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International Conference on Networking and Advanced Systems (ICNAS)

作者： Omar Mechali Ali Zakaria Messaoui Abdelkader Senouci Aimen Abdelhak Messaoui Abdesselam Mechali Jana Petru Ecole Supérieure Ali Chabati Réghaia Algeria School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Laboratoire de Commande des Systèmes Complexes et Simulateurs Ecole Militaire Poly technique Bordj El-Bahri Algeria Department of Computer Science and Technology Faculty of Science and Engineering University of Hull UK Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Os trava Ostrava Czech Republic

This paper presents an effective approach for a quadrotor during trajectory tracking seeking fixed-time stabilization and disturbance rejection. The quadrotor is modeled by Ordinary Differential Equations (ODE) while considering lumped disturbances. Theoretical control design and practical implementation are addressed in the paper. Motivated by the variable structure systems in the form of Sliding Mode Control (SMC) theory and homogeneous systems theory, a homogeneous-SMC-based flight control is proposed to achieve robust trajectory tracking along fixed-time stability. First, a Homogeneous Nonlinear Chattering-Free Terminal Sliding Manifold (HNCFTSM) is synthesized. Second, a Fifth Generation-based Fixed-Time SMC (FGFTSMC) is derived based on the designed HNCFTSM and an appropriate reaching law. Moreover, to suppress the disturbances polluting the system's dynamics, the control scheme is designed within an ADRC approach. The control design and the stability analysis show that the tracking errors are guaranteed to be fixed-time stable at the equilibrium point without being affected by the deviation of the Initial Conditions (ICs) from the origin. Outdoor flight experiments and comparative analysis are drawn to corroborate the findings of this note. The depicted outcomes approve the effectiveness of the suggested controller in the form of chattering alleviation, disturbance rejection, and uniform fast fixed-time convergence.

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Retinal Disorder Diagnosis Based on Hybrid Deep Learning Models

SSRN

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SSRN 2022年

作者： Sedik, Ahmed El-Hag, Noha A. El-Hoseny, Heba M. El-Banby, Ghada M. Khalaf, Ashraf A.M. Abd El-Samie, Fathi E. El-Shafai, Walid Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Egypt Menoufia University Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Deptartment of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtainedwith several modalities such as magnetic resonance (MR) and confocal microscopy need to be classified for anomaly *** MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, braintumors are represented as areas composed of abnormal cells that need to be detected. In addition, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify © 2022, The Authors. All rights reserved.

关键词： Ophthalmology

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Exploring Deep Learning based Object Detection Architectures: A Review 17

Exploring Deep Learning based Object Detection Architectures...

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17th International Bhurban Conference on Applied Sciences and technology, IBCAST 2020

作者： Saddique, Hassan Muhammad Raza, Ahsan Abideen, Zain Ul Khan, Shah Nawaz National University of Computer and Emerging Sciences Department of Electrical Engineering Peshawar Pakistan Ghulam Ishaq Khan Institute of Engineering Science and Technology Faculty of Electronic Engineering Swabi Pakistan University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan

ISBN: (纸本)9781728146768

As one of the hottest fields of computer vision, object detection is employed in many useful applications, including face recognition, autonomous cars and human behaviour analysis. CNN's have played a vital role in the progression of computer vision, especially object detection. In this paper, a review is provided on object detection methods which utilize deep learning to render outstanding results, and different object detection frameworks are discussed and analyzed with respect to their differences amongst each other. The comparisons are carried out based upon the criterion of accuracy and frames per second. The review starts with the explanation of CNNs and their advantages. Then we focus our attention on two types of object detection architectures and dive deep into their respective details. In the end, we draw a conclusion based on the facts detailed in the review. © 2020 IEEE.

关键词： Object detection

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for Power Micro-Application Server

A Web Attack Detection Method Based on DistilBERT and Featur...

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Advanced electronics, Electrical and Green Energy (AEEGE), International Conference on

作者： Li Nige Chen Lu Zhao Lei Teng Zhenning Wang Zhiqiang Shao Yiyang Gui Xiaolin State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China Shaanxi Province Key Laboratory of Computer Network Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency.

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Swin Transformer with Spatial and Local Context Augmentation for Enhanced Semantic Segmentation of Remote Sensing Images

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 608-620页

作者： Ding, Rong-Xing Xu, Yi-Han Yu, Gang Zhou, Wen Zhou, Ding Nanjing Forestry University College of Information Science and Technology Artificial Intelligence Nanjing210037 China Qinghai University School of Computer Technology and Applications Xining810016 China The University of Sheffield Department of Electronic and Electrical Engineering SheffieldS10 2TN United Kingdom University of Surrey 5G/6G Innovation Centre SurreyGU2 7XH United Kingdom Guangzhou Maritime University College of Low Altitude Equipment and Intelligent Control Guangzhou510725 China University of Malaya Faculty of Engineering Kuala Lumpur50603 Malaysia

Semantic segmentation of remote sensing images is extensively used in crop cover and type analysis, and environmental monitoring. In the semantic segmentation of remote sensing images, owning to the specificity of remote sensing images, not only the local context is required, but also the global context information makes an important role in it. Inspired by the powerful global modelling capability of Swin Transformer, we propose the LSENet network, which follows the encoder-decoder architecture of the UNet network. In encoding phase, we propose spatial enhancement module (SEM), which helps Swin Transformer further enhance feature extraction by encoding spatial information. In decoding stage, we propose local enhancement module (LEM), which is embedded in the Swin Transformer to improve the Swin Transformer to assist the network to obtain more local semantic information so as to classify pixels more accurately, especially in the edge region, the adding of LEM enables to obtain smoother edges. The experimental results on the Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. Specifically, the mIoU metric is 78.58% on the Potsdam dataset, 72.59% on the Vaihingen dataset and 64.49% on the OpenEarthMap dataset. © 2020 IEEE.

关键词： Semantic Segmentation

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JointSTNet: Joint Pre-Training for Spatial-Temporal Traffic Forecasting

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IEEE Transactions on Consumer electronics 2024年

作者： Cai, Danqi Chen, Kunwei Lin, Zhizhe Li, Duo Zhou, Teng Ling, Ying Leung, Man-Fai Shantou University Department of Computer Science Shantou China Newcastle University Department of Civil and Geospatial Engineering Newcastle upon TyneNE1 7RU United Kingdom Hainan University School of Cyberspace Security Haikou570228 China University of Electronic Science and Technology of China Yangtze Delta Region Institute Quzhou324003 China Anglia Ruskin University School of Computing and Information Science Faculty of Science and Engineering Cambridge United Kingdom

In recent years, there has been rapid development in autonomous vehicle techniques due to the increasing demands in traffic management and travel planning. Accurate spatiotemporal traffic forecasting is crucial for autonomous vehicles. However, most existing methods are task-specific and tailored for specific cities, making them hard to apply for more downstream applications. These methods fail to accurately simulate the common variation of traffic states at different locations based on their spatial distance and time interval. Compared with most existing spatiotemporal prediction baselines, this paper proposes a joint pre-training framework, named JointSTNet, to further enhance the traffic spatiotemporal prediction accuracy while reducing model complexity for traffic forecasting tasks. Firstly, unlike treating traffic spatiotemporal data on roads as independent patterns, the spatial graph capsule module constructs inter-class traffic patterns based on shared dynamics within the dynamic graph structure. Additionally, the temporal encoding gated module proposed in our work expands the temporal receptive field along the time axis. Finally, we maximize reconstruction losses to handle incomplete connections within data involving intra-cluster and inter-cluster regional semantic relationships that can be enhanced through pre-training. Experimental results on four benchmarking datasets from the real world demonstrate that JointSTNet outperforms various types of state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Autonomous vehicles

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Generative Artificial Intelligence (GAI) for Mobile Communications: A Diffusion Model Perspective

arXiv

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arXiv 2024年

作者： Xu, Xiaoxia Mu, Xidong Liu, Yuanwei Xing, Hong Liu, Yue Nallanathan, Arumugam The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Queen's University Belfast BelfastBT3 9DT United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of Guangzhou511453 China The Department of ECE The Hong Kong University of Science and Technology Hong Kong The Faculty of Applied Sciences Macao Polytechnic University China

This article targets at unlocking the potentials of a class of prominent generative artificial intelligence (GAI) method, namely diffusion model (DM), for mobile communications. First, a DM-driven communication architecture is proposed, which introduces two key paradigms, i.e., conditional DM and DM-driven deep reinforcement learning (DRL), for wireless data generation and communication management, respectively. Then, we discuss the key advantages of DM-driven communication paradigms. To elaborate further, we explore DM-driven channel generation mechanisms for channel estimation, extrapolation, and feedback in multiple-input multiple-output (MIMO) systems. We showcase the numerical performance of conditional DM using the accurate DeepMIMO channel datasets, revealing its superiority in generating high-fidelity channels and mitigating unforeseen distribution shifts in sophisticated scenes. Furthermore, several DM-driven communication management designs are conceived, which is promising to deal with imperfect channels and task-oriented communications. To inspire future research developments, we highlight the potential applications and open research challenges of DM-driven communications. Code is available at https://***/xiaoxiaxusummer/GAI_COMM/. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

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Watermarking and Encryption for electronic Medical Records (EMR) Security Based on mCloud

Watermarking and Encryption for Electronic Medical Records (...

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Cyber Resilience (ICCR), International Conference on

作者： Haider Rasheed Abdulshaheed Yaseen Saadi Abbas Jamal F. Tawfeq Karamath Ateeq Saif. E. A. Alnawayseh Taher M. Ghazal Computer Engineering Techniques Department Baghdad College of Economic Sciences University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Al-Nahrain University Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE Department of Electrical Engineering College of Engineering Mutah University Al-Karak Jordan Computer Science Department Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing “big EMR data” from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article.

关键词： Cloud computing Hospitals Watermarking Big Data Mobile handsets Robustness Safety

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Alpha (α) assumed rotations and shear strains for spatially isotropic polygonal Reissner-Mindlin plate elements (αARS-Poly)

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computers and Structures 2023年 274卷

作者： Nguyen, Son H. Nam, Nguyen N. Hoang, Tien-Dat Nguyen, Tan N. Nguyen-Thoi, T. Division of Computational Mathematics and Engineering Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam Division of English Taught Mechanical Engineering Faculty of International Training Thai Nguyen University of Technology Thai Nguyen City Viet Nam Department of Architectural Engineering Sejong University 209 Neungdong-ro Gwangjin-gu Seoul05006 Korea Republic of Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Engineering and Technology Van Lang University Ho Chi Minh City Viet Nam

This paper proposes simple and efficient alpha assumed rotations and shear strains for polygonal plate elements, named αARS-Poly. In the αARS approach, an alternative assumption of the tangent rotations along element boundaries is applied by using the approximation of the rotations in Timoshenko's beam theory. Then, the quadratic term of this assumed field is linearly scaled up by adding an artificial positive scaling factor α>0. Through examination of the relative errors in the energy (s-) norm regarding α in numerical experiences, the value α=0.5 can be chosen as a general-fixed value that possibly achieves the optimal relative errors of the s-norm. The value α=0.5 seems to be not only mesh-independent but also problem-independent. The αARS-Poly element using α=0.5 passes all critical tests (spatially isotropic, zero-energy mode, and bending path tests) for a finite plate element which ensures the element orientation-independent property, solution stability, and free shear-locking in the thin plate limit. The implementation of the αARS-Poly element is straightforward through a unified form of the stiffness matrix for all arbitrary convex-shaped polygonal meshes. Numerical results show that the proposed element achieves high reliable and optimal results with uniform and excellent convergent rates in the static and free vibration analyses. © 2022 Elsevier Ltd

关键词： Finite element method

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Expected and unexpected routes to synchronization in a system of swarmalators

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Physical Review E 2024年第6期110卷 L062301-L062301页

作者： Steve J. Kongni Thierry Njougouo Patrick Louodop Robert Tchitnga Fernando F. Ferreira Hilda A. Cerdeira Research Unit Condensed Matter Electronics and Signal Processing University of Dschang P.O. Box 67 Dschang Cameroon Dschang Cameroon IMT School for Advanced Studies 55100 Lucca Italy Faculty of Computer Science and Namur Institute for Complex Systems (naXys) University of Namur 5000 Namur Belgium Department of Electrical and Electronic Engineering Faculty of Engineering and Technology (FET) University of Buea Buea Cameroon ICTP South American Institute for Fundamental Research São Paulo State University (UNESP) Instituto de Física Teórica Rua Doutor Bento Teobaldo Ferraz 271 Bloco II Barra Funda 01140-070 São Paulo Brazil Department of Physics-FFCLRP University of São Paulo Ribeirao Preto 14040-901 São Paulo Brazil São Paulo State University (UNESP) Instituto de Física Teórica Rua Doutor Bento Teobaldo Ferraz 271 Bloco II Barra Funda 01140-070 São Paulo Brazil Epistemic Gomez & Gomez Ltda. Micro Empresa (ME) Rua Paulo Franco 520 Vila Leopoldina 05305-031 São Paulo Brazil

Systems of oscillators whose internal phases and spatial dynamics are coupled, swarmalators, present diverse collective behaviors which in some cases lead to explosive synchronization in a finite population as a function of the coupling parameter between internal phases. Near the synchronization transition, the phase energy of the particles is represented by the XY model, and they undergo a transition which can be of the first order or the second depending on the distribution of natural frequencies of their internal dynamics. The first-order transition is obtained after an intermediate state (static wings phase wave state) from which the nodes, in cascade over time, achieve complete phase synchronization at a precise value of the coupling constant. For a particular case of natural frequencies distribution, a new phenomenon, the rotational splintered phase wave state, is observed and leads progressively to synchronization through clusters switching alternatively from one to two and for which the frequency decreases as the phase coupling increases.

关键词： Collective behavior in networks Dynamics of networks Network phase transitions Synchronization transition

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