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Performance Enhancement of High-gain STDA Antennas with Reflector for 4G LTE and Sub-6 GHz 5G Applications: Design, Measurement, and Analysis

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Progress In Electromagnetics Research C 2024年 139卷 87-94页

作者： Wasim, Mohd Khera, Shelej Islam, Tanvir Malik, Praveen Kumar Asha, Sivaji Das, Sudipta School of Electronics and Electrical Engineering Lovely Professional University Phagwara144411 India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Saveetha Engineering College Saveetha Nagar Thandalam Chennai602105 India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

The paper focuses on the design, measurement, and performance analysis of a high-gain cross-orthogonal series fed two dipole antenna (STDA) arrays with side-wall reflectors. The antenna is specifically designed for 4G Long Term Evolution (LTE) and sub-6 GHz 5G band applications. The designed antenna is capable of operating at multiple frequencies aiming to support 4G LTE and the sub-6 GHz 5G application bands. To improve the radiation characteristics and prevent coupling effects in the presence of side-wall reflectors, parasitic strip pair directors are included in the antenna design. Furthermore, the performance of the designed STDA is evaluated by forming different array configurations, such as 2 × 1, 2 × 2, and 2 × 3 arrays. The various array configurations are proposed to investigate the effect of the projected array arrangements on the radiation pattern, impedance bandwidth, and gain characteristics. The results of the measurements show that the radiation characteristics of the antenna have been improved significantly. The proposed antenna operates at six distinct frequencies for S11 ≤ 10 dB. The operating frequencies at 1.8, 2.35, and 2.6 GHz can be utilized for LTE and 3.2, 4.2, and 5.2 GHz which can support sub-6 GHz 5G bands. The antenna is characterized by its compact size, measuring around 89 mm × 71 mm, while still achieving high gain of 12.3 dB for single STDA element with parasites and with reflector. These results emphasize the importance of the proposed design, which incorporates parasitic strip pair directors and side-wall reflectors. This design methodology plays a crucial role in enhancing the performance of the prescribed STDA array for both 4G LTE and sub-6 GHz 5G applications. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Dipole antennas

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Secure mmWave-NOMA Multi-BS Vehicular Communications Using Cooperative Jamming

Secure mmWave-NOMA Multi-BS Vehicular Communications Using C...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yan, Yiting Ju, Ying Hu, Jiawei Liu, Lei Pei, Qingqi Yau, Kok-Lim Alvin Wu, Celimuge Zhang, Ning Xidian University School of Telecommunications Engineering Xi'an710071 China Universiti Tunku Abdul Rahman School of Internet Engineering and Computer Science Selangor 43200 Malaysia The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan University of Windsor Department of Electrical and Computer Engineering WindsorN9B-3P4 Canada

ISBN: (纸本)9798350351255

The fronthaul network architecture is the key to dealing with the massive traffic effectively and providing high-quality service, and the multiple base stations (BSs) deployed by it face the gigantic data transmission, which has given the demand for high-capacity communication and information security in the vehicular network. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technologies to escalate the communication capacity of multiple vehicle users (VUs), and propose a blockage-based cooperative jamming strategy to solve potential security risks in the vehicular network. In particular, with the help of jam-mers selected by this strategy, transmission security is enhanced simultaneously without escalating the instability of connections caused by the time-varying nature of vehicular networks under the NOMA transmission mechanism when the base station (BS) does not fully understand the channel state information (CSI) of VUs. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the performance metrics of the network based on the stochastic geometry method. Numerical results show that the proposed cooperative jamming scheme can effectively improve the secrecy performance of the vehicular network. © 2024 IEEE.

关键词： Jamming

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A Generalized Framework for Brain Tumor and Pneumonia Detection Using Streamlite Application 4

A Generalized Framework for Brain Tumor and Pneumonia Detect...

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4th International Conference for Emerging Technology, INCET 2023

作者： Kishore, S. Nair, Rekha R Mehra, Vishal Babu, Tina Dayananda Sagar University Department of Computer Applications School of Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering School of Engineering Bengaluru India

ISBN: (纸本)9798350335750

One of the dangerous disease pneumonia, which can affect one or both lungs, is frequently brought on by viruses, fungi or bacteria. Radiotherapists with advanced training are required to evaluate chest X-rays used to diagnose pneumonia. Brain tumours are another fatal disease that can have a profound impact on a patient's quality of life and alter everything for them and their loved ones. Therefore, creating an automatic system for diagnosing pneumonia would help treat the disease quickly, especially in remote areas. The work proposes a general framework for MRI and CT images that can automatically detect diseases such as brain tumours and pneumonia with single framework using VGG16. The proposed work focusing on developing streamlit web application for deployment. The proposed work gained an accuracy of 97.87% compared to Inception, Xception and ResNet for brain tumor detection and 94.3% for Pnemonia detection model. The training and testing data are compared using a CNN-based classifier to determine the optimal outcome. © 2023 IEEE.

关键词： Tumors

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Human Activity Recognition Using Deep Learning Techniques for Healthcare Applications 15

Human Activity Recognition Using Deep Learning Techniques fo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shekhar, Raj Tomar, Deepak Singh Sharan, Bhagwati Pateriya, R.K. Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350370249

The idea of smart healthcare is gradually gaining traction with the rapid advancement in information technologies. Smart healthcare is the intelligent transformation of the current medical system to make it more reliable, efficient, and individualized through the intelligent use of next-generation technologies like artificial intelligence, and the Internet of Things (IoT). This study offered the recent advancement of Deep learning (DL) techniques for healthcare systems, and the use of DL techniques to identify human physical activity with wearable sensors. In this paper, convolutional neural networks (CNN), CNN-LSTM, Multi Headed CNN-LSTM, and Multi Headed CNNBiLSTM are used. The obtained outcomes of these Deep Learning models are compared in terms of accuracy, precision, recall and F1 score. The Multi-Headed CNN-BiLSTM model exhibits better performance than the rest of the models when applied to the UCI HAR dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Fraud Detection in Ethereum Transactions: Fusion of Modified Genetic Algorithms and Deep Learning with Limited Attributes

Enhanced Fraud Detection in Ethereum Transactions: Fusion of...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Srivastava, Atul Kumar, Alok Pillai, Anuradha Sharma, Vijay Shankar AUUP Amity School of Engineering and Technology Lucknow India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350385199

Ethereum smart contracts, the new way of transactions and a popular name in the world of cryptocurrencies, have gathered a huge base of research and scientific attention. They are so helpful that they allow us to eliminate the need for a separate third-party library to allow unknown parties to see contract details on a computer. But since we can all see that online commerce is growing day by day and will continue to grow, it can never be fully free from scams and unethical operations. So, to correctly detect all such unethical and malicious transactions, this paper used a deep learning model. And to further enhance the model, this study used metaheuristic optimization as well. It employs an algorithm called Genetic Algorithm and to provide better optimization in the explorations phase of Cuckoo Search (CS) to achieve its goal of detecting fraudulent transactions. The algorithm covers the loopholes in the CS strategy. Furthermore, to provide strong grounds for research in our paper, this proposed model was compared with various types of approaches such as Light Gradient Boosting Machine, Support Vector Classification, Multi-Layer Perceptron, XGBoost, Logistic Regression, and Random Forest. The proposed model outperforms other significant models like SVC, KNN, LGBM Classifier, RF etc. with the accuracy of 98.6%. © 2024 IEEE.

关键词： Genetic algorithms

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Decoding Tomorrow's Gold Prices: A Comparative Study of GRU and CNN-LSTM in the Iranian Market 10

Decoding Tomorrow's Gold Prices: A Comparative Study of GRU ...

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10th International Conference on Web Research, ICWR 2024

作者： Baradaran, Amir Hossein Bohlouli, Masoumeh Motlagh, Mohammad Reza Jahed Iran University of Science and Technology School of Electrical Engineering Tehran Iran Alzahra University Department of Computer Engineering Tehran Iran Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

With the rising prominence of gold as a lucrative investment avenue in Iran, this research delves into predicting the future price of 18-carat gold. In pursuit of this objective, a comprehensive comparison is conducted between two neural network architectures: the Gated Recurrent Unit (GRU) as a single structure and a hybrid model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The evaluation criteria employed focus on error metrics to gauge the accuracy of price predictions. Results reveal that the CNN-LSTM hybrid neural network exhibits superior performance, showcasing lower error values in predicting the price of 18-carat gold in Iran. Consequently, the chosen model, CNN-LSTM, is employed to forecast the following day's gold prices, providing valuable insights for investors navigating the Iranian market. This research contributes to the ongoing discourse on gold investment strategies by highlighting the effectiveness of advanced neural network models in enhancing predictive accuracy. © 2024 IEEE.

关键词： Long short-term memory

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Tailoring Fano resonance characteristics for GSS4T1-based reconfigurable photonics

Tailoring Fano resonance characteristics for GSS4T1-based re...

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2024 Frontiers in Optics, FiO 2024

作者： Foteinopoulou, Stavroula Tsitsas, Nikolaos L. Department of Electrical and Computer Engineering University of New Mexico NM87131-0001 United States School of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece

We present a GSS4T1-based metagrating designed to exhibit a homogeneous-optical-medium response, when GSS4T1 amorphous, or a judicious Fano response, when GSS4T1 crystalline, which enables a negative beam steering tha... 详细信息

关键词： Photonics

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An Ensemble Approach using Self-attention based MobileNetV2 for SAR classification 2

An Ensemble Approach using Self-attention based MobileNetV2 ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Anjali, K.S. Singh, Rimjhim Padam Panda, Manoj Kumar Palaniappan, Kannappan Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical Engineering and Computer Science University of Missouri Columbia United States

The Synthetic Aperture Radar (SAR) image classification has become an essential task in numerous applications. Despite its significance, SAR image classification remains challenging due to the inherent complexity and speckle noise characteristics of SAR data. To address this, an innovative approach is proposed in this study, combining the power of ensemble learning and the efficiency of MobileNetV2 architecture to achieve robust and accurate SAR image classification. The proposed deep learning model consists of two main components: an ensemble learning approach that combines multiple binary classifiers, and the employment of MobilenetV2 architecture with self-attention mechanism as a binary classifier. The ensemble approach uses a max-value selector for predicting final class from the individual classifier output. The MobileNetV2 architecture is finetuned by freezing weights and removing the last four layers. The inclusion of the attention layer within the architecture aids in refining the learning process by focusing on crucial features while disregarding irrelevant background elements in the images. The effectiveness of the model is evaluated using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset with accuracy of classification as performance *** model is trained and tested with images captured at various aspect angles. This approach enhances the robustness of the results, making them more applicable to real-world scenarios when compared to prior methods. This study also compares the performance of the ensemble method using MobileNetV2 with state-of-art individual classifiers and different CNN architectures. The proposed ensemble method OVA-MbNN achieves an impressive classification accuracy of 76.63%, surpassing the performance of state-of-the-art classifiers while maintaining robustness. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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A Deep Learning Framework for Enhanced Liver Tumor Classification Using Convolutional Gated Kronecker Network

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SN computer Science 2025年第5期6卷 1-18页

作者： Shilpa, B. Devadhas, George Glan Satheesha, T.Y. Department of Electronics and Communication Engineering School of Engineering & Technology (SOET) CMR University Bengaluru 562149 India Department of Electronics and Communication Engineering Bangalore Institute of Technology Bengaluru 560004 India School of Engineering & Technology (SOET) and Director-DORI CMR University Bengaluru 562149 India School of Computer Science Engineering REVA University Bengaluru 560064 India

Liver cancer remains a leading cause of cancer-related mortality worldwide, and timely, accurate classification of liver tumors in magnetic resonance imaging (MRI) is critical for improving patient outcomes. However, automated liver tumor classification is challenged by blurred tumor boundaries, variable lesion sizes, and heterogeneous image characteristics. To address these issues, we propose a novel Convolutional Gated Kronecker Network (CGKN) framework that integrates spatial and contextual feature learning for robust liver tumor classification in MRI. The methodology begins with preprocessing MRI images using an Adaptive Wiener Filter to enhance image quality and reduce noise, followed by precise segmentation of liver and lesion regions using a Dynamic Context Encoder Network. After extracting relevant features, the CGKN—combining Convolutional Neural Network-Gated Recurrent Unit and Deep Kronecker Network modules—classifies tumors into mild, moderate, or severe categories. This hybrid approach is designed to overcome the limitations of existing models, such as limited generalizability, high computational cost, and insufficient integration of spatial and contextual cues. Experimental evaluation on a benchmark MRI dataset demonstrates that the CGKN achieves an accuracy of 92.88%, a true positive rate of 91.64%, and a true negative rate of 91.56%, outperforming several state-of-the-art deep learning models. These findings highlight the potential of the CGKN framework as an effective and efficient solution for automated liver tumor classification in MRI, supporting more reliable computer-aided diagnosis and facilitating clinical decision-making. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive Wiener filter Convolutional neural network Deep Kronecker network Liver cancer Magnetic resonance imaging

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Cyber Threat Intelligence in Smart Cities: Bayesian Inference and Energy Optimization in LoRa Networks for Big Data Applications

Cyber Threat Intelligence in Smart Cities: Bayesian Inferenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Karras, Aristeidis Theodorakopoulos, Leonidas Karras, Christos Antonopoulou, Hera University of Patras Computer Engineering and Informatics Department Patras Greece University of Patras Department of Management Science and Technology Patras Greece

ISBN: (纸本)9798350362480

In the evolving landscape of smart cities, optimizing energy consumption and enhancing cybersecurity in Internet of Things (IoT) networks are crucial. This study leverages LoRa (Long Range) technology, Bayesian Inference, and Extreme Learning Machines (ELMs) to advance cyber threat intelligence and energy efficiency in large-scale IoT deployments. The proposed algorithms address key challenges within LoRa networks by implementing a novel energy consumption model, kernel-based ELM fine-tuning, Bayesian parameter tuning, and data fusion. Through Bayesian Inference, our approach dynamically adjusts network parameters to optimize packet transmission and collision rates, ultimately reducing power consumption across smart city applications. The Kernel-ELM algorithm fine-tunes LoRa network applications by adapting kernel parameters to the unique demands of IoT environments. Additionally, our Anomaly Detection and Fusion Algorithm (ADFA) integrates data from multiple sources to detect potential cyber threats, enhancing network security. Experimental results validate these algorithms on various datasets, demonstrating improvements in both energy optimization and threat detection. Ultimately, this study provides actionable insights into the deployment of scalable, energy-efficient, and secure IoT networks for smart city infrastructure. © 2024 IEEE.

关键词： Cyber attacks

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