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Applied Sciences
Special Issues
High Performance Computing, Modeling and Simulation
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High Performance Computing, Modeling and Simulation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 6119

Special Issue Editors

Dr. Stavros Souravlas

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Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: cloud computing; parallel and distributed computing; parallel algorithms; grid computing; digital design; computer architecture
Special Issues, Collections and Topics in MDPI journals
Dr. Stefanos Katsavounis

E-Mail Website
Guest Editor
Department of Production and Management Engineering, Democritous University, 57100 Xanthi, Greece
Interests: scheduling; RCMPSP; project management; graph theory and modeling; heuristics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sofia Anastasiadou

E-Mail Website
Guest Editor
Department of Early Childhood Education, Faculty of Education, University of Western Macedonia, Koila, 50100 Kozani, Greece
Interests: qualitative and quantitative methods in social sciences; applied statistics; implicative statistical analysis; multivariate statistical analysis; biostatistics; meta-analysis; structural equation models; big data; big data applications
Special Issues, Collections and Topics in MDPI journals
Dr. Andreas Masouras

E-Mail Website
Guest Editor
Department of Economics and Business, Neapolis University, Pafos, Cyprus
Interests: digital marketing and communication; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The study of systems with mathematical methods requires full knowledge of the existing or proposed system and the ability to represent the system with such models. However, when the system becomes complex, other methods of study and analysis have been developed. Simulation is one such method. At present, due to the explosion of computer systems, this method has grown in popularity and is widely used in all research areas. The vast majority of research papers in Applied Sciences include experiments conducted via sets of simulations. The purpose of this Special Issue is to invite state-of-the-art methods for the modeling and simulation of a variety of systems, their design, and their performance. The Special Issue invites papers in a number of fields of applied modeling and simulation.

The main topics of interest include but are not limited to the following:

  • Modeling, simulation and evaluation techniques of high-performance systems;
  • Petri net and colored Petri net modeling;
  • Network flow modeling and simulation;
  • Parallel and distributed modeling and simulation;
  • Message passing modeling and simulation;
  • Resource allocation and sharing modeling;
  • General systems modeling and simulation (includes systems from a variety of fields, like digital marketing, economics, healthcare, production, etc.).

Prof. Dr. Stavros Souravlas
Dr. Stefanos Katsavounis
Prof. Dr. Sofia Anastasiadou
Dr. Andreas Masouras
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • simulation
  • Petri nets
  • systems
  • modeling

Published Papers (3 papers)

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Research

25 pages, 1550 KiB  
Article
Accelerating Electromagnetic Field Simulations Based on Memory-Optimized CPML-FDTD with OpenACC
by Diego Padilla-Perez, Isaac Medina-Sanchez, Jorge Hernández and Carlos Couder-Castañeda
Appl. Sci. 2022, 12(22), 11430; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211430 - 10 Nov 2022
Cited by 1 | Viewed by 1201
Abstract
Although GPUs can offer higher computing power at low power consumption, their low-level programming can be relatively complex and consume programming time. For this reason, directive-based alternatives such as OpenACC could be used to specify high-level parallelism without original code modification, giving very [...] Read more.
Although GPUs can offer higher computing power at low power consumption, their low-level programming can be relatively complex and consume programming time. For this reason, directive-based alternatives such as OpenACC could be used to specify high-level parallelism without original code modification, giving very accurate results. Nevertheless, in the FDTD method, absorbing boundary conditions are commonly used. The key to successful performance is correctly implementing the boundary conditions that play an essential role in memory use. This work accelerates the simulations of electromagnetic wave propagation that solve the Maxwell curl equations by FDTD using CMPL boundary in TE mode using OpenACC directives. A gain of acceleration optimizing the use of memory is shows, checking the loops intensities, and the use of single precision to improve the performance is also analyzed, producing an acceleration of around 5X for double precision and 11X for single precision respectively, comparing with the serial vectorized version, without introducing errors in long-term simulations. The scenarios of simulation established are common of interest and are solved at different frequencies supported by a Mid-range cards GeForce RTX 3060 and Titan RTX. Full article
(This article belongs to the Special Issue High Performance Computing, Modeling and Simulation)
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17 pages, 3848 KiB  
Article
Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking
by Seungbong Lee, Jaehoon Jeong, Daechang Kim and Sungmin Kim
Appl. Sci. 2022, 12(16), 8035; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168035 - 11 Aug 2022
Cited by 2 | Viewed by 1693
Abstract
Eye tracking technology has been continuously researched for application in various fields. In the past, studies have been conducted to interpret eye movements in 3D space in order to solve the problem of not being able to find the centre of rotation of [...] Read more.
Eye tracking technology has been continuously researched for application in various fields. In the past, studies have been conducted to interpret eye movements in 3D space in order to solve the problem of not being able to find the centre of rotation of the eye. In this paper, we propose a novel pre-processing method for eye-gaze tracking by monitoring the front of the face with a camera. Our method works regardless of the distance between the eye and the camera. The proposed method includes an analysis technique that simplifies conventional three-dimensional space analysis to two dimensions. The contribution this work presents is a method to simplify gaze direction detection. The errors in our model’s estimations appear to be under 1 pixel. In addition, our approach has an execution time of less than 1 s, enabling an adaptive model that responds to user movements in real time. The proposed method was able to overcome various problems that methods in existing studies still suffer from, including accurately finding the rotational centre of the user’s eye-ball. Moreover, even when a user’s pupil can only be monitored from a distance, our approach still makes it possible to produce accurate estimations. Full article
(This article belongs to the Special Issue High Performance Computing, Modeling and Simulation)
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16 pages, 5290 KiB  
Article
Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction
by Haixia Kang, Yuping Wu, Lan Chen and Xuelian Zhang
Appl. Sci. 2022, 12(3), 1357; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031357 - 27 Jan 2022
Cited by 4 | Viewed by 2336
Abstract
The parameter extraction of device models is critically important for circuit simulation. The device models in the existing parameter extraction software are physics-based analytical models, or embedded Simulation program with integrated circuit emphasis (SPICE) functions. The programming implementation of physics-based analytical models is [...] Read more.
The parameter extraction of device models is critically important for circuit simulation. The device models in the existing parameter extraction software are physics-based analytical models, or embedded Simulation program with integrated circuit emphasis (SPICE) functions. The programming implementation of physics-based analytical models is tedious and error prone, while it is time consuming to run the device model evaluation for the device model parameter extraction software by calling the SPICE. We propose a novel modeling technique based on a neural network (NN) for the optimal extraction of device model parameters in this paper, and further integrate the NN model into device model parameter extraction software. The technique does not require developers to understand the device model, which enables faster and less error-prone parameter extraction software developing. Furthermore, the NN model improves the extraction speed compared with the embedded SPICE, which expedites the process of parameter extraction. The technique has been verified on the BSIM-SOI model with a multilayer perceptron (MLP) neural network. The training error of the NN model is 4.14%, and the testing error is 5.38%. Experimental results show that the trained NN model obtains an extraction error of less than 6%, and its extraction speed is thousands of times faster than SPICE in device model parameter extraction. Full article
(This article belongs to the Special Issue High Performance Computing, Modeling and Simulation)
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