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A Dual Polarization Antenna on Perpendicular Strip Line with Fractal Narrow Slit for GPS, 4G LTE, and 5G Network Applications

A Dual Polarization Antenna on Perpendicular Strip Line with...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Ian Jerome Q. Santos Norakamon Wongsin Paitoon Rakluea Thinnawat Jangjing Nutyada Nantapanich Nonchanutt Chudpooti Chatree Mahatthanajatuphat Prayoot Akkaraekthalin Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Thanyaburi Pathum Thani Thailand Department of Thai-German Pre-Engineering School Faculty of The College of Industrial Technology King Mongkut's University of Technology North Bangkok Bangsue Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangsue Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangsue Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

This article discusses the utilization of a dual polarization antenna on a perpendicular strip line, including a fractal narrow slit, for a global positioning system (GPS), 4G Long Term Evolution (LTE), and a 5G communication network. The proposed antenna consists of a perpendicular strip line that couples the electromagnetic field to a fractal narrow slit, allowing resonance at frequencies of 1.74 GHz and 2.675 GHz. Additionally, it has the capability to facilitate circular polarization for GPS applications and linear polarization for 4G&5G communication networks. The proposed antenna has an impedance characteristic of essentially 22.41% and 9.40% for S11 at frequencies of 1.74 GHz and 2.675 GHz, respectively. At 1.575 GHz, 1.74 GHz, and 2.675 GHz, the antenna's identified peak gains are approximately 2.3 dBi, 2.8 dBi and 3.86 dBi, respectively. Moreover, the proposed antenna has the capability to generate a radiation pattern that propagates in two opposing directions, enabling the efficient exploitation of both GPS and 4G&5G communication network.

关键词： Polarization 5G mobile communication Resonant frequency Fractals Resonance Telecommunications Communication networks

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An Array of Bandpass Detectors for Measuring Beam Spectral Components

An Array of Bandpass Detectors for Measuring Beam Spectral C...

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applied Sensing Conference (APSCON), IEEE

作者： Mojtaba Jahangiri Paolo Sberna Amir Sammak Stoyan Nihtianov A Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft Netherlands B QuTech and Netherlands Organization for Applied Scientific Research (TNO) Stieltjesweg 1 Delft Netherlands

A clear understanding of the spectral components of an irradiated beam, or captured optical emission, is essential to optimize an optical system and increase its performance. Logically, for this purpose a grating-based spectrometer could be the first choice. However, in the case of a wide range spectrum, and for radiation with one dominant wavelength, this option may not work well. In this paper, we present a technique based on an array of bandpass detectors to measure accurately the power of a number of beam-specific spectral components in a wide spectrum range: from soft X-ray to infrared. The main unique features of this technique are: customization for specific wavelengths of interest; vacuum compatibility; and high sensitivity to low-energy spectral components in the presence of one or more dominant highpower spectral components.

关键词： Optical filters Sensitivity Stimulated emission Wavelength measurement Ultraviolet sources Detectors Vacuum technology

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-elghany Zeid Ibrahim Abd Elatif Gomaa Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one.

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Identifying Cell Types in Single-Cell Multimodal Omics Data via Joint Embedding Learning

Identifying Cell Types in Single-Cell Multimodal Omics Data ...

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International Conference on Knowledge and Systems engineering (KSE)

作者： Van Hoan Do Stefan Canzar Center for Applied Mathematics and Informatics Le Quy Don Technical University Hanoi Vietnam Department of Computer Science and Engineering Huck Institutes of the Life Sciences The Pennsylvania State University University Park PA United States

Emerging single-cell technologies profile different modalities of data in the same cell, providing opportunities to study cellular population and cell development at a res-olution that was previously inaccessible. The first and most fundamental step in analyzing single-cell multimodal data is the identification of the cell types in the data using clustering analysis and classification. However, combining different data modalities for the classification task in multimodal data remains a computational challenge. We propose an approach for identifying cell types in multimodal omics data via joint dimensionality reduction. We first introduce a general framework that extends loss based dimensionality reduction methods such as nonnegative matrix factorization and UMAP to multimodal omics data. Our approach can learn the relative contribution of each modality to a concise representation of cellular identity that enhances discriminative features and decreases the effect of noisy features. The precise representation of the multimodal data in a low dimensional space improves the predictivity of classification methods. In our experiments using both synthetic and real data, we show that our framework produces unified embeddings that agree with known cell types and allows the predictive algorithms to annotate the cell types more accurately than state-of-the-art classification methods.

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Exploring 3D Reconstruction with Drone Images: Advances and Challenges in Urban Environments

Exploring 3D Reconstruction with Drone Images: Advances and ...

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Annual Conference of Industrial Electronics Society

作者： Thiago João Miranda Baldivieso Taise Grazielle Da Silva Batista Fabio Suim Chagas Luiz Carlos Pacheco Rodrigues Velho Paulo Fernando Ferreira Rosa Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea/RJ Rio de Janeiro Brazil Department of Science and Industry Systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software—both commercial and open-source—and concluding with a qualitative evaluation of the results.

关键词： Industrial electronics Three-dimensional displays Urban areas Aircraft Image reconstruction Photogrammetry Drones

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Improved recognition rate of different material category using convolutional neural networks

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Materials Today: Proceedings 2023年 81卷 947-950页

作者： Abhay Shukla Gauri Kalnoor Amit Kumar N. Yuvaraj R. Manikandan M. Ramkumar Department of Computer Science EngineeringAxis Institute of Engineering and Technology Kanpur India Central University of Karnatak India Department of Electronics Bhaskaracharya College of Applied Sciences University of Delhi New Delhi 110075 India Training and Research ICT Academy Chennai India Department of CS The Quaide Milleth College for Men Chennai India Gnanamani College of Technology Rasipuram Namakkal India

The recognition a material quality is considered as a process of finding out the constituent material present in an object and it is regarded as a vital part in various applications. Hence, it is considered as a valuable approach for the creation of a system that possesses the ability to achieve recognition of a material. In this paper, we develop a mechanism using convolutional neural networks (CNNs) for material recognition. The CNN model initially trains itself with the features extracted from the image samples. Finally, the classification is carried out with CNN model that learn the classes obtained via CNN of different category of materials. The experimental validation is conducted to test the accuracy of CNN classifiers against various deep learning classifiers. The results on various materials show that the proposed CNN classifier obtains improved recognition accuracy than other methods.

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Full-Duplex Beamforming in the Sub-Terahertz Regime

Full-Duplex Beamforming in the Sub-Terahertz Regime

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International Conference on Infrared and Millimeter Waves

作者： Subhajit Karmakar Atsutse Kludze Jacques Doumani Andrey Baydin Junichiro Kono Yasaman Ghasempour Department of Electrical and Computer Engineering Princeton University Princeton NJ USA Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Smalley-Curl Institute Rice University Houston TX USA Department of Physics and Astronomy Rice University Houston TX USA Department of Material Science and NanoEngineering Rice University Houston TX USA

We propose a novel architecture that realizes full-duplex directional beamforming in the sub-terahertz regime. Our architecture leverages the frequency-controlled beam steering of leaky-wave antenna together with the polarization-dependent response of aligned single-wall carbon nanotubes. We evaluate the performance of this architecture via preliminary experiments.

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Secure 3D Directional Modulation Using Subarrays Based on Planar Frequency Diverse Array with Nonuniform Frequency Offsets

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IEEE Transactions on Communications 2024年

作者： You, Byungha Lee, In-Ho Jung, Haejoon Duong, Trung Q. Shin, Hyundong Kyung Hee University Department Of Electronics And Information Convergence Engineering Yongin-si17104 Korea Republic of Hankyong National University School Of Electronic And Electrical Engineering Anseong17579 Korea Republic of Memorial University Faculty Of Engineering And Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School Of Electronics Electrical Engineering And Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department Of Electronic Engineering Yongin17104 Korea Republic of

Physical-layer security (PLS) is a new paradigm for secure communication without requiring secret key exchange and management. Moreover, PLS with frequency diverse subarray (FDSA) can better control information leakage in the angle-range domain, which mitigates the security weakness of the phased array caused by its lack of range resolution. In this paper, we propose a three-dimensional (3D) directional modulation (DM) using randomized radiation with FDSA for enhanced PLS, employing a planar array. In addition, nonuniform frequency offsets (FOs) are considered as FO configurations (FOCs) for FDSA to concentrate on the mainlobe and suppress the undesired sidelobes in 3D space, where logarithmically increasing FOC (L-FOC), Hamming window-based FOC (H-FOC), and piecewise trigonometric FOC (P-FOC) are introduced. Characterizing the process of selecting the random subsets for randomized radiation, we provide the exact analysis of the secrecy rate of the proposed scheme. Moreover, FOs applied to FDSA and the number of random subsets are optimized with a genetic algorithm (GA)-based optimization strategy. We evaluate the proposed schemes in terms of secrecy rate and vulnerable volume, where the simulation results verify our analysis and show that nonuniform FOCs are a more favorable choice for FDSA compared to uniform FOC (U-FOC). © 1972-2012 IEEE.

关键词： frequency diverse array nonuniform frequency offsets Physical layer security (PLS) randomized radiation

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Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography

arXiv

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arXiv 2025年

作者： Yao, Ni Liu, Xiangyu Sun, Danyang Han, Chuang Li, Yanting Nan, Jiaofen Li, Chengyang Zhu, Fubao Zhou, Weihua Zhao, Chen School of Computer Science and Technology Zhengzhou University of Light Industry Henan Zhengzhou450001 China School of Information Management and Engineering Shanghai University of Finance and Economics China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States Department of Computer Science Kennesaw State University MariettaGA United States

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://***/chenzhao2023/MGFA_CCTA. © 2025, CC BY.

关键词： Image segmentation

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