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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

computer Modeling in engineering & Sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Self-excitation of Terahertz Plasmons in Graphene FETs Enabled by Transit-time Negative Dynamic Conductance 2

Self-excitation of Terahertz Plasmons in Graphene FETs Enabl...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Ryzhii, Maxim Ryzhii, Victor Otsuji, Taiichi Mitin, Vladimir Shur, Michael S. University of Aizu Department of Computer Science and Engineering Aizu-Wakamatsu Japan Research Institute of Electrical Communication Tohoku University Sendai Japan University at Buffalo Department of Electrical Engineering SUNY BuffaloNY United States Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States

ISBN: (纸本)9781665475174

The interband Zener-Klein interband tunneling in the field-effect transistors (FETs) with the graphene channels consisting of the reverse-biased p-i-n junctions enables the negative dynamic conductance associated with the electrons and hole propagation delays. This can lead to the self-excitation of plasmons in the gated p- and n-regions. For the submicrometer gate lengths, the frequency of these plasmons is in the terahertz (THz) range. The plasmon self-excitation in this regime can be used for the generation of THz radiation. © 2023 IEEE.

关键词： Plasmons

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Monocular vision navigation system for UAV autonomous mission: a real-time window-based obstacle avoidance approach

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Neural Computing and Applications 2025年第16期37卷 9843-9860页

作者： Javaid, Abdulrahman Alnaser, Mustafa Baroudi, Uthman Alfaraj, Amjad Research and Development Department Yokogawa Saudi Arabia Company Al Khobar Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Center for Intelligent Secure Systems King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia

The need for autonomous unmanned aerial vehicles (UAVs) is rapidly increasing for various industrial applications today. However, the realization of UAV autonomy requires a careful mix of Artificial Intelligence (AI) and computer vision (CV) techniques to be implemented to address end-user challenges and satisfy field requirements. This study aims to develop an intelligent system for UAV to achieve autonomy with standard low-cost hardware components. We have infused state-of-the-art AI and CV technologies into our developed system architecture to improve autonomous navigational capabilities of UAVs. The proposed architecture has been implemented and tested on the DJI Tello UAV equipped only with a monocular camera on-board and an inertial measurement unit (IMU). We focused on solving multiple practical technical problems that have arisen during this study due to the hardware limitations of the UAV. The study investigated methods for representing obstacles within an environmental map, which are crucial for effective avoidance and path planning, such as their edges, width, and shape, to ensure accurate detection and navigation. The experiments were carried out in an environment that included dynamic obstacles that were not considered in the map. The experimental results showed outstanding performance in terms of successful autonomous navigation for a wide range of light intensity. These results show that the proposed approach has a high potential to achieve autonomous UAV operation in the field with low hardware and energy requirements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Invertibility of Discrete-Time Linear systems with Sparse Inputs 63

Invertibility of Discrete-Time Linear Systems with Sparse In...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Poe, Kyle Mallada, Enrique Vidal, Rene University of Pennsylvania Applied Mathematics and Computational Science Group PA19104 United States Johns Hopkins University Mallada Is with the Department of Electrical and Computer Engineering MD21218 United States University of Pennsylvania Radiology Computer and Information Science Statistics and Data Science Departments of Electrical and Systems Engineering PA19104 United States

ISBN: (纸本)9798350316339

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024 IEEE.

关键词： State space methods

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Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

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computers, Materials & Continua 2022年第3期70卷 4691-4705页

作者： Ibrahim M.Mehedi Mohd Heidir Mohd Shah Soon Xin Ng Abdulah Jeza Aljohani Mohammed El-Hajjar Muhammad Moinuddin Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Next Generation Wireless Research Group University of SouthamptonUK

This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible *** proposed AGDI control law will be comprised of two control *** baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control *** control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized *** boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode *** discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering *** closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is *** computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.

关键词： Adaptive control generalized dynamic inversion moore-penrose generalized inverse sliding mode control lyapunov stability semi-global asymptotic stability

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Comprehensive IoT-Based Monitoring System for Improving Worker's Health and Safety at the High-Risk Working Environment

Comprehensive IoT-Based Monitoring System for Improving Work...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Benny, Wan Jasmine Chew, Ing Ming Wong, W.K. Sivamkumar, Saaveethya Juwono, Filbert H. Tiong, Teck Chai Wehaya Sdn Bhd Project Department Miri Malaysia Curtin University Malaysia Sensor Research Group Miri Malaysia Xi'an Jiaotong University Department of Electrical and Electronic Engineering Suzhou China Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350357905

For all engineering endeavours, workplace safety comes as a top priority. Even with careful planning, accidents and fatalities still occur due to little mitigation in place or personnel on duty cannot be continuously monitored. This research project uses wearable devices, which are enabled by IoT technologies, to monitor physiological and environmental variables in real-time mainly applied to the Hazardous Area Zone 2, which is unlikely to cause an explosion in the existence of dangerous gases. SpO2 and heart rate measurements are categorized under the physiological monitoring approach, whereas AQI, combustible gas, and temperature measurements are categorized under the environmental monitoring approach. To reflect the different levels of health and safety problems, the design is adopted with the safety measurements, and the conditions are then displayed on the Blynk application dashboards. The analysis's findings demonstrate the effectiveness of both physiological and environmental monitoring because erroneous readings will activate warning LEDs on the dashboards and convey an alert message to the line manager. However, this research project still could be extended and upgraded by implanting machine learning, displaying the battery level of the devices and monitoring more workers at the same time in the high-risk environment for the massive production lines. © 2024 IEEE.

关键词： Wearable technology

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Geometric-Spectral Reconstruction Learning for Multi-Source Open-Set Classification With Hyperspectral and LiDAR Data

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1892-1895页

作者： Leyuan Fang Dingshun Zhu Jun Yue Bob Zhang Min He IEEE the College of Electrical and Information Engineering Hunan University the Peng Cheng Laboratory the Department of Geomatics Engineering Changsha University of Science and Technology the PAMI Research Group the Department of Computer and Information ScienceUniversity of Macao

Dear editor,This letter presents an open-set classification method of remote sensing images(RSIs)based on geometric-spectral reconstruction *** specifically,in order to improve the ability of RSI classification model to adapt to the open-set environment,an openset classification method based on geometric and spectral feature fusion is proposed.

关键词： editor letter Open

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Secure Communication With UAV-Enabled Aerial RIS: Learning Trajectory With Reflection Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-10页

作者： Tang, Xiao Jiang, Tianqi Liu, Jinxin Li, Bin Zhai, Daosen Yu, F. Richard Han, Zhu School of Electronics and Information Northwestern Polytechinical University Xi'an China China Helicopter Research and Development Institute Jingdezhen China School of Mechanical Engineering Xi'an Jiaotong University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Electrical and Computer Engineering University of Houston Houston USA

Reconfigurable intelligent surfaces (RISs) have manifested huge potential in enhancing information security by actively intervening the wireless propagation, yet the security gain may still be limited depending on the RIS deployment. In this paper, we propose to employ an unmanned aerial vehicle (UAV) mounting a RIS to enable on-demand reflection, noted as an aerial RIS (ARIS). The ARIS is then exploited to assist the anti-eavesdropping communications established through a conventional fixed-deployed RIS to further enhance the wireless secrecy. The secure communication is investigated by jointly optimizing the reflection at both RISs as well as the trajectory of the ARIS to maximize the average secrecy rate during the flight. To facilitate effective algorithm design, the formulated security problem is decomposed and solved in a double-layer framework. The outer layer tackles the flying trajectory through deep reinforcement learning while the inner layer solves for reflection phase shift design with manifold optimization. Finally, simulation results demonstrate the learned trajectory in various topologies as well as the superior performance of our proposal in terms of security provisioning. IEEE

关键词： Reinforcement learning

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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Run-Time Detection of Malicious Behavior Based on Exploit Decomposition Using Deep Learning: A Feasibility Study on SysJoker 23rd

Run-Time Detection of Malicious Behavior Based on Exploit De...

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23rd International Conference on Embedded computer systems: Architectures, Modeling, and Simulation, SAMOS 2023

作者： Tsakoulis, Thanasis Haleplidis, Evangelos Fournaris, Apostolos P. Industrial Systems Institute Research Center ATHENA Patra Greece Department of Digital Systems University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (纸本)9783031460760

As malicious operations become gradually very complex and involve advanced attack campaigns even on embedded systems and IoT, there is an increasing need for detecting malicious behavior on a system as it dynamically as it happens. In this paper, a Deep Learning based malicious behavior dynamic, run-time detection methodology is proposed that rely on the collection of Linux OS execution flow metrics/features like CPU, disk and memory usage and their association with ATT&CK MITRE knowledge base exploits. Using that approach, we can emulate the attack sequence of complex malicious activity and use the collected features to train Deep Learning models in order to classify execution operations at run-time as malicious or not. In the paper, we provide a feasibility study of the proposed solution based on the ATT&CK MITRE exploit attack graph emulation of the SysJoker backdoor malware and we train several Deep Learning models acting as classifiers. The provided results showed that in SysJoker use-case study of the proposed approach we managed to obtain more than 99% accuracy and less that 0.5% False Positive and False Negative Rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Embedded systems

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