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Multi-Scale Analysis Combined Operando Elemental/Spectroscopic Measurement Techniques in Oxide-Type All-Solid-State Na Batteries

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Energy & Environmental Materials 2025年第2期8卷 38-47页

作者： Koji Hiraoka Kazuo Yamamoto Takeshi Kobayashi Tetsuo Sakamoto Shiro Seki Graduate School of Engineering Applied Chemistry and Chemical Engineering Program Kogakuin University Nanostructures Research Laboratory Japan Fine Ceramics Center Energy Transformation Research Laboratory Central Research Institute of Electric Power Industry Graduate School of Engineering Electrical Engineering and Electronics Program Kogakuin University

Understanding the charge/discharge mechanism of batteries plays an important role in the development of high-performance systems,but extremely complicated reactions are *** these complex phenomena are also bottlenecks for the establishment of all-sol id-state batteries(ASSB),we conducted multi-scale analysis using combined multi-measurement techniques,to directly observe charge/discharge reactions at hierarchical scales for the oxide-type ASSB using Na as the carrier *** particular,all of measurement techniques are applied to cross-section ASSB in the same cell,to complementarily evaluate the elemental distributions and structural *** Operando scanning electron microscopy-energy-dispersive X-ray spectroscopy,the Na concentration in the electrode layers changes on the micrometer scale under charge/discharge reactions in the first ***,Operando Raman spectroscopy reveal changes in the bonding states at the atomic scale in the active material,including changes in reversible structural *** cycling the ASSB,the elemental distributions are clearly observed along with the particle shapes and can reveal the Na migration mechanism at the nanometer scale,by time-of-flight secondary ion mass ***,this study can provide a fundamental and comprehensive understanding of the charge/discharge mechanism by observing reaction processes at multiple scales.

关键词： Operando oxide-type all-solid-state battery Raman SEM-EDS sodium battery TOF-SIMS

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Interfacial-Layer-Free Ge0.95Si0.05Nanosheet FeFETs

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IEEE Transactions on Electron Devices 2024年第3期71卷 1758-1763页

作者： Hsieh, Wan-Hsuan Chen, Yu-Rui Liu, Yi-Chun Zhao, Zefu Lee, Jia-Yang Tu, Chien-Te Huang, Bo-Wei Wang, Jer-Fu Lee, M.H. Liu, C.W. Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Institute and Undergraduate Program of Electro-Optical Engineering Graduate School of Advance Technology National Taiwan University Taipei10617 Taiwan National Taiwan University Graduate School of Advance Technology Taipei10617 Taiwan Graduate Institute of Electronics Engineering Graduate Institute of Photonics and Optoelectronics Graduate School of Advanced Technology National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan

The substantial memory window (MW) of 1.8 V is achieved by utilizing stacked two nanosheet (NS) gate-all-around (GAA) Ge0.95 Si0.05 ferroelectric field-effect transistors (FeFETs), employing a low write voltage of 2 V. The channel phosphorus concentration 1E18 cm-3 is used to erase the absence of interfacial layer (IL) can reduce the operation voltage. An isotropic wet etching process is employed for channel release. The stacked two NSs offer the advantages of reducing cell-to-cell variation and doubling the read current. Additionally, the stability of data storage is demonstrated with the data retention of >1E4 s, linearly extrapolated to a span of 10 years, and exhibiting high endurance >1E11 cycles. The thermal budget is as low as 400 °C. © 1963-2012 IEEE.

关键词： III-V semiconductors

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A Study on Optimization and Augmentation Techniques for Improving the Performance of CADx System for Cat Skin Disease

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 135-141页

作者： Won, Hyeong-Sik Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

In modern society, the number of households raising pets is increasing. As pet ownership increases, the cost of treating companion cats is also rising, with a significant portion of these costs going toward the treatment of skin diseases. Skin diseases are among the common ailments in pets, and swift action is required if they occur in cats. However, early lesions lack distinctive characteristics, making accurate diagnosis difficult. Therefore, this study proposes a CADx(Computer-Aided Diagnosis) system that classifies images of cat skin conditions into inflammatory lesions, non-inflammatory lesions, and normal images using a dataset of cat skin diseases. We selected the ConvNeXt model, based on CNN, which can learn regional information and features. To learn various patterns of skin lesions, we applied Mixup, an image augmentation technique. When Mixup was applied, the model accuracy was 0.8679, showing a 3.35% improvement compared to the original dataset. Additionally, Lookahead Optimizer was applied to ensure stable learning of Mixup. When both Mixup and Lookahead Optimizer were applied, the model accuracy was 0.8741, showing a high improvement of 3.97% compared to the original dataset. Therefore, the Lookahead Optimizer helps with the stable training of mixup, which can improve the performance of the model. Copyright © The Korean Institute of Electrical Engineers.

关键词： Dermatology

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Development of a Deep Learning-Based System for Cat Skin Disease Classification and Grad-CAM Visualization

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Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 339-344页

作者： Won, Hyeong-Sik Cho, Hyun-Chong Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of

Skin diseases in companion cats can worsen if not treated promptly, and this can increase the financial burden on pet owners. To prevent this, early and accurate diagnosis is essential. This study introduces a deep learning-based Computer-Aided Diagnosis (CADx) system designed to classify cat skin diseases into non-inflammatory and inflammatory lesions by comparing them with normal images. The system employs the EfficientNetV2 model and incorporates image augmentation techniques like AutoAugment and AugMix to enhance classification performance. The study results indicate that the developed model achieved an 84.68% accuracy for non-inflammatory lesions, reflecting a 10.06% improvement, and a 97.19% accuracy for inflammatory lesions, reflecting a 2.45% improvement. Furthermore, we applied Grad-CAM to visualize the regions of interest in the images, offering veterinarians critical insights into the location and characteristics of the lesions. This system has the potential to significantly improve the precision of diagnosing skin diseases in companion cats, thereby supporting better veterinary care. Copyright © The Korean Institute of Electrical Engineers.

关键词： Veterinary medicine

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Development of a Deep Learning-Based Model for Canine Skin Disease Diagnosis Using RandAugment and Patterned-GridMask

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 142-148页

作者： Kim, Min-Jun Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

In contemporary society, pets are increasingly regarded as integral family members, contributing significantly to human quality of life. The growing prevalence of dog ownership has concurrently escalated the economic burden associated with veterinary care, particularly in managing common conditions like skin diseases. This study introduces an advanced deep learning-based diagnostic system for canine skin diseases, designed for practical application in home environments. We employed RandAugment to enhance data augmentation, thereby increasing the diversity of the training dataset. Furthermore, the implementation of Patterned-GridMask significantly improved the model's generalization capabilities. The use of the AdamW optimization algorithm was instrumental in mitigating overfitting, thus enhancing the model's overall learning efficiency. The proposed Transformer-based model, ViT/B-16, achieved an accuracy of 78.65% with the original dataset. With the integration of RandAugment and Patterned-GridMask techniques, the model's accuracy improved to 84.33%, underscoring its potential effectiveness for practical veterinary applications. Copyright © The Korean Institute of Electrical Engineers.

关键词： Medical imaging

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Comparison of Bioinspired MLP Controllers for Mobile Robots Obstacle Following/Avoidance

Comparison of Bioinspired MLP Controllers for Mobile Robots ...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Luiz, Matheus De S. Pastrana, M.A. Da Silva, Lukas. A. Campagnucci, Pamela. R. A. N. Mendoza-Penaloza, Jose Benevides, Rafael R.L. Munoz, Daniel M. University of Brasilia Mechatronics Graduate Program Department of Mechanical Engineering Brasilia Brazil University of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program Brasilia Brazil

ISBN: (纸本)9798350374575

Proportional, integral, and derivative (PID) controllers have been widely adopted for industrial applications. However, these controllers are not very efficient for non-linear systems. Artificial neural networks (ANN) based on the Multilayer Perceptron (MLP) have great potential to replace PID controllers due to their polynomial structure, allowing complex non-linear systems to be controlled. This article introduces the integration of four MLPs as alternatives to a traditional PID controller. These MLPs were trained through four bioinspired algorithms tailored for following tasks in mobile robots. The bioinspired algorithms employed for MLP network training include Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Month-Flame Optimization (MFO), and Artificial Hummingbird Algorithm (AHA). A comparative analysis was conducted between these MLPs and a classic PID controller, focusing on parameters such as overshoot (OS), settling time (ST), and steady-state error for different simulated scenarios. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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EfficientSpeech: An On-Device Text to Speech Model 48

EfficientSpeech: An On-Device Text to Speech Model

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Atienza, Rowel University of the Philippines Electrical and Electronics Engineering Institute Ai Graduate Program Philippines

ISBN: (纸本)9781728163277

State of the art (SOTA) neural text to speech (TTS) models can generate natural-sounding synthetic voices. These models are characterized by large memory footprints and substantial number of operations due to the long-standing focus on speech quality with cloud inference in mind. Neural TTS models are generally not designed to perform standalone speech syntheses on resource-constrained and no Internet access edge devices. In this work, an efficient neural TTS called EfficientSpeech that synthesizes speech on an ARM CPU in real-time is proposed. EfficientSpeech uses a shallow non-autoregressive pyramid-structure transformer forming a U-Network. EfficientSpeech has 266k parameters and consumes 90 MFLOPS only or about 1% of the size and amount of computation in modern compact models such as Mixer-TTS. EfficientSpeech achieves an average mel generation real-time factor of 104.3 on an RPi4. Human evaluation shows only a slight degradation in audio quality as compared to FastSpeech2. © 2023 IEEE.

关键词： CMOS FLOPS parameters RTF TTS

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Design and Performance Comparison of Current References for a 32-bit Microcontroller Using 22nm Technology 16

Design and Performance Comparison of Current References for ...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Barcelos, Eduardo A. Campagnucci, Pamela R. A. N. Munoz, Daniel M. Amaral, Wellington A. Haddad, Sandro A. P. Mechatronics Graduate Program Faculty of Technology Department of Mechanical Engineering Electronics Engineering Undergraduate Program University of Brasilia Faculty of Gama DF Brasilia Brazil

ISBN: (纸本)9798331522124

With the advancement of semiconductor technologies, the need for efficient power management solutions in integrated systems, such as RISC-V architecture-based microcontrollers, becomes increasingly critical. However, there is a lack of studies comparing current reference architectures in terms of performance and robustness, particularly in 22nm technologies. In this paper, we present a comparative analysis of two current reference designs implemented in the same technology-referred to as Reference Design A and Reference Design B. We focus on their approaches to thermal compensation, supply stability, and layout optimizations. Through simulations and experimental tests, we evaluate the performance of each design within a power management system that includes DC-DC converters and LDO regulators. Our results indicate that Design A exhibits superior power supply rejection with a PSRR of -85 dB, while Design B demonstrates enhanced energy efficiency and thermal stability, achieving a nominal current of 5.14 μA and a temperature coefficient of 16.21 ppm. These findings underscore the importance of balancing energy efficiency, thermal stability, and power supply variation rejection when selecting current references for low-power, high-reliability applications, such as those in the Internet of Things (IoT). © 2025 IEEE.

关键词： Integrated circuit layout

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A 22nm CMOS PFM Buck Converter Used in a 32-Bit RISC-V Microcontroller for IoT Applications 16

A 22nm CMOS PFM Buck Converter Used in a 32-Bit RISC-V Micro...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Oliveira, Luiz H. N. Amaral, Wellington A. Haddad, Sandro A. P. Munoz, Daniel M. Barcelos, Eduardo A. Sotero, Vinicius V. M. Jonhson, Emmanuel K. M. Campagnucci, Pamela R. A. N. Lima, Luis V. N. Electronics Engineering Undergraduate Program Faculty of Gama Brazil Mechatronics Graduate Program University of Brasilia Faculty of Technology Department of Mechanical Engineering DF Brasilia Brazil

ISBN: (纸本)9798331522124

The demand for efficient power management solutions in electronic devices has increased significantly, especially for microcontrollers operating in low-power modes. This article presents the design and implementation of a Pulse Frequency Modulation (PFM)-based buck converter for applications in 32-bit microcontrollers using 22nm CMOS technology. The methodology involves the development of a Constant On Time (COT) architecture that maintains a fixed duration for the activation of the high-side MOSFET, using a monostable multivibrator to generate the necessary timing signals. The converter's performance was simulated under various load conditions and process corners to evaluate its efficiency and output characteristics. The results show that the proposed buck converter achieves high efficiency, with peak values above 95%, while maintaining a low output ripple. Specifically, the converter exhibits an efficiency of 93.4% at a load of 1mA, with output ripple voltages as low as 2.20 mV under ideal conditions. The contributions of this work lie in the advancement of PFM converter technology for modern microcontroller applications, demonstrating the potential of 22nm CMOS processes to improve power efficiency and performance. © 2025 IEEE.

关键词： CMOS integrated circuits

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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