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Computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： Mohit Yadav Surendra Kumar Rajkumar Yadav Dinesh Yadav Ramesh Kumar Garg Ashish Kaushik Akash Ahlawat Deepak Chhabra Department of Applied Sciences Mathematics UIET Maharshi Dayanand University Rohtak India Department of Applied Sciences Mathematics University Institute of Engineering and Technology M.D. University Rohtak India Department of Computer Science & Engineering University Institute of Engineering and Technology M.D. University Rohtak India Department of Mechanical Engineering Deenbandhu Chhotu Ram University of Science and Technology Sonipat India Department of Mechanical Engineering UIET Maharshi Dayanand University Rohtak India

The results of enormous climate change and other industrial carbon emissions have been driven by the rise of nonrenewable traditional energy sources. The scientific community is looking at unconventional renewable techniques that can harvest energy in the external atmosphere. This research article provides a critical analysis of the growth in the field of piezoelectric energy harvesting. Recent advances in bio-inspired piezo-smart equipment are critically analyzed. A comprehensive overview of harvesting power from mechanical vibrations and its renewable sources, such as human body movements, flowing fluid, and circuitry arrangements of structures with piezoelectric materials have been discussed. Capturing and converting mechanical energy into useful electric power is the easiest and most prevalent way to harness the massive amounts of ambient energy around us. With the help of piezoelectric materials, vibrations induced by mechanical sources can convert into electrical energy. Various industries, including transportation, construction, aeronautical, smart systems, micro-fluids, biomedical devices, and implantable electronics, employ piezoelectric energy generating at the micro level. Piezoelectric energy generating technique’s applications along with their achievements, and future possibilities are critically analyzed. This overview concisely presents the extensive range of piezoelectric materials for wireless electronic devices that use eco-friendly, renewable, and sustainable energy harvesting sources.

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LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Zhang, Shengdong Jia, Fan Li, Xiang Zhang, Hao Shi, Jun Ma, Liyan Ying, Shihui Department of Mathematics School of Science Shanghai University Shanghai200444 China Institute University of Utah Salt Lake CityUT84102 United States School of Computer Science and Technology East China Normal University Shanghai China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Shanghai Institute of Applied Mathematics and Mechanics The School of Mechanics and Engineering Science Shanghai University Shanghai200072 China

Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called the Learned Mumford-Shah Network (LMSNet), for the FSS task. Specifically, motivated by the effectiveness of pixel-to-prototype comparison in prototypical FSS methods and the capability of deep priors to model complex spatial structures, we leverage our learned Mumford-Shah model (LMS model) as a mathematical foundation to integrate these insights into a unified framework. By reformulating the LMS model into prototype update and mask update tasks, we propose an alternating optimization algorithm to solve it efficiently. Further, the iterative steps of this algorithm are unfolded into corresponding network modules, resulting in LMS-Net with clear interpretability. Comprehensive experiments on three publicly available medical segmentation datasets verify the effectiveness of our method, demonstrating superior accuracy and robustness in handling complex structures and adapting to challenging segmentation scenarios. These results highlight the potential of LMS-Net to advance FSS in medical imaging applications. Our code will be available at: https:// ***/SDZhang01/LMSNet Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Lightweight Deep Residual Network for Motorcyclist Behavior Recognition Using Inertial Sensors

Lightweight Deep Residual Network for Motorcyclist Behavior ...

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International Conference on Information Technology (INCIT)

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Motorcyclist behavior recognition (MBR) is a significant factor in improving road safety and preventing accidents. Inertial sensors, such as accelerometers, gyroscopes, and magnetometers, have shown promising potential for capturing the dynamic behavior of motorcyclists. However, existing approaches often require complex models and significant computational resources, limiting their deployment in real-world scenarios. This study presents a MotoNeXt architecture, which is a light deep residual network for correctly identifying the behavior of motorcyclists using data from inertial sensors. Our approach leverages the power of deep learning while maintaining a compact model size, making it suitable for resource-constrained environments. The proposed network incorporates multi-kernel residual blocks to facilitate the learning of hierarchical features and improve gradient flow during training. We evaluate our method on a publicly available benchmark dataset, MB-IMU, collected from multiple riders under various riding conditions. Experimentation has shown that our lightweight model achieves cutting-edge performance, outperforming a variety of existing techniques. In Scenario I, using accelerometer, gyroscope, and magnetometer data, MotoNeXt achieved an accuracy result of 78.90088% along with an F1-score of 81.56738%. According to Scenario II, which included additional sensor data such as VelInc, OriInc, and Euler angles, MotoNeXt further improved its performance, achieving an accuracy result of 83.29735% along with an F1-score of 84.95352%. The proposed approach offers a practical solution for real-time MBR, enabling the development of intelligent transportation systems and rider assistance technologies. Our experiments highlight the effectiveness of lightweight deep learning models in extracting meaningful patterns from inertial sensor data. This opens up opportunities for further investigation in this field.

关键词： Deep learning Accelerometers Training Accuracy Inertial sensors Magnetometers Computational modeling Transportation Gyroscopes Residual neural networks

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Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

arXiv

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arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

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Open set label noise learning with robust sample selection and margin-guided module

arXiv

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arXiv 2025年

作者： Zhao, Yuandi Xia, Qianxi Sun, Yang Wen, Zhijie Ma, Liyan Ying, Shihui College of Air Traffic Management Civil Aviation University of China Tianjin300300 China Department of Mathematics College of Sciences Shanghai University Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute of Applied Mathematics and Mechanics Shanghai University Shanghai200072 China

In recent years, the remarkable success of deep neural networks (DNNs) in computer vision is largely due to large-scale, high-quality labeled datasets. Training directly on real-world datasets with label noise may result in overfitting. The traditional method is limited to deal with closed set label noise, where noisy training data has true class labels within the known label space. However, there are some real-world datasets containing open set label noise, which means that some samples belong to an unknown class outside the known label space. To address the open set label noise problem, we introduce a method based on Robust Sample Selection and Margin-Guided Module (RSS-MGM). Firstly, unlike the prior clean sample selection approach, which only select a limited number of clean samples, a robust sample selection module combines small loss selection or high-confidence sample selection to obtain more clean samples. Secondly, to efficiently distinguish open set label noise and closed set ones, margin functions are designed to filter open-set data and closed set data. Thirdly, different processing methods are selected for different types of samples in order to fully utilize the data's prior information and optimize the whole model. Furthermore, extensive experimental results with noisy labeled data from benchmark datasets and real-world datasets, such as CIFAR-100N-C, CIFAR80N-O, WebFG-469, and Food101N, indicate that our approach outperforms many state-of-the-art label noise learning methods. Especially, it can more accurately divide open set label noise samples and closed set ones. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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The State of Lithium-Ion Battery Health Prognostics in the CPS Era

arXiv

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arXiv 2024年

作者： Shinde, Gaurav Mohapatra, Rohan Krishan, Pooja Garg, Harish Prabhu, Srikanth Das, Sanchari Masum, Mohammad Sengupta, Saptarshi Department of Engineering San Jose State University San JoseCA United States Department of Computer Science San Jose State University San JoseCA United States School of Mathematics Thapar Institute of Engineering & Technology Punjab Patiala147004 India Department of Computer Science and Engineering Manipal Institute of Technology KA Manipal India Department of Computer Science University of Denver DenverCO United States Department of Applied Data Science San Jose State University San JoseCA United States

Lithium-ion batteries (Li-ion) have revolutionized energy storage technology, becoming integral to our daily lives by powering a diverse range of devices and *** high energy density, fast power response, recyclability, and mobility advantages have made them the preferred choice for numerous sectors. This paper explores the seamless integration of Prognostics and Health Management within batteries, presenting a multidisciplinary approach that enhances the reliability, safety, and performance of these powerhouses. Remaining useful life (RUL), a critical concept in prognostics, is examined in depth, emphasizing its role in predicting component failure before it occurs. The paper reviews various RUL prediction methods, from traditional models to cutting-edge data-driven techniques. Furthermore, it highlights the paradigm shift toward deep learning architectures within the field of Li-ion battery health prognostics, elucidating the pivotal role of deep learning in addressing battery system *** applications of PHM across industries are also explored, offering readers insights into real-world *** paper serves as a comprehensive guide, catering to both researchers and practitioners in the field of Li-ion battery *** Codes 68 Copyright © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Lowering the Entry Bar to HPC-Scale Uncertainty Quantification

arXiv

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arXiv 2023年

作者： Seelinger, Linus Reinarz, Anne Bénézech, Jean Lykkegaard, Mikkel Bue Tamellini, Lorenzo Scheichl, Robert Institute for Applied Mathematics Heidelberg University Heidelberg Germany Department of Computer Science Durham University Durham United Kingdom Department of Mechanical Engineering University of Bath Bath United Kingdom digiLab Exeter United Kingdom Institute of Applied Mathematics and Information Technologies National Research Council Pavia Italy

Treating uncertainties in models is essential in many fields of science and engineering. Uncertainty quantification (UQ) on complex and computationally costly numerical models necessitates a combination of efficient model solvers, advanced UQ methods and HPC-scale resources. The resulting technical complexities as well as lack of separation of concerns between UQ and model experts is holding back many interesting UQ applications. The aim of this paper is to close the gap between advanced UQ methods and advanced models by removing the hurdle of complex software stack integration, which in turn will offer a straightforward way to scale even prototype-grade UQ applications to high-performance resources. We achieve this goal by introducing a parallel software architecture based on UM-Bridge, a universal interface for linking UQ and models. We present three realistic applications from different areas of science and engineering, scaling from single machines to large clusters on the Google Cloud Platform. Copyright © 2023, The Authors. All rights reserved.

关键词： Software architecture

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Global-Local Active Contour for Segmenting Images with Simultaneous Inhomogeneity and Texture

SSRN

引用

SSRN 2024年

作者： Shao, Pei-Chiang Department of Applied Mathematics Chung Yuan Christian University Zhongli District Taoyuan City320314 Taiwan Undergraduate Program in College of Electrical Engineering and Computer Science Chung Yuan Christian University Zhongli District Taoyuan City320314 Taiwan

Severe intensity inhomogeneity (InH) and complex real-world textures cause great difficulties and become two important issues in image segmentation and object extraction applications. Plenty of methods are proposed to tackle these issues, however, they usually perform poorly on images possess both InH and texture. In this paper, we propose a region-based active contour model for segmenting images with simultaneous inhomogeneity and texture. A new bias field possesses both global and local smoothness properties is proposed to handle the InH issue, and a pixel inhomogeneity factor (PIF) is employed to jointly deal with InH and textured images. We also employ the total variation (TV) regularization to further smooth and denoise the transformed PIF image, making the segmentation process to have the nice properties of global convergence and independence of contour initialization. A large number of numerical experiments and comparisons are conducted to demonstrate the effective performance of the newly proposed method. © 2024, The Authors. All rights reserved.

关键词： Textures

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Application of HPC and big data in post-pandemic times

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Earthquake Research Advances 2021年第3期1卷 21-28页

作者： Henry M.Tufo David A.Yuen Gabriele Morra Matthew G.Knepley Bei Zhang Shi Chen Dept.of Computer Science University of ColoradoBoulderUSA Dept.of Applied Physics and Applied Mathematics Columbia UniversityUSA College of Information Science and Engineering and College of Marine Geosciences Ocean University of ChinaQingdaoChina Dept.of Physics and Earth Sciences University of LouisianaLafayetteLouisianaUSA Dept.of Computer Science and Engineering State University of New YorkBuffaloUSA Institute of Geophysics China Earthquake AdministrationBeijingChina Beijing Baijiatuan Earth Science National Observation and Research StationBeijingChina

We lay out the ramifications of the 2020 pandemic for all people in geosciences,especially the young,and argue for significant changes on training and career *** focus primarily on its devastating impact in USA and compare with that in other countries especially *** review the potential effect for the next four years or so on the aspirations of an academic career versus more realistic career *** urge people in mid-career about the need to reassess previous *** stress the need for students or researchers to acquire technical skills in high-performance computing(HPC),data analytics,artificial intelligence,and/or visualization along with a broad set of technical skills in applied computer science and *** give advice about hot prospects in several areas that have great potential for advancement in the coming decade,such as visualization,deep learning,quantum computing and information,and cloud computing,all of which lie within the aegis of *** forecast is that the pandemic will significantly reshape the job landscape and career paths for both young and established researchers and we discuss bluntly the dire situation facing junior people in geosciences in the aftermath of the pandemic around the world until 2024.

关键词： Geophysics High-performance computing Higher education Post-pandemic era

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Understanding Sign Language Recognition: An Overview

Understanding Sign Language Recognition: An Overview

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Shivani Saini Meenu Prjapati Bhupal Arya Ravinder Kumar Sharma Manoj Kumar Department of Mathematics Lovely Professional University Jalandhar India School of Applied & Life Sciences Uttranchal University Computer Science and Engineering RIT Roorkee Roorkee Institute of Technology Roorkee India Department of Mathematics Chandigarh University India India CSE RIT Roorkee Roorkee Institute of Technology Roorkee India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Communication is only the way which is used by human to express their needs, feelings and thoughts to people mostly by speech. But this world also has the people with some disability like deaf and dumb people. For these people communication is not possible by speech. The only language which is used by deaf and dumb people to communicate with each other is Sign Language. Sign Language is completely different from generally spoken languages, it has its own grammar. Different body parts are involved in sign language but the important parameters are hand gestures, body movement and also facial expression. Many researches have been done in sign language recognition. But still Sign Language Recognition (SLR) is the most challenging area for practitioners and researchers, because sign language is not an international language. For e.g., Indian Sign Language is differed from Ethiopian Sign Language, Chinese Sign Language, American Sign Language and other country’s sign language. The SLR is mostly required because of its ability to remove the communication barrier for deaf and dumb people. This review paper contains various techniques of AI (Artificial Intelligence) used for SLR for human understanding that have employed in recent past. The main approaches are i) Vision based ii) Glove based. In vision-based approach one or more camera is used to capture the image sequence, and then image processing is used to recognize the signs. In glove-based approach instrumental gloves with sensor is used to track the hand movement

关键词： Support vector machines Sign language Image segmentation Tracking Reviews Image edge detection Feature extraction Classification algorithms Artificial intelligence Speech processing

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