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IEEE Electron Device Letters 2025年第5期46卷 705-708页

作者： Chen, Yi-Ho Chu, Fu-Chuan Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung-Shan Institute of Science and Technology Solid State Devices Section Electronic System Research Division Taoyuan325 Taiwan 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 National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Recess gate etching is a critical technique for achieving enhancement-mode (E-mode) AlGaN/GaN high-electron mobility transistors (HEMTs) because the interface is susceptible to the etching damage. This study fabricates recess gates using the neutral beam etching (NBE) technique. By adjusting the aperture thickness in the NBE apparatus, we simulate both NB-mode and plasma-mode etching. The electrical characteristics of E-mode HEMTs fabricated using these two modes are analyzed and compared through DC, noise, and pulsed IV measurements. The results demonstrate that NB-recessed HEMTs exhibit superior performance. © 1980-2012 IEEE.

关键词： Aluminum gallium nitride

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Design and Performance Comparison of Low Dropout Regulators for a 32-bit Microcontroller Using 22nm Technology

Design and Performance Comparison of Low Dropout Regulators ...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Emmanuel K. M. Johnson Luis Victor N. Lima Pamela. R. A. N. Campagnucci Sandro A. P. Haddad Wellington A. Amaral Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper presents a comparison between two LDOs (Low Dropout Regulators) developed for a PMIC (Power Management Integrated Circuit) responsible for powering a 32-bit microcontroller based on the RISC-V microarchitecture, designed in 22 nm technology. The microcontroller will be integrated with a radio frequency interface for IoT (Internet of Things) applications. Two LDOs were designed: one with external compensation and the other a capacitor-less LDO. Both operate with an input voltage of 2.5V, an output voltage of 0.8V, and a maximum current of 150mA. The externally compensated LDO exhibited greater stability in response to load variations and a higher PSRR (Power Supply Rejection Ratio), while the capacitor-less LDO demonstrated faster regulation speed and occupied a chip area similar to the externally compensated LDO.

关键词： Regulators Microcontrollers Power supplies Power system management Simulation Stability analysis Regulation Circuit stability Internet of Things Voltage control

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A review of hybrid EEG-based multimodal human–computer interfaces using deep learning: applications, advances, and challenges

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Biomedical engineering Letters 2025年 1-32页

作者： Lee, Hyung-Tak Shim, Miseon Liu, Xianghong Cheon, Hye-Ran Kim, Sang-Gyu Han, Chang-Hee Hwang, Han-Jeong Department of Electronics and Information Engineering Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Department of Artificial Intelligence Tech University of Korea Siheung Korea Republic of Department of Computer Information Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea Republic of

Human–computer interaction (HCI) focuses on designing efficient and intuitive interactions between humans and computer systems. Recent advancements have utilized multimodal approaches, such as electroencephalography (EEG)-based systems combined with other biosignals, along with deep learning to enhance performance and reliability. However, no systematic review has consolidated findings on EEG-based multimodal HCI systems. This review examined 124 studies published from 2016 to 2024, retrieved from the Web of Science database, focusing on hybrid EEG-based multimodal HCI systems employing deep learning. The keywords used for evaluation were as follows: ‘Deep Learning’ AND ‘EEG’ AND (‘fNIRS’ OR ‘NIRS’ OR ‘MEG’ OR ‘fMRI’ OR ‘EOG’ OR ‘EMG’ OR ‘ECG’ OR ‘PPG’ OR ‘GSR’). Main topics explored are: (1) types of biosignals used with EEG, (2) neural network architectures, (3) fusion strategies, (4) system performance, and (5) target applications. Frequently paired signals, such as EOG, EMG, and fNIRS, effectively complement EEG by addressing its limitations. Convolutional neural networks are extensively employed for spatio-temporal-spectral feature extraction, with early and intermediate fusion strategies being the most commonly used. Applications, such as sleep stage classification, emotion recognition, and mental state decoding, have shown notable performance improvements. Despite these advancements, challenges remain, including the lack of real-time online systems, difficulties in signal synchronization, limited data availability, and insufficient explainable AI (XAI) methods to interpret signal interactions. Emerging solutions, such as portable systems, lightweight deep learning models, and data augmentation techniques, offer promising pathways to address these issues. This review highlights the potential of EEG-based multimodal HCI systems and emphasizes the need for advancements in real-time interaction, fusion algorithms, and XAI to enhance their adaptability, interpreta

关键词： Convolutional neural networks

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Characterization of a new triaxial nanofiber membrane and osteogenic differentiation capacity of human bone marrow mesenchymal stem cells for bone repair

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Journal of Materials Research 2025年 1-18页

作者： Altundag-Erdogan, Özlem Çetinkaya, Hayrullah Çelebi-Saltik, Betül Altun, Belgin Şahin, Mehtap Mağden, Gamze Kara Öteyaka, Mustafa Özgür Department of Stem Cell Sciences Graduate School of Health Sciences and Center for Stem Cell Research and Development Hacettepe University Sihhiye Ankara06100 Turkey Department of Chemical Engineering Eskisehir Osmangazi University Eskişehir26040 Turkey Vocational School Health Services Hacettepe University Sihhiye Ankara06100 Turkey BUGAMED Biotechnology Industry and Trade Inc Eskişehir26380 Turkey Eskişehir Vocational School Electronics and Automation Eskisehir Osmangazi University Mechatronic Program Eskişehir26250 Turkey

A triaxial nanofiber membrane was developed using a custom triaxial nozzle during electrospinning. The membrane consisted of three layers (outer middle (PVA-Collagen Type I-BMP-2), and inner (PVA-nHAP)). TEM revealed the diameters of the outer, middle, and inner layers as 167.5, 142.6, and 119.3 nm, respectively. The triaxial nanofiber membrane exhibited superior tensile strength (36 MPa) and elastic modulus (2.5 GPa), along with a roughness of 359 nm and hydrophilic properties. In addition, it demonstrated a 200% swelling ratio and reduced bacterial colonization by 10.8-fold. Both Calcium deposition and ALP activity were significantly higher and increased expression of BSP (eightfold), OCN (13-fold), and OPN (488-fold) genes were observed in membrane + OST groups (p © The Author(s), under exclusive licence to The Materials Research Society 2025.

关键词： Bone

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Development of EEG-Based Optimal Biomarkers for Decoding Fatigue during Motor Imagery Tasks

Development of EEG-Based Optimal Biomarkers for Decoding Fat...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Jun-Seok Lee Seung-Won Lee Sang-Rok Kim Han-Jeong Hwang Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

This study aimed to identify electroencephalography (EEG)-based neurophysiological biomarkers for monitoring mental fatigue during motor imagery (MI) tasks. We used EEG data recorded from 29 healthy adults during MI tasks. For the preprocessing of the EEG data, we sequentially performed down-sampling into 200 Hz, independent component analysis (ICA), band-pass filtering between 0.5 and 50 Hz, and common average reference (CAR). The preprocessed EEG data were analyzed using a fast Fourier transform (FFT) to calculate spectral power in the δ (0.5-3 Hz), θ (4–7 Hz), $a$ (8–13 Hz), and β (13–30 Hz) frequency bands. Subsequently, seventeen fatigue-related metrics were calculated using individual spectral powers and their combinations. These fatigue-related metrics were averaged for the frontal lobe (F3 and F4), central lobe (FCC3h and FCC4h), parietal lobe (P3 and P4), and occipital lobe (POO1 and POO2). The statistical significance of the average fatigue metrics was evaluated through Pearson correlation analysis with the sequence of trials. As a result, we observed strong positive correlations (r < 0.9, p < 0.001) between beta power in the frontal and central regions and the sequence of trials. These findings suggest that central and frontal beta activity can serve as reliable biomarkers for estimating fatigue during MI tasks, supporting their potential for real-time fatigue monitoring.

关键词： Measurement Correlation Fast Fourier transforms Biomarkers Fatigue Motors Electroencephalography Decoding Reliability Monitoring

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. This research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. The usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Development of a Smartphone-Linked Immunosensing System for Oxytocin Determination

Biosensors

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Biosensors 2025年第4期15卷 261-261页

作者： Sarubo, Miku Suzuki, Yoka Numazaki, Yuka Kudo, Hiroyuki Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki City214-8571 Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki City214-8571 Japan

We report an optical immunosensing system for oxytocin (OXT) based on image analysis of color reactions in an enzyme-linked immunosorbent assay (ELISA). We employed a miniaturized optical immunosensing unit that was functionally connected to an LED and a smartphone camera. Our system measures OXT levels using a metric called the RGBscore, which is derived from the red, green, and blue (RGB) information in the captured images. By calculating the RGBscore regressively using the brute-force method, this approach can be applied to smartphones with various CMOS image sensors and firmware. The lower detection limit was determined to be 5.26 pg/mL, and the measurement results showed a higher correlation (r = 0.972) with those obtained from conventional ELISA. These results suggest the potential for its application in a simplified health management system for individuals. © 2025 by the authors.

关键词： Firmware

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CyclicAugment: Optimized Medical Image Analysis via Adaptive Augmentation Intensity

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IEEE Access 2025年 13卷 86562-86575页

作者： Min-Jun Kim Jung-Woo Chae Hyun-Chong Cho Department of Data Science Kangwon National University Chuncheon-si Republic of Korea Department of Electronics Engineering Kangwon National University Chuncheon-si Republic of Korea Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Chuncheon-si Republic of Korea

Computer-aided diagnosis (CADx) systems play a crucial role in accurately diagnosing and monitoring diseases through medical imaging. However, there are many challenges, such as data scarcity and complex structural patterns, limiting the performance of deep-learning models. Although conventional data-augmentation techniques can help mitigate these limitations, excessive augmentation may distort critical diagnostic information, leading to suboptimal performance. To address this issue, this study proposes CyclicAugment, a novel data-augmentation strategy that dynamically adjusts augmentation intensity in a cyclic manner throughout training. The method gradually transitions from using original data as weak augmentation, subsequently applying strong augmentation before reverting to weak augmentation again, reintroducing the original data. This process ensures stable training through an adaptive learning rate. By cyclically adjusting augmentation intensity, our approach enhances data diversity while preserving essential diagnostic information, thereby improving the generalizability of deep learning models. We validated CyclicAugment across six different medical imaging datasets and multiple deep learning architectures, including convolutional neural networks, vision transformers, and multilayer perceptrons. Our framework achieved a maximum accuracy improvement of 8.8%, reaching a peak performance of 0.927 and thereby demonstrating its effectiveness in enhancing medical image analysis.

关键词： Biomedical imaging Medical diagnostic imaging Accuracy Data augmentation Training Computed tomography Lesions Deep learning Imaging Data models

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Reducing Errors and Powers in LPDDR for DNN Inference: A Compression and IECC-Based Approach

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Journal of Systems Architecture 2025年 163卷

作者： Hong, Jae-Youn Jang, Je-Woo Cho, Sung-Hyuk Kong, Youngbae Kim, Sungkyu Kang, Youngjung Ko, Jaehyung Chung, Jaeyong Yang, Joon-Sung Department of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Memory Division Samsung Electronics Hwaseong Korea Republic of Department of System Semiconductor Engineering Yonsei University Seoul Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul Korea Republic of

In modern edge systems, the demand for data processing, especially for complex DNN tasks, is rapidly increasing. To address this, various compression schemes have been proposed to enable on-device AI while meeting the strict power and storage constraints of edge devices. However, despite these advancements, the compatibility of the compression methods with edge device memory, such as LPDDR, has not been thoroughly investigated. LPDDR operates at low voltage and faces reliability challenges like cell leakage, which is particularly concerning for applications where accuracy is critical, such as Advanced Driver Assistance Systems (ADAS) or medical devices. To address these reliability concerns, an ECC engine, known as IECC, is employed within each LPDDR bank. While IECC improves reliability, it also incurs performance penalties due to Read-Modify-Write (RMW) operations and parity storage overheads. This paper introduces RELIA, a DNN weight compression scheme with three-stage protection, aimed at enabling power-efficient and reliable DNN operations in mobile environments. RELIA reduces the operation granularity of the IECC engine to eliminate RMW overhead. Additionally, it proposes a SEC-FOEC(72,64) scheme (Single Error Correction-Frequently Occurring Error Correction) that can correct 99.97% of LPDDR errors. To mitigate the added storage overhead, a compression scheme based on DNN weight characteristics is introduced. Experimental results show RELIA outperforms traditional IECC schemes, reducing power by 16.12%, cycles by 12.6%, energy by 30.62%, and storage by 22.78%, while offering superior reliability in DNN inference. © 2025

关键词： Advanced driver assistance systems

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Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils

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Computers in Biology and Medicine 2025年 184卷 109410-109410页

作者： Park, Jaehee Cheong, Da Yeon Lee, Gyudo Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and various forms of amyloidosis. Atomic force microscopy (AFM) stands as the most representative method for studying amyloid fibril morphology. However, obstacles in AFM images, including noise, salt, and amorphous aggregates, often impede accurate sample quantification. In this study, we developed denoising software employing a U-Net deep learning architecture to address this issue. The software efficiently eliminated various impediments that interfere with fibril analysis in noisy AFM images, thereby facilitating precise quantification of amyloid fibrils. We also developed automated fibril analysis technologies using the denoised AFM images, leading to quicker, more precise, and more objective assessments of fibril morphology. Furthermore, we presented a method for fibril stiffness extraction from a modulus image through mask creation based on a denoised height image. Our approach secures time efficiency and precision in analyzing amyloid morphology, and we believe it will significantly advance the currently stagnant research on amyloid-related diseases. © 2024 The Authors

关键词： Neurodegenerative diseases

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