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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Wang, Ziheng Ren, Haoyang Geng, Wenpu Peng, Qinru Zhao, Wenqian Liu, Yuanpeng Pan, Zhongqi Yue, Yang School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China Institute of Modern Optics Nankai University Tianjin300350 China Department of Electrical & Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States

A graded-index fiber with two concentric-ring cores is proposed, which features a - 4479.31 ps/(nmkm) negative dispersion for OAM2,1 mode at 1419 nm. The impact of the fiber material and structural parameters on dispe... 详细信息

ISBN: (纸本)9781957171296

关键词： Effective refractive index Graded index fibers Nonlinear optical fibers Optical fibers Orbital angular momentum multiplexing Refractive index

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Index Profile Effect on OAM Mode Purity in Annular-core Optical Fiber

Index Profile Effect on OAM Mode Purity in Annular-core Opti...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Liu, Yuanpeng Geng, Wenpu Zhao, Wenqian Pan, Zhongqi Yue, Yang Institute of Modern Optics Nankai University Tianjin300350 China Department of Electrical & Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China

The OAM mode purity in the annular-core optical fiber with different graded-high-index profiles is investigated. Up to 11.83% and 13.07% purity improvement for the OAM1,1 mode can be achieved by adopting a graded-inde... 详细信息

ISBN: (纸本)9781957171296

关键词： Effective refractive index Fiber optic communications Light beams Mode division multiplexing Optical fibers Orbital angular momentum multiplexing

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A Novel Machine Learning-Based Classification Framework for Age-Related Macular Degeneration (AMD) Diagnosis from Fundus Images

A Novel Machine Learning-Based Classification Framework for ...

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IEEE International Symposium on Biomedical Imaging

作者： Aya A. Abd El-Khalek Hossam Magdy Balaha Ali Mahmoud Norah Saleh Alghamdi Mohammed Ghazal Abeer T. Khalil Mohy Eldin A. Abo-Elsoud Ayman El-Baz Communications and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Bioengineering Department J.B. Speed School of Engineering University of Louisville Louisville KY USA Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Communications and Electronics Engineering Department Faculty of Engineering Mansoura University Egypt

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The increasing prevalence of eye disorders among the elderly has underscored the importance of early detection through routine eye examinations. Age-related Macular Degeneration (AMD), a common ailment affecting those over 45, is a leading cause of vision impairment in older individuals. This study introduces a comprehensive computer-Aided Diagnosis (CAD) system that leverages ML techniques to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. We developed an innovative system that extracts both local and global appearance markers from fundus images. These markers are derived from the entire retina and iso-regions aligned with the optical disc, utilizing the fast marching level sets model. The features include markers from the Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) models. The integration of weighted majority voting with the best classifiers enhances performance, resulting in an impressive overall accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 score of 93.72%, ROC of 95.85%, BAC of 95.81%, and WSM of 95.38%.

关键词： Macular degeneration Solid modeling Accuracy Sensitivity Feature extraction Retina Optical imaging

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Extended Physarum Solver for Capacity-Constraint Routing

Extended Physarum Solver for Capacity-Constraint Routing

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International Conference on Computing, Networking and communications (ICNC)

作者： Kohsuke Sonoda Hiroshi Katada Shigeru Shimamoto Taku Yamazaki Takumi Miyoshi College of Systems Engineering and Science Shibaura Institute of Technology Saitama Japan Department of Communications and Computer Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Physarum solver (PS), which is inspired by the foraging behavior of Physarum polycephalum, has been attracting attention as a metaheuristic for solving optimization problems such as finding the shortest path. However, it could not solve optimization problems such as routing with link capacity constraint due to the lack of the link capacity concept in the PS. To address this problem, this paper introduces an extended Physarum solver (EPS) that can recognize and maintain link bandwidth resources. The EPS makes minimal changes to the existing PS adaptive equations but enhances the PS to solve optimization problems involving capacity-constraint routing by introducing the concept of link capacity. Experiments with various traffic scenarios show that the EPS achieves the optimal routing and further extends the link capacity of the PS. It was also confirmed that the EPS is consistent with the existing research. This suggests that metaheuristics based on the foraging behavior of Physarum polycephalum can be promising solutions to optimization problems in communication networks.

关键词： Adaptation models Computational modeling Metaheuristics Bandwidth Routing Mathematical models Communication networks

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Image Classification of Natural Disasters Using Different Deep Learning Models 10

Image Classification of Natural Disasters Using Different De...

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10th International Japan-Africa Conference on Electronics, communications, and Computations, JAC-ECC 2022

作者： Abraham, Kibitok Abdelwahab, Moataz Abo-Zahhad, Mohammed School of Electronics Communications & Computer Engineering New Borg El Arab City Alexandria21934 Egypt Assiut University Electrical Engineering Department Faculty of Engineering Assiut71515 Egypt

ISBN: (纸本)9781665464635

Natural disasters continue to affect the world through wildfires, cyclones, earthquakes, and floods. The advent of photography has provided us with valuable images of how disasters happen and their impact. Many deep-learning models have been developed to classify images. However, the classification of natural disasters still lags. Through transfer learning, eleven existing deep learning models and two optimizers were adapted, analyzed and tested on images based on natural disasters. We explore the impact of data augmentation on deep learning model performance. Based on experimental results, ResNet-50 coupled with SGDM optimizer achieved an accuracy of 98.6%. However, AlexNet converge faster in 4109 seconds, compared to all adopted deep learning models. © 2022 IEEE.

关键词： Floods

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Survey of BERT (Bidirectional Encoder Representation Transformer) types 2

Survey of BERT (Bidirectional Encoder Representation Transfo...

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2nd International Conference on Physics and Applied Sciences, ICPAS 2021

作者： Mohammed, Athar Hussein Ali, Ali H. Department of Computer Science Faculty of Computer Science and Maths University of Kufa Iraq Department of Electronic and Communications Engineering Faculty of Engineering University of Kufa Iraq

There are many algorithms used in Natural Language Processing( NLP) to achieve good results, such as Machine Learning (ML), Deep Learning(DL) and many other algorithms. In Natural Language Processing,the first challenges is to convert text to numbers for using by any algorithm that a researcher choose. So how can convert text to numbers? This is happen by using Word Embedding algorithms such as skip gram,bags of words,BERT and etc. Representing words as numerical vectors by relying on the contents has become one of the effective methods for analyzing texts in machine learning, so that each word is represented by a vector to determine its meaning or to know how close or distant this word from the rest of the other word. BERT(Bidirectional Encoder Representation Transformer) is one of the embedding methods. It is designed to pre-trained form left and right in all layer deep training. It is a deep language model that is used for various tasks in natural language processing. In this paper we will review the different versions and types of BERT. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Maximizing Stable Throughput in Age of Information-Based Cognitive Radio

Maximizing Stable Throughput in Age of Information-Based Cog...

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2022 IEEE International Conference and Expo on Real Time communications at IIT, RTC 2022

作者： Mohamed, Ali Gaber Gamal, Mahmoud Alexandria University Department of Electrical Engineering Communications and Electronics Section Faculty of Engineering Alexandria Egypt Damanhour University Faculty of Engineering Department of Electrical Engineering Damanhour Egypt Higher Institute of Engineering and Technology Department of Computer Engineering El-Behira Egypt

ISBN: (数字)9781665475037

ISBN: (纸本)9781665475037

Cognitive radio can be considered as a viable frequency access framework that overcomes the disadvantages of the licensed-based transmission procedure by allowing the secondary users to access the spectrum to transmit their data. However, due to the evolution of latency-sensitive real-time communication applications such as gaming and extended reality, it becomes more vital that cognitive radio networks should be studied with latency requirements on the data transmission. Recently, Age of Information has introduced itself as a important metric for evaluating the freshness of the transmitted data. In this paper, we investigate the latency and stability analysis of a two-user cognitive radio network that consists of one primary user, one secondary user and their destinations. The latency requirements of the transmitted data packets are taken into consideration by imposing Age of Information constraints on the data transmission of the users. We present two optimization problems, in the first problem, the secondary user stable throughput is maximized under an Age of Information constraint imposed on the data transmission of the secondary user. While, in the second problem, we maximize the stable throughput of the secondary user with respect to Age of Information constraints set on the data transmission of both the primary and secondary users. The resultant problems are found to be non-linear programming optimization problems. An appropriate algorithm is used to solve the problems and provide the numerical solutions. Our results characterize the impact of setting Age of Information constraints on the stability region of the network;we demonstrate that the stability region, in certain cases, is reduced by only 11% with strict Age of Information restrictions if compared to the scenario where no latency requirements is considered. Our results also show the potential accuracy of the algorithm adopted in this paper to solve the formulated optimization problems. © 2022 IEEE.

关键词： Cognitive radio

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Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control 22

Physics-Guided Reinforcement Learning System for Realistic V...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Nhu, Anh N. Le, Ngoc-Anh Li, Shihang Truong, Thang D.V. University of Maryland Department of Computer Science College ParkMD20742 United States Institute of Mechanical Engineering University of Transport and Communications Hanoi Viet Nam School of Mechanical Engineering Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9798350345346

The suspension system is a crucial part of the automotive chassis, improving vehicle ride comfort and isolating passengers from rough road excitation. Unlike passive suspension, which has constant spring and damping coefficients, active suspension incorporates electronic actuators into the system to dynamically control stiffness and damping variables. However, effectively controlling the suspension system poses a challenging task that necessitates real-time adaptability to various road conditions. This paper presents the Physics-Guided Deep Re-inforcement Learning (DRL) for adjusting an active suspension system's variable kinematics and compliance properties for a quarter-car model in real time. Specifically, the outputs of the model are defined as actuator stiffness and damping control, which are bound within physically realistic ranges to maintain the system's physical compliance. The proposed model was trained on stochastic road profiles according to ISO 8608 standards to optimize the actuator's control policy. According to qualitative results on simulations, the vehicle body reacts smoothly to various novel real-world road conditions, having a much lower degree of oscillation. These observations mean a higher level of passenger comfort and better vehicle stability. Quantitatively, DRL out-performs passive systems in reducing the average vehicle body velocity and acceleration by 43.58% and 17.22%, respectively, minimizing the vertical movement impacts on the passengers. © 2023 IEEE.

关键词： Active Suspension System Deep Reinforcement Learning Mechanical En-gineering Vehicle Vertical Dynamics

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Innovative methods of signal processing for hydrogen-filled proportional counters

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IEEE Sensors Journal 2025年第11期25卷 19159-19167页

作者： Horna, Aleš Kolář, Ondřej Kubíček, Michal Czakoj, Tomáš Košťál, Michal Mazánková, Věra Petr, Jiří Cvachovec, František Ančář, Aleš Král, Jan Přenosil, Václav Matěj, Zdeněk Masaryk University Faculty of Informatics Department of Computer Systems and Communications Brno602 00 Czech Republic Brno University of Technology Faculty of Electrical Engineering and Communication Department of Radioelectronics Brno616 00 Czech Republic Research Centre Řež Husinec-Řež250 68 Czech Republic University of Defence Faculty of Military Technology Department of Mathematics and Physics Brno602 00 Czech Republic VF a.s. Černá Hora 679 21 Czech Republic

Proportional counters filled with hydrogen are suitable for fast neutron detection and spectrometry. As their sensitivity is not limited only to this type of ionizing radiation as desired, an approach suitable for event discrimination was investigated. Several counters of the given construction were subjected to various radiation fields and the effort was directed to the examination of pulse-shape discrimination method intended to distinguish neutrons from gamma rays and parasitic events (microdischarges) if these also occurred. Rising edge duration of the output pulses and their amplitudes were mapped into diagrams enabling visual discrimination while the criteria for further computational processes are to be determined. Pulse waveforms from charge-sensitive preamplifier with the proportional counters connected were recorded using an FPGA-based custom-build acquisition unit and then post-processed. Moreover, the possibility of operation verification and energy calibration based on the introduction of helium-3 admixture into the gas filling was studied, as well as the energy resolution abilities. Also, the theoretical basis for particle discrimination is presented in the form of description of events and principles involved. © 2001-2012 IEEE.

关键词： Gamma rays

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1-Bit Reconfigurable Phase Shifter for mm-Wave Antenna Beam-Steering Applications

1-Bit Reconfigurable Phase Shifter for mm-Wave Antenna Beam-...

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

作者： Bilal, Asif Quddious, Abdul Kanno, Atsushi Kawanishi, Tetsuya Antoniades, Marco A. Iezekiel, Stavros EMPHASIS Research Centre University of Cyprus Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Technische Universität Dresden RF and Photonics Engineering Dresden Germany Nagoya Institute of Technology Aichi Japan National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Computer and Biomedical Engineering Department of Electrical Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9781665442282

A 1-bit reconfigurable phase shifter for 28 GHz mm-wave beam steering antennas is proposed. The design uses two beam-lead GaAs PIN diodes loaded on a microstrip transmission line in parallel, to provide two discrete phases for the transmitted signal. When the diodes are switched OFF, no shift in the phase of the transmitted signal is observed while a phase shift of 90° is achieved when the diodes are switched ON. The total length of the phase shifter is ∼λ/2. The compact size, low cost, reduced complexity and easy adaptation in antenna and phased array technology makes it a suitable candidate for 5G mm-wave beam steering applications. © 2023 IEEE.

关键词： Phase shifters

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