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arXiv 2025年

作者： Xie, Mengda Zhong, Chengzhi He, Yiling Qin, Zhan Fang, Meie School of Computer Science and Cyber Engineering Guangzhou University Guangdong Guangzhou510006 China State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou310027 China Information Security Research Group University College London LondonNW1 2AE United Kingdom

Color constancy estimates illuminant chromaticity to correct color-biased images. Recently, Deep Neural Network-driven Color Constancy (DNNCC) models have made substantial advancements. Nevertheless, the potential risks in DNNCC due to the vulnerability of deep neural networks have not yet been explored. In this paper, we conduct the first investigation into the impact of a key factor in color constancy—brightness—on DNNCC from a robustness perspective. Our evaluation reveals that several mainstream DNNCC models exhibit high sensitivity to brightness despite their focus on chromaticity estimation. This sheds light on a potential limitation of existing DNNCC models: their sensitivity to brightness may hinder performance given the widespread brightness variations in real-world datasets. From the insights of our analysis, we propose a simple yet effective brightness robustness enhancement strategy for DNNCC models, termed BRE. The core of BRE is built upon the adaptive step-size adversarial brightness augmentation technique, which identifies high-risk brightness variation and generates augmented images via explicit brightness adjustment. Subsequently, BRE develops a brightness-robustness-aware model optimization strategy that integrates adversarial brightness training and brightness contrastive loss, significantly bolstering the brightness robustness of DNNCC models. BRE is hyperparameter-free and can be integrated into existing DNNCC models, without incurring additional overhead during the testing phase. Experiments on two public color constancy datasets—ColorChecker and Cube+—demonstrate that the proposed BRE consistently enhances the illuminant estimation performance of existing DNNCC models, reducing the estimation error by an average of 5.04% across six mainstream DNNCC models, underscoring the critical role of enhancing brightness robustness in these models. Copyright © 2025, The Authors. All rights reserved.

关键词： Digital elevation model

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A novel augmented reality-based simulator for enhancing orthopedic surgical training

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computers in Biology and Medicine 2025年 185卷 109536-109536页

作者： Wu, Luohong Seibold, Matthias Cavalcanti, Nicola A. Hein, Jonas Gerth, Tatiana Lekar, Roni Hoch, Armando Vlachopoulos, Lazaros Grabner, Helmut Zingg, Patrick Farshad, Mazda Fürnstahl, Philipp Research in Orthopedic Computer Science Balgrist University Hospital University of Zurich Lengghalde 5 Zurich8008 Switzerland Department of Orthopaedics Balgrist University Hospital University of Zurich Forchstrasse 340 Zurich8008 Switzerland Computer Vision and Geometry Group ETH Zurich Ramistrasse 101 Zurich8092 Switzerland Institute of Computer Science School of Engineering Zurich University of Applied Sciences Obere Kirchgasse 2 Winterthur8401 Switzerland Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 81 Winterthur8400 Switzerland

Background: Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for instructors. Methods: The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann–Whitney U tests. Results: The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22±1.32mm with LoA −2.37 to 2.81 mm for distance deviation, 0.94±2.41 degrees with LoA −3.78 to 5.66 degrees for yaw deviation, −0.34±1.30 degrees with LoA −2.90 to 2.22 degrees for pitch deviation) and p-values of Mann–Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of 0.05. Conclusion: This work sh

关键词： Augmented reality

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Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

Optimizing Ego Vehicle Trajectory Prediction: The Graph Enha...

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IS and T International Symposium on Electronic Imaging 2024: Autonomous Vehicles and Machines, AVM 2024

作者： Sharma, Sushil Singh, Aryan Sistu, Ganesh Halton, Mark Eising, Ciarán Data-Driven Computer Engineering Group Dept. of Electronic and Computer Engineering University of Limerick Ireland

Predicting the trajectory of an ego vehicle is a critical component of autonomous driving systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) and sequential models to process front-view images for future trajectory prediction. However, these approaches often struggle with perspective issues affecting object features in the scene. To address this, we advocate for the use of Bird’s Eye View (BEV) perspectives, which offer unique advantages in capturing spatial relationships and object homogeneity. In our work, we leverage Graph Neural Networks (GNNs) and positional encoding to represent objects in a BEV, achieving competitive performance compared to traditional DNN-based methods. While the BEV-based approach loses some detailed information inherent to front-view images, we balance this by enriching the BEV data by representing it as a graph where relationships between the objects in a scene are captured effectively. © 2024, Society for Imaging Science and Technology.

关键词： Deep neural networks

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Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving 26

Deformable Convolution Based Road Scene Semantic Segmentatio...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Manzoor, Anam Singh, Aryan Sistu, Ganesh Mohandas, Reenu Grua, Eoin Scanlan, Anthony Eising, Ciarán Data-Driven Computer Engineering Group Dept. of Electronic and Computer Engineering University of Limerick Ireland

ISBN: (纸本)9781837242672

This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios with fisheye images. These images, providing a wide field of view, pose unique challenges for extracting spatial and geometric information due to dynamic changes in object attributes. Our experiments focus on segmenting the WoodScape fisheye image dataset into ten distinct classes, assessing the Deformable Networks' ability to capture intricate spatial relationships and improve segmentation accuracy. Additionally, we explore different loss functions to address class imbalance issues and compare the performance of conventional CNN architectures with Deformable Convolution-based CNNs, including Vanilla U-Net and Residual U-Net architectures. The significant improvement in mIoU score resulting from integrating Deformable CNNs demonstrates their effectiveness in handling the geometric distortions present in fisheye imagery, exceeding the performance of traditional CNN architectures. This underscores the significant role of Deformable convolution in enhancing semantic segmentation performance for fisheye imagery. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Semantic Segmentation

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Dense Neural Network Based Arrhythmia Classification on Low-cost and Low-compute Micro-controller

arXiv

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arXiv 2025年

作者： Zishan, Md. Abu Obaida Shihab, H.M. Islam, Sabik Sadman Riya, Maliha Alam Rahman, Gazi Mashrur Noor, Jannatun Research Group Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

The electrocardiogram (ECG) monitoring device is an expensive albeit essential device for the treatment and diagnosis of cardiovascular diseases (CVD). The cost of this device typically ranges from $2000 to $10000. Several studies have implemented ECG monitoring systems in micro-controller units (MCU) to reduce industrial development costs by up to 20 times. However, to match industry-grade systems and display heartbeats effectively, it is essential to develop an efficient algorithm for detecting arrhythmia (irregular heartbeat). Hence in this study, a dense neural network is developed to detect arrhythmia on the Arduino Nano. The Nano consists of the ATMega328 microcontroller with a 16MHz clock, 2KB of SRAM, and 32KB of program memory. Additionally, the AD8232 SparkFun Single-Lead Heart Rate Monitor is used as the ECG sensor. The implemented neural network model consists of two layers (excluding the input) with 10 and four neurons respectively with sigmoid activation function. However, four approaches are explored to choose the appropriate activation functions. The model has a size of 1.267 KB, achieves an F1 score (macro-average) of 78.3% for classifying four types of arrhythmia, an accuracy rate of 96.38%, and requires 0.001314 MOps of floating-point operations (FLOPs). This study has achieved notable advancements compared to recent studies, particularly in terms of neural network metrics and the efficient utilization of computational resources. © 2025, CC BY-NC-ND.

关键词： Static random access storage

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Fusion+: A Multi-Sensor Image Fusion for Very High-Resolution Satellite Imagery

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IEEE Access 2025年 13卷 62856-62869页

作者： Hestrio, Yohanes Fridolin Surya Candra, Danang Prima Brahmantara, Randy Prabowo, Yudhi Dimara Sakti, Anjar Luthfi Ramadhan, Mgs M. Loong Yap, Yong Azizah, Kurniawati Hafizhuddin Hilman, Muhammad Phinn, Stuart Liang Lim, Sin Jatmiko, Wisnu University of Indonesia Faculty of Computer Science Depok16424 Indonesia Research Center for Geoinformatics Bogor16912 Indonesia Center for Data and Information Bogor16912 Indonesia Research Center for Hydrodynamics Technology Bogor16912 Indonesia Remote Sensing and Geographic Information Science Research Group Faculty of Earth Sciences and Technology Bandung40132 Indonesia Multimedia University Faculty of Engineering Cyberjaya75450 Malaysia The University of Queensland Remote Sensing Research Center School of Earth and Environmental Sciences BrisbaneQLD4072 Australia

A very detailed image, such as very high-resolution satellite imagery, is necessary for many applications. By enhancing the image’s detail and reducing its color distortion, we can optimize its use. Image fusion is a method to achieve this goal. This paper proposes a novel method of multi-sensor image fusion called Fusion+. This method enhances spatial resolution, sharpens the image using the kernel of image enhancement, and preserves radiometric consistency using the histogram specification. In addition, the Fusion+ has a shift adjustment between the input images to address the geometric issue, especially for images from multiple sensors. The proposed method can be used for both single-sensor and multi-sensor image fusion. The fused image obtained from the proposed method also increased the image’s detail and was sharper than the other methods. The universal image quality index, peak signal-to-noise ratio, and structural similarity index measure are also sufficient to maintain the radiometric consistency of the original image. The results also showed that the fused image had maximum detail increase, minimum color distortion, and a natural color appearance. Fusion+ obtained an average SSIM score of 0.779, an average PSNR of 29.342, and an average UQI of 0.973 on single-sensor fused images, as well as SSIM averages of 0.556, an average PSNR of 27.896, and an average UQI of 0.947 on multi-sensor fused images. The Fusion+ method demonstrates the potential for improved image fusion quality, which can contribute to improved accuracy and reliability in the analysis of very high-resolution remote sensing data, especially in multi-sensor applications. © 2013 IEEE.

关键词： Satellite imagery

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Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Development, implementation and evaluation of a smartphone application aimed to reduce sedentary time and increase physical activity among Indian sedentary office workers – findings from SMART-STEP trial

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BMC Public Health 2025年第1期25卷 1-16页

作者： Chandrasekaran, Baskaran Arumugam, Ashokan Pesola, Arto J. Kundapur, Poornima Panduranga Rao, Chythra R. Department of Exercise and Sports Sciences Manipal College of Health Professions Manipal Academy of Higher Education Manipal India Department of Physiotherapy College of Health Sciences University of Sharjah Sharjah United Arab Emirates Neuromusculoskeletal Rehabilitation Research Group RIMHS – Research Institute of Medical and Health Sciences University of Sharjah Sharjah United Arab Emirates Sustainable Engineering Asset Management Research Group RISE - Research Institute of Sciences and Engineering University of Sharjah Sharjah United Arab Emirates Adjunct Faculty Department of Physiotherapy Manipal College of Health Professions Manipal Academy of Higher Education Manipal India Active Life Lab South-Eastern Finland University of Applied Sciences Mikkeli Finland Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Community Medicine Kasturba Medical College Manipal Manipal Academy of Higher Education Manipal India

Excessive sedentary time (ST) is linked to an increased risk of cardiometabolic diseases. Although various behavioral interventions have emerged to reduce ST, the potential of smartphone (SmPh)-based interventions remains relatively unexplored in workplace settings. This study aimed to explore the development, implementation and acceptability of a SmPh application among Indian desk-based office workers. One hundred thirty-six office workers were randomised to one of three interventions for six months: (1) SmPh-based ST and physical activity (PA) reminders (SMART); (2) traditional education (TRADE) and (3) usual work group (CONT). A threshold of 70% adherence (equivalent to responding to at least 580 out of 828 assigned prompts) was used to classify participants as ‘compliant’. Of 44 SMART group participants who were assigned to 24 weeks of intervention, nine participants were purposefully selected based on compliance, Moreover, they were interviewed for potential barriers associated with the SmPh application using semi-deductive approach. The SMART STEP application was developed over eight months, during which three versions were created and pilot tested. The cost of application development was reasonable (≈ $1,860). Of 44 participants who received SmPh application-based reminders, 37 completed the two follow-ups at 3rd and 6th month. Mean prompt engagement rates, defined as ‘the percentage of prompts participants actively responded to’, during the 1st, 2nd, 3rd, 4th, 5th, and 6th months were 77% (n = 107), 59% (n = 82), 54% (n = 74), 45% (n = 63), 43% (n = 59), and 31% (n = 43), respectively. Barriers such as workload, lack of movement sensing, and insufficient organizational and peer support were key factors contributing to the decline in long-term engagement among office workers. The SMART-STEP application appears to be an affordable and promising solution for reducing ST and promoting PA among office workers in low-resource settings. However, enhancements such a

关键词： Acceptability Productivity Safe work Sedentary time Smartphone application Sustainable industry

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Educational data mining: a 10-year review

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Discover Computing 2025年第1期28卷 1-25页

作者： Kalita, Emi Oyelere, Solomon Sunday Gaftandzhieva, Silvia Rajesh, Kandala N. V. P. S. Jagatheesaperumal, Senthil Kumar Mohamed, Asmaa Elbarawy, Yomna M. Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif Theodorou, Paraskevi Hilčenko, Slavoljub Hazarika, Jiten Ali, Tazid Centre for Computer Science and Applications Dibrugarh University Dibrugarh India Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå Sweden Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter England Research Group On Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School (JBS) University of Johannesburg Johannesburg South Africa Faculty of Mathematics and Informatics University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria School of Electronics Engineering VIT-AP University Vijayawada India Department of Electronics & Communication Engineering Mepco Schlenk Engineering College Sivakasi India Department of Mathematics Faculty of Science Al-Azhar University (Girls Branch) Cairo Egypt Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir Türkiye Engineering and Informatics Department Klaipėdos Valstybinė Kolegija/Higher Education Institution Klaipeda Lithuania Department of Digital Systems University of Piraeus Piraeus Greece College for Vocational Education of Preschool Teachers and Coaches Subotica Serbia Department of Statistics Dibrugarh University Dibrugarh India

This systematic review comprehensively examines the application and impacts of Educational data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

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