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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Baidya, Dayarnab Dhopte, Shreya Bhattacharjee, Mitradip Iiser I Lab Electrical Engineering and Computer Science MP Bhopal462066 India

ISBN: (纸本)9781665461634

Electric vehicles (EVs) are a clean, sustainable alternative for transportation energy emissions. Modelling and controlling electric vehicles are gaining interest. PID controllers are widely used for their simplicity, robustness, and practical application. However, there are errors between the controlled signal and the desired signal. This system controls EV speed using sensor feedback supported novel controller based on Dandelion Optimization (DO) and the PID control approach. This article aims to reduce system error between the reference and output signals. The proposed model is compared to PID control, Fire Fly-PID control, Teaching Learning Based Optimization (TLBO)PID control, and Particle Swarm Optimization (PSO)-PID control. Simulation results show the implemented technique outperforms existing methods. It is found that the Integral Absolute Error (IAE) in controlling speed of EV with PID controller is obtained as 0.3028, for PSO-PID controller it is 0. 1 3 7 2, 0. 1 2 2 2 f o r Fire Fly - P I D c o n t r o l l e r, 0.1218 for TLBOPID controller and 0.1114 for proposed DO-PID controller. The LAE in proposed DO-PID controller is 63.2 % less than PID controller, 18.8 % less than PSO-PID controller, 8.83 % less than Fire Fly-PSO controller, 8.53 % less than TLBOPID controller. © 2023 IEEE.

关键词： Three term control systems

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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Real-Time Freezing of Gait Detection: Harnessing Advanced AI for Better Mobility

Real-Time Freezing of Gait Detection: Harnessing Advanced AI...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Yang, Luyao Amin, Osama Shihada, Basem Networking Lab Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9798350350548

Freezing of Gait (FoG) represents a critical and debilitating symptom of Parkinson’s disease, posing significant challenges in patient mobility and safety. Numerous research efforts have focused on predicting the onset of FoG using wearable sensors and computational aids. However, the unpredictability and brief nature of FoG episodes complicate the ability to predict their onset in real-time, rendering timely detection a complex goal. In our study, we employed an Autoencoder-Wavenet network architecture designed to effectively utilize data from tri-axial accelerometers and tri-axial gyroscopes positioned at the ankle. This approach allowed for a more nuanced analysis of movement patterns associated with FoG. Our findings indicated that this model successfully achieved a high level of accuracy in detecting FoG, with a specificity of 0.81, and sensitivity of 0.87. Furthermore, the model demonstrated the capability to provide real-time warnings of FoG onset, achieving a specificity of 0.68 and a sensitivity of 0.86. And an accuracy of 0.67 within a 1-second timeframe on the test set. Consequently, these results underscore the potential of our model in contributing to the real-time detection and management of FoG in Parkinson’s disease patients. © 2024 IEEE.

关键词： Accelerometers

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Theoretical analysis and design of a dual-wavelength and selectable all-optical broadband QDs semiconductor optical amplifier (QDs-SOA) with inhomogeneous broadening

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Optical and Quantum Electronics 2025年第2期57卷 1-26页

作者： Eyvazi, Mahdiyeh Yadipour, Reza Rostami, Ali Rostami, Parisa Mirtagioglu, Hamid Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran SP-EPT Lab ASEPE Company Industrial Park of Advanced Technologies Tabriz Iran Faculty of Medicine Tabriz University of Medical Science Tabriz Iran Department of Statistics Faculty of Science and Literature University of Bitlis Eren Bitlis Turkey

This study delves into the significant role played by Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) in meeting the ever-growing bandwidth demands. QD-SOAs offer a unique blend of cost-effectiveness, integration capabilities, wide bandwidth, rapid responsiveness, robust power output, stability, and spectral adaptability, driving notable advancements in optical communication systems. In this work, we introduce a novel two-wavelength amplifier structure based on Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) that utilizes quantum dots of different sizes to achieve efficient amplification at specific mid-infrared wavelengths. This innovative approach, which incorporates quantum dots with varying sizes, enables enhanced performance by optimizing the amplification process for each specific wavelength. Furthermore, this work demonstrates the use of tailored optical pumping mechanisms that enhance the carrier recovery process and reduce carrier relaxation times in the active region. This novel optical pumping technique leads to a significant increase in the efficiency and speed of the amplifier, distinguishing this study from others in the field. Simulation results provide detailed insights into the complex interplay between carrier interactions and gain spectra, offering a comprehensive understanding of QD-SOA operational dynamics. The proposed Quantum Dot Semiconductor Optical Amplifier (QD-SOA) achieves significant advancements in optical communication, offering maximum amplification rates of 26.9 and 19.9 times for QD1 and QD2, respectively, and bandwidths of 12 THz and 15 THz. The study highlights the role of quantum dot size, homogeneous and inhomogeneous broadening, and optical pumping in enhancing gain and performance, demonstrating the potential of QD-SOAs for high-gain, wide-bandwidth applications in photonic systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semiconductor quantum dots

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Generalized Multi-protocol label Switching based Virtualization for cloud Computing 1

Generalized Multi-protocol Label Switching based Virtualizat...

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1st International Conference on Connected Systems and Intelligence, CSI 2022

作者： Saxena, Rajat Patel, Ajay Singh Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Cloud computing Lab Department of Computer Science and Engineering Indore India Cloud computing Lab Department of Computer Science and Engineering Indore India

ISBN: (纸本)9781665458153

Generalized Multi Protocol label Switching (GMPLS) or Multi-Protocol Lambda Switching is a new technology which produce amplification in Multi Protocol label Switching (MPLS) to provide support network switching, space switching, and packet switching for time and wavelength. Thus, we can say that GMPLS is extension of MPLS which provides resilience and restoration by automatic switching. In this paper, we provide a Docker based simu-1ation environment which emulates a complex network and create insight functioning of GMPLS. The Docker based simulation environment is light-weight and scalable. It emulates large and complex network with minimal specifications. We have tested this Docker based GMPLS simulation testbed that depends on scaled network resources. Our method has shown tremendous improvement over the other virtualization methods. In this paper, we do a comparative study based on UML, Virtual Box, and Docker and found that Docker consumes very less resources when compared to UML and Virtual Box. In a system with 1 TB Hard disk space and 16 GB RAM, we can design and study a topology with 30-40 nodes using Virtual Box and a topology with 10-15 nodes using UML. Whereas, with Docker a topology with 300-nodes can be designed and tested. Thus scalability is limited by system specifications. © 2022 IEEE.

关键词： multi-protocol label switching

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks 38

AdvAD: Exploring Non-Parametric Diffusion for Imperceptible ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Jin He, Ziqiang Luo, Anwei Hu, Jian-Fang Wang, Z. Jane Kang, Xiangui Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-Sen University China Electrical and Computer Engineering Dept University of British Columbia Canada

Imperceptible adversarial attacks aim to fool DNNs by adding imperceptible perturbation to the input data. Previous methods typically improve the imperceptibility of attacks by integrating common attack paradigms with specifically designed perception-based losses or the capabilities of generative models. In this paper, we propose Adversarial Attacks in Diffusion (AdvAD), a novel modeling framework distinct from existing attack paradigms. AdvAD innovatively conceptualizes attacking as a non-parametric diffusion process by theoretically exploring basic modeling approach rather than using the denoising or generation abilities of regular diffusion models requiring neural networks. At each step, much subtler yet effective adversarial guidance is crafted using only the attacked model without any additional network, which gradually leads the end of diffusion process from the original image to a desired imperceptible adversarial example. Grounded in a solid theoretical foundation of the proposed non-parametric diffusion process, AdvAD achieves high attack efficacy and imperceptibility with intrinsically lower overall perturbation strength. Additionally, an enhanced version AdvAD-X is proposed to evaluate the extreme of our novel framework under an ideal scenario. Extensive experiments demonstrate the effectiveness of the proposed AdvAD and AdvAD-X. Compared with state-of-the-art imperceptible attacks, AdvAD achieves an average of 99.9% (+17.3%) ASR with 1.34 (-0.97) l2 distance, 49.74 (+4.76) PSNR and 0.9971 (+0.0043) SSIM against four prevalent DNNs with three different architectures on the ImageNet-compatible dataset. Code is available at https://***/XianguiKang/AdvAD. © 2024 Neural information processing systems foundation. All rights reserved.

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Square tessellation for stochastic connected k- coverage in planar wireless sensor networks 28

Square tessellation for stochastic connected k- coverage in ...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Nakka, Kalyan Ammari, Habib M. Research Lab Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States

ISBN: (纸本)9798350300482

In this paper, we focus on the problem of connected k-coverage in planar wireless sensor networks (PWSNs), where every point in a field of interest (FoI) is covered by at least k sensors simultaneously, while all the participating sensors are mutually connected, where k > 1. To this end, we develop a global framework using a square tessellation that considers both deterministic and stochastic sensing models. Initially, we tessellate a planar FoI into adjacent and congruent square tiles. In each tile of this tessellation, we construct a cusp-square area for sensor placement to achieve k-coverage. Based on this cusp-squared square tile configuration, we compute the minimum sensor density that is required for deterministic and stochastic k-coverage in PWSNs. Then, we establish the necessary relationship that should exist between the sensing and communication ranges of the sensors to maintain network connectivity in k-covered PWSNs. Finally, we propose our stochastic k-coverage protocol for sensor scheduling and substantiate our theoretical analysis with simulation results. © 2023 IEEE.

关键词： Wireless sensor networks

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