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Dual-Band Reflectarray Antennas Using Integrated Resonant and Non-Resonant Natures of Metallic Waveguide Elements at Millimeter Wave Frequencies

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Radio Science 2023年第1期58卷 1-13页

作者： Chou, H.-T. Wang, N.-N. Fang, M. Wang, L.-Q. Akkaraekthalin, P. Torrungrueng, D. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Research Center of Innovation Digital and Electromagnetic Technology Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut's University of Technology North Bangkok Bangkok Thailand

Pure metallic reflectarray antennas have good power efficiency and low fabrication cost, limited to single-band operation in the past. A dual-band reflectarray antenna is developed for two directional beams by two feeds. The reflecting elements are metal waveguides and have novel properties of both resonant and non-resonant modes at the two frequency bands. The low and high-frequency bands can be easily separated by the cutoff frequency of waveguide modes. The phase-changing mechanisms via ray optics and fundamental waveguide modes, respectively, provide simple formulations for the elemental structure design of directional beams. Radiation characteristics of the linear and circular polarizations are cross-examined with a good performance by full-wave simulations using HFSS, FEKO, and CST at 28 and 60 GHz bands for 5G front-haul network applications. In particular, we have used HFSS to simulate the reflecting elements' basic characteristics of phase variation, FEKO to simulate the linear polarization antenna case, and CST to simulate the circular polarization antenna case, respectively. © 2023. American Geophysical Union. All Rights Reserved.

关键词： Cutoff frequency

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Field of view and angular-resolution enhancement in microlens array type virtual reality near-eye display using polarization grating

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Optics Express 2025年第1期33卷 263-278页

作者： Shin, Kwang-Soo Hong, Jisoo Han, Woongseob Park, Jae-Hyeung Department of Electrical and Computer Engineering Inha University 100 Inha-ro Michuhol-gu Incheon22212 Korea Republic of Hologram Research Center Korea Electronics Technology Institute Seoul03924 Korea Republic of Department of Electrical and Computer Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

In this paper, we propose what we believe to be a novel method to enhance both the field of view and angular resolution of a microlens array-based thin virtual reality near-eye display using a polarization grating. The proposed system employs a polarization grating to steer the field angle according to the polarization states, simultaneously enhancing the field of view and angular resolution. Optical experiments and Zemax simulations validate the effectiveness of our approach, with the field of view in the experimental setup expanding from 58.8° to 95.17°. Additionally, the improvement in angular resolution was demonstrated experimentally by positioning two images with right-handed and left-handed circular polarization states at a half-pixel diagonal offset. The proposed method is expected to contribute to the advancement of virtual reality technology significantly by addressing the trade-off between field of view and resolution. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Virtual environments

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Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction

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IEEE Transactions on Cloud Computing 2024年第1期12卷 186-199页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Shi, Yun-Qing Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974301 Taiwan New Jersey Institute of Technology ECE Department NewarkNJ07102 United States

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably using k-out-of-n marked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable. © 2013 IEEE.

关键词： Image reconstruction

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A 1.7 kV Full-SiC Reverse-Blocking Module Enabling Efficient Multiport Operation in Soft-Switching Current Source Converters

A 1.7 kV Full-SiC Reverse-Blocking Module Enabling Efficient...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Marellapudi, Aniruddh Mauger, Mickael J. Benzaquen, Joseph Divan, Deepak Georgia Institute of Technology Center for Distributed Energy Department of Electrical & Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350376067

The Soft-Switching Solid-State Transformer (S4T) is a flexible, multiport, and isolated ZVS soft-switching topology with applications in multiport energy routers for data centers, EV fast charging, storage integration, and solar farms. Prior work investigated a commercial 1.7 kV reverse-blocking (RB) phase-leg module based on a series combination of a Si IGBT and a SiC Schottky diode. While soft switching was achieved, significant residual switching losses due to IGBT tail current limited achievable efficiency. This work presents a high-current, full-SiC 1.7 kV RB half-bridge module with each switch composed of a SiC MOSFET and a SiC Schottky diode. Characterization of the full-SiC 1.7 kV RB module in the S4T resulted in total switching losses of 1.3 mJ at 1000 V, 150 A, Tbaseplate=110 °C, a >9.7X reduction from the previous IGBT-based RB structure (800 V, 150 A, Tbaseplate=90 °C). Critically, the full-SiC RB structure enables near-ideal soft-switching between any set of sources and loads in each S4T switching cycle - unlocking scalable, efficient multiport operation. © 2024 IEEE.

关键词： Schottky barrier diodes

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A novel deep learning approach for automated grading of knee osteoarthritis severity

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Multimedia Tools and Applications 2024年 1-20页

作者： Kaur, PrabSimran Kohli, Guneet Singh Bedi, Jatin Wasly, Saud Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Electrical and Computer Engineering Department King Abdulaziz University Jeddah22254 Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah22254 Saudi Arabia

Knee Osteoarthritis (OA) is a prevalent musculoskeletal disorder that affects the knee joint that causes pain, stiffness, and reduced mobility. It is also known as "Degenerative Joint Disease" and is caused by the degeneration of cartilage in the knee joint, leading to bone-on-bone contact and further damage. Knee OA is prevalent in the population, affecting around 22% to 39% of people in India, and there is currently no treatment available that can halt the progression of the disease. Therefore, early diagnosis and management of symptoms are essential to reduce its impact on an individual’s quality of life. To address this issue, have introduced a framework that leverages ConvNeXt architecture, a modernization of ResNets (ResNet-50) architecture towards Hierarchical Transformers (Swin Transformers), to provide accurate identification and classification of knee osteoarthritis. The classification of knee osteoarthritis was done using the Kellgren and Lawrence (KL) graded X-ray images. These images of the damaged knees are preprocessed and augmented, creating a scaled, enhanced, and varied version of the features, thus making the data fitter and more significant for classification. The performance estimation of the proposed strategy is conducted on the Osteoarthritis Initiative (OAI), a research project focused on knee osteoarthritis that works in partnership with NIH and other private industries to develop a public domain dataset that can facilitate research and evaluation. It involves training the prepared data using various hyper-tuned versions of ConvNeXt. The different fine-tuned results of the ConvNeXt models on each KL Grade are evaluated against the other state-of-the-art models and vision transformers. The comparative assessment of widely used performance measures shows that the proposed approach outperforms the conventional models by generating the highest score for all the KL grades. Lastly, an approach is employed to statistically confirm the validity of t

关键词： Joints (anatomy)

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Cognitive Radio Receiver Design Employing Pipeline Successive Approximation ADC 22

Cognitive Radio Receiver Design Employing Pipeline Successiv...

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22nd International Learning and technology Conference, L and T 2025

作者： Baig, Muhammad Samee Hussein, Aziza I. Effat University Electrical and Computer Engineering Department Saudi Arabia Effat University College of Engineering Energy and Technology Research Center Saudi Arabia

ISBN: (纸本)9798331532215

Smart cities require better cellular communication, incorporating a high data rate that satisfies the Internet, cloud computing, and the Internet of Things (IoT) requirements. A high data rate demands higher bandwidth with low latency. However, the already saturated or underutilized frequency spectrum hinders this realization. 5G and cognitive radio technology can enhance spectrum utilization to meet the high data demands of future cellular communication. Cognitive radio uses spectrum sensing to identify underutilized frequencies without interfering with licensed users, addressing the scarcity of the electromagnetic spectrum. In this paper, a cognitive radio receiver design with a SAR analog-to-digital converter, which is recognized for having a modest resolution for high bandwidth signals, is implemented. Moreover, the pipeline technique in the SAR design is proposed to increase the resolution to be suitable for 5G applications. The high-level model of the system is simulated using MATLAB Simulink software. The quantization error observed in the regular SAR ADC design is compared to that in the proposed pipeline SAR ADC, highlighting the enhanced accuracy and efficiency of the pipeline SAR ADC, which makes it suitable for spectrum sensing in next-generation cognitive radio systems. © 2025 IEEE.

关键词： Bandwidth

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Nutation-based longitudinal-sensing protocols for high-field NMR with nitrogen-vacancy centers in diamond

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Physical Review Applied 2024年第2期22卷 024043-024043页

作者： Declan Daly Stephen J. DeVience Emma Huckestein John W. Blanchard Johannes Cremer Ronald L. Walsworth Department of Physics Quantum Technology Center Department of Electrical and Computer Engineering

Nitrogen-vacancy (N-V) centers in diamond enable nuclear magnetic resonance (NMR) spectroscopy of samples at the nano- and microscale. However, at typical tesla-scale NMR magnetic field strengths, N-V–NMR protocols become difficult to implement due to the challenge of driving fast N-V pulse sequences sensitive to nuclear Larmor frequencies above a few megahertz. We perform simulations and theoretical analysis of the experimental viability of N-V NMR at tesla-scale magnetic fields using a measurement protocol called DRACAERIS (Double Rewound ACquisition Amplitude Encoded Radio Induced Signal). DRACAERIS detects the longitudinal magnetization of the NMR sample at a much lower driven Rabi frequency, more suitable technically for N-V detection. We discuss how pulse errors, finite pulse lengths, and nuclear spin-spin couplings affect the resulting NMR spectra. We find that DRACAERIS is less susceptible to pulse imperfections and off-resonance effects than previous protocols for longitudinal magnetization detection. We also identify reasonable parameters for experimental implementation.

关键词： Analytical chemistry Magneto-optics Quantum sensing Nitrogen vacancy centers in diamond Nuclear magnetic resonance Radio frequency techniques

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

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IEEE Access 2025年 13卷 86768-86780页

作者： Saleem, Muhammad Asim Javeed, Ashir Benjapolakul, Watit Srisiri, Wattanasak Chaitusaney, Surachai Kaewplung, Pasu Chulalongkorn University Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden

Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting techniques for improved accuracy. The N-XGB model achieves an accuracy of 94.8% and an average F1 score of 0.95 on a real-world dataset that includes wildfires, floods and earthquakes, significantly outperforming baseline models such as random forest, Support vector machine and logistic regression 85% accuracy. The balanced F1 scores for wildfires 0.96, floods 0.93, and earthquakes 0.96 demonstrate the model's robustness in multi-class classification. The Synthetic Minority Oversampling Technique (SMOTE) balances datasets and improves model efficiency and capability. The proposed N-XGB model provides a reliable and accurate solution for predicting disasters and contributes to improving preparedness, resource allocation and risk management strategies. © 2013 IEEE.

关键词： Risk management

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