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Development of LiFx/Al2O3/ITO Multilayer Antireflection Coatings for Reflectance Reduction in Silicon Heterojunction Solar Cells

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Physica Status Solidi (A) Applications and Materials Science 2025年第0期

作者： Aida, Maha Nur Khokhar, Muhammad Quddamah Yousuf, Hasnain Chu, Mengmeng Rahman, Rafi Ur Alamgeer, Khan Bae, Junhan Jang, Seokjin Yi, Junsin Department of Future Energy Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of

This study focuses on the optimization of antireflection coatings (ARCs) to enhance the performance of silicon heterojunction (SHJ) solar cells. SHJ solar cells face a significant challenge in achieving their theoretical efficiency limits due to light reflection at the air–cell interface. This reflection reduces photon absorption, which limits photocurrent generation and hinders the widespread adoption of SHJ technology. To address this, the optimization of ARCs using single, double, and triple-layer designs with a graded refractive index material as the active layer is investigated. The optimization process is simulated by calculating the layer thicknesses using OPAL2 software and validated through experimental deposition. The results demonstrate that a triple-layer ARC structure consisting of 80/50/95 nm of indium tin oxide (ITO), aluminum oxide (Al2O3), and lithium fluoride (LiFx) significantly improves SHJ performance. This configuration leads to a 1.04 mA cm−2 increase in short-circuit current density (Jsc), reaching 39.85 mA cm−2, and an efficiency of 21.62%, compared to a single-layer ARC. This study presents a promising solution to improve SHJ solar cell efficiency by reducing reflection losses and optimizing optical properties, contributing to the advancement of SHJ technology and facilitating the development of more efficient and cost-effective photovoltaic systems for sustainable energy transition. © 2025 Wiley-VCH GmbH.

关键词： Antireflection coatings

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Balanced Spherical Grid for Egocentric View Synthesis

Balanced Spherical Grid for Egocentric View Synthesis

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Changwoon Choi Sang Min Kim Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Korea Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficiently build neural radiance fields. Motivated by the recent acceleration of NeRF using feature grids, we adopt spherical coordinate instead of conventional Cartesian coordinate. Cartesian feature grid is inefficient to represent large-scale unbounded scenes because it has a spatially uniform resolution, regardless of distance from viewers. The spherical parameterization better aligns with the rays of egocentric images, and yet enables factorization for performance enhancement. However, the naive spherical grid suffers from singularities at two poles, and also cannot represent unbounded scenes. To avoid singularities near poles, we combine two balanced grids, which results in a quasi-uniform angular grid. We also partition the radial grid exponentially and place an environment map at infinity to represent unbounded scenes. Furthermore, with our resampling technique for grid-based methods, we can increase the number of valid samples to train NeRF volume. We extensively evaluate our method in our newly introduced synthetic and real-world egocentric 360 video datasets, and it consistently achieves state-of-the-art performance.

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Visual Control and Mapping for UAV-Based Platform Inspection

Visual Control and Mapping for UAV-Based Platform Inspection

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2025 International Conference on Unmanned Aircraft Systems, ICUAS 2025

作者： Fagundes, Leonardo A. Soria, Carlos M. Vassallo, Raquel F. Brandão, Alexandre S. Federal University of Viçosa Graduate Program on Computer Science Department of Electrical Engineering MG Viçosa 36570-009 Brazil Instituto de Automática Universidad Nacional de San Juan Facultad de Ingeniería Argentina Federal University of Espirito Santo Department of Electric Engineering ES Vitória Brazil

ISBN: (纸本)9798331513283

Autonomous take-off and landing capabilities are crucial in UAV vision-based missions, ensuring adaptive navigation, especially in challenging environments where realtime identification and interaction with a variety of landing platforms are required. In this context, this paper presents a servo-visual controller that uses pattern detection and color segmentation techniques to identify take-off/landing platforms and estimate their current orientation. The proposed system was subjected to experimental validation with four platforms positioned in different orientations, heights, and positions, demonstrating its versatility in various conditions. Our study addresses the Flying Robots Trial League challenge, which emulates mapping and inspection tasks in offshore platforms. © 2025 IEEE.

关键词： Target drones

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Learning-Based Tone Mapping to Improve 3D SAS ATR

Learning-Based Tone Mapping to Improve 3D SAS ATR

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gregory D. Vetaw Benjamin Cowen Daniel C. Brown David P. Williams Suren Jayasuriya School of Electrical Computer and Energy Engineering Arizona State University The Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University

Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment. Conventional dynamic range compression (DRC) techniques such as log-compression, which is a type of tone mapping intended to appeal to the human visual system, can further obscure the sonar signatures of these already physically occluded objects and lead to suboptimal downstream ATR performance, particularly for convolutional neural networks (CNNs). In this paper, we present a novel machine learning-based approach for tone mapping sub-bottom SAS imagery as a pre-processing stage in the 3D SAS ATR pipeline. This learned tone mapping function can be jointly optimized with a CNN-based ATR algorithm. We train and validate our method on measured volumetric SAS data captured by the Sediment Volume Search Sonar (SVSS) system.

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Disentangled Motion Modeling for Video Frame Interpolation

arXiv

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arXiv 2024年

作者： Lew, Jaihyun Choi, Jooyoung Shin, Chaehun Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of AIIS ASRI and INMC Seoul National University Korea Republic of

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI. © 2024, CC BY-NC-SA.

关键词： Pixels

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes 7

Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using ...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Pohan, Husni Iskandar Suparta, Wayan Heryadi, Yaya Wibowo, Antoni Lukas Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). The purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor's recommendation. The advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. The method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. The dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency. © 2022 IEEE.

关键词： Decision trees

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Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection

arXiv

引用

arXiv 2024年

作者： Lee, Saehyung Mok, Jisoo Park, Sangha Shin, Yongho Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm Korea YH Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT © 2024, CC BY.

关键词： Data assimilation

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A 4-Channel 0.23$mm^{2}$ Voltage-to-Time Converter AFE with $3.7\mu V_{rms}$ Noise and $480nW$ Galvanic Impulse Uplink

A 4-Channel 0.23$mm^{2}$ Voltage-to-Time Converter AFE with ...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Mehdi Bandali Morgan Riley Benjamin C. Johnson Dept. of Electrical and Computer Engineering Boise State University Boise ID USA Morgan Riley is with Biomedical Engineering Doctoral Program Boise State Universitvy Boise ID USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

We present a 4-channel 15 kS/s Voltage-to-Time Converter (VTC) analog front-end (AFE) with a $0.49\mu W$ impulse-based galvanic uplink for a peripheral nerve interface. Multiple, low-noise, high-data-rate channels are needed to sense compound action potentials and measure their conduction velocity as they propagate down a peripheral nerve. To achieve high energy efficiency for these constraints, the AFE encodes and transmits data with time-domain charge-balanced impulses through an implantable galvanic link. Each channel consists of an integrator with charge-based sampling and amplification for rapid multiplexing. A shared VTC encodes the amplitude-domain outputs of each integrator into differential time-domain impulses. Since the timing can be synchronized with stimulation, this AFE achieves instant artifact recovery after rail-to-rail stimulation events. We designed this AFE in a 180nm CMOS process, and the simulation results show an SNDR of 60dB and noise of $3.7\mu Vrms$ . Thanks to the new galvanic uplink protocol, this front-end only consumes $11.28\mu W$ including wireless data transmission for four channels.

关键词： Wireless communication Wireless sensor networks Protocols Simulation Noise Data communication Velocity measurement

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Comparisons of Two-stage Models for Flood Mitigation of electrical Substations

arXiv

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arXiv 2023年

作者： Austgen, Brent Kutanoglu, Erhan Hasenbein, John J. Santoso, Surya The University of Texas at Austin Operations Research and Industrial Engineering Program United States The University of Texas at Austin Chandra Department of Electrical and Computer Engineering United States

We compare stochastic programming and robust optimization decision models for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. In our models, the first stage captures the deployment of a fixed quantity of flood mitigation resources, and the second stage captures the operation of a potentially degraded power grid with the primary goal of minimizing load shed. To model grid operation, we introduce adaptations of the DC and LPAC power flow approximation models that feature relatively complete recourse by way of an indicator variable. We apply our models to a pair of geographically realistic flooding case studies, one based on Hurricane Harvey and the other on Tropical Storm Imelda. We investigate the effect of the mitigation budget, the choice of power flow model, and the uncertainty perspective on the optimal mitigation strategy. Our results indicate the mitigation budget and uncertainty perspective are impactful whereas choosing between the DC and LPAC power flow models is of little to no consequence. To validate our models, we assess the performance of the mitigation solutions they prescribe in an AC power flow model. © 2023, CC BY.

关键词： Stochastic programming

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