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Abusive adversarial agents and attack strategies in cyber-physical systems

CAAI Transactions on Intelligence Technology 2023年第1期8卷 149-165页

作者： Viraj Singh Gaur Vishal Sharma John McAllister School of Electronics Electrical Engineering and Computer ScienceQueen's University BelfastBelfastUK

The exponential increase in IoT device usage has spawned numerous cyberspace *** devices,sensors,and actuators bridge the gap between physical processes and the cyber network in a cyber-physical system(CPS).Cyber-physical system is a complex system from a security perspective due to the heterogeneous nature of its components and the fact that IoT devices can serve as an entry point for *** adversaries design their attack strategies on systems to gain an advantage at a relatively lower cost,whereas abusive adversaries initiate an attack to inflict maximum damage without regard to cost or *** this paper,a sensor spoofing attack is modelled as a malicious adversary attempting to cause system failure by interfering with the feedback control *** is accomplished by feeding spoofed sensor values to the controller and issuing erroneous commands to the *** on a Simulink-simulated linear CPS support the proof of concept for the proposed abusive ideology,demonstrating three attack *** impact of the evaluations stresses the importance of testing the CPS security against adversaries with abusive settings for preventing ***,the research concludes by highlighting the limitations of the proposed work,followed by recommendations for the future.

关键词： intelligent systems security security evaluation

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The Development of Dental Problem Detection Device Using Inception V3 and Data Augmentation 8

The Development of Dental Problem Detection Device Using Inc...

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8th International Conference on electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Almodovar, Jacob P. Gloria, Germaine Beatrix A. Paglinawan, Charmaine C. School of Electrical Electronics and Computer Engineering Bachelor of Science in Electronics Engineering Manila Philippines

ISBN: (纸本)9798331531249

This study focuses on the development of a dental problem detection device using the Inception V3 deep learning model and advanced data augmentation techniques. Dental problems such as cavities, impacted wisdom teeth, and crowded teeth pose significant health risks, often requiring advanced imaging for accurate diagnosis. The proposed device utilizes an intraoral camera to capture digital tooth images, which are then processed using the Inception V3 model to identify three dental problems- dental caries, crowded teeth, and partially erupted wisdom teeth. By employing image data augmentation and leveraging datasets from various dental repositories, the study seeks to enhance the accuracy and reliability of dental diagnostics. As a result, the model achieved a total accuracy of 87.2%, in which the model attained a precision of 92%, a recall of 93%, and an Fl-score of 92.5% for caries detection. In detecting crowded teeth, it recorded a precision of 81.1 %, a recall of 90.9%, and an Fl-score of 85.7%. For partially erupted wisdom teeth detection, the precision was 88.8%, the recall 78%, and the Fl-score 83 %. These results indicate that while the model demonstrates strong overall performance, further improvements are necessary to improve the detection of crowded teeth and partially erupted wisdom teeth. This innovative approach endeavors to provide a non-invasive, efficient, and precise tool for dentists, potentially revolutionizing dental healthcare through the integration of advanced AI technologies into routine dental practices. © 2024 IEEE.

关键词： Diagnosis

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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An efficient single unit for virtual-machine placement in cloud data centres

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International Journal of Information and Communication Technology 2024年第1期25卷 1-24页

作者： Ismaeel, Salam Miri, Ali Al-Khazraji, Ayman Department of Computer Science Ryerson University Toronto Canada Department of Electrical and Electronics Engineering University of Bahrain Manama Bahrain

There are numerous energy minimisation plans that are adopted in today’s data centres (DCs). The highest important ones are those that depend on switching off unused physical machines (PMs). This is usually done by optimal distribution and/or reallocating of virtual machines (VMs) on the selected servers, while maintaining the quality of service (QoS) to ensure the performance of a DC. In this work, a novel server machine condition index (MCI) has been proposed, which includes all resources related to servers available in the DC using a single unit. The MCI represents a dynamic tool to compare services, increase effectiveness, reflect PM adequation, and ensure the optimal management of heterogeneous DC resources. The MCI will be used to convert the multi-objective VM allocation optimisation problem into a single-objective problem. This work will identify the MCI components and the way that can be used as a cloud resource unit, and modified VMP algorithms. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Electric power utilization

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Deep learning and machine learning based highly accurate reflection prediction model for multi layers anti-reflection coatings

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Optical and Quantum electronics 2025年第1期57卷 1-22页

作者： Oktay, Semih Duru, İremnur Bakır, Halit Tabaru, Timuçin Emre Department of Electrical-Electronics Engineering Sivas University of Science and Technology Sivas Turkey Computer Engineering Department Sivas University of Science and Technology Sivas Turkey

This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (ML) and Deep Learning (DL) techniques. This model aims to shed light on the design process of a multilayer optical filter, making it more cost-effective by providing faster and more precise production. In creating this model, a dataset containing data obtained from 3000 (1500 Ge–Al2O3, 1500 Ge–SiO2) simulations previously performed on a computer based on the thicknesses of multilayer structural materials was used. The data are generated using Computational Electromagnetic simulation software based on the Finite-Difference Time-Domain method. To understand the mechanism of the proposed model, two different two-layer coating simulations were studied. While Ge was used as the substrate in both coatings, Al2O3 and SiO2 were used as the second layers. The data set consists of the 3–5 µm and 8–12 µm bands typical for the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands and includes reflectance values for wavelengths ranging between these spectra. In the specified 2-layer data set, the average reflectance was obtained with a minimum of 0.36 at 515 nm Ge and 910 nm SiO2 thicknesses. This value can be increased by adapting the proposed model to more than 2 layers. Six ML algorithms and a DL model, including artificial neural networks and convolutional neural networks, are evaluated to determine the most effective approach for predicting reflectance properties. Furthermore, in the proposed model, a hyperparameter tuning phase is used in the study to compare the efficiency of ML and DL methods to generate dual-band ARC and maximize the prediction accuracy of the DL algorithm. To our knowledge, this is the first time this has been implemented in this field. The results show that ML models, particularly decision tree (MSE: 0.00000069, RMSE: 0.00083), rand

关键词： Antireflection coatings

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From Perception to Action: Building a Robust AI System for Safe and Adaptive Autonomous Driving 15

From Perception to Action: Building a Robust AI System for S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Taha, Adnan Sabith Hossain, Md Rafat Esha, Sheikh Aysha Khatun Pantha, Iffat Mahmud Riya, Aparna Sarker Uddin, Md.Jamil Shakur, Md. Abdus Computer Science and Engineering Bangladesh Pace University Data Science United States North South University Computer Science and Engineering Bangladesh Brac University Computer Science and Engineering Bangladesh Eastern University Electrical and Electronics Engineering Bangladesh Electrical and Electronics Engineering Bangladesh

ISBN: (纸本)9798350370249

This study explores the development of a self-driving car using a combination of deep learning (DL), machine learning (ML), computer vision (CV), and convolutional neural networks (CNN). The proposed system aims to simulate human-like decision making in response to external conditions encountered during autonomous driving. The approach involves real-time on-road testing and a self-training mechanism to enable the car to continuously learn and adapt. Furthermore, the text suggests an investigation into the fundamental principles of artificial intelligence (AI) and their role in the autonomous car's functionality. © 2024 IEEE.

关键词： Control Perception Planning Self-driving car

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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

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IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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Smart parking occupancy detection method using smart app

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International Journal of Wireless and Mobile Computing 2024年第1期26卷 99-105页

作者： Rahman, Atiqur Ufiteyezu, Emmanuel Department of Computer Science and Engineering University of Chittagong Hathazari Chittagong Bangladesh Electrical and Electronics Department University of Rwanda Kigali Rwanda

Population growth in cities results in a demand for parking lots from an increasing number of automobiles, which frequently contributes to the global problem of traffic congestion. This study presents the smart parking occupancy detection system as a technique to scientifically enhance the existing parking lot, i.e., parking guidance and information method or something else, occupancy detection system. The smart app named rPark is promoted as a cutting-edge method of parking detection. Ultra-high frequency static radio frequency identification chips are placed on the asphalt and cross-examined by Radio Frequency Identification interrogator aerials over the parking areas to initially assess utilisation status. This plan eliminates the majority of the challenges associated with current up-to-the-minute parking usage identification techniques. The parking detection accuracy of rPark, which was tested and used to illustrate an experimental analysis in a real-world outdoor parking space, has been confirmed to be about 100%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Radio frequency identification (RFID)

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Tsunami tide prediction in shallow water using recurrent neural networks: model implementation in the Indonesia Tsunami Early Warning System

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Journal of Reliable Intelligent Environments 2024年第2期10卷 177-195页

作者： Dharmawan, Willy Diana, Mery Tuntari, Beti Astawa, I. Made Rahardjo, Sasono Nambo, Hidetaka Centre of Electronics BRIN Puspiptek Serpong South Tangerang15314 Indonesia Electrical Engineering and Computer Science Kanazawa University Kakuma Kanazawa Ishikawa9201192 Japan Computer Science and Electrical Engineering Kumamoto University Kumamoto Kurokami City8600862 Japan

Near-field tides prediction for tsunami detection in the coastal area is a significant problem of the cable-based tsunami meter system in north Sipora, Indonesia. The problem is caused by its shallow water condition and the unavailability of an applicable model or research for tsunami detection in this area. The problem foundation of shallow water area is its ambient noise level-dependent property that requires preprocessing to improve its feature representation. Moreover, because this shallow water is close to the land area, we must consider a model that can accommodate low prediction time for a Tsunami Early Warning System. Therefore, we propose a recurrent neural network (RNN) model because of its reliable performance for time series forecasting. Our report evaluates variants of the RNN model (the vanilla RNN, LSTM and GRU models) in tides prediction and z-score analysis for tsunami identification. The GRU model overwhelms the other two variants in error scores and time processed (training and prediction). It can achieve median error score distribution of 7.8×10-5 on the L1000-P250 configuration with time prediction under 0.1 s. This lower-time prediction is necessary to ensure the early warning system is going well. Moreover, the GRU model can identify all synthetic tsunami tide spikes (compared with the ground truth result) from magnitude 7.2–8.2 by applying a z-score on the GRU’s prediction. © The Author(s) 2023.

关键词： Deep neural networks

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A ROS-2 based Path Planning and Maze Solving of Autonomous Robots

A ROS-2 based Path Planning and Maze Solving of Autonomous R...

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2024 International Conference on Advances in Computing, Communication, electrical, and Smart Systems, iCACCESS 2024

作者： Nuhel, Ahsan Kabir Hossain, Sazzad Hassan, Md.Tonim Haider, Iftekhar Saha, Ankur Taha, Adnan Sabith Ony, Asma-UI Husna Electrical and Electronics Engineering Bangladesh University of Hertfordshire Computer Networks and Systems Security United Kingdom Electrical and Electronics Engineering Bangladesh Computer Science and Engineering Bangladesh University of Information Technology and Sciences Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350350289

The goal of this study is to build a self-driving robot that can effectively navigate mazes by utilizing sophisticated computer vision algorithms with ROS2. Fusion 360 is used to create the robot model, and ROS2 launch files are used to initialize the sensor and control nodes. Attaining optimal path planning and accurate localization are the main objectives. The combination of novel motion planning based on the orientation of the robot with graph search algorithms like DFS, A∗, and Dijkstra's leads to effective maze discovery and traversal. Thorough testing shows that the system can successfully navigate unfamiliar maze configurations, demonstrating the dependability of ROS2 and modern robotics techniques. By completing this study, a foundation for future advancements in perception and planning to manage more complex settings is laid. © 2024 IEEE.

关键词： computer vision

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