectively detects plant diseases. We also have put forth the evaluation metrics to investigate the model. The suggested method has potential applications in real-world scenarios and can help farmers detect diseases in their crops at an early stage, allowing them to take timely action to minimize crop losses. © 2024 The Authors. Published by Elsevier B.V.

关键词： Losses

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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MINIMUM-RANK POSITIVE SEMIDEFINITE MATRIX COMPLETION WITH CHORDAL PATTERNS AND APPLICATIONS TO SEMIDEFINITE RELAXATIONS

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2023年第2期5卷 265-283页

作者： Jiang, Xin Sun, Yifan Andersen, Martin S. Vandenberghe, Lieven Department of Industrial and Systems Engineering Lehigh University BethlehemPA18015-1582 United States Department of Computer Science Stony Brook University Stony BrookNY11794-2424 United States Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States

We present an algorithm for computing the minimum-rank positive semidefinite completion of a sparse matrix with a chordal sparsity pattern. This problem is tractable, in contrast to the minimumrank positive semidefinite completion problem for general sparsity patterns. We also present a similar algorithm for the Euclidean distance matrix completion with minimum embedding dimension. The two algorithms use efficient recursions over a clique tree associated with the chordal sparsity pattern. As an application, we use the minimum-rank completion method as a rounding technique to convert the solution of a sparse semidefinite optimization problem with non-unique solutions to an optimal solution of lower rank. In experiments with semidefinite relaxations of optimal power flow problems, the minimum-rank completion often results in solutions of lower rank than the solutions computed by interior-point solvers. ©2023 Applied Set-Valued Analysis and Optimization.

关键词： Electric load flow

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IoT-Based Smart Beehive Monitoring System 6

IoT-Based Smart Beehive Monitoring System

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Liyanage, Nisal Attanayaka, Chamindra Perera, Tarinya Neilkumara, Daniru Bandara, Isuru Sri Chandrasiri, Lakshman NSBM Green University Town Department of Computer Science and Data Science Homagama Sri Lanka NSBM Green University Town Department of Computer Security and Network Systems Homagama Sri Lanka NSBM Green University Town Department of Electrical Electronic and Systems Engineering Homagama Sri Lanka

ISBN: (纸本)9798331517878

In recent years, there has been an alarming decline in bee populations both globally and in Sri Lanka, severely impacting ecosystems and agriculture worldwide. Traditional methods in Sri Lankan beekeeping are often time-consuming and less efficient, making hive management and health assessment challenging. This paper introduces a smart IoT-based beehive monitoring system tailored to the Sri Lankan beekeeping industry. The system integrates various sensors to measure key hive parameters, such as temperature, humidity, and weight, offering beekeepers a user-friendly web interface for real-time monitoring of hive conditions. It also provides insights into hive management aspects, including feeding needs, optimal harvesting times, and abnormal temperature detection within the hive. This paper highlights the initial steps towards implementing a robust IoT-based beehive monitoring system, acknowledging its current limitations. By addressing these limitations in the future, the authors aim to develop a more effective and efficient monitoring method for Sri Lankan beekeepers, saving time and enhancing the productivity and health of bee colonies. © 2024 IEEE.

关键词： Electronic health record

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Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

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Sampling-Based Distributed Control for Quantized Consensus Tracking Under Denial-of-Service Attacks 7

Sampling-Based Distributed Control for Quantized Consensus T...

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7th International Symposium on Autonomous systems, ISAS 2024

作者： Wang, Xinhe Wang, Linan Wang, Shuai Southeast University School of Cyber Science and Engineering Nanjing211189 China Southeast University Department of Systems Science Nanjing211189 China Beihang University School of Computer Science and Engineering Beijing100191 China

ISBN: (纸本)9798350363173

In this paper, the quantized consensus tracking problem for continuous-time multi-agent systems (MASs) under denial-of-service (DoS) attacks is studied. Based on the dynamical quantized strategy, a sampling-based control protocol is developed, which is suitable for the high-order MASs under the general directed communication topology with limited bandwidth. To resist the DoS attacks, the scaling function is designed with a zooming-in and zooming-out approach. Sufficient conditions for consensus tracking problem under DoS attacks are provided, and the quantizer is unsaturated under the specified quantization level. Finally, simulation examples are provided to validate the theoretical results. © 2024 IEEE.

关键词： Denial-of-service attack

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PalateTouch : Enabling Palate as a Touchpad to Interact with Earphones Using Acoustic Sensing 25

PalateTouch : Enabling Palate as a Touchpad to Interact with...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Zhao, Yankai Zhang, Jin Li, Jiao Sun, Tao Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798400713941

This paper introduces PalateTouch, a hands-free earphone interaction system that leverages acoustic sensing technology to detect gestures resulting from the interaction between the tongue and the palate. By transmitting Zadoff-Chu signals and analyzing ear canal transfer function features, PalateTouch can capture subtle ear canal deformation and recognize various palate gestures used for interaction. Our proposed palate touch screening method ensures the system remains unaffected by unintended gestures from daily activities and the calibration mechanism enables our system to achieve user-independent recognition. Using only the earphone's built-in microphone and speaker, our system can distinguish nine gestures with an average F1 score of 0.92 and a false alarm rate of 0.02 across diverse conditions with 16 participants. Additionally, we have enabled real-time functionality and conducted a user study with 11 participants to evaluate PalateTouch's effectiveness in a demo application. The results demonstrate the superior performance and high usability of PalateTouch. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Gesture recognition

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