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Simulation of Photonic Metasurfaces using a Surface Integral Equation Solver with Generalized Sheet Transition Conditions

Simulation of Photonic Metasurfaces using a Surface Integral...

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Antennas and Propagation (LACAP), IEEE Latin American Conference on

作者： Sebastian Celis Sierra Partha Mondal Ran Zhao Hakan Bagci Electrical and Computer Engineering (ECE) Program King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331527587

ISBN: (纸本)9798331527594

Photonic metasurfaces are efficiently and accurately simulated using a surface integral equation (SIE) solver. This solver models the metasurface as an infinitesimally thin sheet on which generalized sheet transition conditions (GSTCs) are enforced. GSTCs connect the electromagnetic fields on both sides of the metasurface using equivalent susceptibility tensors and avoid modelling of geometrically intricate metasurface unit cells by the SIE solver. Susceptibility tensors are obtained from the transmission and the reflection coefficients associated with a unit cell. A numerical example, involving a metasurface that supports high-quality factor resonances and excited by a Gaussian beam, is presented to demonstrate the accuracy of the proposed approach.

关键词： Tensors Accuracy Integral equations Metasurfaces Mathematical models Numerical models Reflection coefficient Electromagnetic fields Dispersion Photonics

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Smart Electronic Assistive Device for Visually Impaired Individual through Image Processing 13

Smart Electronic Assistive Device for Visually Impaired Indi...

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2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Flores, Ira Lacdang, Gary Chris Undangan, Chessa Adtoon, Jetron Linsangan, Noel B. University of Mindanao Electronics Engineering Program College of Engineering Education Davao Philippines Mapua University School of Electrical Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9781665401678

According to World Health Organization, it is assessed that around 1.3 billion individuals live with some distance or near vision impairment. People with visual impairment encounter different issues in their everyday life as advanced assistive devices are frequently not meeting the consumer prerequisites in terms of price and level of assistance. The paper's primary goal is to build a smart electronic assistive device specifically for visually impaired individuals. The study consists of two devices, which are the glasses and the smart cane. The glasses are responsible for text detection and recognition, which falls under image processing. At the same time, the smart cane is the one responsible for obstacle detection using combination sensors known as VL53L0X and Ultrasonic. This study found that the smart cane and smart glass functions well as intended, in alerting users through a voice alert about the obstacles around and navigation and reading the text captured by the camera. The smart cane's obstacle detection accuracy is 100%, while the smart glasses' overall accuracy is 98.13% for document text and 91.33% for natural scene text. © 2021 IEEE.

关键词： Obstacle detectors

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Outage Performance Analysis of Terahertz Band Drone-to-Ground Communications

Outage Performance Analysis of Terahertz Band Drone-to-Groun...

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Akhtar Saeed Ozgur Gurbuz Fatih Alagoz Department of Electrical Engineering DHA Suffa University Karachi Pakistan Electronics Engineering Program Sabanci University Istanbul Turkiye Department of Computer Engineering Bogazici University Istanbul Turkiye

Recent studies have shown the potential of em-ploying the Terahertz (THz) band for 6G non-terrestrial communications including drones. This paper assesses the outage performance of the THz band Drone-to-Ground (D2G) vertical communications up to 100 m altitude at four practically feasible THz transmission windows (TWs): f 1 = 0.140 THz; [0.110-0.170] THz, f 2 = 0.300 THz; [0.275-0325] THz, f 3 = 0.850 THz; [0.790-0.910] THz, and f 4 = 0.935 THz; [0.930-03940] THz. We consider vertical D2G communication, where realistic THz-based absorption gains of the four THz TWs at the drone altitudes are obtained via the International Telecommunications Union (ITU) model using the Standard Weather Profile. Additionally, multipath (MP) fading and beam misalignment (BM) fading are incorporated into the overall channel gain for assessing the outage performance. Numerical results reveal that under severe BM fading and Rayleigh fading, THz band D2G communication employing 60 dBi total antenna gains at $f_{1},\ f_{2},\ f_{3}$, and f 4 can be reliably established up to the drone altitudes: 76.9 m, 59.2m, 34.4 m, and 43.2 m, and up to 85.1 m, 60.3m, 36.9m, 45.5m under the severe BM fading and the presence of a strong Line-of-Sight component, truly showcasing the achievable altitude and range limits for the THz band D2G communications among 6G non-terrestrial cognitive networks.

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A Comprehensive Key Features Analysis and Recommendations based Cyber Intrusion Detection for Satellite-Terrestrial Networks

A Comprehensive Key Features Analysis and Recommendations ba...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Esraa Shehab Shaimaa Ahmed Elsaid Ahmed M. Mattar Dept. of Electrical Engineering Computer Engineering and Systems program Badr University in Cairo (BUC) Cairo Egypt Dept. of Electronics and Communications Engineering Zagazig University Zagazig Egypt Dept. of Computer engineering and artificial intelligence military technical college Cairo Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The integration of Satellite-Terrestrial Networks (ISTN) necessitates advanced security measures, particularly Intrusion Detection Systems (IDSs). This study introduces hybrid sequential intrusion detection models for ISTNs, combining Deep Learning (DL) and Machine Learning (ML) techniques. The models employ both anomaly-based and signature-based detection to enhance accuracy, utilizing methods such as Extra Trees (ET), Decision Trees (DT), Random Forest (RF), XGBoost (XGB), and Gated Recurrent Units (GRU). These models are chosen for their superior performance and are used sequentially to improve IDSs effectiveness. RF-based Sequential Feature Selection (RF-SFS) is also utilized to reduce dataset dimensionality, which in turn decreases the computational costs for each model. Evaluations using UNSW-NB15 and STIN datasets-representing terrestrial and satellite traffic, respectively-demonstrate the models' superiority over traditional IDSs. The XGB-ET model achieved 99.99% accuracy in anomaly detection, while the XGB-GRU model attained 89% accuracy in signature-based detection on the UNSW-NB15 dataset. On the STIN dataset, the ET-DT-GRU model reached 96.47% accuracy in signature-based detection. Additionally, RF-SFS reduced execution times, with training and testing speedups up to 2.8x.

关键词： Training Accuracy Satellites Computational modeling Intrusion detection Space-air-ground integrated networks Feature extraction Security Random forests Testing

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social Sciences (7.8%), engineering (3.9%), computer Science (2.6%), Environmental Science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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Arabic text sentiment analysis and emotion classification using transformers

Arabic text sentiment analysis and emotion classification us...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Nada Mossad Youssef Mohamed A Fares Ahmed B. Zaky Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt The Department of Electrical Engineering The Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately.

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks 22

FedClassAvg: Local Representation Learning for Personalized ...

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Proceedings of the 51st International Conference on Parallel Processing

作者： Jaehee Jang Heoneok Ha Dahuin Jung Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Republic of Korea Department of Electrical and Computer Engineering and Interdisciplinary Program in Artificial Intelligence Seoul National University Republic of Korea and AIIS ASRI and INMC Seoul National University Republic of Korea

ISBN: (纸本)9781450397339

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneous models remain understudied. In this study, we propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg). Deep neural networks for supervised learning tasks consist of feature extractor and classifier layers. FedClassAvg aggregates classifier weights as an agreement on decision boundaries on feature spaces so that clients with not independently and identically distributed (non-iid) data can learn about scarce labels. In addition, local feature representation learning is applied to stabilize the decision boundaries and improve the local feature extraction capabilities for clients. While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient. Moreover, FedClassAvg does not require extra optimization problems such as knowledge transfer, which requires intensive computation overhead. We evaluated FedClassAvg through extensive experiments and demonstrated it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks.

关键词： Communication Efficient Resource Constraint Representation Learning Neural Networks Model Heterogeneity Federated Learning

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Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter 6

Social Media Analysis using Sentiment Analysis on COVID-19 f...

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6th International Conference on Information Technology, Information Systems and electrical engineering, ICITISEE 2022

作者： Iqbal Maulana, Fairuz Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter. © 2022 IEEE.

关键词： COVID-19 python sentiment analysis social media twitter

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Evaluating the Influence of Phase-Locked Loops on the Response of Grid-Following Inverter-Based Resources to Interconnection Transmission Line Faults

Evaluating the Influence of Phase-Locked Loops on the Respon...

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Workshop on Communication Networks and Power Systems (WCNPS)

作者： Pedro Henrique de Morais Martins Luciano Sales Barros Felipe Vigolvino Lopes Postgraduate Program in Renewable Energies Federal University of Paraíba Department of Computer Systems Federal University of Paraíba Department of Electrical Engineering Federal University of Paraíba

ISBN: (数字)9798331507152

ISBN: (纸本)9798331507169

In this paper, the influence of different Phase-Locked Loops (PLLs) on the fault response of a grid-following (GFL) Inverter-Based Resource (IBR) is evaluated. The analyzed IBR represents a wind power plant with rated power equal to 100 MVA, being composed of 50 turbines with 2 MVA each, whose controls follow the Brazilian grid code. Two PLL models are taken into account to conduct the proposed studies, which include the analysis of voltage and frequency estimated by each PLL, and currents injected into the IBR interconnection transmission line. The Synchronous Reference Frame (SRF) PLL and the Quadrature Signal Generator-based Second-Order Generalized Integrator (QSG-SOGI) PLL are considered, assessing their influence under different transmission line fault scenarios. The obtained results indicate that the PLL can present severe instabilities during faults, which can be more or less critical depending on the fault type and on the PLL type. Indeed, the results reveal that different PLLs can result in distinct behaviors of currents injected into the interconnection line, raising the importance of knowing the PLL structures when studies on IBR fault responses are of interest.

关键词： Power transmission lines Grid following Conferences Voltage Wind power generation Power system stability Frequency estimation Phase locked loops Inverter-based resource Turbines

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