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2023 IEEE International Conference on Emerging Trends in engineering, Sciences and technology, ICES and T 2023

作者： Azam, Tahir Tahir, Farooq Ahmed Sarwar, Asima Qayyum, Muhammad Aqib National University of Science and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan GIK Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Topi Swabi Pakistan

ISBN: (纸本)9781665455602

This paper presents a high gain, compact size and dual band rectangular patch antenna for 5G applications. To enhance the gain of antenna, an equilateral triangle slots on the upper rectangular patch are constructed. The broadband antenna is operating at 27.4 GHz to 35.5 GHz and 38GHz to 41.8GHz having fractional bandwidth of 25.7% and 9% respectively. The maximum gain of 8.19 dBi is observed at 28 GHz and the antenna has radiation efficiency greater than 90% over the entire band. Moreover, the Voltage Standing Wave Ratio (VSWR) is lower than 2 dB at operational bands. © 2023 IEEE.

关键词： Radiation efficiency

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New Radio Polar Coding Performance Enhancement Study Using QAM and APSK Modulation Schemes for 6G 4

New Radio Polar Coding Performance Enhancement Study Using Q...

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4th International Conference on electrical, Communication and computer engineering, ICECCE 2023

作者： Tadesse, Edom Wondimu Annamalai, Pushparaghavan Mengistu, Fikreselam Gared Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia

ISBN: (纸本)9798350369694

In various regions of the world, the 5G network has been operational since 2020. In 5G better spectrum and power resources are included as extra features. A better network is still required, nevertheless, to support the Internet of Everything and the needs of the holographic society. Consequently, it is anticipated that the 6G network will debut in 2030 to meet future demand. The new 6G technologies will improve the air interface and transmission technologies. Enhanced channel coding is one of these new technologies. For 5G, the 3GPP chose the channel coding techniques to be LDPC and Polar codes. By enhancing the 5G polar codes performance it can be applied as channel coding techniques in 6G. Using higher-order modulation techniques with these polar codes is one approach. We have used polar codes with 16APSK and QAM with low code rates to improve the performance of 5G polar codes. The result showed better BER performance for polar codes with 16APSK and QAM at a low coding rate than BPSK modulation with a high code rate. © 2023 IEEE.

关键词： 5G mobile communication systems

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Evaluating Optimization Approaches for Deep-Reinforcement-Learning-based Navigation Agents 10

Evaluating Optimization Approaches for Deep-Reinforcement-Le...

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10th IEEE International Conference on Cybernetics and Intelligent Systems and IEEE Conference on Robotics, Automation and Mechatronics, CIS-RAM 2023

作者： Kästner, Linh Roberts, Liam Bhuiyan, Teham Lambrecht, Jens Berlin Institute of Technology Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350303087

In recent years, Deep Reinforcement learning has made remarkable progress in various application areas such as control of robots and vehicles, simulation, and natural language processing. In recent years, various research works applied DRL to conduct different kinds of tasks for autonomous navigation of vehicles and robots such as lane changing, cruise control, or obstacle avoidance. However, DRL training is still a tedious and difficult process due to long training times, catastrophic forgetfulness and the myopic nature of DRL agents. In this paper, we integrate and explore the effect of a variety of state-of-the-art optimization approaches for DRL agents such as imitation learning, behavior cloning, frame stacking, and hindsight replay for autonomous obstacle avoidance. We evaluate the effect of each of these changes to the DRL agent in terms of training- as well as navigational performance. The resulting agents are compared against baseline approaches without those optimizations and we found an increase in navigational performance for some of these methods while other optimization approaches surprisingly resulted in decreased performing. The findings of this paper should aid in development of DRL approaches for autonomous navigation approaches. © 2023 IEEE.

关键词： Natural language processing systems

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An Exploratory Study of Diverse Models and Datasets for Transfer Learning based Image Classification on Sparse Data 10

An Exploratory Study of Diverse Models and Datasets for Tran...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Pathmarasa, Jathurshan Wijenayake, Udaya Wijesinghe, Ruchire Eranga Silva, Bhagya Nathali University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331529048

Transfer learning is a powerful technique for image classification, especially when dealing with limited data. However, selection of the best transfer learning approach and model remains challenging, since the strategy is highly influenced by the scenario. This work conducted a thorough analysis of four transfer learning approaches for binary classification, and multiclass classification using four pretrained models i.e. MobileNetV2, MobileNetV3Large, EfficientNetB0, and EfficientNetB5. The test accuracy was measured with respect to the sample size considering the effects of data augmentation and fine tuning. The key finding of the study revealed that data augmentation has improved the test accuracy by up to 11.8%. Moreover, the lightweight models marked a breakthrough by outperforming large scale models' accuracy for sparse data settings when fine tuning (FT) and data augmentation (DA) are applied appropriately. Hence, this work provides a comprehensive overview on selecting different transfer learning approaches for sparse data classification by exploring diverse models and datasets, while discussing the consequences and challenges for future real-world applications. © 2024 IEEE.

关键词： Data accuracy

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Cavity-Excited Metagrating Antennas with Controlled Frequency Diverse Patterns

Cavity-Excited Metagrating Antennas with Controlled Frequenc...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Hu, Fengming Epstein, Ariel Technion - Israel Institute of Technology Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9781665442282

We present a semianalytical approach to design frequency diverse cavity-excited antennas, using metagratings (MGs, sparse arrays of subwavelength scatterers). Harnessing the reliable frequency response prediction of the underlying analytical model, tying the MG parameters to the waveguide eigenmode composition at the aperture, a single MG design that radiates prescribed (significantly different) patterns at different frequencies is obtained. This systematic method and resultant devices, validated via full-wave simulations, could find use in mm-wave imaging systems and emerging security schemes. © 2023 IEEE.

关键词： Frequency response

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Macroscopic Nonlocality Makes Dielectric Slabs Omniderctionally Transparent via Printed-Circuit-Board- (PCB-) Compatible electrically Polarizable Coatings 17

Macroscopic Nonlocality Makes Dielectric Slabs Omniderctiona...

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17th International Congress on Artificial Materials for Novel Wave Phenomena, Metamaterials 2023

作者： Shaham, A. Epstein, A. Technion - Israel Institute of Technology Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9798350332445

We present a rigorous scheme to render planar dielectric slabs omnidirectionally transparent by coating them with adequately engineered simple electrical impedance sheets compatible with standard printed-circuit-board (PCB) technology. In contrast to cylindrical or spherical cloaks, which practice angular stability via symmetry, our approach leverages macroscopic nonlocality to defy the intrinsic spatial dispersion of Fresnel reflection due to planar slabs. To this end, we analytically invoke the generalized Huygens' condition derived in our previous work to tune these coatings;we thus accomplish a composite of extremely low reflectance and, importantly, desired angular behavior of the transmission phase that mimics free-space propagation over all the range of incident angles and preserves wavefronts. Verified via full-wave simulations, these results are expected to facilitate efficient radomes for wide-angle beam-scanning applications and novel optical elements of extreme nonlocality and low reflectance. © 2023 IEEE.

关键词： Reflection

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A Regression-Based Approach to Optimize Curved Beam Parameters for Micro-Scale Bistable Actuation 21

A Regression-Based Approach to Optimize Curved Beam Paramete...

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21st International Bhurban Conference on Applied Sciences and technology, IBCAST 2024

作者： Tariq, Muhammad Owais Bazaz, Shafaat Ahmad Ahmed, Hussain The Superior University Faculty of Engineering and Technology Lahore Pakistan Sir Syed CASE Institute of Technology Department of Electrical and Computer Engineering Islamabad Pakistan Sir Syed CASE Institute of Technology Department of Sciences & Humanities Islamabad Pakistan

ISBN: (纸本)9798331516680

The research described the design and analysis of a bistable actuator for MEMS devices using a curved beam structure. The investigation revolves around the optimization of length, width, and apex height of the microstructure to deflect it to the actuated position. The mechanical behavior was validated using Finite Element Analysis using Ansys Multiphysics with a deviation of 1.94%. The result shows that the beam maintains its second stable state without continuous application of the force which significantly reduces the power consumption. © 2024 IEEE.

关键词： Curved beams and girders

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CARA: A Hybrid Framework Integrating Swarm AI Agents and Knowledge Graphs for Advanced LLM Reasoning 6

CARA: A Hybrid Framework Integrating Swarm AI Agents and Kno...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Pussadeniya, Nirodya Wijesinghe, Ruchira Wijenayake, Udaya Silva, Bhagya University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the Cognitive Adaptive Reasoning Architecture (CARA), a framework that enhances reasoning by integrating a modular swarm architecture with knowledge graphs (KGs) and specialized agents. These agents, equipped with diverse tools, dynamically adapt to various tasks, actively engaging with real-world data. CARA's continuous memory update cycle ensures each reasoning step is based on up-to-date, contextual KG knowledge, enhancing decision-making. Incorporating human insights further aligns outcomes with nuanced cognition. Experimental results demonstrate CARA's superior performance over larger models, advancing language models' reasoning capabilities for more context-aware, informed decision-making in dynamic environments. © 2024 IEEE.

关键词： Knowledge graph

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A Study on the Potency of Hybrid Models: Detecting Diseases in Cucumber Leaves with Pre-trained CNNs and SVM 12

A Study on the Potency of Hybrid Models: Detecting Diseases ...

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12th International electrical engineering Congress, iEECON 2024

作者： Yodrot, Teerapon Sutacha, Chalermkiat Orachon, Teerapong Jangjongdee, Nattapon Boonyasuwanno, Sompol Rajamangala University Of Technology Rattanakosin Faculty Of Industry And Technology Department Of Electrical Engineering Technology Prachuap Khiri Khan Thailand Ubon Ratchathani Rajabhat University Faculty Of Industrial Technology Department Of Computer Network Engineering Ubon Ratchathani Thailand Kosen Of King Mongkut's Institute Of Technology Ladkrabang Department Of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This study investigates a hybrid machine learning approach for the classification of cucumber leaf diseases, combining the strengths of pre-trained Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM). Focusing on a publicly available dataset, we evaluated four CNN architectures - VGG16, ResNet50, AlexNet, and EfficientNetV2 - for feature extraction, followed by SVM for the final classification task. The performance of each model was assessed using F1 scores and confusion matrices, with ResNet50 yielding the highest F1 score, indicative of a strong balance between precision and recall. VGG16 and AlexNet showed potential for improvement, which could be realized through model tuning or integration into an ensemble framework. EfficientNetV2, despite a slightly lower F1 score, highlighted the importance of further training to enhance class distinction. The research underlines the importance of a hybrid approach, which harnesses deep learning's feature extraction capabilities with SVM's classification strength, to improve the accuracy of disease detection in cucumber plants. © 2024 IEEE.

关键词： Convolutional neural networks

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