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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Abdelmounim, Sellidj Said, Gaoua Abderrahim, Belalia Rachida, Touhami Yagoub, Mustapha Cherif Eddine USTHB Faculty of Electronics and Computer Science LINS laboratory Algeria School of eletrical engineering and computer science University of Ottawa Ottawa Canada

ISBN: (纸本)9798350351736

The ever-increasing demand for higher and higher 5G wireless communication system performance has a direct impact on the design of highly efficient RF blocks, in which mixers play a key role. In fact, with the exponentially grow of data traffic and the need for high data-rate and low latency, mixers should maintain a high performance while working in challenging conditions. The main requirements for mixers to work properly in 5G wireless networks are related to their conversion gain, noise figure, and linearity. These are indeed the key design elements to consider but the technology to be used as well as the mixer compactness and integration into mobile devices must be carefully evaluated. In this paper, recent CMOS Gilbert-Cell mixer designs for 5G applications are discussed, while highlighting the techniques used to improve their electrical parameters. Some limitations are mentioned as well. © 2024 IEEE.

关键词： CMOS integrated circuits

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The Institute of electronics and Information Engineers.

关键词： Cavity resonators

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Design of a Crescent Moon-Shaped Reconfigurable Patch Antenna Using a PIN Diode for 5G Sub-6 GHz and Multistandard Wireless Applications

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Progress in Electromagnetics Research B 2024年 109卷 81-93页

作者： El Aoud, Salah E. Abbaoui, Hind Benkhadda, Omaima Attioui, Sanae Assri, Nasima El Ibnyaich, Saida Zeroual, Abdelouhab Ismail, Mohd M. Al-Gburi, Ahmed J.A. Department of Physics Faculty of Sciences Semlalia Cadi Ayyad University Marrakesh Morocco Department of Physics Faculty of Sciences and Techniques Gueliz Cadi Ayyad University Marrakesh Morocco Moroccan School of Engineering Sciences Marrakesh Morocco Faculty of Technology and Engineering Electronics and Computer Universiti Teknikal Malaysia Melaka Malaysia

This research explores the versatility of a miniature reconfigurable antenna designed for a variety of wireless applications: 5G (IEEE 802.15.3), WLAN (IEEE 802.11), V2X (IEEE 802.11 p), WiMAX (IEEE 802.166), Wi-Fi 6E (IEEE 802.11 ax), Wi-Fi 7 (IEEE 802.11 be), C-band (from 4 GHz to 8 GHz), and X-band (from 8 GHz to 12 GHz). To enable frequency reconfigurability, the patch is equipped with two PIN diodes, which can be positioned at different locations to adjust the antenna’s operational frequency range. This reconfigurability allows the antenna to maintain its size while changing its frequency range according to the state of the PIN diodes. The strength of our work lies in achieving exceptional electrical performance while maintaining a small size, cost-effective, and compact design. The antenna demonstrates an almost omnidirectional radiation pattern across all frequency ranges. Additionally, the simulated reflection coefficient remains within the ideal range for every frequency band. The antenna’s overall dimension is 22 × 18 × 1.6 mm3 (Formula presented) (with λo being the free space wavelength at the lowest resonating frequency and for the proposed antenna its value equal to 91.18 mm) with a miniaturization rate equal to 75.25%. This compact antenna is designed to operate across multiple frequencies, making it suitable for various applications, particularly in wireless communication systems. Its versatility also makes it a promising candidate for future portable devices, sensor networks, and telecommunication applications. The performance metrics, including return loss and radiation pattern, are presented, demonstrating strong performance across these parameters. The analyses were conducted using the CST Studio Suite, which provided detailed insights into the antenna’s functionality and effectiveness. © (2024), (Electromagnetics Academy). All rights reserved.

关键词： Wi-Fi

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A New Comparative Approach Based on Features of Subcomponents and Machine Learning Algorithms to Detect and Classify Power Quality Disturbances

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Electric Power Components and Systems 2024年第8期52卷 1269-1292页

作者： Akkaya, Sıtkı Yüksek, Emre Akgün, Hasan Metehan Department of Electrical and Electronics Engineering of Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas Turkey Department of Computer Engineering of Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas Turkey Department of Aircraft Technology of Sivas Vocational School Sivas University of Science and Technology Sivas Turkey

Current measurement systems based on the IEEE-1159 standard have some limitations and robustness problems under noisy and fast-changing conditions. Besides, applying different methods for each Power Quality Disturbance (PQD) to every window is required but time-consuming and not feasible. Therefore, different kinds of two-stage methods, Detection and Classification (D&C), have been improved in many studies. Then, the required measurement can be performed to define disturbance. For this purpose, a new approach based on features of subcomponents with Machine Learning Algorithms (MLAs) to detect and classify PQDs is proposed. 21-class dataset including single and multiple PQDs under different noisy conditions was prepared randomly. Of this dataset, determined features were extracted and some of these were selected. Then, selected features were trained and tested with some MLAs in a workstation. Results obtained from comparative MLAs and the other classification methods show that the best MLA with related features is Random Forest with 96.97% while LightGBM, k-Nearest Neighbors, and XGBoost 96.85%, 96.73%, and 92.82% accuracy, respectively. Because the selected features, optimized parameters, and the related MLA were obtained by investigating for features provided from the PQDs in the whole parameter space, this approach brings the advantages of high accuracy, low D&C complexity, and computing load. © 2023 Taylor & Francis Group, LLC.

关键词： Power quality

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Federated Semi-Supervised Medical Image Classification Using Improved Inter-Client Relation Matching 8

Federated Semi-Supervised Medical Image Classification Using...

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8th International Conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Ozdemir, Muhammed Said Sabbagh, Mais Erdem, Cigdem Eroglu Korcak, Omer Uludag, Kubra Marmara University Faculty of Eng. Department of Computer Engineering İstanbul Turkey Ozyegin University İstanbul Turkey Dept. of Electrical and Electronics Eng. Istanbul Turkey Marmara University Faculty of Engineering Department of Computer Engineering İstanbul Turkey

ISBN: (纸本)9798331539696

Traditional centralized machine learning faces chal-lenges with 'data label scarcity' and 'data privacy', especially in medical data classification. Semi-supervised learning leverages unlabeled data, while federated learning protects data privacy by decentralizing model training. In this study, we introduce an improved inter-client relation matching algorithm for semi-supervised federated medical image classification (iFedIRM). The FedIRM algorithm defines a loss function to transfer the knowledge of disease relationships from labeled clients to unlabeled clients to extract discriminative information from unlabeled data using per-category mean feature vectors. Our approach enhances the FedIRM algorithm by incorporating per-category feature covariance matrices for better feature representation and using group-based averaging for model aggregation. We utilize the ConvNeXt architecture as the backbone, and apply a confidence threshold to filter unreliable pseudo-labels. Experiments on three medical datasets show improved accuracy, F1, and AUC metrics. Code will be available at https://***/msoz7/iFedIRM. © 2025 IEEE.

关键词： Federated learning

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On the Application of Inexact Computation for Low Power SHA-256

On the Application of Inexact Computation for Low Power SHA-...

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2023 Workshop on electronics Communication engineering, WECE 2023

作者： Ismail, Ahmed Shaaban Shalaby, Ahmed Abu-Elyazeed, Mohamed Fathy Electronics and Electrical Communications Dept Faculty of Engineering Cairo University Cairo Egypt Computer Science Dept. Faculty of Computer and Artificial Intelligence Benha University Benha Egypt

ISBN: (纸本)9781510672574

The adoption of IoT has raised concerns about security as life-threatening attacks become more common. To address this, ultra-lightweight secure SoCs become a demand to ensure trusted operation in the presence of untrusted IPs while meeting power and area constraints. Integrity, one of the main components of security, is the prevention of protected data modification without detection. Hash functions are used to ensure data integrity in secure systems. In this paper, we propose a low-power implementation of a hash function by using approximate adders in SHA-256 implementation. First, approximate adders’ designs were investigated, where we tested the output uniqueness of each of them, as the output uniqueness of an adder improves the properties of hash results. Then, the chosen adders are utilized for hash implementation. Next, the Avalanche test is applied to evaluate the properties of the proposed hash functions. Synthesis and simulation results show that using an approximate adder reduces the power consumed in the combinational part by up to 66% while the avalanche test results are reduced gradually from 50% to 25%. © 2024 SPIE. All rights reserved.

关键词： Adders

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Optimizing Automotive Cybersecurity with Cost-Sensitive Decision Forest Algorithm in IoV 6

Optimizing Automotive Cybersecurity with Cost-Sensitive Deci...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Qureshi, Salwa Umar Souri, Alireza Inanç, Nihat Department of Software Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey

ISBN: (纸本)9798331539603

In the quickly changing world of connected cars, having strong cybersecurity is essential. To protect vehicular networks from cyber threats, this study provides an Embedded Intrusion Detection System (IDS) that has been optimized using the Cost-Sensitive Decision Forest (CSForest) optimization algorithm. To improve data usefulness and model performance, a thorough preprocessing and feature engineering process was carried out using a real vehicular GPS tracking process from ToN-IoT dataset. After evaluating several machine learning algorithms, CSForest received the best results, scoring 100% accuracy along with flawless precision, recall, and F-measure scores. Our results highlight how well the CSForest-based IDS can detect and stop possible cyberattacks in connected cars, which will improve automotive cybersecurity and create a safer driving environment for vehicular communication systems. The study also emphasizes how crucial it is to keep updating IDS models to handle the ever-changing nature of cyberattacks and maintain the dependability and resilience of vehicular communication systems in the future. The discovery has practical consequences as it can improve connected and autonomous car cybersecurity infrastructure substantially, lowering vulnerabilities and guaranteeing a safer transportation ecosystem. © 2024 IEEE.

关键词： Cybersecurity

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Enhancing Solar Energy Production Forecasting with Ensemble-based Learning Techniques 6

Enhancing Solar Energy Production Forecasting with Ensemble-...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Abdelsalam, Hany Souri, Alireza İnanç, Nihat Department of Software Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey

ISBN: (纸本)9798331539603

The global trend in renewable energy solutions has emphasized the urgent need for accurate forecasting of solar energy production. This study examines the potential of ensemble-based learning techniques in predicting solar energy production by leveraging historical data collected from 11 solar photovoltaic installations in Calgary, Canada, spanning from September 2015 to March 2023. The dataset was carefully preprocessed to handle missing values, duplicates, and to extract meaningful features that align with the nature of time series data. A range of ML models were implemented and assessed by several forecasting evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2). The experimental results demonstrate the superior performance of ensemble methods, particularly XGBoost and Light GBM, in solar energy forecasting. XGBoost achieved the best performance, with an MAE of 25.36, an MSE of 3599.66, and R2 of 0.957, outperforming models such as Random Forest and Neural Networks. The practical implications of these findings suggest that accurate solar energy forecasting can significantly enhance operational planning and optimize energy management in solar power plants, contributing to more reliable and efficient renewable energy systems. © 2024 IEEE.

关键词： ensemble methods machine learning renewable energy solar energy forecasting time series analysis XGBoost

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Difussion-Weighted Images Analysis Using Regional Convolutional Neural Network for Stroke Lesion Classification 4

Difussion-Weighted Images Analysis Using Regional Convolutio...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Saad, Norhashimah Mohd Abdullah, Abdul Rahim Kandaya, Shaarmila Mohd Noor, Nor Shahirah Samsudin, Adam Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology and Engineering Melaka Malaysia Universiti Teknikal Malaysia Melaka Faculty of Electrical Technology and Engineering Melaka Malaysia

ISBN: (纸本)9798350395914

Stroke is a major type of brain disorder, and the MRI sequence known as diffusion-weighted imaging (DWI) is often used to evaluate early changes related to strokes. Typically, stroke diagnosis requires manual assessment by neuroradiologists, which is subjective and time-consuming. Therefore, developing an automated stroke diagnosis system is crucial to streamline this process. This study presents a method for automatically detecting and categorizing stroke lesions using deep learning techniques applied to DWI. The dataset comprises 61 samples from Hospital Kuala Lumpur (HKL) and 69 samples from the public ISLES database, including acute hemorrhage, acute ischemic, chronic ischemic, and sub-acute ischemic strokes. The method employs a Regional Convolutional Neural Network (R-CNN) on DWI images, involving four stages: input image, region proposal network (RPN), computation of CNN features, and classification. Performance is evaluated using metrics such as accuracy, sensitivity, and specificity. The technique achieves a classification accuracy of 64.62% with an average training time of 16.3 seconds. Overall, this stroke classification method shows promise for enhancing the diagnosis and categorization of brain stroke lesions using deep learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Privacy, Security, Trust and Applications in Internet of Things 6

Privacy, Security, Trust and Applications in Internet of Thi...

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6th IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Yaklaf, Sliman K.A.A. Elmezughi, Abdurrezagh S. Naas, Salah Mohamed H. Ekreem, Nasser Bashir Azzaytuna University Faculty of Engineering Electrical and Electronics Engineering Department Tarhuna Libya Azzaytuna University Faculty of Engineering Computer Engineering Department Tarhuna Libya

ISBN: (纸本)9798350321029

Internet of Things (IoT) is a network to connect anything with the Internet. While its purpose is to provide efficient and effective solutions, security of the network devices is a challenging issue. Therefore, security and privacy has emerged as a significant challenge for the IoT. Within the available Internet infrastructure, IoT devices collecting data through wireless technology. Now Days, IoT is research area with many devices are connected through internet. IoT concept brings a new challenge concerning privacy, trust, security, and reliability. To build smart systems, IoT technology has become important technology for people use. In this paper, IoT technologies, applications and characteristics will be briefly discussed. This paper gives a complete image of IoT security issues and its applications. The platform is required to secure exchange between consumers of their information and IoT devices. © 2023 IEEE.

关键词： Internet of things

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