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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Aldaeabool, Sally. R. Al-Zubaedi, Wesam H. Abbod, M.F. Al-Raweshidy, H.S. Brunel University London Dep. of Electronic and Computer Engineering United Kingdom University of Kerbala Collage of Engineering Dep. of Biomedical Engineering Iraq

ISBN: (纸本)9798331530938

The exponential expansion of mobile communication traffic, fuelled by a rising number of smart devices and sophisticated applications, has put significant pressure on existing cellular network architectures. The 6G networks need creative ways to meet these difficulties effectively. This study offers a Distributed Open Radio Access Network (DO-RAN) architecture that uses Open-RAN and Software-Defined Networking (SDN) to reduce signalling burden and boost network performance. DO-RAN improves responsiveness, reduces signalling load on the 5GC, and reduces backhaul congestion by decentralising key functionalities such as the Access and Mobility Management Function (AMF) and the Session Management Function (SMF) from the 5G Core Network (5GC) to the Open-RAN framework's Central Units (CUs). Our proposal compares DO-RAN to Central Open RAN (CO-RAN) and other architectures, such as 4G LTE networks. The findings show that DO-RAN considerably reduces signalling load in a variety of situations, such as network- and user-triggered service events. This architecture provides enhanced efficiency and scalability for 6G cellular networks in addition to meeting the growing demands for signalling. © 2025 IEEE.

关键词： Radio access networks

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Signaling Load Reduction in 5G Network Based Using Open-RAN Architecture and SDN 4

Signaling Load Reduction in 5G Network Based Using Open-RAN ...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

ISBN: (纸本)9798331530938

This paper addresses the challenges associated with signaling load in 5G Core (5GC) by proposing innovative architectural solutions based on Open Radio Access Network (Open-RAN), and Software-Defined Networking (SDN). The work introduces two novel approaches, Partially Distributed Open RAN (PDO-RAN) and Fully Distributed Open RAN (FDO-RAN), aiming to optimize network performance and efficiency by reduce the signaling load on 5GC. The architectures involve the strategic relocation of critical components, such as the Access and Mobility Management Function (AMF) and Session Management Function (SMF) from 5GC to the gNB Central Unit (gNB-CU) within the Open RAN architecture. The research conducts a comprehensive evaluation of these proposed architectures, comparing them with the traditional Central Open RAN (CO-RAN), and existing LTE designs. Signaling load analyses, particularly during the initial attachment event, reveal that FDO-RAN consistently outperforms other architectures, showcasing its efficiency in handling signaling processes. The study provides valuable insights into the scalability and effectiveness of distributed and fully distributed Open RAN architectures, offering crucial guidance for future advancements in 5G network optimization. © 2025 IEEE.

关键词： Efficiency

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss dep.oyment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： electronic health record

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Sensing voltage of MEMS Accelerometer using Polycrystalline Silicon (Si),Gallium Arsenide (GaAs) and Germanium (Ge) 6

Sensing voltage of MEMS Accelerometer using Polycrystalline ...

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6th International Youth Conference on Radio electronics, Electrical and Power engineering, REEPE 2024

作者： Elsayed, Amr N. Morsi, Iman G. Abdelrassoul, Roshdy A. Mahmoud, Ahmed K. Arab Academy for Science and Technology Dep. of Electronics and Communications Engineering Alexandria Egypt Air Defense College Dep. of Electrical and Electronic Engineering Alexandria Egypt

ISBN: (纸本)9798350382891

Accelerometers are inertial sensors that detect acceleration and forces along three orthogonal axes, then the readings are produced as a digital or analog signal and used in various applications. Microelectromechanical systems (MEMS) have recently been utilized to perform inertial sensing techniques on an integrated circuit (IC) at micro size. Due to the various characteristics of MEMS materials and how they affect the sensitivity of the MEM Accelerometer with these variations. In this study, a MEMS accelerometer model was created using the COMSOL program. The design was then applied to polycrystalline Silicon (Si), Gallium Arsenide (GaAs), and Germanium (Ge), and the sensing voltage was determined using each material to clarify which of these materials performed best. © 2024 IEEE.

关键词： Germanium

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CNN-based Human Activity Recognition on Edge Computing Devices

CNN-based Human Activity Recognition on Edge Computing Devic...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Singh, Amandeep Margaria, Tiziana Demrozi, Florenc University of Limerick Dep. of Computer Science and Information Systems Ireland University of Stavanger Dep. of Electrical Engineering and Computer Science Norway

ISBN: (纸本)9798350346473

Human Activity Recognition (HAR) is a research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as healthcare, sports, industry, and entertainment. However, executing HAR algorithms on remote devices or the cloud can lead to issues such as latency, bandwidth requirements, and energy consumption. Transitioning towards Edge HAR can be a more effective and versatile solution, overcoming the challenges of traditional HAR techniques. We present a novel HAR model for computation on edge devices: we design a Convolutional Neural Network (CNN) Deep Learning approach and compare its performance with cloud-computing HAR models. The paper is accompanied by a self-collected dataset. The experiments on this dataset demonstrate that the proposed edge computing model achieves promising results (\geq 92 %) in terms of Precision, Recall, and Fl-score. Furthermore, the model exhibits significantly reduced latency, with only 117 ms, and utilizes minimal memory, with a peak of 18.8 Kb RAM and 956 Kb Flash memory. © 2023 IEEE.

关键词： Edge computing

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Traffic sign recognition based on CNN vs different transfer learning techniques 5

Traffic sign recognition based on CNN vs different transfer ...

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5th Symposium on Pattern Recognition and Applications, SPRA 2024

作者： Ghouse, Mohammed Farag, Saber Butt, Usman Engineering and Environment Dep. Northumbria University London United Kingdom Computer and Information Science Dep. British University in Dubai Dubai United Arab Emirates

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Traffic sign detection and recognition (TSDR) is a pivotal component of contemporary transportation systems, designed to enhance road safety and operational efficiency. In an era where automation is indispensable, TSDR stands out as a critical tool for optimizing driving experiences. This study presents the development of a TSDR model employing advanced deep learning techniques, with a particular emphasis on convolutional neural networks (CNNs). Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which comprises annotated images of various traffic signs captured under diverse environmental conditions, we achieved remarkable results. Our CNN models demonstrated a 99% detection rate, underscoring their efficacy in traffic sign recognition tasks. Furthermore, transfer learning models yielded impressive accuracies, with VGG19 achieving 99.64%, EfficientNetB7 reaching 99%, and ASNet attaining 99.72%. Despite these promising results, we identified several limitations, including dataset-specific dep.ndencies and the need for further enhancements in model design and training methodologies. Addressing these challenges is necessary for ensuring the robustness and reliability of intelligent transportation systems, thereby significantly improving road traffic management, safety, and efficiency. Copyright © 2025 SPIE.

关键词： Convolutional neural networks

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SANA at NADI 2023 shared task: Ensemble of Layer-Wise BERT-based models for Dialectal Arabic Identification 1

SANA at NADI 2023 shared task: Ensemble of Layer-Wise BERT-b...

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1st Arabic Natural Language Processing Conference, ArabicNLP 2023

作者： Almarwani, Nada Aloufi, Samah Dep. of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (纸本)9781959429272

Our system, submitted to the Nuanced Arabic Dialect Identification (NADI-23), tackles the first sub-task: Closed Country-level dialect identification. In this work, we propose a model that is based on an ensemble of layer-wise fine-tuned BERT-based models. The proposed model ranked fourth out of sixteen submissions, with an F1-macro score of 85.43. © 2023 Association for Computational Linguistics.

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Comparative Analysis of ML Algorithms for Fraud Detection 4

Comparative Analysis of ML Algorithms for Fraud Detection

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Abitova, Gulnara Abalkanov, Miras Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University BinghamtonNY United States Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350374865

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss. © 2024 IEEE.

关键词： Logistic regression

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Study on the Robotic Arm Trajectory Compensation System with Deviation Sensing 3

Study on the Robotic Arm Trajectory Compensation System with...

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3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Liu, Chao-Chung Liu, Jun-Chi Liu, Chao-Shu Dep. of Computer Science Zhaoqing University Zhaoqing526061 China Dep. of Industrial Design National United University Miaoli360301 Taiwan Dep. of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan

ISBN: (纸本)9781643685649

Robotic arms play an important role in the automation industry. Robotic arms plan processing paths based on the shape of the workpiece. However, the processing path often deviates due to inaccurate placement of the workpiece. To solve the issue of deviations, this paper develops a trajectory compensation system for a robotic arm with offset perception. This system uses the concept of workpiece boundary projection and designs an offset calculation algorithm by comparing the dimensions of the workpiece drawings, correcting errors caused by missing corners of the workpiece. In terms of operation, the system uses photoelectric sensors to quickly scan the workpiece boundary and immediately calculate the displacement and rotational offset of the workpiece. Subsequently, when deviations occur due to different placement positions of the workpiece, the robotic arm's motion path and action must be corrected and compensated to maintain precision. Tests on selected workpieces show that the corrected rotational angle error is less than 0.4 degrees, and the translational error is less than 0.7 millimeters. Since these correction error values are lower than the 1.0 mm error threshold for the robotic arm's trajectory tracking control. The offset-sensing robotic arm trajectory compensation system developed in this study can satisfy the requirements of smart automation production lines and arrive at the manufacturer's application standards. © 2024 The Authors.

关键词： Robotic arms

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Detecting 5G Signal Jammers with Autoencoders Based on Loose Observations

Detecting 5G Signal Jammers with Autoencoders Based on Loose...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Varotto, Matteo Valentin, Stefan Tomasin, Stefano University of Padova Dep. of Information Engineering Padova Italy Darmstadt University of Applied Sciences Dep. of Computer Science Darmstadt Germany University of Padova Dep. of Information Engineering Dep. of Mathematics Padova Italy

ISBN: (纸本)9798350370218

Fifth-generation (5G) networks are prone to jamming attacks, which are particularly dangerous in mission-critical applications such as factory automation. In this paper, we present a simple method to detect jamming attacks with machine learning techniques operating on in-phase and quadrature (IQ) modulated signals. In particular, a convolutional autoencoder (CAE) learns the structure of the clean signal to distinguish it from jammed signals in real time. This approach requires only a loose synchronization to the OFDM symbol, while equalization and decoding are not necessary. Despite its simplicity, our technique has shown high detection rates in experiments on a 5G testbed. © 2023 IEEE.

关键词： 5G Convolutional Autoencoder IQ Signals Jamming Detection New Radio Wireless Intrusion Prevention

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