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2023 Novel Optical Materials and Applications, NOMA 2023

作者： Lee, Dayeong Kim, Jungmin Park, Hyungchul Lee, Ikbeom Yu, Sunkyu Park, Namkyoo Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

We propose a theoretical approach for the realization of unidirectional light scattering without spatial patterning, enabled by correlated photonic disorder in time domain. Our study enables novel photonic devices suc... 详细信息

关键词： Photonic devices

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Average Current Control Design of Active Power Decoupling Converter with Low Filter Inductance Based on Double Sampling Techniques for PV Microinverter

Average Current Control Design of Active Power Decoupling Co...

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2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future, ICUE 2024

作者： Shen, Yidi Hasnain, Arafat Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland College Park Department of Electrical and Computer Engineering The Institute for Systems Research College ParkMD United States

ISBN: (纸本)9798331517076

Active power decoupling (APD) converter is a power dense and reliable solution in dc-ac power conversion systems to balance the fluctuating ac-side power and constant dc-side power. In boost type APD converter, filter inductor with low inductance can be beneficial in achieving efficient and compact designs or allows the implementation of zero voltage switching (ZVS) based modulation. On the other hand, the reduced inductance increases the difficulty of controller design due to reduction of the average current control loop phase margin and higher requirement in low-pass-filter (LPF). This paper proposes an average current control based on double sampling techniques to reduce the phase delay of LPF and improve the current tracking performance under low inductance. The implementation of double sampling in the digital signal processor (DSP) and the design of control loops are discussed. The proposed approach is experimentally verified using a 40 V, 400 W APD converter prototype. © 2024 IEEE.

关键词： Low pass filters

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Modeling and Analysis of The Balance Network for Common Mode EMI Noise Suppression

Modeling and Analysis of The Balance Network for Common Mode...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yang, Qiuzhe Wang, Shuo Li, Qiang Virginia Tech Center for Power Electronics Systems Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798350376067

This paper presents the model of the balance technique for common mode noise suppression. Considering the resonance due to the self-parasitic capacitance of the inductor in the balance network, coupling between the inductors is essential to compensate for the impact of self resonance. The derived model illustrates how the coupling coefficient affects the balance condition. To verify the model, three coupled inductors with different coupling coefficients are designed. A voltage-gain based method is proposed to simulate the real noise source in the balance network. And the voltage ratio between the balance bridges can be experimentally measured. The result matches the prediction very well and verifies the balance model. © 2024 IEEE.

关键词： Interference suppression

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Analysis and Comparison of Integrated Planar Transformers for CLLC Resonant Converters

Analysis and Comparison of Integrated Planar Transformers fo...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yuan, Tianlong Jin, Feng Yu, Gibum Li, Qiang Virginia Polytechnic and State University Center for Power Electronics Systems Bradley Department of Electrical and Computer Engineering Blacksburg United States

ISBN: (纸本)9798350376067

The advancement of electric vehicles and green energy technologies necessitates on-board chargers (OBCs) with higher power density and reduced costs. Integrated planar magnetics emerge as a highly competitive option due to their superior power density and ease of manufacturing compared to traditional magnetic designs. However, detailed flux analysis, particularly the distribution on the plate, remains essential for evaluating various transformer structures. This paper presents a comprehensive comparison of different core structures for integrated transformers, validated through an 11-kW CLLC resonant converter prototype with a 98.4% peak efficiency and 420 W/inch3 power density. © 2024 IEEE.

关键词： Resonant converters

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Fake News Detection Approach Using Hybrid Deep Learning Framework 9

Fake News Detection Approach Using Hybrid Deep Learning Fram...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Sanida, Maria Vasiliki Sanida, Theodora Sideris, Argyrios Dossis, Michael Dasygenis, Minas University of Piraeus Department of Digital Systems Piraeus18534 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece

ISBN: (纸本)9798350342482

The rapid expansion of social media has facilitated individuals' widespread distribution of information, frequently without sufficient verification or filtering mechanisms. This has exacerbated the problem of misinformation, raising significant concerns about the harmful impacts of fake news on communities. To counteract this issue, a focused effort has been made to develop automated detection systems that harness artificial intelligence (AI) and machine learning (ML) capabilities. Given the success of deep learning (DL) techniques in managing complex natural language processing (NLP) tasks, these methods offer a promising avenue for detecting fake news. This study introduces an innovative DL framework that effectively combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to detect fake news. The proposed approach attains 99.19% accuracy and significantly superior detection outcomes compared to existing methods, highlighting its potential to significantly enhance the reliability of news verification processes and mitigate the spread of misinformation. © 2024 IEEE.

关键词： Adversarial machine learning

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High capacity,low power,short reach integrated silicon photonic interconnects

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Photonics Research 2024年第11期12卷 59-76页

作者： ANDREW NETHERTON MARIO DUMONT ZACHARY NELSON JAHYUN KOO JINESH JHONSA ALICE MO DAVID MCCARTHY SKYLAR DECKOFF-JONES YUN GAO NOAH PESTANA JORDAN GOLDSTEIN REN-JYE SHIUE CHRISTOPHER POULTON MJKENNEDY MARK HARRINGTON BOZHANG DONG JOCK BOVINGTON MICHAEL FRANKEL LUKE THEOGARAJAN MICHAEL WATTS DANIEL BLUMENTHAL JOHN EBOWERS Department of Electrical and Computer Engineering University of CaliforniaSanta BarbaraSanta BarbaraCalifornia 93106USA Analog Photonics BostonMassachusetts 02210USA Cisco Systems San JoseCalifornia 95134USA Ciena Corporation HanoverMaryland 21076USA

The architecture and component technology of a low power,high capacity,short reach optical interconnect are *** from high-performance 300 mm silicon photonics components that comprise the system are shown,along with a quantum-dot mode-locked laser 20-channel comb source with free space wall plug efficiencies up to 17%,advanced packaging techniques for 3D silicon photonic-electronic integration,and schematics for integrated electronics that control the photonic integrated *** for operating such a system in the presence of changing ambient temperature are *** on a 1 Tbps design are conducted with an optical link experiment indicating sub-picojoule/bit energy consumption at scale.

关键词： power system ambient

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A High Gain GaAs pHEMT Low Noise Amplifier for Ka-Band Active Phase Shifters

A High Gain GaAs pHEMT Low Noise Amplifier for Ka-Band Activ...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Karmakar, Nemai C. Lin, Chia-Han Trinh, Kim Tuyen Chiu, Hsien-Chin Kao, Hsuan-Ling Department of Electrical & Computer Systems Engineering Monash University Clayton MelbourneVIC3800 Australia Department of Electronic Engineering Chang Gung University Tao-Yuan Taiwan

ISBN: (纸本)9798350379693

A two-stage common source topology, 36 to 38 GHz GaAs pHEMT Low Noise Amplifier (LNA) was designed, fabricated, and tested. This LNA is an essential part of the proposed active phase shifter for soil moisture radiometer phased array antenna. The LNA provides 23 dB gain. A typical noise figure (NF) of 5 dB was measured at 37 GHz. DC power consumption is about 154 mW. The MMIC chip was fabricated by using 0.15 µm GaAs pHEMT process. The total chip area including all the pads and scribe lines is about 1.5 mm x 0.9 mm. Measured gain flatness is about ±1 dB within 36-38 GHz bandwidth. The high gain, small gain flatness makes this device well suited for the phase shifters in the soil moisture radiometer phased array antenna. © 2024 IEEE.

关键词： III-V semiconductors

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Automated lumpy skin grading in bovine images using novel deep convolutional neural networks

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Multimedia Tools and Applications 2024年 1-26页

作者： Muhammad, Abdul Malik ur Rehman, Muhammad Zia Mushtaq, Zohaib Qureshi, Muhammad Farrukh Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha40100 Pakistan Department of Electrical Engineering Namal University Mianwali Mianwali42250 Pakistan Center of AI and BigData Namal University Mianwali Mianwali42250 Pakistan

The diagnosis and prevention of lumpy skin disease, a viral ailment affecting cattle and buffalo, present significant financial implications for the livestock industry. Traditional methods for identifying lumpy skin disease rely on manual visual inspection by veterinarians, which can be labor-intensive, subjective, and prone to errors. To address these challenges, this study proposes a novel deep convolutional neural network (DCNN) model for the automatic recognition and grading of lumpy skin disease from bovine images. The primary contributions of this research include the development of a DCNN architecture specifically tailored for this task, comprising five convolutional layers, five max pooling layers, two fully connected layers with ReLU activation, and a final fully connected layer with softmax activation. The model’s detection accuracy is further enhanced by applying image cropping and patching techniques, which divide each input image into 12 patches to improve local feature extraction. The proposed model was trained and tested using a publicly available dataset from Kaggle. Comparative analysis was conducted against several state-of-the-art models, including InceptionV3, ResNet50, MobileNetV3, VGG19, and Xception. The DCNN model demonstrated superior performance, achieving the highest validation accuracy of 0.96875, outperforming the compared models in terms of accuracy, precision, recall, and F1 score. Additionally, the study explores the potential of transitioning from binary to multiclass classification, which would allow for the assessment of the severity of lumpy skin disease. This future direction aims to provide more nuanced and actionable information for veterinary diagnostics. The significance of this research lies in its potential to offer an objective, efficient, and scalable solution for early disease detection and prevention in livestock, thereby presenting considerable economic benefits for farmers and the livestock industry as a whole. The me

关键词： Convolutional neural networks

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Accelerating CNNs for Pneumonia Disease Diagnosis via Heterogeneous FPGA systems 9

Accelerating CNNs for Pneumonia Disease Diagnosis via Hetero...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Sanida, Theodora Sideris, Argyrios Sanida, Maria Vasiliki Dossis, Michael Dasygenis, Minas University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350342482

Early and accurate diagnosis of pneumonia is critical for effective treatment and improved patient outcomes. This paper introduces a cutting-edge framework for accelerating the diagnosis of pneumonia using Convolutional Neural Networks (CNNs) on heterogeneous Field-Programmable Gate Array (FPGA) systems. The framework is designed to leverage FPGAs' parallel processing capabilities and reconfigurability, enabling significant enhancements in speed and efficiency for processing medical images. Our approach involves the development of an optimized CNN model tailored to the features and characteristics typical of pneumonia in radiographic images. This model is then mapped onto an FPGA, utilizing high-level synthesis and custom hardware descriptions to maximize data throughput and minimize latency. We conducted testing using a diverse chest X-ray dataset, comparing our FPGA-based system's performance against traditional computing platforms such as GPUs and CPUs. Results indicate that our system accelerates the diagnostic process over 5 times and achieves a 97.58% accuracy rate comparable to state-of-the-art methods. The proposed framework enhances medical imaging devices' capabilities, leading to quicker diagnoses, thus reducing hospitalization time and improving patient care. © 2024 IEEE.

关键词： Digital storage

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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