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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Chaudhary, Preeti Banda, Latha Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350338027

Frequency selective surface (FSS) was first created by G. Marconi and C. S. Franklin in 1919 and is known as the "Reflector for Wireless Telegraph and Telephone,"making a significant contribution to our way of life. In terms of the 1960s, it is impossible to overlook the success attained with the employment of regularly selective surfaces in military intelligence units. These structures, which were occasionally applied to land, air, and sea platforms, displayed low pass, high pass, band pass, or band stop characteristics for particular frequencies and served as electromagnetic filters. Many accomplishments have been made, particularly with platforms that are invisible to target detection systems. The geometry and arrangement of the unit cell in these structures, which are successfully used in many fields such as wireless communication systems, radars, and microwaves, allows for the reflection or transmission of the incoming electromagnetic wave. This is made possible by the advancement of technology. High-speed internet connectivity is possible using millimeter wave, a frequency spectrum between 30 GHz and 300 GHz. Its significance has grown in the fields of radio astronomy, radar, remote sensing, and wireless communications. © 2023 IEEE.

关键词： Millimeter waves

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Software-Defined Ultrasonic Communication System using PPM-EMAT with High Transmission Rate

Software-Defined Ultrasonic Communication System using PPM-E...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Huang, Xin Saniie, Jafar Bakhtiari, Sasan Heifetz, Alexander Illinois Institute of Technology Ecasp Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States Argonne National Laboratory Nuclear Science and Engineering Division LemontIL United States

ISBN: (纸本)9798350346459

A novel approach to ultrasonic communication systems introduces a software-defined system that utilizes Periodic-permanent-magnet electromagnetic acoustic transducers (PPM-EMATs) as transmitters and receivers. Traditional systems had limitations in adaptability and flexibility. The software-defined system provides enhanced versatility, adaptability, and portability, allowing customization for diverse applications. The use of PPM-EMATs is explored for optimizing energy transfer through metal plate channels, generating shear horizontal waves. Experimental tests validate the efficiency of PPM-EMATs, showing successful communication at a 10-kbps bitrate using ASK modulation with a high signal-to-noise ratio (SNR). The research highlights the potential of PPM-EMAT technology in high-speed software-defined ultrasonic communication systems. © 2023 IEEE.

关键词： Signal to noise ratio

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Optimization of Microwave Resonant Cavity Flowmeter Design for High Temperature Fluid Sensing Applications

Optimization of Microwave Resonant Cavity Flowmeter Design f...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Fang, Tianyang Saniie, Jafar Bakhtiari, Sasan Heifetz, Alexander Illinois Institute of Technology Ecasp Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States Nuclear Science and Engineering Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9781665493765

This paper investigates optimization of parameters to enhance performance of a microwave resonant cavity transducer for high temperature fluid flow sensing in advanced reactors. The cylindrical microwave cavity flowmeter is a novel sensor that measures fluid flow velocity through deflection of the cavity wall due to dynamic fluid pressure. This sensor is designed to operate in harsh environments of a nuclear reactor because sensor material is resilient to high temperatures, ionizing radiation, and corrosion. Parameters that determine performance characteristics of the transducer include dimensions of the microwave cavity, cavity electromagnetic excitation method, and cavity material. We investigate transducer design through computer simulations, with the objective of maximizing cavity Q-factor. © 2023 IEEE.

关键词： Microwave resonators

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Mobile edge fog, Blockchain Networking and Computing-A survey 1

Mobile edge fog, Blockchain Networking and Computing-A surve...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatla, Shruti Mishra, Divya Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350338027

Mobile edge computing (MEC) and blockchain technology are two rapidly evolving areas in computer science that have the potential to significantly impact the design and operation of distributed systems. MEC involves the deployment of computing resources and services at the edge of the network, closer to the end users, enabling lowlatency, high-bandwidth applications such as augmented reality and machine learning. Blockchain technology, on the other hand, is a distributed database that allows for secure, transparent, and immutable record-keeping, with potential applications in a wide range of industries. In this project, we explore the integration of MEC and blockchain technology for the management of distributed computing resources at the edge of the network. We review the state of the art in these two areas, including existing projects and prototypes, and describe our materials and method for evaluating their potential for integration. Our results show that the combination of MEC and blockchain has the potential to enable new applications and services that require both low latency and secure, decentralized data management. We conclude by summarizing the main contributions of our work and discussing future directions for research in this area. Our findings have implications for the design and operation of distributed systems, and have the potential to enable new applications and services in a wide range of industries. © 2023 IEEE.

关键词： Blockchain

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D Semantic Geometry Voting

V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tuan Dang Khang Nguyen Manfred Huber Department of Computer Science and Engineering Learning and Adaptive Robotics Laboratory University of Texas at Arlington Arlington TX USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Simultaneous localization and mapping (SLAM) in highly dynamic environments is challenging due to the correlation complexity between moving objects and the camera pose. Many methods have been proposed to deal with this problem; however, the moving properties of dynamic objects with a moving camera remain unclear. Therefore, to improve SLAM’s performance, minimizing disruptive events of moving objects with a physical understanding of 3D shapes and dynamics of objects is needed. In this paper, we propose a robust method, V3D-SLAM, to remove moving objects via two lightweight reevaluation stages, including identifying potentially moving and static objects using a spatial-reasoned Hough voting mechanism and refining static objects by detecting dynamic noise caused by intra-object motions using Chamfer distances as similarity measurements. Through our experiment on the TUM RGB-D benchmark on dynamic sequences with ground-truth camera trajectories, the results show that our methods outperform most other recent state-of-the-art SLAM methods. Our source code is available at https://***/tuantdang/v3d-slam.

关键词： Simultaneous localization and mapping Three-dimensional displays Source coding Dynamics Noise Benchmark testing Cameras Feature extraction Trajectory Object recognition

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Fast and Accurate Interrogation of Vernier Effect-based Optical Fiber Sensors Enabled by Machine Learning

Fast and Accurate Interrogation of Vernier Effect-based Opti...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Zhu, Chen Huang, Jie Research Center for Optical Fiber Sensing Zhejiang Laboratory Hangzhou311100 China Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

The possibility of employing a light source with a small wavelength bandwidth (35 nm) and a coarsely resolved spectrometer (~166 pm) for the interrogation of a Vernier effect-based high-sensitivity optical fiber senso... 详细信息

关键词： Spectrometers

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, J.E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications 15772 Greece Keio University Department of Information and Computer Science Kanagawa 2238522 Japan Tohoku University International Research Institute of Disaster Science Sendai9808576 Japan

As synthetic aperture radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this article proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network. First, employing Multiscale Superpixel Reconstructed Difference Image effectively enhances the edge information of the images. Second, a two-stage Weighted Center Constrained Fuzzy C-Means clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Third, a Convolutional Squeeze-and-Excitation Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model is introduced into the CSWNN to expand pseudolabeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2% –9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Volumetric Mapping with Panoptic Refinement using Kernel Density Estimation for Mobile Robots

Volumetric Mapping with Panoptic Refinement using Kernel Den...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Khang Nguyen Tuan Dang Manfred Huber Department of Computer Science and Engineering Learning and Adaptive Robotics Laboratory University of Texas at Arlington Arlington TX USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Reconstructing three-dimensional (3D) scenes with semantic understanding is vital in many robotic applications. Robots need to identify which objects, along with their positions and shapes, to manipulate them precisely with given tasks. Mobile robots, especially, usually use lightweight networks to segment objects on RGB images and then localize them via depth maps; however, they often encounter out-of-distribution scenarios where masks over-cover the objects. In this paper, we address the problem of panoptic segmentation quality in 3D scene reconstruction by refining segmentation errors using non-parametric statistical methods. To enhance mask precision, we map the predicted masks into a depth frame to estimate their distribution via kernel densities. The outliers in depth perception are then rejected without the need for additional parameters in an adaptive manner to out-of-distribution scenarios, followed by 3D reconstruction using projective signed distance functions (SDFs). We validate our method on a synthetic dataset, which shows improvements in both quantitative and qualitative results for panoptic mapping. Through real-world testing, the results furthermore show our method’s capability to be deployed on a real-robot system. Our source code is available at: https://***/mkhangg/refined_panoptic_mapping.

关键词： Image segmentation Three-dimensional displays Statistical analysis Source coding Semantics Mobile robots Kernel Image reconstruction Testing Synthetic data

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Fast and Accurate Interrogation of Vernier Effect-based Optical Fiber Sensors Enabled by Machine Learning

Fast and Accurate Interrogation of Vernier Effect-based Opti...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical fibers

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