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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Scattering Guided Image Despeckling : A Score-Based Diffusion Approach in Frequency Domain

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Dong, Sihao Xiao, Xiayang Pan, Mingjun Inner Mongolia University of Science and Technology School of Automation and Electrical Engineering Inner Mongolia Baotou014010 China School of Information Science and Technology Shanghai200433 China China Mobile Internet Company Limited Guangzhou510030 China Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen China

When it comes to deep learning-based methods for despeckling Synthetic Aperture Radar (SAR) images, maintaining spatial structure, ensuring process stability as well as achieving fast inference are still major challenges. To address these issues, a novel method named SGID (Scattering Guided Image Despeckling), which is based on the wavelet - transform - conditioned diffusion model for SAR despeckling, has been proposed. First, the core component of this approach is the Adaptive Scattering Extraction Module (ASEM), which is designed to preserve the spatial structure of images effectively. It achieves the preservation of spatial structure by leveraging SAR imaging principles to adaptively extract scattering points, thereby retaining critical spatial information. Second, considering that speckle noise in SAR images is mainly concentrated in the high-frequency region, this study has designed a High-Frequency Despeckling Module (HFDM). This module adopts a direction-aware adaptive weight allocation strategy, aiming to precisely regulate the weight allocation of high-frequency components in all directions during the processing, so as to maximize the coherence and consistency of the high-frequency detail structures in the images. At last, the entire processing workflow is conducted in the frequency domain, which contributes significantly to the improvement of inference speed. Experimental results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance in both spatial structure preservation and despeckling quality. Besides, its inference speed is 0.19 seconds, which is 67 times faster than that of comparable diffusion model-based methods. The code for this study will be publicly available at https://***/SihaoDong/SGID. © 2004-2012 IEEE.

关键词： Speckle

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Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading 30th

Efficient Compute Resource Sharing of RISC-V Packed-SIMD Us...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Takata, Shogo Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan Division of Advanced Information Technology Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan

ISBN: (纸本)9783031856372

AI tasks are gaining popularity in the area of IoT and edge devices. To run such tasks on devices, QNNs are used because of their reduced size and ability to be computed with simple integer arithmetic. There have been many implementations to support such a network format. However, when considering thread-level parallelism to speedup the program, many often implement a multi-core architecture or clusters which needs to copy all resources for each core. In this paper, we introduce a new RISC-V Out-of-Order Simultaneous Multi-Threading core "B4SMT" with RISC-V Packed-SIMD extension for evaluation. We also show that even a single executor could increase the performance of a 1D median filter by over 100×, and a matrix multiplication by over 30 on more than 16 threads efficiently. Furthermore, we suggest that other infrequently used executors may be placed as a shared resource efficiently in an SMT core. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Median filters

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An Implementation of Naming Rule Checking Function and its Applications to Java programming Codes

An Implementation of Naming Rule Checking Function and its A...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Khaing Hsu Wai Nobuo Funabiki Mustika Mentari Soe Thandar Aung Htoo Htoo Sandi Kyaw Shingo Yamaguchi Wen-Chung Kao Graduate School of Science and Engineering Akita University Akita Japan Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Graduate School of Sciences and Technology for Innovation Yamaguchi University Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Currently, a lot of universities are offering Java programming courses for teaching the first-step object-oriented programming language. For novice students, to master writing readable codes using proper names for variables, classes, and methods is crucial in Java programming, to improve under-standability and maintainability. In this paper, we consider four naming rules to write a readable code and implement the naming rule checking function to check whether the given code follows them. For evaluations, we applied the proposal to 2, 908 source codes submitted in Java programming courses from students in Okayama University, Japan, Malang State Polytechnic, Indonesia, and Yamaguchi University, Japan. The results confirm the effectiveness of the proposal.

关键词： Java Codes Source coding Education programming Writing Proposals Object oriented programming Consumer electronics

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Effects of Incident Angle on Single-Event Leakage Current Degradation in Heavy-Ion Irradiated 1800 v and 4500 v SiC Power MOSFETs

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IEEE Transactions on Nuclear science 2025年

作者： Sengupta, A. Martinella, C. Ball, D.R. Niskanen, K. Senarath, A.S. Islam, S. Harris, P.M. Hutson, J.M. Osheroff, J.M. Sternberg, A.L. Kettunen, H. Kosier, S.L. Grossner, U. Javanainen, A. Galloway, K.F. Schrimpf, R.D. Witulski, A.F. Zürich8092 Switzerland Vanderbilt University Interdisciplinary Materials Science Program NashvilleTN37235 United States Lipscomb University Electrical and Computer Engineering Department NashvilleTN37204 United States NASA/GSFC Code 561.4 GreenbeltMD20771 United States Vanderbilt University Electrical Engineering and Computer Science Department B 351825 NashvilleTN37235 United States University of Jyväskylä Department of Physics Jyväskylä40014 Finland

Experimental heavy-ion responses of two variants of SiC power MOSFETs are evaluated. The devices have similar epitaxial thickness but different doping. The higher doping in the epitaxial region results in lower breakdown voltage, and typically leads to a lower threshold drain voltage at which single-event leakage current (SELC) occurs. Furthermore, the heavy-ion-induced SELC degradation in the drain and gate currents of the SiC MOSFETs exhibit a strong dependence on the heavy-ion's angle of incidence. The devices' behavior significantly alters when irradiated with heavy-ions at different incident angles under the same fixed bias and total fluence, as also confirmed by 3-D Technology computer Aided Design (TCAD) simulations. © 1963-2012 IEEE.

关键词： Power MOSFET

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Periodic phase diagrams in micromagnetics with an eigenvalue solver

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IEEE Transactions on Magnetics 2025年

作者： Ai, Fangzhou Lin, Zhuonan Duan, Jiawei Lomakin, Vitaliy University of California Department of Electrical and Computer Engineering San DiegoCA92093 United States University of California Center for Memory and Recording Research San DiegoCA92093 United States University of California Program in Materials Science and Engineering San Diego92093 United States

This work introduces an approach to compute periodic phase diagram of micromagnetic systems by solving a periodic linearized Landau-Lifshitz-Gilbert (LLG) equation using an eigenvalue solver with the Finite Element Method formalism. The linear operator in the eigenvalue problem is defined as a function of the periodic phase-shift wave vector. The dispersion diagrams are obtained by solving the eigenvalue problem for complex eigenfrequencies and corresponding eigenstates for a range of prescribed wave vectors. The presented approach incorporates a calculation of the periodic effective field, including the exchange and magnetostatic field components. The approach is general in that it allows 3D problems to be handled with any 1D, 2D, and 3D periodicities. The ability to calculate periodic diagrams provides insights into the spin wave propagation and localized resonances in complex micromagnetic structures. © 1965-2012 IEEE.

关键词： Spin waves

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Effects of Néel and Brownian relaxations on dynamic magnetization empirically characterized in single-core and multicore structures of magnetic nanoparticles

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Nanoscale 2025年 2025 May 2页

作者： Goto, Haruki Futagawa, Masato Takemura, Yasushi Ota, Satoshi Department of Optoelectronics and Nanostructure Science Graduate School of Science and Technology Shizuoka University Hamamatsu432-8561 Japan Department of Electrical and Electronic Engineering Shizuoka University Hamamatsu432-8561 Japan Department of Electrical and Computer Engineering Yokohama National University Yokohama240-8501 Japan

This study takes a novel approach toward understanding the diagnostic and therapeutic efficacy of magnetic nanoparticles for cancer theranostics. We focused on the parameters influencing the dynamic magnetization response, such as particle core size and magnetic anisotropy. Our experimental investigation on the relationship between magnetic relaxation and these particle parameters provides fresh insights for developing biomedical applications. The magnetic relaxation time was estimated from the magnetic relaxation process and measured by applying a pulsed magnetic field over a wide time range of 20 ns to 200 ms. Magnetic nanoparticles with single-core and multicore structures under viscous fluid and solid conditions were investigated to evaluate the Néel and Brownian relaxations, respectively, associated with the magnetization and physical particle rotations. We observed a distinct magnetization response associated with the complex magnetic relaxation mechanisms, which challenged the concept described by the conventional theory of effective relaxation time. Moreover, we clarified the relationship between the effective magnetic anisotropy energy and attempt time for controlling the magnetization dynamics dependent on particle structures. Our novel measurement technique and investigation of the magnetic relaxation time provide guidance for significantly optimizing material design and determining the magnetic field conditions for the biomedical applications of magnetic nanoparticles, particularly in cancer theranostics. © 2025 The Royal Society of Chemistry.

关键词： Magnetic nanoparticles

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Spectral Extremal Connectivity of Two-State Seizure Brain Waves

arXiv

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arXiv 2025年

作者： Talento, Mara Sherlin D. Richards, Jordan Pinto-Orellana, Marco Huser, Raphael Ombao, Hernando C. Statistics Program Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia Neuro-AI Laboratory Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia School of Mathematics Maxwell Institute for Mathematical Sciences University of Edinburgh EdinburghEH9 3FD United Kingdom Department of Biomedical Engineering University of California Irvine92697 United States

Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading findings when ignoring variations of connectivity in the tails of the distribution. This limitation becomes important when investigating extreme neural events that are characterized by large signal amplitudes. The focus of this paper is to examine connectivity in the tails of the distribution, as this reveals salient information that may be overlooked by standard methods. We develop a novel notion of spectral tail association of periodograms to study connectivity in the network of electroencephalogram (EEG) signals of seizure-prone neonates. We further develop a novel non-stationary extremal dependence model for multivariate time series that captures differences in extremal dependence during different brain phases, namely burst-suppression and non-burst-suppression. One advantage of our proposed approach is its ability to identify tail connectivity at key frequency bands that could be associated with outbursts of energy which may lead to seizures. We discuss these novel scientific findings alongside a comparison of the extremal behavior of brain signals for epileptic and non-epileptic patients. © 2025, CC BY.

关键词： Electroencephalography

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Structure-from-Sherds++: Robust Incremental 3D Reassembly of Axially Symmetric Pots from Unordered and Mixed Fragment Collections

arXiv

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arXiv 2025年

作者： Yoo, Seong Jong Liu, Sisung Arshad, Muhammad Zeeshan Kim, Jinhyeok Kim, Young Min Aloimonos, Yiannis Fermüller, Cornelia Joo, Kyungdon Kim, Jinwook Hong, Je Hyeong Department of Computer Science University of Maryland College ParkMD United States Department of Artificial Intelligence Hanyang University Korea Republic of Faculty of Health Sciences University of Ottawa ON Canada Artificial Intelligence Graduate School UNIST Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Intelligence and Interaction Research Center Korea Institute of Science and Technology Korea Republic of Artificial Intelligence Graduate School The Department of Computer Science and Engineering UNIST Korea Republic of Department of Electronic Engineering Hanyang University Korea Republic of

Reassembling multiple axially symmetric pots from fragmentary sherds is crucial for cultural heritage preservation, yet it poses significant challenges due to thin and sharp fracture surfaces that generate numerous false positive matches and hinder large-scale puzzle solving. Existing global approaches, which optimize all potential fragment pairs simultaneously or data-driven models, are prone to local minima and face scalability issues when multiple pots are intermixed. Motivated by Structure-from-Motion (SfM) for 3D reconstruction from multiple images, we propose an efficient reassembly method for axially symmetric pots based on iterative registration of one sherd at a time, called Structure-from-Sherds++ (SfS++). Our method extends beyond simple replication of incremental SfM and leverages multi-graph beam search to explore multiple registration paths. This allows us to effectively filter out indistinguishable false matches and simultaneously reconstruct multiple pots without requiring prior information such as base or the number of mixed objects. Our approach achieves 87% reassembly accuracy on a dataset of 142 real fragments from 10 different pots, outperforming other methods in handling complex fracture patterns with mixed datasets and achieving state-of-the-art performance. Code and results can be found in our project page https://***/sfs/. Copyright © 2025, The Authors. All rights reserved.

关键词： Historic preservation

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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