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Advances in Enterprise Architecture (NCAEA), National Conference on

作者： Fatemehzahra Feizi Farhoud Jafari Kaleibar Fatemeh Rahimi Houman Kashfi Amirhossein Hossein Nia Artificial Intelligence Laboratory Irancell Labs Tehran Iran Faculty of Electrical & Computer Engineering Tabriz University Tabriz Iran

The telecommunications industry (telcos) is undergoing a significant transformation driven by digital technologies and changing customer expectations. Telcos are compelled to embrace digital transformation to remain competitive and meet evolving customer demands. This paper presents a comprehensive roadmap for telcos to transition from telco to tech co (technological company), recognizing the strategic and holistic approach required for successful transformation. The roadmap encompasses key aspects such as technological infrastructure, customer-centricity, agile business models, ecosystem partnerships, and organizational culture. By following this roadmap, telcos can unlock new revenue streams, deliver innovative digital services, and maintain competitiveness in the dynamic digital landscape. Finally, this paper conducted a case study on the practical implementation of the digital transformation roadmap by MTN Irancell, providing real-world insights into its effectiveness and outcomes.

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Unlocking the Power of Data in Telecom: Building an Effective MLOps Infrastructure for Model Deployment

Unlocking the Power of Data in Telecom: Building an Effectiv...

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Advances in Enterprise Architecture (NCAEA), National Conference on

作者： Amirhossein Hossein Nia Farhoud Jafari Kaleibar Fatemehzahra Feizi Fatemeh Rahimi Houman Kashfi Artificial Intelligence Laboratory Irancell Labs Tehran Iran Faculty of Electrical & Computer Engineering Tabriz University Tabriz Iran

The telecom industry is experiencing a data revolution, with vast amounts of data being generated from various sources such as customer interactions, network logs, and sensor data. To leverage the power of this data and gain a competitive edge, telecom companies are increasingly turning to machine learning models for various tasks. However, deploying and managing these models at scale is a complex and challenging task. This paper explores the construction of an effective MLOps infrastructure for model deployment in the telecom industry. It discusses the architecture, tools, and components involved in establishing a scalable and robust infrastructure. The paper also addresses industry-specific challenges such as real-time data processing and compliance requirements. The paper provides telecom professionals with a comprehensive guide to unlocking the potential of data through MLOps, enabling innovation and a competitive edge in the telecom sector.

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Sensorless Torque Control of an Electric Vehicle Driven by an Open End Winding Induction Motor: An Experimental Study 2

Sensorless Torque Control of an Electric Vehicle Driven by a...

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2nd International Conference on Energy Transition and Security, ICETS 2023

作者： Younes, Djellouli Ahmed El Mehdi, Ardjoun Sid Houcine, Chafouk Mouloud, Denai Djilali Liabes University Irecom Laboratory Faculty of Electrical Engineering Sidi Bel-Abbes22000 Algeria Normandy University Unirouen Esigelec Irseem Rouen76000 France University of Hertfordshire School of Physics Engineering and Computer Science HatfieldAL10 9AB United Kingdom

ISBN: (纸本)9798350372182

Electric Vehicles (EVs) are anticipated to play a pivotal role in addressing global climate change objectives and offering a solution for improved fuel efficiency. However, enhancing the performance, energy efficiency and cost remain the major challenges for EV technology. Identifying the operating points of the EVs motor drive system is crucial for optimizing its performance. Sensorless electric motor control for EVs is one of the promising research areas for cost reduction and performance improvement. This work's primary objective is to develop, implement and validate experimentally a torque estimator based on Luenberger observer on an EV driven by an open-end winding induction motor (OEWIM) fed by two inverters. The proposed observer structure uses the complete OEWIM mathematical model. The rotor fluxes are estimated using the electrical model whilst the torque is observed using the mechanical model. This observer has been implemented using a board signified for a 1.5 kW OEWIM. The obtained experimental findings demonstrate the efficacy of the method put forward. © 2023 IEEE.

关键词： Torque

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Visualizing Code Syntax: Empowering Developers with a Global View for Enhanced Code Quality Evaluation

Visualizing Code Syntax: Empowering Developers with a Global...

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International Conference on Advanced computer Theory and engineering, ICACTE

作者： Chaima Adjel Fadila Atil Djamel Meslati Computer science departement Faculty of Electrical Engineering and Computing Laboratory LISCO University Badji Mokhtar-Annaba Annaba Algeria

This article discusses the issues that come with system evolution, specifically the challenges of comprehending and maintaining old code created by unreachable developers. In this instance, we want to highlight the value of using code visualization to get a global perspective of the components and their dependencies, since this can assist in recognizing any possible structural problems. Additionally, visualization can shed light on the code's scalability and performance properties. A well-written program with clear syntax that respects low coupling and separation of concerns makes system maintenance and evolution easier, which saves time and money. Adopting a scalable and flexible language also makes maintenance and development smoother. In this paper, we made a visualization of Mage code in order to evaluate its syntax flexibility and scalability.

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Real-Time Implementation of the Luenberger Torque Observer for an Induction Motor 2

Real-Time Implementation of the Luenberger Torque Observer f...

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Younes, Djellouli El Mehdi, Ardjoun Sid Ahmed Mouloud, Denai Houcine, Chafouk Djillali Liabes University Irecom Laboratory Faculty of Electrical Engineering Sidi Bel-Abbes22000 Algeria School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Normandy University Unirouen Esigelec Irseem Rouen76000 France

ISBN: (纸本)9798350314243

Currently, the technology of high-performance variable speed drives without mechanical sensors is widely used in the industrial environment. This preference arises from the exorbitant costs and fragility associated with mechanical sensors. This investigation aims to establish, implement, and empirically validate a Luenberger Observer for wheel torque within an electric propulsion framework that employs induction motors. The proposed observer structure focuses on the complete dynamic model of an induction motor. Concerning the rotor fluxes, they are estimated using the electrical model. On the other hand, the torque is observed using the mechanical model. A real-time implementation was performed on a DSpace 1104 board designed for a 1.5 kW induction motor to verify the proposed observer's effectiveness. The experiment results show that the proposed method exhibits satisfactory performance, affirming its effectiveness and robustness and giving valuable insights into its practical applicability. © 2023 IEEE.

关键词： Induction motors

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Design of A Third-order Inline Waveguide Bandpass Filter With Cross Coupling and Spurious Mode Suppression

Design of A Third-order Inline Waveguide Bandpass Filter Wit...

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IEEE International Wireless Symposium (IWS)

作者： Wangtao Ye Zai-Cheng Guo Lei Zhu Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing Normal University Nanjing Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (数字)9798350389999

ISBN: (纸本)9798350390001

A third-order inline waveguide bandpass filter (BPF) with cross coupling and spurious mode suppression is designed in this paper. Based on the conventional cascaded-coupled filter, the cross coupling is realized by reducing the height of the second resonating cavity and introducing a stacked cavity for cross coupling, which generates a transmission zero (TZ) on the right side of passband. In order to eliminate the spurious modes caused by the cross-coupled cavity, a short-circuited post is introduced to make TE101 and TE102 mode of the cavity introduced for cross coupling self-suppressed. Therefore, the proposed inline waveguide BPF is with one TZ on the right side of passband while preserving almost the same bandwidth of stopband when compared to the conventional cascaded-coupled filter. The design process is illustrated in detail and the measured results verify the feasibility of the proposed inline waveguide BPF.

关键词： Band-pass filters Couplings Wireless communication Bandwidth Filtering theory Passband

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An intelligent solar flare prediction model based on X-ray flux curves using Long Short-Term Memory

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Astronomical Techniques and Instruments 2025年第2期2卷 65-72页

作者： Yan Gao Li Zhang Long Xu College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic ***,it is very important to accurately predict the time period of solar *** paper proposes a flare prediction model,based on physical images of active solar *** employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 *** reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 *** True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and *** model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.

关键词： Neural Network Long Short-Term Memory Solar flare prediction X-ray flux curve

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Enhanced Breast Cancer Molecular Biomarker Classification: A Novel Two-Stage Machine Learning Pipeline for Accurate Histological Analysis of Whole Slide Images

Enhanced Breast Cancer Molecular Biomarker Classification: A...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Aboudessouki, A. Ali, Kh.M. Alksas, A. Elsharkawy, M. Rahma, M. Abo Ghazal, M. Mekky, N. El-Daydamony, E. Gondim, D. El-Baz, A. BioImaging Laboratory Bioengineering Department University of Louisville United States Pathology Department Faculty of Medicine Mansoura University Egypt Mansoura University Hospital Mansoura University Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University United Arab Emirates Department of Information Technology Faculty of Computers and Information Mansoura University Egypt Department of Pathology and Laboratory Medicine University of Louisville LouisvilleKY United States

ISBN: (纸本)9798350368741

Breast cancer, a prevalent and diverse form of cancer, is characterized by unique clinicopathologic features. Accurate classification of its molecular subtype, essential for targeted treatment and improved survival rates, relies on testing molecular biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 antigen. This study introduces an innovative two-stage machine learning pipeline for breast cancer PR molecular biomarker classification using whole slide immunohistochemistry images. Our approach consists of two specialized stages. In the first stage, a watershed algorithm segments the Cells from the input image. Morphological and statistical features extracted from these segments are then used by machine learning classifiers to distinguish between tumor and nontumor tissues [1]-[3]. The second stage focuses on the tumor tissues that were identified in the first stage, extracting texture and appearance features to classify them into positive or negative biomarker responses, again using machine learning classifiers [4], [5]. This method not only automates the classification of PR biomarkers in tumor regions but also generates a detailed image map for each cell in the Whole Slide Image (WSI). By doing so, it can determine whether the tissue represents tumor cells and whether the tumor tissue exhibits positive or negative PR status. The results show the ability of our machine learning-based approach to augment pathologists' diagnostic capabilities, offering significant advancements in the automated classification of histopathological images. © 2025 IEEE.

关键词： Breast cancer Histopathology Immunohistochemistry Machine Learning Marker-based Watershed Segmentation Molecular biomarker Status Molecular biomarkers Tissue classification Tumor Detection Watershed Segmentation

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Redefining the Classification of Extravasation Severity Using CLIP Linear Probe with Few-shot Instances

Redefining the Classification of Extravasation Severity Usin...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： A. Munthuli M. Thepsoparn P. Pooprasert P. Phienphanich P. Pongcharoen D. Sakonlaya P. Sittiwattanawong S. Weschawalit P. Chakkavittumrong B. Thongthawee T. Pathomjaruwat C. Tantibundhit Department of Electrical and Computer Engineering Faculty of Engineering Medical Invention and Innovation Laboratory (MeDii) Thammasat School of Engineering Thammasat University Thailand MeDii and Bangkok Patana School Thailand Faculty of Medicine Thammasat University Thailand Faculty of Nursing Thammasat University Thailand

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study addresses extravasation, a critical issue requiring prompt detection for effective management to avoid severe complications. This work harnesses the capability of zero-shot capabilities from pre-trained vision transformer models–specifically, GroundingDINO and Segment Anything Model (SAM) for segment human skin regions, and Contrastive Language-Image Pretraining (CLIP) for extracting rich features from these regions using its frozen image encoder. Our methodology applies linear probe techniques to feature vectors obtained from CLIP using few-shot instances. The results demonstrate accurate classification of extravasation severities with training using only 64 instances per class, achieving average F1 macro scores of 74.08% for GroundingDINO-CLIP. This marks an improvement result to the previous study which utilized dual U-Nets for skin and lesion segmentation alongside DenseNet-121 models for classification with training using 975 instances. Notably, this approach increases previous F1 macro scores by 3.27% in mild extravasation cases. This research advances fine-grained extravasation classification, specifically in early detection, achieved through few-shot models in a unique context.

关键词： Training Image segmentation Feature extraction Transformers Skin Vectors Lesions Probes engineering in medicine and biology Context modeling

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