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CLEO: Science and Innovations, S and I 2022

作者： Arnold, Kellen P. Halimi, Sami I. Allen, Joshua A. Hu, Shuren Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States

We report a split ring photonic crystal that demonstrates an order of magnitude larger peak energy density compared to traditional photonic crystals. The split ring offers highly focused optical energy in an accessibl...

ISBN: (纸本)9781557528209

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Audio-aided Character Control for Inertial Measurement Tracking

Audio-aided Character Control for Inertial Measurement Track...

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46th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2025

作者： Jang, Hojun Bae, Jinseok Kim, Young Min Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University Korea Republic of

ISBN: (纸本)9783038682684

Physics-based character control generates realistic motion dynamics by leveraging kinematic priors from large-scale data within a simulation engine. The simulated motion respects physical plausibility, while dynamic cues like contacts and forces guide compelling human-scene interaction. However, leveraging audio cues, which can capture physical contacts in a costeffective way, has been less explored in animating human motions. In this work, we demonstrate that audio inputs can enhance accuracy in predicting footsteps and capturing human locomotion dynamics. Experiments validate that audio-aided control from sparse observations (e.g., an IMU sensor on a VR headset) enhances the prediction accuracy of contact dynamics and motion tracking, offering a practical auxiliary signal for robotics, gaming, and virtual environments. © 2025 The Authors.

关键词： Virtual environments

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PUCA: patch-unshuffle and channel attention for enhanced self-supervised image denoising 23

PUCA: patch-unshuffle and channel attention for enhanced sel...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Hyemi Jang Junsung Park Dahuin Jung Jaihyun Lew Ho Bae Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Cyber Security Ewha Womans University Department of Electrical and Computer Engineering Seoul National University and Interdisciplinary Program in Artificial Intelligence Seoul National University

Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image pairs from the real world are extremely costly to gather, self-supervised learning, which utilizes noisy input itself as a target, has been studied. To prevent a self-supervised denoising model from learning identical mapping, each output pixel should not be influenced by its corresponding input pixel; This requirement is known as J-invariance. Blind-spot networks (BSNs) have been a prevalent choice to ensure J-invariance in self-supervised image denoising. However, constructing variations of BSNs by injecting additional operations such as downsampling can expose blinded information, thereby violating J-invariance. Consequently, convolutions designed specifically for BSNs have been allowed only, limiting architectural flexibility. To overcome this limitation, we propose PUCA, a novel J-invariant U-Net architecture, for self-supervised denoising. PUCA leverages patch-unshuffle/shuffle to dramatically expand receptive fields while maintaining J-invariance and dilated attention blocks (DABs) for global context incorporation. Experimental results demonstrate that PUCA achieves state-of-the-art performance, outperforming existing methods in self-supervised image denoising.

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An Improved Vision Transformer Architecture-Based computer Recognition Platform for Lilium Classification

An Improved Vision Transformer Architecture-Based Computer R...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Huang, Xiang-Rui Ke, Huai-Jhan Kuo, Szu-Yu Chen, Liang-Bi Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan

ISBN: (纸本)9798331521165

Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer (ViT) architecture. The proposed platform uses an improved vision transformer (ViT) architecture to classify different types of lilies, allowing consumers to access information and names of various Lilium species. The experimental results show that the proposed lily classification model achieved a 96.4% accuracy rate in classifying six lily species. © 2025 IEEE.

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Learning 3D Scene Analogies with Neural Contextual Scene Maps

arXiv

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arXiv 2025年

作者： Kim, Junho Bae, Gwangtak Lee, Eun Sun Kim, Young Min Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University Korea Republic of

Understanding scene contexts is crucial for machines to perform tasks and adapt prior knowledge in unseen or noisy 3D environments. As data-driven learning is intractable to comprehensively encapsulate diverse ranges of layouts and open spaces, we propose teaching machines to identify relational commonalities in 3D spaces. Instead of focusing on point-wise or object-wise representations, we introduce 3D scene analogies, which are smooth maps between 3D scene regions that align spatial relationships. Unlike well-studied single instance-level maps, these scene-level maps smoothly link large scene regions, potentially enabling unique applications in trajectory transfer in AR/VR, long demonstration transfer for imitation learning, and context-aware object rearrangement. To find 3D scene analogies, we propose neural contextual scene maps, which extract descriptor fields summarizing semantic and geometric contexts, and holistically align them in a coarse-to-fine manner for map estimation. This approach reduces reliance on individual feature points, making it robust to input noise or shape variations. Experiments demonstrate the effectiveness of our approach in identifying scene analogies and transferring trajectories or object placements in diverse indoor scenes, indicating its potential for robotics and AR/VR applications. © 2025, CC BY.

关键词： Adversarial machine learning

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Guest Editorial: Special Issue on Technology Trends in Control, Robotics and Systems - For the 30th Anniversary of the Institute of Control, Robotics and Systems -

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Journal of Institute of Control, Robotics and Systems 2024年第4期30卷 295-295页

作者： Ko, Nak Yong Kang, Taesam Song, Jin Woo Lim, Myo Taeg Jo, Kang-Hyun Department of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Korea Republic of Department of Mechanical and Aerospace Engineering Konkuk University Korea Republic of Department of Intelligent Mechatronics Engineering Sejong University Korea Republic of School of Electrical Engineering Korea University Korea Republic of Department of Electrical Electronic and Computer Engineering University of Ulsan Korea Republic of

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An Improved Vision Transformer Architecture-Based computer Recognition Platform for Lilium Classification

An Improved Vision Transformer Architecture-Based Computer R...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiang-Rui Huang Huai-Jhan Ke Szu-Yu Kuo Liang-Bi Chen Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

关键词： computer vision Electric potential Accuracy Computational modeling computer architecture computer applications Flowering plants Network architecture Transformers Consumer electronics

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Expanding the Surgical Robotics Community: An Intuitive Sim-to-Real Control Framework for Raven-II with a Budget-Friendly Gamepad Controller

Expanding the Surgical Robotics Community: An Intuitive Sim-...

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International Symposium on Medical Robotics (ISMR)

作者： Mai Bui Natalie Chalfant Cuiling Sun Sean Fabrega Haonan Peng Kevin Huang Yun-Hsuan Su Department of Computer Science Mount Holyoke College South Hadley MA Department of Electrical and Computer Engineering University of Washington Seattle WA Picker Engineering Program Smith College Northampton MA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

This paper proposes a low-cost interface and refined digital twin for the Raven-II surgical robot. Previous simulations of the Raven-II, e.g. via the Asynchronous Multibody Framework (AMBF), presented salient drawbacks, including control inputs inconsistent with Raven-II software, and lack of stable, high-fidelity physical contact simulations. This work bridges both of these gaps, both (1) enabling robust, simulated contact mechanics for dynamic physical interactions with the Raven-II, and (2) developing a universal input format for both simulated and physical platforms. The method furthermore proposes a low cost, commodity game-controller interface for controlling both virtual and real realizations of Raven-II, thus greatly reducing the barrier to access for Raven-II research and collaboration. Overall, this work aims to eliminate the inconsistencies between simulated and real representations of the Raven-II. Such a development can expand the reach of surgical robotics research. Namely, providing end-to-end transparency between the simulated AMBF and physical Raven-II platforms enables a software testbed previously unavailable, e.g. for training real surgeons, for creating digital synthetic datasets, or for prototyping novel architectures like shared control strategies. Experiments validate this transparency by comparing joint trajectories between digital twin and physical testbed given identical inputs. This work may be extended and incorporated into recent efforts in developing modular or common software infrastructures for both simulation and control of real robotic devices, such as the Collaborative Robotics Toolkit (CRTK).

关键词： Training Medical robotics Costs Collaboration Surgery computer architecture Software

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Distributed temperature sensing for special induction motor applied in harsh environment 11

Distributed temperature sensing for special induction motor ...

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Photonic Instrumentation engineering XI 2024

作者： Mazzo, Paulo H.R. Torelli, Gabriel A. Dreyer, Uilian J. Pelegrin, Jessé Stel, Henrique Da Silva, Luiz C.T. Romero, Gabriel A. Morales, Rigoberto E.M. da Silva, Jean C.C. Graduate Program in Electrical and Computer Engineering Federal University of Technology UTFPR Paraná PR Curitiba Brazil Graduate Program in Energy Systems Federal University of Technology UTFPR Paraná PR Curitiba Brazil Multiphase Flow Research Center - NUEM Federal University of Technology UTFPR Paraná PR Curitiba Brazil CENPES/PDIEP EE Petrobras Brazil Federal Institute Catarinense IFC Santa Catarina Brazil

ISBN: (纸本)9781510670464

This paper presents a study and development on the thermal mapping of a Three-phase Induction Motor for harsh environments application. These machines’ exceptional performance and precise speed control make them essential equipment in multiple industrial processes, such as Artificial Lift, which integrates with a multistage centrifugal pump to elevate fluids from the reservoirs to the surface. Increased motor speeds for higher production requirements can result in temperature rise, damaging the stator winding. Employing the Raman Distributed Temperature Sensing System (RDTS), we generate a comprehensive heat distribution map of the motor. ANSYS Motor-CAD was applied for computational simulations to perform steady-state thermal analysis, giving an overview of heat transfer among internal components and thermal limits for the instrumented motor. This preliminary study presents a series of experimental tests to evaluate the advantages of fiber optic temperature sensing in harsh environments, emphasizing its significance in improving electrical machines’ performance and reliability. © 2024 SPIE.

关键词： Centrifugal pumps

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Fully Geometric Panoramic Localization

arXiv

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arXiv 2024年

作者： Kim, Junho Jeong, Jiwon Kim, Young Min Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University Korea Republic of Dept. of Electrical Engineering Stanford University United States

We introduce a lightweight and accurate localization method that only utilizes the geometry of 2D-3D lines. Given a pre-captured 3D map, our approach localizes a panorama image, taking advantage of the holistic 360◦ view. The system mitigates potential privacy breaches or domain discrepancies by avoiding trained or hand-crafted visual descriptors. However, as lines alone can be ambiguous, we express distinctive yet compact spatial contexts from relationships between lines, namely the dominant directions of parallel lines and the intersection between nonparallel lines. The resulting representations are efficient in processing time and memory compared to conventional visual descriptor-based methods. Given the groups of dominant line directions and their intersections, we accelerate the search process to test thousands of pose candidates in less than a millisecond without sacrificing accuracy. We empirically show that the proposed 2D-3D matching can localize panoramas for challenging scenes with similar structures, dramatic domain shifts or illumination changes. Our fully geometric approach does not involve extensive parameter tuning or neural network training, making it a practical algorithm that can be readily deployed in the real world. Project page including the code is available through this link: https://***/fgpl/. © 2024, CC BY.

关键词： Neural networks

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