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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Chekari, Tassadit Fekik, Arezki Azar, Ahmad Taher Ahmed, Saim Djeddi, Abdelghani Kamal, Nashwa Ahmad Ben Njima, Chakib L2CSP Laboratory Mouloud Mammeri University of Tizi-Ouzou Algeria Mhamed Bougara University of Boumerdes Algeria Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Rue Drissi Yahia Bouira 10000 Algeria Nantes Université École Centrale Nantes Cnrs LS2N NantesF-44000 France College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Prince Sultan University Riyadh11586 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Sciences and Technology Echahid Cheikh Larbi Tebessi University Tebessa12002 Algeria Faculty of Engineering Cairo University Giza Egypt University of Sousse Tunisia

ISBN: (纸本)9798350361025

In this paper, a combination of fractional order control and a classical vector control scheme is proposed to drive the PMSM speed. The new proposed control scheme with fractional order property is based on IMC structure which is known for its robustness to modelling error and Bode's ideal transfer function chosen as the reference model which exhibits iso-damping property. Simulation results show that the proposed control scheme tracks the PMSM speed at specified values and compensates the effect of load torque. Moreover, the time constant and fractional order of the Bode's ideal transfer function can be used as tuning parameters to improve the PMSM speed and current responses. © 2024 IEEE.

关键词： Synchronous motors

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A 7nm-Based Decodable Self-Resetting Regfile Circuit

A 7nm-Based Decodable Self-Resetting Regfile Circuit

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International Conference on ASIC

作者： Wanlong Zhao Yuejun Zhang Mingze Ren Liang Wen Pengjun Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo University Ningbo China Department of Electronic Technology China Coast Guard Academy Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China

This paper presents a novel design scheme for a decodable self-resetting Regfile circuit that aims to reduce the power consumption caused by massive data and high computing power. The scheme consists of three steps: first, it analyzes the working principle and the energy consumption process of the decoding circuit; second, it proposes a self-resetting decoding circuit structure that uses the clock to control the decoding state and automatically resets when the clock is low, thus avoiding the extra power consumption due to the inversion of the memory cell state during the non-working period of the decoding circuit; third, it implements a two-read, two-write, decodable self-resetting Regfile circuit using a full-custom design method under the TSMC 7nm FinFET process. The experimental results demonstrate that the circuit has an area of 5593μm 2 and an energy-delay product of 0.41pJ•nS, and it can operate stably at 4GHz under various temperatures and process corner conditions.

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Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

arXiv

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning

arXiv

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arXiv 2025年

作者： Ivanovska, Marija Todorov, Leon Damer, Naser Jain, Deepak Kumar Peer, Peter Štruc, Vitomir Faculty of Electrical Engineering University in Ljubljana Slovenia Faculty of Computer and Information Science University in Ljubljana Slovenia Dalian University of Technology China Fraunhofer Institute for Computer Graphics Research Germany

With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in identity fraud and other malicious activities. Contemporary Morphing Attack Detection (MAD) approaches frequently rely on supervised, discriminative models trained on examples of bona fide and morphed images. These models typically perform well with morphs generated with techniques seen during training, but often lead to sub-optimal performance when subjected to novel unseen morphing techniques. While unsupervised models have been shown to perform better in terms of generalizability, they typically result in higher error rates, as they struggle to effectively capture features of subtle artifacts. To address these shortcomings, we present SelfMAD, a novel self-supervised approach that simulates general morphing attack artifacts, allowing classifiers to learn generic and robust decision boundaries without overfitting to the specific artifacts induced by particular face morphing methods. Through extensive experiments on widely used datasets, we demonstrate that SelfMAD significantly outperforms current state-of-the-art MADs, reducing the detection error by more than 64% in terms of EER when compared to the strongest unsupervised competitor, and by more than 66%, when compared to the best performing discriminative MAD model, tested in cross-morph settings. The source code for SelfMAD is available at https://***/LeonTodorov/SelfMAD. © 2025, CC BY-NC-ND.

关键词： Self-supervised learning

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Sensor Fusion of Camera and 2D LiDAR for Self-Driving Automobile in Obstacle Avoidance Scenarios 2

Sensor Fusion of Camera and 2D LiDAR for Self-Driving Automo...

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2nd International Workshop on Intelligent Systems, IWIS 2022

作者： Nguyen, Tan-Thien-Nien Phan, Thanh-Danh Duong, Minh-Thien Nguyen, Chi-Tam Ly, Hong-Phong Le, My-Ha Hcmc University of Technology and Education Intelligent System Laboratory Ho Chi Minh City Viet Nam Soongsil University Department of Information and Telecommunication Engineering Seoul Korea Republic of University of Information Technology Computer Engineering Embedded Club Ho Chi Minh City Viet Nam Hcmc University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (数字)9798350398939

ISBN: (纸本)9798350398939

Obstacle dodging and overtaking are the pivotal tasks ensuring safety for self-driving automobiles. Multi-sensors fusion is the must-required condition to explore the entire surrounding information. This paper proposes a novel frontal dynamic car dodging strategy for automobiles with the left-hand side steering wheel by fusion of a camera and 2D LiDAR features. To begin with, we improve the LiteSeg model to extract the segmented mask, which can determine the drivable area and the avoiding direction. In addition to a camera, 2D LiDAR is used to check the scene information on the right side, which the camera's range cannot cover. As for point clouds output of 2D LiDAR, we adopt the Adaptive Breakpoint Detection (defined as ABD) algorithm to cluster the objects in a scanning plane. Subsequently, the RANSAC algorithm forms a straight line from the clustered point clouds to determine the boundary of the right-side obstacle. Besides, we compute the distance from LiDAR to the estimated straight line to maintain a safe distance when overtaking. Last but not least, the post-processing results of the two devices are fused to decide on the obstacle dodging and overtaking. The comprehensive experiments reveal that our self-driving automobile could perform well on the university campus in diverse scenarios. © 2022 IEEE.

关键词： Cameras

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Economic and environmental analysis of a solar-powered ev charging system in india—a case study 2nd

Economic and environmental analysis of a solar-powered ev ch...

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2nd International Conference on electrical and Electronics engineering, ICEEE 2021

作者： Shrivastava, Prashant Soon, Teykok Mekhilef, Saad Ahmad, Furkan Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Power Electronics and Renewable Energy Laboratory Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Software and Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Indian Institute of Technology New Delhi Delhi110016 India

ISBN: (纸本)9789811607486

With the growing environmental concern, the demand for electric vehicle is increasing in India. However, the limited existing charging facilities slow down the rate of adoption in the market. Presently, the solar-based charging system is gaining popularity, due to its low greenhouse gas emission. In this study, a case study for different solar charging systems suited in Delhi is presented. The economic and environmental analysis of different solar-based EV charging systems utilized for charging three types of commercial EVs such as 2, 3, and 4-W is explained. The cost of energy required for 100 km drive is lower with vehicle integrating PV charging system;however, it can act as a supportive system for driving range improvement. The analysis shows that the economically solar rooftop charging system is suitable for all types of EVs. The CO2 emission produced by all types of solar-based EV charging systems is lower than coal-based charging systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Solar energy

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Population Size in Differential Evolution 1

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7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019

作者： Alić, Amina Berkovič, Klemen Bošković, Borko Brest, Janez Faculty of Electrical Engineering and Computer Science Computer Architecture and Languages Laboratory Institute of Computer Science University of Maribor Maribor2000 Slovenia

ISBN: (数字)9783030378387

ISBN: (纸本)9783030378370

In this paper we examined how the population size affects the performance of the differential evolution algorithm. First, we tested the original differential evolution algorithm, and then the improved self-adaptive differential evolution algorithm, on ten benchmark functions, that have been proposed for the CEC 2019 competition. We used six different population sizes. Afterwards, we tested the newly created algorithm with population reinitialization. The results show that the population size affects the algorithm’s efficiency, and that we need to tune it to obtain the best results. In the paper, we demonstrate that the newly created algorithm with reinitialization gives better, or at least comparable, results than the two algorithms without reinitialization. © Springer Nature Switzerland AG 2020.

关键词： Population statistics

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A novel data standards platform using the ISO core components technical specification 41

A novel data standards platform using the ISO core component...

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41st computers and Information in engineering Conference, CIE 2021, Held as Part of the ASME 2021 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2021

作者： Ivezic, Nenad Kulvatunyou, Boonserm Jelisic, Elena Oh, Hakju Frechette, Simon Srinivasan, Vijay Engineering Laboratory National Institute of Standards and Technology GaithersburgMD United States Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States

ISBN: (纸本)9780791885376

It is generally observed in inter-organizational communication that present-day data exchange standards are too costly, large, slow to respond to industry demands, and complex to develop and use. These problems in data exchange are felt keenly by the manufacturing industry and its vast supply chains. In addressing these challenges, a successful attempt was recently made that involves two major developments. The first is the Core Components Technical Specification (CCTS), which is an ISO-approved meta-model for data exchange standards. CCTS introduces common data types, uniform structure for data models, and data usage semantics. The second is Score, which is a novel open-source software tool. Score was developed by National Institute of Standards and Technology researchers as a platform to take advantage of the CCTS for data exchange standards development and usage. This paper describes the potential of the CCTS and the Score platform, and describes the current status of the Score platform and Score-enabled industry interactions. Copyright © 2021 by The United States Government

关键词： Electronic data interchange

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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Clustered Vehicular Federated Learning: Process and Optimization

arXiv

引用

arXiv 2022年

作者： Taïk, Afaf Mlika, Zoubeir Cherkaoui, Soumaya The INTERLAB Research Laboratory Faculty of Engineering Department of Electrical Engineering and Computer Engineering Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada

Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML model among edge devices on their distributed datasets while keeping data locally. While FL requires less communication compared to classical distributed learning, it remains hard to scale for large models. In vehicular networks, FL must be adapted to the limited communication resources, the mobility of the edge nodes, and the statistical heterogeneity of data distributions. Indeed, a judicious utilization of the communication resources alongside new perceptive learning-oriented methods are vital. To this end, we propose a new architecture for vehicular FL and corresponding learning and scheduling processes. The architecture utilizes vehicular-to-vehicular(V2V) resources to bypass the communication bottleneck where clusters of vehicles train models simultaneously and only the aggregate of each cluster is sent to the multi-access edge (MEC) server. The cluster formation is adapted for single and multi-task learning, and takes into account both communication and learning aspects. We show through simulations that the proposed process is capable of improving the learning accuracy in several non-independent and-identically-distributed (non-i.i.d) and unbalanced datasets distributions, under mobility constraints, in comparison to standard FL. © 2022, CC BY.

关键词： Autonomous vehicles

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