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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Zamri, Fatin Najihah Muhamad Gunawan, Teddy Surya Yusoff, Siti Hajar Mustafah, Yasir Mohd. Kartiwi, Mira Yusoff, Nelidya Md International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Mechatronics Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur51400 Malaysia

ISBN: (纸本)9798350365283

Drones present substantial detection challenges due to their capacity to operate in various conditions, including low lighting, harsh weather, and similar objects like birds. Existing datasets frequently fail to address all of these challenges comprehensively. The BirDrone dataset, specifically designed to improve the accuracy of distinguishing between drones and birds, is introduced to address this issue, with a particular emphasis on small-scale objects. The dataset comprises images with intricate backgrounds and lighting conditions to enhance detection reliability. By utilizing the YOLOv9 model to assess the dataset, we achieved a high level of accuracy and significantly reduced the number of false alarms. The BirDrone dataset's development process, data augmentation methodologies, and performance outcomes of YOLOv9 are all detailed in this paper, which serves as a testament to its efficacy in practical applications. © 2024 IEEE.

关键词： Drones

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 720-733页

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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Wireless control of a D.C. Motor Used for Driving a Conveyor Belt

Wireless control of a D.C. Motor Used for Driving a Conveyor...

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2024 International Conference on Circuit, systems and Communication, ICCSC 2024

作者： Livint, Petru Banu, Ioan-Viorel Culea, George Manale, Bouderbala Majout, Btissam Dusa, Alexandru University 'Vasile Alecsandri' of Bacau Department of Energetics and Science Computer Romania Sidi Mohammed Ben Abdellah University Fez Limas Laboratory Faculty of Sciences Dhar El Mahraz Morocco Sidi Mohammed Ben Abdellah University Fez Laboratory of Engineering Modellingand Systems Analysis Morocco 'Dunarea de Jos' University of Galati Department of Automatic and Electrical Engineering Romania

ISBN: (纸本)9798350365306

This article presents an experimental stand for the remote control of a D.C. motor used to drive a conveyor belt. This is based on two development boards Arduino Uno and two shields NRF24L01. The D. C. motor is supplied from a D.C. voltage source by means of the H type bridge. The emitter circuit sends the control signal generated through a potentiometer to the receptor circuit by means of radio waves at a frequency of 2.4 GHz. The receptor circuit receives the analogue signal emitted by the emitting circuit, transforms it into a PWM (pulse width modulation) signal and uses it for controlling the transistors of the H-bridge. For implementing and validating the remote control system of the D.C. motor two programs have been issued in the programming environment Arduino IDE for the two circuits. emitter and receptor. The remote control system of the D.C. motor has been running in the same way as the direct control of this. The remote control is used in the case of the electric actuation systems made with D.C. motors continuous used in spaces inaccessible to the human operator. © 2024 IEEE.

关键词： Belts

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Sensitivity Analysis on Green Hydrogen as Energy Storage: A Techno-Economic Case study

Sensitivity Analysis on Green Hydrogen as Energy Storage: A ...

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IEEE General Meeting Power& Energy Society

作者： Rahul R Urs Assia Chadly Ahmad Mayyas Ameena Al Sumaiti Department of Industrial Systems and Engineering Khalifa university Abu Dhabi UAE Department of Electrical & Computer Science Engineering Khalifa university Abu Dhabi UAE

Production of green Hydrogen is increasingly helping the world achieve its energy transition goals. Compared to conventional methods, producing Hydrogen using green energy produces fewer carbon emissions. Furthermore, green Hydrogen can be produced from several renewable sources depending on the region’s potential. Photovoltaic systems are considered in this study to estimate green hydrogen production in the United Arab Emirates. The impact of electrolyzer and hydrogen tank capital expenses on green Hydrogen production has been extensively researched in the literature. However, the impact of the varying capital cost is often overlooked. In this work, we analyze the sensitivity of renewable systems’ costs to changes in the costs of producing Hydrogen, and employing it as energy storage.

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Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?

Seizure prediction with long-term iEEG recordings: What can ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Yang, Hongliu Eberlein, Matthias Muller, Jens Tetzlaff, Ronald TU Dresden Faculty of Electrical and Computer Engineering Institute of Circuits and Systems Dresden01062 Germany

ISBN: (纸本)9781665401265

Repeated epileptic seizures impair around 65 million people worldwide and a successful prediction of seizures could significantly h elp p atients suffering from refractory epilepsy. For two dogs with yearlong intracranial electroencephalography (iEEG) recordings, we studied the influence of time series nonstationarity on the performance of seizure prediction using in-house developed machine learning algorithms. We observed a long-term evolution on the scale of weeks or months in iEEG time series that may be represented as switching between certain meta-states. To better predict impending seizures, retraining of prediction algorithms is therefore necessary and the retraining schedule should be adjusted to the change in meta-states. There is evidence that the nature of seizure-free interictal clips also changes with the transition between meta-states, which has been shown relevant for seizure prediction. © 2021 IEEE.

关键词： Electroencephalography

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Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression

arXiv

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arXiv 2023年

作者： Anjarlekar, Ameya Etesami, Rasoul Srikant, R. Department of Electrical and Computer Engineering Coordinated Science Lab University of Illinois Urbana-Champaign United States Departments of Industrial and Systems Engineering and Electrical and Computer Engineering The Coordinated Science Lab University of Illinois Urbana-Champaign United States

We address the challenge of solving machine learning tasks using data from privacy-sensitive sellers. Since the data is private, we design a data market that incentivizes sellers to provide their data in exchange for payments. Therefore our objective is to design a mechanism that optimizes a weighted combination of test loss, seller privacy, and payment, striking a balance between building a good privacy-preserving ML model and minimizing payments to the sellers. To achieve this, we first propose an approach to solve logistic regression with known heterogeneous differential privacy guarantees. Building on these results and leveraging standard mechanism design theory, we develop a two-step optimization framework. We further extend this approach to an online algorithm that handles the sequential arrival of sellers. © 2023, CC BY-NC-ND.

关键词： Differential privacy

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NFTs for Trusted Traceability and Management of Digital Twins for Shipping Containers

NFTs for Trusted Traceability and Management of Digital Twin...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Feruz K. Elmay Mohammad Madine Khaled Salah Raja Jayaraman Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Industrial and Systems Engineering Khalifa University Abu Dhabi UAE

The distribution, traceability, and management of shipping container logistics require secure data flow and trusted transactions. Digital Twins (DTs) can realize these features by offering shipping tracking and traceability, process flow and status monitoring, and management of the physical containersall in a remote manner. However, the data of a DT itself is typically stored, controlled, and managed by a centralized entity, which is often the original creator of the physical container. Having a centralized entity can cause mistrust. The centralized entity may alter, tamper, or delete the digital twin data. To overcome this problem, this paper proposes trusted sharing and management of DTs for shipping containers by using Non-Fungible Tokens (NFTs). NFTs are digital tokens that hold unique data stored, controlled, and managed in a decentralized and immutable blockchain ledger. We extend in this paper the use of NFTs to tokenize shipping container DTs and their metadata. The proposed solution uses NFTs and Ethereum blockchain smart contracts to offer decentralization, security, transparency, traceability, and immutability to the data and processes involved in the creation, storage, and management of DTs of shipping containers. To demonstrate our solution, we create a DT of a shipping container using Microsoft Azure Digital Twins services and showed how to tokenize it using NFT. We assess the system using various test cases to evaluate its main functionalities. Furthermore, we analyze the cost of transactions and the security of the smart contracts code. We have made the code of our smart contracts publicly available on GitHub.

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Is it necessary to calculate young’s modulus in afm nanoindentation experiments regarding biological samples?

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Micro and Nanosystems 2021年第1期13卷 3-8页

作者： Kontomaris, Stylianos-Vasileios Malamou, Anna Stylianou, Andreas Faculty of Architecture Engineering & Built Environment Athens Metropolitan College Athens Greece Radar Systems and Remote Sensing Lab School of Electrical & Computer Engineering National Technical University Athens Greece Cancer Biophysics Laboratory Department of Mechanical and Manufacturing Engineering Faculty of Engineering University of Cyprus Nicosia Cyprus

Background: The determination of the mechanical properties of biological samples using Atomic Force Microscopy (AFM) at the nanoscale is usually performed using basic models arising from the contact mechanics theory. In particular, the Hertz model is the most frequently used theoretical tool for data processing. However, the Hertz model requires several assumptions, such as homogeneous and isotropic samples and indenters with perfectly spherical or conical shapes. As it is widely known, none of these requirements are 100 % fulfilled for the case of indentation experiments at the nanoscale. As a result, significant errors arise in the Young’s modulus calculation. At the same time, an analytical model that could account complexities of soft biomaterials, such as nonlinear behavior, anisotropy, and heterogeneity, may be far-reaching. In addition, this hypothetical model would be ‘too difficult’ to be applied in real clinical activities since it would require a very heavy workload and highly specialized personnel. Objective: In this paper, a simple solution is provided to the aforementioned dead-end. A new approach is introduced in order to provide a simple and accurate method for mechanical characterization at the nanoscale. Method: The ratio of the work done by the indenter on the sample of interest to the work done by the indenter on a reference sample is introduced as a new physical quantity that does not require homogeneous, isotropic samples or perfect indenters. Results: The proposed approach provides an accurate solution from not only a physical perspective but also a simpler solution which does not require activities such as the determination of the cantilever’s spring constant and the dimensions of the AFM tip. Conclusion: It has been observed from this opinion paper that the solution aims to provide a significant opportunity to overcome the existing limitations provided by Hertzian mechanics and apply AFM techniques in real clinical activities. © 2021 Bentham

关键词： Indentation

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Adaptive Oracle-Efficient Online Learning

arXiv

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arXiv 2022年

作者： Wang, Guanghui Hu, Zihao Muthukumar, Vidya Abernethy, Jacob College of Computing United States School of Electrical and Computer Engineering School of Industrial and Systems Engineering

The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitations when implemented at scale. More recent sophisticated techniques, which we refer to as oracle-efficient methods, address this problem by dispatching to an offline optimization oracle that can search through an exponentially-large (or even infinite) space of decisions and select that which performed the best on any dataset. But despite the benefits of computational feasibility, oracle-efficient algorithms exhibit one major limitation: while performing well in worst-case settings, they do not adapt well to friendly environments. In this paper we consider two such friendly scenarios, (a) "small-loss" problems and (b) IID data. We provide a new framework for designing follow-the-perturbed-leader algorithms that are oracle-efficient and adapt well to the small-loss environment, under a particular condition which we call approximability (which is spiritually related to sufficient conditions provided in (Dudík et al., 2020)). We identify a series of real-world settings, including online auctions and transductive online classification, for which approximability holds. We also extend the algorithm to an IID data setting and establish a "best-of-both-worlds" bound in the oracle-efficient setting. © 2022, CC0.

关键词： Decision making

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