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Minimum and maximum packets’ delays determination for communication flows’ delays jitters computation

Australian Journal of electrical and Electronics engineering 2023年第1期20卷 27-34页

作者： Eyinagho, Monday Ofori Electrical and Computer Engineering Department Afe Babalola University Ado-Ekiti Nigeria

Delay Jitter is a communication networks’ performance issue which occurs when the delays’ of the packets of a traffic stream that has traversed from a transmitter to a receiver are different. Jitter is measured in terms of the difference between a minimum and a maximum value of delay. A large and unaccounted for, value of delay jitter is a problem for some time sensitive applications, for example, real-time audio/video. Delay Jitter is eliminated in some applications by the delayed-playback approach. But this approach requires that the maximum and minimum packets’ delay of a flow should be known in advance. However, it is challenging to a priori know a flow’s minimum and maximum packets’ delays. In this paper, the derivation of simple, practically applicable formulas that can be used to compute the minimum and maximum packets’ delays of a flow, with a view to accounting for, and mitigating the effects of delays’ jitters is reported. The maximum delay value that is obtained with the formulas is compared with three maximum delay values of models that were reported in the literature. The comparison indicates that the formulas that are reported in this paper are superior in terms of practical reality and applicability. ©, Engineers Australia.

关键词： Jitter

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Physics-informed transient stability assessment of microgrids

iEnergy

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iEnergy 2023年第3期2卷 231-239页

作者： Priyanka Mishra Peng Zhang Department of Electrical and Computer Engineering Stony Brook UniversityNY 11794USA

With the integration of a voltage source converter(VSC),having variable internal voltages and source impedance,in a microgrid with high resistance to reactance ratio of short lines,angle-based transient stability techniques may find *** such a situation,the Lyapunov function can be a viable option for transient stability assessment(TSA)of such a VSC-interfaced ***,the determination of the Lyapunov function with the classical method is very challenging for a microgrid with converter controller *** overcome such challenges,this paper develops a physics-informed,Lyapunov function-based TSA framework for VSC-interfaced *** method uses the physics involved and the initial and boundary conditions of the system in learning the Lyapunov *** method is tested and validated under faults,droop-coefficient changes,generator outages,and load shedding on a small grid-connected microgrid and the CIGRE microgrid.

关键词： Physics-informed neural network Lyapunov function voltage source converter transient stability assessment microgrid

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Enhanced Vehicle Detection by Optimized Image Compression in NextG Wireless Network Autonomous Vehicles System 100

Enhanced Vehicle Detection by Optimized Image Compression in...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Hossain, Md Junayed Barkatullah, Mohammad Hoque, Md Mozammal Hassan, Zoheb Ahmed, Tarem Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh University of Houston Department of Engineering Technology United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331517786

Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, the demand for performing resource-intensive edge-assisted Deep Learning tasks in AVS with limited computational resources becomes increasingly challenging. The advent of 5G and future cellular networks (NextG) offers the promise of facilitating the seamless execution of these tasks. This research aims to diminish dataset size, in harmony with the integration capabilities of the Semantic and Flexible Open Radio Access Network (SEMO-RAN) framework, which is anticipated to reduce latency and refine resource allocation in vehicle image processing. The focus of this study is on optimizing image compression without compromising the accuracy of vehicle classification, leveraging esteemed Convolutional Neural Network models like YOLOv5. By employing Generative Adversarial Network compression and Wavelet Image Compression methods, our study achieves an impressive 81.82% reduction in data size while maintaining a 96.97% accuracy in classification tasks. This underscores the potential for significant efficiency gains in AVS through improved data management, supported by Digital Twins (DT), Integrated Sensing and Communication (ISAC), and O-RAN. © 2024 IEEE.

关键词： Radio access networks

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Image-to-Image Translation with Deep Neural Networks for the Enhancement of Monostatic Synthetic-Aperture Ultrasound Images

Image-to-Image Translation with Deep Neural Networks for the...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Bosco, Edoardo Stellino, Chiara Cotogni, Marco Ramalli, Alessandro Cusano, Claudio Matrone, Giulia University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy University of Florence Department of Information Engineering Florence Italy

ISBN: (纸本)9798350346459

In synthetic aperture (SA) ultrasound imaging, whether monostatic or multistatic, all elements of the transducer array are used in turn one-by-one during the transmission phase. However, during reception, the monostatic technique activates only the same element used in transmission, whereas the multistatic one employs the entire transducer array. The monostatic approach thus represents a potential solution for the implementation of an ultra-portable device, given that it would require a single-channel system to work. However, a downside would be a significant loss in terms of image contrast and signal-to-noise ratio caused by single-element transmission/reception. In a previous investigation, we introduced a deep neural network (DNN) for reconstructing high-quality images from radio-frequency (RF) signals acquired using a monostatic SA approach. Differently, in the current study, we propose a similar DNN to perform contrast enhancement of the images obtained from the same RF signals but through delay-and-sum (DAS) beamforming, addressing an image-to-image translation task. The dataset used for training the network consisted of pairs of monostatic/multistatic SA images of elliptical targets simulated in Field II. We also evaluated the effects of training the DNN in an adversarial framework and we compared the results in terms of contrast on a test set comprising both simulated and experimental scans. The results show that the DNN effectively enhances the contrast between the targets and the surrounding background. Additionally, the adversarial training introduces an improvement in speckle pattern and fine-grained details reproduction within the output images. © 2023 IEEE.

关键词： Image enhancement

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Vision-based Markerless Robot-to-robot Relative Pose Estimation Using RGB-D Data

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Journal of Institute of Control, Robotics and Systems 2023年第6期29卷 495-503页

作者： Kim, Joo-Hyun Kim, Kwang-Ki Department of Electrical and Computer Engineering Inha University Incheon22212 Korea Republic of

In this paper, a vision-based robot-to-robot relative pose estimation method is proposed for markerless wheeled mobile robots using an RGB-D camera. The proposed method comprises three parts: (i) object detection using a machine vision technique with RGB data, (ii) relative position estimation averaging over the point cloud with outlier rejection, and (iii) relative orientation estimation using point cloud scan matching through iterative closest point (ICP) methods. The neighboring or target robots are detected using YOLOv3-tiny so that the position of the robot can be determined in pixel coordinates. To estimate the 3D position of the target robot in the camera coordinates of the ego-robot, the object detection results are used along with the depth information and the precalibrated pinhole camera model. To estimate the orientation of the target robot, a point cloud is generated using the depth image, downsampled, and then used for scan matching by using ICP methods. For experimental verification, the proposed relative pose estimation method was implemented as a robot operating system package and tested on a small-scale differential-driving wheeled mobile robot, TurtleBot3;the estimation results were compared with the ground truth in real-world environments. © ICROS 2023.

关键词： Multipurpose robots

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A Singly Curved Conformal Phased Array with Integrated Particles-Based CRLH Phase Shifters

Electronics

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Electronics 2023年第2期27卷 43-49页

作者： Ayaz, Muhammad Ullah, Irfan Department of Electrical & Computer Engineering COMSATS University Islamabad Abbottabad Campus Pakistan

In this paper, a four unit cell micron-sized magnetic particles embedded composite right/left-handed (CRLH) transmission line metamaterial phase shifter is proposed. The variable phases are achieved by activating the micron-sized magnetic particles along the length of stub transmission lines of the CRLH transmission line structure. This causes to effectively change the inductance of stub transmission lines, which changes the phase of the RF signal. These structures can be activated in a symmetric or asymmetrical manner to obtain wide range of phase shifts. A prototype of cascaded four unit cell CRLH transmission line phase shifter with embedded silver-coated ferrites (magnetic particles) is designed, fabricated and tested for phase shifters applications. The measured phase response of the proposed phase shifter is in close agreement with the simulated phase response. Next, for the performance analysis of proposed phase shifter, a 1 × 4 conformal curved-shaped phased array operating at 5.8 GHz is fed with the four unit cell cascaded phase shifter in CST full-wave simulator. The conformal phased array with proposed integrated phase shifters was simulated for broadside pattern correction and main beam scanning at 20 degree on curved-shaped surface with different bend angles. The radiation pattern results obtained with the proposed integrated phase shifters are in close agreement with those obtained with direct excitation of the array. © 2023, University of Banja Luka. All rights reserved.

关键词： Phase shifters

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Parrondo's Paradox-Based Enhanced Beacon Transmission in 6TiSCH Networks

IEEE Networking Letters

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IEEE Networking Letters 2023年第4期5卷 204-207页

作者： Kalita, Alakesh Hazra, Abhishek Gurusamy, Mohan National University of Singapore Department of Electrical and Computer Engineering Queenstown Singapore

The standardized 6TiSCH network offers high throughput, reliable, delay-bounded, and energy-efficient communication in Internet of Things networks. For 6TiSCH bootstrapping, 6TiSCH minimal configuration standard has been released, which uses a fixed transmission rate of Enhanced Beacon (EB) control frame throughout the network's lifetime. However, our theoretical analysis shows both high and low EB rates affect new joining nodes' synchronization time and energy consumption. To address this, we propose Parrondo's Paradox-based EB Transmission (PPET) scheme, which allows already joined nodes to transmit their EB frames probabilistically. Both theoretical and experimental results show the performance improvement by PPET compared to benchmark schemes. © 2019 IEEE.

关键词： Synchronization

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Brain Fingerprinting-Based Community Structure 22

Brain Fingerprinting-Based Community Structure

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Athamnah, Sema Aviyente, Selin Michigan State University Department of Biomedical Engineering Department of Electrical and Computer Engineering East LansingMI United States

ISBN: (纸本)9798331520526

The ability to uniquely characterize individual subjects based on their functional connectome (FC), i.e., fingerprinting, is key for progress toward precision medicine. Over the past few years, a variety of methods have been proposed for fingerprinting. While early approaches focused on using either the whole brain connec-tivity or pre-defined subnetwork connectivity to discriminate between subjects, more recently different feature extraction methods such as principal component analysis (PCA) have been used to ex-tract discriminative features. However, these methods do not iden-tify functionally interpretable network components, i.e., commu-nities, that contribute most to discrimination across subjects. In this paper, we introduce a fingerprinting approach based on community structure. In particular, we introduce a signed multilayer modularity optimization approach to identify the community structure across a group of subjects from resting state fMRI FCs. The optimal community structure for identifying subjects and discrimi-nating between different tasks is learned through a community in-dicator vector which quantifies the relevance of each community for a subject. The proposed framework is tested on the Midnight Scan Club (MSC) data across different tasks. High identification rates between resting state and different tasks indicate that an indi-vidual's community indicator vector is intrinsic to that individual independent of the task. The proposed framework also provides interpretability to fingerprinting by determining the most discrimi-native brain regions between task pairs. © 2025 IEEE.

关键词： community detection fingerprinting Functional connectivity multilayer graphs signed graph

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Modified Frequency Scheduling Droop Control for Decentralized State of Charge Balancing of Battery in Islanded Microgrid 25

Modified Frequency Scheduling Droop Control for Decentralize...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Rahman, Irham Izzatur Miyauchi, Hajime Takano, Hirotaka Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Kumamoto University Department of Computer Science and Electrical Engineering Kumamoto Japan

ISBN: (纸本)9798350378573

This study proposes a decentralized frequency scheduling method to achieve SoC balancing with no communication protocol. Since the frequency scheduling method is based on modifying droop control of inverter, to make the power output of inverter to be proportional to the SoC, the frequency output of inverter would be affected. This study considers to minimize the frequency deviation within European Norm EN50160 (±1% of the nominal value of the frequency), while also able to satisfy SoC balancing. To achieve it, we propose fuzzy logic controller to weight the value of droop coefficient ( ) instead of linear scaling in conventional frequency scheduling. The proposed method is studied in several test to show the performance and compare to conventional method. From the result, the proposed method performs better in SoC balancing, and results in better system reliability. We also study the proposed battery balancing system in various condition of PV and load profile, as well "plug and play"condition of microgrid. The proposed method can perform well in both conditions. © 2024 IEEE.

关键词： State of charge

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Automating skin cancer screening: a deep learning

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Journal of engineering and Applied Science 2025年第1期72卷 1-18页

作者： Rashad, Nada M. Abdelnapi, Noha MM. Seddik, Ahmed F. Sayedelahl, M.A. Department of Computer Science Faculty of Computers and Information Suez University Suez43221 Egypt Biomedical Engineering Department Faculty of Engineering Helwan University Cairo11795 Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

Skin cancer presents in various forms, including squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. Established risk factors include ultraviolet (UV) radiation exposure from solar or artificial sources, lighter skin pigmentation, a history of sunburns, and a family history of the disease. Early detection and prompt intervention are crucial for achieving a favorable prognosis. Traditionally, treatment modalities include surgery, radiation therapy, and chemotherapy. Recent advancements in immunotherapy have revolutionized skin cancer diagnosis, but manual identification remains time-consuming. Artificial intelligence (AI) has shown potential in skin cancer classification, leading to automated screening methods. To support dermatologists, we improved the model for classifying images. This model is able to recognize seven different kinds of skin lesions. On the ISIC dataset, an analysis has been done. This study offers a novel approach to early skin cancer diagnosis based on image processing. Our approach leverages the high accuracy of a specific convolutional neural network architecture, utilizing transfer learning with pre-trained data to further enhance detection performance. Our findings demonstrate that the employed ResNet-50 transfer learning model achieves a remarkable accuracy of 97%, while ResNet50 without augmentation gives an accuracy of 81.57% and an F1-score of 75.75%. © The Author(s) 2025.

关键词： Convolutional neural networks

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