Innovations in High-Performance Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2931

Special Issue Editors


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Guest Editor
Institute of Informatics, Faculty of Electrical Engineering and Computer Science from the University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia
Interests: ubiquitous systems; high performance computing; machine learning; computer languages; computer games

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Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: differential evolution; multiobjective optimization; evolutionary robotics; artificial life; cloud computing
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Special Issue Information

Dear Colleagues,

High-performance computing (HPC) solves complex performance-intensive problems much faster and at less cost than traditional computing. It has been crucial to academic research and industry innovations for decades. All the newest discoveries in materials science, biology, drug development, artificial intelligence, and others could not be accomplished without HPC.

We kindly invite the authors to contribute innovative ideas, novel approaches, and outcomes of advanced research projects that present the next frontier of high-performance computing.

Topics of interest include but are not limited to:

  • new computing paradigms for HPC;
  • new HPC algorithms;
  • innovative applications of HPC;
  • high-performance networking and storage;
  • exascale data analytics;
  • HPC for digital twinning and real-time simulations;
  • HPC architectures for neuromorphic computing;
  • distributed and hybrid applications of HPC on the edge;
  • novel approaches to non-functional aspects of HPC (safety, security, fault-tolerance, etc.).

Dr. Domen Verber
Dr. Aleš Zamuda
Guest Editors

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Keywords

  • high-performance computing
  • computing paradigms
  • algorithm
  • exascale computing
  • digital twinning
  • neuromorphic computing
  • edge computing

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Published Papers (1 paper)

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Research

28 pages, 1008 KiB  
Article
High-Performance Real-Time Human Activity Recognition Using Machine Learning
by Pardhu Thottempudi, Biswaranjan Acharya and Fernando Moreira
Mathematics 2024, 12(22), 3622; https://doi.org/10.3390/math12223622 - 20 Nov 2024
Cited by 3 | Viewed by 1867
Abstract
Human Activity Recognition (HAR) is a vital technology in domains such as healthcare, fitness, and smart environments. This paper presents an innovative HAR system that leverages machine-learning algorithms deployed on the B-L475E-IOT01A Discovery Kit, a highly efficient microcontroller platform designed for low-power, real-time [...] Read more.
Human Activity Recognition (HAR) is a vital technology in domains such as healthcare, fitness, and smart environments. This paper presents an innovative HAR system that leverages machine-learning algorithms deployed on the B-L475E-IOT01A Discovery Kit, a highly efficient microcontroller platform designed for low-power, real-time applications. The system utilizes wearable sensors (accelerometers and gyroscopes) integrated with the kit to enable seamless data acquisition and processing. Our model achieves outstanding performance in classifying dynamic activities, including walking, walking upstairs, and walking downstairs, with high precision and recall, demonstrating its reliability and robustness. However, distinguishing between static activities, such as sitting and standing, remains a challenge, with the model showing a lower recall for sitting due to subtle postural differences. To address these limitations, we implement advanced feature extraction, data augmentation, and sensor fusion techniques, which significantly improve classification accuracy. The ease of use of the B-L475E-IOT01A kit allows for real-time activity classification, validated through the Tera Term interface, making the system ideal for practical applications in wearable devices and embedded systems. The novelty of our approach lies in the seamless integration of real-time processing capabilities with advanced machine-learning techniques, providing immediate, actionable insights. With an overall classification accuracy of 90%, this system demonstrates great potential for deployment in health monitoring, fitness tracking, and eldercare applications. Future work will focus on enhancing the system’s performance in distinguishing static activities and broadening its real-world applicability. Full article
(This article belongs to the Special Issue Innovations in High-Performance Computing)
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