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Search Results (14)
  • Open Access

    ARTICLE

    HealthyBrain: A Scalable Microservices-Based Smart Healthcare System for Remote Patient Monitoring

    Shounak Mandal1, Subhadip Pati1,#, Nirmallyadeb Ray1,#, Bipasha Guha Roy2,#, Priyanka Saha3, Deepsubhra Guha Roy2,*

    Digital Engineering and Digital Twin, Vol.4, pp. 27-47, 2026, DOI:10.32604/dedt.2026.081859 - 14 August 2026

    Abstract HealthyBrain is a scalable, interoperable, and intelligent Remote Patient Monitoring (RPM) platform built on Internet of Things (IoT) technologies and a modular microservices architecture. The system integrates wearable IoT devices, MQTT (Message Queuing Telemetry Transport)-based lightweight messaging, and high-throughput real-time data streaming via Apache Kafka. Edge-side preprocessing enables low-latency analytics, while machine learning-based anomaly detection models facilitate early identification of critical health events. To ensure clinical interoperability, the platform adheres to the HL7 FHIR (Fast Healthcare Interoperability Resources) standard for electronic health record exchange. The system’s novel contribution lies in the unified integration of edge… More >

  • Open Access

    ARTICLE

    Experimental Frame–System Under Test (EFSUT): A Principled Foundation for Model Choice and Lifecycle Management in Digital Twins

    Bernard P. Zeigler*

    Digital Engineering and Digital Twin, Vol.4, pp. 1-26, 2026, DOI:10.32604/dedt.2026.082492 - 02 June 2026

    Abstract As Digital Twin (DT) applications expand into complex, dynamic environments, a formal methodology is lacking to ensure that the embedded digital models remain adequate for specific stakeholder goals over time. This article introduces the Experimental Frame–System Under Test (EFSUT) methodology, providing a principled foundation for linking high-level stakeholder questions to the specific models capable of answering them. EFSUT organizes the digital engineering process around three core constructs: stakeholder questions, experimental frames that formalize observational requirements, and models related through morphisms. This structure allows developers to reason about model choice, reduction, and adequacy with technical rigor… More >

  • Open Access

    ARTICLE

    A Systematic Comparison of Discrete Cosine Transform-Based Approaches for Multi-Focus Image Fusion

    Muhammad Osama1, Sarwar Shah Khan2,*, Sajid Khan2, Muzammil Khan3, Mian Muhammad Danyal4, Reshma Khan1

    Digital Engineering and Digital Twin, Vol.3, pp. 17-34, 2025, DOI:10.32604/dedt.2025.066344 - 19 August 2025

    Abstract Image fusion is a technique used to combine essential information from two or more source images into a single, more informative output image. The resulting fused image contains more meaningful details than any individual source image. This study focuses on multi-focus image fusion, a crucial area in image processing. Due to the limited depth of field of optical lenses, it is often challenging to capture an image where all areas are in focus simultaneously. As a result, multi-focus image fusion plays a key role in integrating and extracting the necessary details from different focal regions.… More >

  • Open Access

    ARTICLE

    Advancing Brain Tumor Classification: Evaluating the Efficacy of Machine Learning Models Using Magnetic Resonance Imaging

    Khalid Jamil1, Wahab Khan1, Bilal Khan2, Sarwar Shah Khan2,*

    Digital Engineering and Digital Twin, Vol.3, pp. 1-16, 2025, DOI:10.32604/dedt.2025.058943 - 28 February 2025

    Abstract Brain tumors are one of the deadliest cancers, partly because they’re often difficult to detect early or with precision. Standard Magnetic Resonance Imaging (MRI) imaging, though essential, has limitations, it can miss subtle or early-stage tumors, which delays diagnosis and affects patient outcomes. This study aims to tackle these challenges by exploring how machine learning (ML) can improve the accuracy of brain tumor identification from MRI scans. Motivated by the potential for artificial intillegence (AI) to boost diagnostic accuracy where traditional methods fall short, we tested several ML models, with a focus on the K-Nearest More >

  • Open Access

    ARTICLE

    Orthogonal Probability Approximation for Highly Accurate and Efficient Orbit Uncertainty Propagation

    Pugazhenthi Sivasankar1,*, Austin B. Probe2, Tarek A. Elgohary1

    Digital Engineering and Digital Twin, Vol.2, pp. 169-205, 2024, DOI:10.32604/dedt.2024.052805 - 31 December 2024

    Abstract In Space Situational Awareness (SSA), accurate and efficient uncertainty quantification and propagation are essential for various applications, such as conjunction analysis, track correlation, and orbit prediction. The propagation of the probability density function (PDF) in nonlinear systems results in non-Gaussian distributions, which are difficult to approximate. Furthermore, the computational cost of approximating the PDF increases exponentially with the number of random variables, a phenomenon known as the curse of dimensionality. To address these challenges, the Orthogonal Probability Approximation (OPA) method is presented for high-fidelity uncertainty propagation and PDF approximation in nonlinear dynamical systems. The method… More >

  • Open Access

    ARTICLE

    Research on Substation Siting Based on a 3D GIS Platform and an Improved BP Neural Network

    Yao Jin1,2,*, Jie Zhao1,2, Xiaozhe Tan1,2, Linghou Miao1,2, Wenxing Yu1,2

    Digital Engineering and Digital Twin, Vol.2, pp. 131-144, 2024, DOI:10.32604/dedt.2024.048142 - 31 December 2024

    Abstract Substation siting is an important foundation and a key task in power system planning. The article is based on a three-dimensional GIS platform combined with an improved BP neural network algorithm and proposes a substation siting method that is more efficient, accurate and provides a better user experience. Firstly, the BP algorithm is enhanced to improve its convergence speed and computational efficiency for a more accurate and reasonable calculation of optimal site selection. Then, a 24-item selection index system with 7 categories is proposed, which provides quantifiable data support and an evaluation basis for substation… More >

  • Open Access

    ARTICLE

    Approach for the Simulation of Linear PDEs with Constant Coefficients, Testing Multi-Dimensional Helmholtz and Wave Equations

    Chein-Shan Liu, Chung-Lun Kuo*

    Digital Engineering and Digital Twin, Vol.2, pp. 145-167, 2024, DOI:10.32604/dedt.2024.042804 - 31 December 2024

    Abstract A new concept of projective solution is introduced for the second-order linear partial differential equations (PDEs) endowed with constant coefficients. In terms of a projective variable the PDE is transformed to a second-order ordinary differential equation (ODE) with constant coefficients at the first time. The characteristic form appears as the coefficient preceding the second-order derivative term. Depending on the characteristic form and coefficients we can derive various parameters-dependent particular solutions, which can be adopted as the bases to expand the solution. The Helmholtz and wave equations are solved by the projection method. We project the… More >

  • Open Access

    ARTICLE

    Supervised Learning for Finite Element Analysis of Holes under Biaxial Load

    Wai Tuck Chow*, Jia Tai Lau

    Digital Engineering and Digital Twin, Vol.2, pp. 103-130, 2024, DOI:10.32604/dedt.2024.044545 - 06 May 2024

    Abstract This paper presents a novel approach to using supervised learning with a shallow neural network to increase the efficiency of the finite element analysis of holes under biaxial load. With this approach, the number of elements in the finite element analysis can be reduced while maintaining good accuracy. The neural network will be used to predict the maximum stress for holes of different configurations such as holes in a finite-width plate (2D), multiple holes (2D), staggered holes (2D), and holes in an infinite plate (3D). The predictions are based on their respective coarse mesh with… More >

  • Open Access

    REVIEW

    A Review on Finite Element Alternating Methods for Analyzing 2D and 3D Cracks

    Jai Hak Park*

    Digital Engineering and Digital Twin, Vol.2, pp. 79-101, 2024, DOI:10.32604/dedt.2024.047280 - 25 March 2024

    Abstract A finite element alternating method has been known as a very convenient and accurate method to solve two and three-dimensional crack problems. In this method, a general crack problem is solved by a superposition of two solutions. One is a finite element solution for a finite body without a crack, and the other is an analytical solution for a crack in an infinite body. Since a crack is not considered in a finite element model, generating a model is very simple. The method is especially very convenient for a fatigue crack growth simulation. Over the More >

  • Open Access

    ARTICLE

    Physics Based Digital Twin Modelling from Theory to Concept Implementation Using Coiled Springs Used in Suspension Systems

    Mohamed Ammar1,*, Alireza Mousavi1, Hamed Al-Raweshidy2,*

    Digital Engineering and Digital Twin, Vol.2, pp. 1-31, 2024, DOI:10.32604/dedt.2023.044930 - 31 January 2024

    Abstract The advent of technology around the globe based on the Internet of Things, Cloud Computing, Big Data, Cyber-Physical Systems, and digitalisation. This advancement introduced industry 4.0. It is challenging to measure how enterprises adopt the new technologies. Industry 4.0 introduced Digital Twins, given that no specific terms or definitions are given to Digital Twins still challenging to define or conceptualise the Digital Twins. Many academics and industries still use old technologies, naming it Digital Twins. This young technology is in danger of reaching the plateau despite the immense benefit to sectors. This paper proposes a… More >

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