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2024 4th International Conference on computer, Remote Sensing and Aerospace, CRSA 2024

作者： Liu, Haoxing Xie, Renjie Qin, Haoshen Li, Yizhou Flight Department Shanghai Jixiang Airlines Co. Ltd. Shanghai201315 China Department of Computer & Information Science & Engineering Herbert Wertheim College of Engineering University of Florida Gainesville32611 United States Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland44106 United States

With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. To meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions. © Published under licence by IOP Publishing Ltd.

关键词： Radial basis function networks

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Examining the economic feasibility of hybrid Photovoltaic - Battery Energy Storage System in public building with flexible demand: Cyprus case study 3

Examining the economic feasibility of hybrid Photovoltaic - ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Chatzigeorgiou, Nikolas G. Makrides, George Theocharides, Spyros Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (纸本)9798350375923

Photovoltaic - Battery Energy Storage systems (PVBESS) are a promising solution against technical bottlenecks of high PV penetration. Public buildings are suitable for installation, although currently underexploited, while high BESS costs limit technology diffusion. This paper assesses the economic feasibility of PV-BESS in public buildings with flexible demand, through simulations showcasing BESS subsidisation impact and self-sufficiency contribution. The results derived that smaller PV-BESS sizes are more attractive investments, as larger systems are characterised by reduced profitability, while BESS subsidisation enhances system profitability. Shifting a small portion of load demand improves economic feasibility by decreasing BESS size, while combining PV production, energy storage, and energy flexibility is the optimal solution for public buildings against electricity prices. Finally, a trade-off between economic feasibility and energy self-sufficiency is observed. The results can be utilised by policymakers to design strategies for BESS promotion, while professionals can perform accurate system sizing, thus avoiding excess system capacities. © 2024 IEEE.

关键词： Battery storage

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Research of Sequential Algorithms for Graph Model Decomposition 32

Research of Sequential Algorithms for Graph Model Decomposit...

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32nd International Scientific Conference Electronics, ET 2023

作者： Dinev, Matyo Kukenska, Valentina Minev, Petar Technical University of Gabrovo Faculty of Electrical Engineering and Electronics Department of Computer Systems and Technology 4 Hadji Dimitar street Gabrovo Bulgaria

ISBN: (纸本)9798350302004

The subject of this paper is sequential decomposition algorithms. They are applied to graph models of various sizes. A study of their speed and applicability was made with software applications developed for the purpose. The obtained results are presented in tabular and graphical form. Conclusions have been made about their applicability in the design automation of circuit and systems. © 2023 IEEE.

关键词： Application programs

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Sequential Patch Analysis Framework for Lung Disease Classification 11th

Sequential Patch Analysis Framework for Lung Disease Classi...

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11th International Conference on Future Data and Security engineering, FDSE 2024

作者： Le, Kim-Ngoc T. Le, Duc-Tai Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of College of Computing and Informatics Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819604364

Deep neural network (DNN) models have shown promise in lung disease classification using spectrogram transformations of respiratory sounds. However, these models typically process entire images, which can be computationally expensive, and they require large datasets for effective training. With the largest available respiratory dataset, ICBHI, containing only 920 recordings, training a robust DNN becomes challenging. To address this issue, this paper introduces a simplified DNN framework that incorporates novel techniques such as patch splitting and transfer learning. Specifically, the proposed framework splits the spectrogram image into a sequence of fixed-size patches, effectively reducing computational demands. These patches are processed with the position embeddings by DNN encoders to capture acoustic characteristics, leveraging a pre-trained ImageNet model to make efficient use of the limited dataset. Evaluation experiments on the ICBHI dataset show that the proposed method improves the state-of-the-art 8-class classification accuracy by 1.91%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan Department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

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Practical FSS-Based Sensor Sensitivity

Practical FSS-Based Sensor Sensitivity

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Mahmoodi, Mahboobeh Donnell, Kristen M. Laboratory Department of Electrical and Computer Engineering RollaMO65409 United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

Frequency selective surfaces are a periodic array of unit cells that, when illuminated externally, have a specific frequency response that depends on element geometry, spacing, and substrate properties. Theoretically, FSS is assumed as an infinite array of unit cells with a plane wave excitation. However, in practice, an FSS is finite and hence, due to edge effects, the limited number of unit cells, and non-uniform illumination, the response will deviate from the theoretical. As it relates to FSS-based sensing in particular, a localized illumination is often used in order to improve the sensing resolution. However, due to the aforementioned factors, the sensitivity of the sensor may suffer as a result. Hence, the effect of these factors is studied on the FSS sensor response. Then, taking strain measurement as an example, the degradation in the sensor sensitivity to strain is evaluated in comparison with that of a theoretical FSS. The simulation results show that a finite FSS with non-uniform illumination has reduced sensitivity to strain. This degradation in sensitivity of reduces as the number of illuminated unit cells increases. However, the sensitivity of a finite FSS with uniform illumination is nearly constant with respect to the number of illuminated unit cells. © 2022 IEEE.

关键词： Frequency selective surfaces

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Deep Learning in Remote Sensing for Climate-Induced Disaster Resilience: A Comprehensive Interdisciplinary Approach

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Remote Sensing in Earth systems Sciences 2024年第1期8卷 145-160页

作者： Padmaja, S.M. Naveenkumar, R. Kumari, N. P. Lavanya Pimo, Er. S. John Bindhu, M. Konduri, Bhagavan Jangir, Pradeep Department of Electrical and Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Systems Engineering AUCE Andhra University Visakhapatnam India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Climate-induced disasters pose significant threats to human lives, infrastructure, and ecosystems. Deep learning techniques, combined with remote sensing, offer powerful tools for predicting, detecting, and mitigating the impacts of such disasters. This study presents a comprehensive interdisciplinary approach using a hybrid cascaded CNN-RNN model to perform spatio-temporal analysis, enhanced image segmentation with U-Net and SegNet architectures, and a hybrid CNN-LSTM-XGBoost ensemble for multi-hazard prediction. The proposed CNN-RNN model achieves an accuracy of 96.78%, significantly outperforming traditional models in predicting flood-prone areas, wildfires, and droughts. Key metrics such as Intersection over Union (IoU), precision, recall, and F1-scores are used to evaluate model performance, highlighting the model’s ability to accurately identify disaster-prone areas and improve disaster resilience. This research not only demonstrates the efficacy of deep learning for climate-induced disaster prediction but also paves the way for future integration of socio-economic data to refine disaster risk assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate-induced disasters CNN-RNN Deep learning Disaster resilience Hybrid ensemble Multi-hazard prediction Remote sensing SegNet Spatio-temporal analysis U-Net

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Computing Riemann zeros with light scattering

arXiv

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arXiv 2024年

作者： Yu, Sunkyu Piao, Xianji Park, Namkyoo Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Wave Engineering Laboratory School of Electrical and Computer Engineering University of Seoul Seoul02504 Korea Republic of Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Finding hidden order within disorder is a common interest in material science1,2, wave physics3,4, and mathematics5. The Riemann hypothesis, stating the locations of nontrivial zeros of the Riemann zeta function6, tentatively characterizes statistical order in the seemingly random distribution of prime numbers. This famous conjecture has inspired various connections with different branches of physics7, recently with non-Hermitian physics8,9, quantum field theory10, trapped-ion qubits11, and hyperuniformity12,13. Here we develop the computing platform for the Riemann zeta function by employing classical scattering of light. We show that the Riemann hypothesis suggests the landscape of semi-infinite optical scatterers for the perfect reflectionless condition under the Born approximation. To examine the validity of the scattering-based computation, we investigate the asymptotic behaviours of suppressed reflections with the increasing number of scatterers and the emergence of multiple scattering. The result provides another bridge between classical physics and the Riemann zeros, exhibiting the design of wave devices inspired by number theory. © 2024, CC BY.

关键词： Born approximation

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Design Flow of Blowfish Symmetric-Key Block Cipher on FPGA

Design Flow of Blowfish Symmetric-Key Block Cipher on FPGA

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Palka, Piotr Perez, Rafael A. Fang, Tianyang Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

Cryptography is an essential topic for modern digital systems. In the past two decades, FPGA-based encryptions has proved to be effective. Through this project, we explore a cryptographic application of FPGAs by implementing the Blowfish symmetric-key block cipher on a Xilinx ZedBoard. The motivation behind this is to explore how FPGAs can be used to accelerate the performance of a symmetric-key block cipher. Our goal is to compare the performance of a hardware implementation of Blowfish to a software implementation. Our Blowfish block cipher is implemented in VHDL and is functionally verified against a Python reference model. The design as a whole supports user input of various keys for key initialization along with full encryption and decryption functionality. Furthermore, the design was successfully synthesized and implemented on the ZedBoard FPGA. A performance evaluation was conducted between the hardware implementation and a Blowfish library running in Python by comparing the time it takes to perform the key initialization and encrypt/decrypt operation. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Staggered Microstrip Grating Structure for Sheet-Beam TWTs 25

Staggered Microstrip Grating Structure for Sheet-Beam TWTs

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25th Joint IEEE International Vacuum Electronics Conference and 15th International Vacuum Electron Sources Conference, IVEC + IVESC 2024

作者： Marosi, Robert Zuboraj, Muhammed Capolino, Filippo University of California Department of Electrical Engineering and Computer Science IrvineCA United States Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350348705

We introduce a simple geometry for slow-wave structures (SWSs) in sheet-beam traveling-wave tubes (TWTs). The staggered microstrip grating SWS is a space harmonic structure with geometry and bandwidth with analogous to the staggered vane SWS commonly used for sheet beam TWTs. The staggered microstrip grating TWT is designed to operate in V-band with a center frequency of 74 GHz and electron beam voltage of 20 kV. © 2024 IEEE.

关键词： Slow wave structures

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