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International Conference on Computing, engineering, and Design (ICCED)

作者： Agus Pratondo Nanang Ismail Aprianti Putri Sujana Astri Novianty Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Dept. of Electrical Engineering Faculty of Engineering UIN Sunan Gunung Djati Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia

It’s critical to correctly identify Sukun (Artocarpus altilis) and Kluwih (Artocarpus camansi) leaves to avoid misunderstandings about their traditional medical use and to prevent fraud in the sale of plant seedlings. This study uses the well-known deep-learning algorithms VGG16 and Inception v3 to identify Sukun and Kluwih leaves. A varied collection of leaf photos in a range of sizes was gathered and used in the experiment. The results of the experiment demonstrate how profoundly useful deep learning is at identifying leaves. A remarkable accuracy rate of 99.52 percent was demonstrated by the VGG16 and Inception v3 models. The models’ dependability for practical, real-world applications is highlighted by this level of accuracy. Due to the excellent accuracy attained by both models, it can be concluded that they are suitable for practical applications such as preventing the improper use of leaves and preventing potential fraud in the trading of plant saplings. The work underlines the viability of adopting these models to address relevant societal concerns while showcasing the promise of deep learning approaches in botanical classification.

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Disturbance Observer and Tube-based Model Reference Adaptive Control for Active Suspension Systems with Non-ideal Actuators

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IFAC-PapersOnLine 2023年第2期56卷 1075-1081页

作者： Yashar Mousavi Amin Zarei Ibrahim Beklan Kucukdemiral Afef Fekih Alireza Alfi Department of Applied Science School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow G4 0BA UK Department of Electrical and Computer Engineering University of Sistan and Baluchestan Zahedan Iran Electrical and Computer Engineering Department University of Louisiana at Lafayette P.O. Box 43890 Lafayette LA 70504 USA Faculty of Electrical Engineering Shahrood University of Technology Shahrood 36199-95161 Iran

Active suspension systems undertake a vital role in ensuring passengers’ ride comfort and vehicle vibration attenuation. This work proposes a tube-based model reference adaptive control (TMRAC) that identifies a correctional control component at each time instant for performance amplification of active suspension systems with non-ideal actuators. The TMRAC is augmented by a disturbance observer (DO) to compensate for the effects of dead-zone and hysteresis. Closed-loop stability analysis of the developed method is also investigated in the sense of Lyapunov theory. The developed control scheme's feasibility with regard to suspension safety, road handling, and ride comfort is assessed through simulations by using a class C road profile. Comparison results with DO-based conventional MRAC are also conducted. The obtained results demonstrate that the proposed DO-based control strategy provides a superior ride comfort quality and better road disturbance mitigation with less control effort than the conventional method.

关键词： Tube-based model reference adaptive control Disturbance observer Active suspension system Vibration Non-ideal actuator

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An Alzheimer's disease classification method using fusion of features from brain Magnetic Resonance Image transforms and deep convolutional networks

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Healthcare Analytics 2023年 4卷 100223-100223页

作者： Asgharzadeh-Bonab, Akbar Kalbkhani, Hashem Azarfardian, Sina Department of Electrical and Computer Engineering Urmia University Urmia Iran Faculty of Electrical Engineering Urmia University of Technology Urmia Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Iran

Alzheimer's is a progressive and irreversible brain degenerative disorder, and presenting an accurate early-stage diagnosis tool is vital for preventing disease progression. The previous research considered the features only from the brain Magnetic Resonance Image (MRI) or its transforms. This study presents two efficient fusion schemes to combine the deep features obtained from the brain MR image and its transforms. We consider the Orthogonal Ripplet II Transform (ORT-II), the Two-Dimensional Discrete Orthonormal Stockwell Transform (2D DOST), and the original brain MR image. We present the decision- and feature-level fusion schemes to combine the information of different inputs. In decision-level fusion, Convolutional Neural Networks (CNNs) separately classify each matrix, where all CNNs have the same structure, and the majority role makes the final decision. In feature-level fusion, the same CNNs extract each matrix's features and then concatenate them to construct the feature vector. Since this vector contains many features, we employ Neighborhood Component Analysis (NCA) to select the more relevant features for classification to determine the level of dementia. We consider the different CNN models and classifiers to classify the brain MR images in three two-class scenarios. The results demonstrate that EfficientNet-B7 with an Artificial Neural Network (ANN) provides the highest accuracy. Also, feature-level fusion reaches a higher accuracy compared to the decision-level one. © 2023 The Authors

关键词： Alzheimer's disease classification Artificial Neural Network Convolutional Neural Network Deep learning Feature-fusion RippletII transform

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E-Learning Study Support and Accessible Documents Creation for Students with Special Needs 16

E-Learning Study Support and Accessible Documents Creation f...

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16th IEEE International Scientific Conference on Informatics, Informatics 2022

作者： Jencik, Marian Grincova, Anna Simsik, Dusan Galajdova, Alena Access Centre Technical University of Košice Košice Slovakia Technical University of Košice Faculty of Electrical Engineering and Informatics Department of Mathematics and Theoretical Informatics Košice Slovakia Technical University of Košice Faculty of Mechanical Engineering Department of Automation and Human Machine Interactions Košice Slovakia

ISBN: (纸本)9798350310344

This paper discusses the need for automated accessible documents creation in a world of disabled people inclusion and their education. Converting study materials into more accessible formats is not a trivial problem. We will try to review several possible solutions to this problem. The first very used solution for producing study scientific documents is to produce them in LaTeX language, but when we generate PDF documents directly from LaTeX, math expressions are not accessible by screen readers and braille displays. They can be made accessible by using LaTeX package, which automatically produces a hidden text corresponding to the LaTeX commands that generate the math expressions. The second solution is used the system LAMBDA for reading and writing mathematics especially for people with visual impairments. This system does not help to produce accessible math documents but it can be used as e-learning study system. The third solution is RoboBraille service. RoboBraille is automatized system for decentralized study materials production and automated document conversion service. RoboBraille can very easily convert study documents into digital Braille, MP3 audio files, DAISY structured audio books and various types e-books. Finally yet importantly, solution for e-learning study support students with special needs is to use computer algebra system. The main features of using computer algebra system MAPLE in the e-learning process as a supporting tool in the education of people with disabilities. We will illustrate on the solution of the differential equation in the chapter five. © 2022 IEEE.

关键词： Students

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Robust Secure ISAC: How RSMA and Active RIS Manage Eavesdropper's Spatial Uncertainty

arXiv

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arXiv 2024年

作者： Salem, A. Abdelaziz Abdallah, Saeed Saad, Mohamed Alnajjar, Khawla Albreem, Mahmoud A. University of Sharjah PO Box 26666 Sharjah United Arab Emirates Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menoufia Menouf32952 Egypt Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

Incorporating rate splitting multiple access (RSMA) into integrated sensing and communication (ISAC) presents a significant security challenge, particularly in scenarios where the location of a potential eavesdropper (Eve) is unidentified. Splitting users' messages into common and private streams exposes them to eavesdropping, with the common stream dedicated for sensing and accessible to multiple users. In response to this challenge, this paper proposes a novel approach that leverages active reconfigurable intelligent surface (RIS) aided beamforming and artificial noise (AN) to enhance the security of RSMAenabled ISAC. Specifically, we first derive the ergodic private secrecy rate (EPSR) based on mathematical approximation of the average Eve channel gain. An optimization problem is then formulated to maximize the minimum EPSR, while satisfying the minimum required thresholds on ergodic common secrecy rate, radar sensing and RIS power budget. To address this non-convex problem, a novel optimization strategy is developed, whereby we alternatively optimize the transmit beamforming matrix for the common and private streams, rate splitting, AN, RIS reflection coefficient matrix, and radar receive beamformer. Successive convex approximation (SCA) and Majorization-Minimization (MM) are employed to convexify the beamforming and RIS subproblems. Simulations are conducted to showcase the effectiveness of the proposed framework against established benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Navigation Design to Improve Interaction Technique in Multi-Browser Virtual Environment 15

Navigation Design to Improve Interaction Technique in Multi-...

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15th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2024

作者： Ichsan, Mochammad Hannats Hanafi Sik-Lanyi, Cecilia Guzsvinecz, Tibor Department of Electrical Engineering and Information Systems Faculty of Information Technology University of Pannonia Egyetem u. 10 Veszprem8200 Hungary Department of Informatics Faculty of Computer Science Brawijaya University Malang Indonesia Hungarian Research Network Piarista u. 4 Budapest1052 Hungary Department of Information Technology and its Applications Faculty of Information Technology University of Pannonia Gasparich M. u. 18/A Zalaegerszeg8900 Hungary

ISBN: (纸本)9798350378245

Efficient, precise, and intuitive navigation in a Virtual Environment (VE) or in desktop Virtual Reality (VR) is critical to improving user experience. This research introduces three navigation designs for physical embodiment inside VE using a computer mouse for multi-browser VE in desktop VR based on navigation possibilities. Navigation is intended for the First-Person Shooter (FPS) perspective: Intuitive Navigation (IntuNav), Edge Shift Navigation (EdgeNav), and Twist Shift Navigation (TwistNav). IntuNav uses mouse x and y coordinate movements to move and rotate the camera. EdgeNav relies on the cursor positioning at the edge of the screen for camera rotation and movement. TwistNav uses a middle click and right mouse click for camera movement and rotation. The current navigation design has been implemented in Unity with C#. This research proposed three model designs based on feasible navigation possibilities, which shows that each navigation model has unique advantages that can be adapted to user needs and preferences. Based on the test results using the System Usability Scale, IntuNav and EdgeNav obtained good results and can be used for further development. © 2024 IEEE.

关键词： Virtual environments

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A Real-Time Obstacle Detection and Classification System for Assisting Blind and Visually Impaired People Based on Yolo Model 5

A Real-Time Obstacle Detection and Classification System for...

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5th International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Erdaw, Helawe Behailu Taye, Yesuneh Getachew Lemma, Dereje Teferi Bahir Dar Institute of Technology Bahir Dar University Faculty of Computing Bahir Dar Ethiopia Bahir Dar Institute of Technology Bahir Dar University Faculty of Electrical and Computer Engineering Bahir Dar Ethiopia Addis Ababa University Department of Information Science Addis Ababa Ethiopia

ISBN: (纸本)9798350328486

computer vision is one of the state-of-The-Art technologies for object detection problems. Accurate detection of obstacles could assist blind and visually impaired people to n avigate safely while they walk. However, object detection and t racking are some of the challenging tasks in computer vision. I n video analysis, there are basic steps to be done: detection of target objects in consecutive frames, and analysis of objects to understand their behavior. Recently, several methods of object detection based convolutional neural networks have improved performance undue different conditions like speed and accurac y. However, most of these methods have slow recognition speed that limits their use in real-Time situations. Recently, a unified object detection model You Only Look Once (YOLO) was prop osed, which could directly regress from input image to object cl ass scores and positions. In this paper, we applied YOLOv2 to our prepared datasets of three different classes namely pothole, garbage bin, and pole and we proposed a technique called Short-Term Memory, which considers information between every f rame, to reinforce the detection capability of YOLO in video st reaming by including obj ect location and size estimation tasks. Through this, we achieved a mean average precision of 60.17% accuracy with an average detection speed of 34.6 fps. © 2023 IEEE.

关键词： Object detection

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Transient Stability Analysis and Fault Clearing Angle Estimation of VSG Based on Domain of Attraction Estimated by Trajectory Reversing Method

arXiv

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arXiv 2025年

作者： Lyu, Jiayue Fang, Tianzhi Lin, Zhiheng Han, Jingxue Zhu, Yantao Center for More-Electrical-Aircraft Power System College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada

The virtual synchronous generator (VSG), with the analogous nonlinear power-angle relationship to the synchronous generator (SG), has attracted much attention as a promising solution for converter-based power systems. In this paper, a large signal model of the grid-connected VSG is first established. The trajectory reversing method (TRM) is then introduced to estimate the domain of attraction (DOA) of VSG. Subsequently, the transient instability mechanism is revealed in detail based on the estimated DOA boundary. The impacts of system parameters on the DOA range are further investigated. It is found that loss of synchronization (LOS) occurs if the system trajectory lies outside the post-fault DOA range. In scenarios where no equilibrium points exist after a grid fault, system stability can be reestablished only when the fault clearing angle (FCA) does not exceed the critical clearing angle (CCA). Finally, the CCA derived from the DOA and that from the conventional equal area criteria (EAC) are compared. The results show that CCA obtained by our solution has a higher accuracy. Time-domain simulations are performed to verify the effectiveness of the proposed transient stability analysis method of grid-connected VSG. Copyright © 2025, The Authors. All rights reserved.

关键词： Transient stability

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Multiclass data plane recovery using different recovery schemes in SDN: A simulation analysis

Multiclass data plane recovery using different recovery sche...

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2022 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2022

作者： Jorge, Luisa Melo, Paulo Gomes, Teresa Instituto Politecnico de Bragança Bragança5300-253 Portugal Faculty of Economics University of Coimbra Coimbra3004-512 Portugal University of Coimbra Department of Electrical and Computer Engineering Polo 2 Coimbra3030-290 Portugal INESC Coimbra University of Coimbra Department of Electrical and Computer Engineering Polo 2 Coimbra3030-290 Portugal

To provide dependable services SDN networks need to be resilient to link or switching node failures. This entails, when faults occur, ensuring differentiated types of recovery, according to carried traffic, to routing paths. However, the choice of the recovery scheme best suited to each traffic class is not direct, nor is obvious the impact of the combination of various recovery schemes, according to traffic classes. We explore the usage of different recovery schemes for traffic with distinct requirements Simulation analysis confirms that using different recovery schemes for distinct types of traffic does create differentiated effects in terms of traffic carried and bandwidth usage. © 2022 The Author(s).

关键词： Recovery

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