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European Conference on Antennas and Propagation, EuCAP

作者： Savvas I. Raptis Alexandros Papadopoulos Loizos Symeonidis Antonios Lalas Christos K. Liaskos Konstantinos Votis Dimitrios Tzovaras Traianos V. Yioultsis Information Technologies Institute Centre for Research and Technology Hellas (CERTH) Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece Computer Science and Engineering Department University of Ioannina Greece

Tunable metasurfaces, that is, 2D metamaterial structures with programmable functionalities have been a significant subject of research. The consideration of non-local effects in metasurfaces has been proven to be highly important in modeling the electromagnetic response of such structures. In this paper a highly accurate semi-analytical model is presented, taking into consideration the non-local effects of a periodic metasurface. The model is validated through comparison with full wave simulations of tunable metasurfaces' supercells, providing accurate calculations of the radiating Floquet modes. The efficiency of the proposed model allows optimization algorithms to run fast, without multiple repeats of full wave simulations, rendering an effortless extraction of the tunable variables' pattern for the desired response. This advantage is used for the design of programmable anomalous reflectors' for V2X communications.

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Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles 5

Data Analytics for Core Temperature Estimation in Battery Ma...

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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Chhetri, Ahilya Surya, Sumukh Mohan Krishna, S. Rao, Vidya Cactus Communications Engineering and Technology Mumbai India Bosch Global Software Technologies Pvt. Limited Bengaluru India Aced Alliance University Department of Electrical and Electronics Engineering Bengaluru India Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798350313123

Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz;lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation. © 2023 IEEE.

关键词： Simulink

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Plasmonic Pentamer-Arranged Nanohole Arrays

Plasmonic Pentamer-Arranged Nanohole Arrays

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2024 Photonics North, PN 2024

作者： Wong, Wei Ru Choi, Hyung Woo Mahani, Fatemeh Fouladi Astorga, Luis Angel Mayoral Mokhtari, Arash Berini, Pierre Integrated Lightwave Research Group Department of Electrical Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Nexus for Quantum Technologies Institute University of Ottawa ONK1N 6N5 Canada Lab Electrical Engineering Dept. Kerman Iran Department of Physics University of Ottawa OttawaONK1N 6N5 Canada

ISBN: (纸本)9798350366365

We report the theoretical and experimental investigation of pentamer arrangements of nanoholes in a thin Au film arrayed over a large area. The pentamer arrangements support coupled plasmonic resonances localized to the nanoholes that exhibit a high near-field sensitivity and low polarization dependance. The structures are compelling for applications in biosensing and in surface enhanced Raman scattering (SERS). © 2024 IEEE.

关键词： nanoholes pentamer sensing SERS surface plasmon

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Regional Datasets for Air Quality Monitoring in European Cities

Regional Datasets for Air Quality Monitoring in European Cit...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： G.- F. Angelis A. Emvoliadis T.-I. Theodorou A. Zamichos A. Drosou D. Tzovaras Information & Technologies Institute Centre for Research & Technology Hellas Thessaloniki Greece Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005°. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics. The ready-to-use data, processing code & examples are available at https://***/angelisgiorgos/RegionalAQDatasets

关键词： Urban areas Europe Land surface Air pollution Solids Spatiotemporal phenomena Pollution measurement

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Classification of Intrusion Affected Stego Images over a Channel Using Deep Learning Techniques 2

Classification of Intrusion Affected Stego Images over a Cha...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking technologies, ViTECoN 2023

作者： Manikandan, C. Lahari, M. Sai Meera, S. Bhavitha, R. Rajesh, A. Amirtharajan, R. Veeramalai, Thanikaiselvan Sidharthan, Rakesh Kumar Mahalingam, Hemalatha School of EEE SASTRA Deemed University Thanjavur613401 India School of Electronics Engineering Vellore Institute of Technology Vellore India Engineering R&D Services HCL Technologies Ltd Tamil Nadu India King Abdulaziz University Department of Electrical and Computer Engineering Jeddah22254 Saudi Arabia

ISBN: (纸本)9798350347982

Image Steganography is a widely used technique to hide secret information in the cover image for secure transmission. A stego image is subject to many intrusions such as noise and packet loss during transmission, which may alter the characteristics of the transmitted image. Hence, it is necessary to classify the intrusion type to determine the mitigation strategy. This paper proposes a deep learning-based approach to organise the stego images based on the intrusion type. Three different deep learning techniques, as Convolutional Neural Networks (CNNs), Inception V3, and autoencoders, are trained and comparatively analysed to determine the optimal classifier. The proposed approach is evaluated using the standard BOSSBASE dataset, and the experimental results showed the highest classification accuracy of 84.533 % for Inception V3. © 2023 IEEE.

关键词： Convolutional neural networks

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Zak-OTFS for Identification of Linear Time-Varying Systems

arXiv

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arXiv 2025年

作者： Nisar, Danish Mohammed, Saif Khan Hadani, Ronny Chockalingam, Ananthanarayanan Calderbank, Robert Department of Electrical Engineering Indian Institute of Technology Delhi India Department of Mathematics University of Texas Austin United States Cohere Technologies Inc. CA United States Department of Electrical and Communication Engineering Indian Institute of Science Bangalore India Department of Electrical and Computer Engineering Duke University United States IIT Delhi India

Linear time-varying (LTV) systems model radar scenes where each reflector/target applies a delay, Doppler shift and complex amplitude scaling to a transmitted waveform. The receiver processes the received signal using the transmitted signal as a reference. The self-ambiguity function of the transmitted signal captures the cross-correlation of delay and Doppler shifts of the transmitted waveform. It acts as a blur that limits resolution, at the receiver, of the delay and Doppler shifts of targets in close proximity. This paper considers resolution of multiple targets and compares performance of traditional chirp waveforms with the Zak-OTFS waveform. The self-ambiguity function of a chirp is a line in the delay-Doppler domain, whereas the self-ambiguity function of the Zak-OTFS waveform is a lattice. The advantage of lattices over lines is better localization, and we show lattices provide superior noise-free estimation of the range and velocity of multiple targets. When the delay spread of the radar scene is less than the delay period of the Zak-OTFS modulation, and the Doppler spread is less than the Doppler period, we describe how to localize targets by calculating cross-ambiguities in the delay-Doppler domain. We show that the signal processing complexity of our approach is superior to the traditional approach of computing cross-ambiguities in the continuous time / frequency domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Chirp modulation

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Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture 9

Image-Based Soil Health Analysis Using Deep Learning for Pre...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Saranya, V.S. Mangali, Saikiran Srinija, K. Medabalimi, Galeiah Devi, Meena Venkata Ramana, N.R. Lakkshmanan, Ajanthaa School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603203 India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Mlrit Department: Data science Dundigal India Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Chinna Amiram Andhra Pradesh Bhimavaram India Kcg college of technology Department of electrical and electronics engineering Chennai600097 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India

ISBN: (纸本)9798350377972

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management. © 2024 IEEE.

关键词： Convolutional neural networks

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Non-Invasive Skin Cancer Detection: A High-Resolution Millimeter-Wave Time Reversal Approach

Non-Invasive Skin Cancer Detection: A High-Resolution Millim...

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European Conference on Antennas and Propagation, EuCAP

作者： Fatemeh Ravanbakhsh Alireza Madannejad Javad Ebrahimizadeh School of Medical Science and Technologies Islamic Azad University Tehran Iran School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Electrical Engineering Katholieke Universiteit Leuven Leuven Belgium

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper introduces a novel non-invasive skin cancer detection system using a high-resolution millimeter-wave time reversal imaging approach. Operating in the 2–30 GHz frequency range, the system utilizes two moving Vivaldi antennas to scan the imaging area, significantly reducing hardware complexity and cost compared to conventional systems. The system features a skin-removal technique that eliminates surface reflections, improving subsurface imaging accuracy. Through time-reversal methods, particularly the DORT algorithm, the system achieves high-resolution imaging capable of detecting both single and multiple tumors. In tests with phantoms, the system successfully identified single tumors with diameters of 5 mm and 8 mm and multiple tumors of 8 mm in close proximity. The worst-case positioning error for single tumor detection was 4 mm, and for multiple targets, 14 mm. The system also exhibited a high signal-to-clutter ratio (SCR) of up to 10.8 dB, demonstrating its effectiveness in distinguishing between benign and malignant tissues.

关键词： Costs Millimeter wave technology Vivaldi antennas Imaging Reflection Hardware Complexity theory Tumors Skin cancer Signal to noise ratio

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Nanophotonic-structured front contact for high-performance perovskite solar cells

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Science China Materials 2022年第7期65卷 1727-1740页

作者： Md.Akhtaruzzaman Mohammad Ismail Hossain Mohammad Aminul Islam Md.Shahiduzzaman Ghulam Muhammad A.K.Mahmud Hasan Yuen Hong Tsang Kamaruzzaman Sopian Solar Energy Research Institute(SERI) Universiti Kebangsaan Malaysia43600 BangiSelangorMalaysia Department of Applied Physics Hong Kong Polytechnic University(PolyU)KowloonHong KongChina Centre for Integrated Systems Engineering and Advanced Technologies(Integra) Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia(UKM)43600 BangiSelangorMalaysia Graduate School of Pure and Applied Sciences University of TsukubaTsukubaIbaraki 305-8573Japan Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaJalan Universiti50603 Kuala LumpurSelangorMalaysia Nanomaterials Research Institute(NanoMaRi) Kanazawa UniversityKakumaKanazawa 920-1192Japan Department of Computer Engineering College of Computer and Information SciencesKing Saud University11543 RiyadhSaudi Arabia Department of Electrical and Computer Engineering University of CaliforniaDavisCA 95616USA Institute of Sustainable Energy Universiti Tenaga Nasional(@The National Energy University)Jalan IKRAM-UNITEN43000 KajangSelangorMalaysia

We report the design of a nanophotonic metaloxide front contact aimed at perovskite solar cells(PSCs)to enhance optoelectronic properties and device stability in the presence of ultraviolet(UV)***-quality Cr-doped ZnO film was prepared by industrially feasible magnetron sputter deposition for the electron transport layer of *** a means,the influence of the Cr content on the film and device was systematically ***-depth device optics and electrical effects were studied using advanced three-dimensional opto-electrical multiphysics rigorous simulations,optimizing the front contact for realizing high *** numerical simulation was validated by fabricating PSCs optimized to reach high performance,energy conversion efficiency(ECE)=17.3%,open-circuit voltage(V_(OC))=1.08 V,short-circuit current density(J_(SC))=21.1 mA cm^(-2),and fillfactor(FF)=76%.Finally,a realistic front contact of nanophotonic architecture was proposed while improving broadband light absorption of the solar spectrum and light harvesting,resulting in enhanced quantum efficiency(QE).The nanophotonic PSC enables J_(SC)improvement by~17%while reducing the reflection by 12%,resulting in an estimated conversion efficiency over 23%.It is further demonstrated how the PSCs’UV-stability can be improved without considerably sacrificing optoelectronic *** of nanophotonic designed ZnO:Cr front contact,PSCs device,and fabrication process are described.

关键词： ZnO:Cr front contact magnetron sputtering perovskite solar cells UV stability optics and electrical effects

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End-to-End Speech Recognition with Pre-trained Masked language Model

arXiv

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arXiv 2024年

作者： Higuchi, Yosuke Ogawa, Tetsuji Kobayashi, Tetsunori Watanabe, Shinji Department of Communications and Computer Engineering Waseda University Tokyo162-0042 Japan Language Technologies Institute Carnegie Mellon University PittsburghPA15213-3891 United States

We present a novel approach to end-to-end automatic speech recognition (ASR) that utilizes pre-trained masked language models (LMs) to facilitate the extraction of linguistic information. The proposed models, BERT-CTC and BECTRA, are specifically designed to effectively integrate pre-trained LMs (e.g., BERT) into end-to-end ASR models. BERT-CTC adapts BERT for connectionist temporal classification (CTC) by addressing the constraint of the conditional independence assumption between output tokens. This enables explicit conditioning of BERT’s contextualized embeddings in the ASR process, seamlessly merging audio and linguistic information through an iterative refinement algorithm. BECTRA extends BERT-CTC to the transducer framework and trains the decoder network using a vocabulary suitable for ASR training. This aims to bridge the gap between the text processed in end-to-end ASR and BERT, as these models have distinct vocabularies with varying text formats and styles, such as the presence of punctuation. Experimental results on various ASR tasks demonstrate that the proposed models improve over both the CTC and transducer-based baselines, owing to the incorporation of BERT knowledge. Moreover, our in-depth analysis and investigation verify the effectiveness of the proposed formulations and architectural designs. Copyright © 2024, The Authors. All rights reserved.

关键词： Transducers

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