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Mathematics
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Information Theory Applied in Scientific Computing
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Information Theory Applied in Scientific Computing

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 6401

Special Issue Editors

Prof. Dr. Tiberiu Letia

E-Mail Website
Guest Editor
Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400020 Cluj-Napoca, Romania
Interests: software engineering; real-time systems; distributed control systems; artificial intelligence
Prof. Dr. Alexandru Onea

E-Mail Website
Guest Editor
Departament of Automatic Control and Applied Informatics, Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania
Interests: automatic control; adaptive control; robust control; embedded systems; automotive control systems

Special Issue Information

Dear Colleagues,

The development of information theory in recent years has involved many relevant topics at the intersection of computing and mathematics. These concern mathematical models for problem conception and experiment contraption, as well as computer simulations for solutions or algorithm validation. This approach has led to sound theoretical results becoming standard in contemporary scientific research. Information theory is more oriented toward the fundamental limitations on the processing and communication of information and less oriented toward the detailed operation of devices. The field deals with the development of fast and accurate algorithms for solving a wide variety of problems related to computers.

The realm of information theory and scientific computing covers the domains of computer science, communication, system engineering, electrical engineering, etc.

The main objective of this Special Issue is to gather the most advanced approaches that push the scientific research further. We expect a selection of relevant, quality proposals that provide valuable recommendations for unselected articles with the aim of guiding their improvement for further publication.

Proposed scope of this Special Issue:

  • Communication—networking, transmission, processing, extraction of information, and security;
  • Quantum or classical computation—models, methods, and algorithms;
  • Artificial intelligence—inference and learning algorithms;
  • System engineering—discrete events and discrete and continuous time.

Prof. Dr. Tiberiu Letia
Prof. Dr. Alexandru Onea
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. Mathematics 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 2600 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.

Published Papers (4 papers)

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Research

17 pages, 504 KiB  
Article
Generalized Data–Driven Predictive Control: Merging Subspace and Hankel Predictors
by M. Lazar and P. C. N. Verheijen
Mathematics 2023, 11(9), 2216; https://doi.org/10.3390/math11092216 - 08 May 2023
Cited by 1 | Viewed by 1515
Abstract
Data–driven predictive control (DPC) is becoming an attractive alternative to model predictive control as it requires less system knowledge for implementation and reliable data is increasingly available in smart engineering systems. Two main approaches exist within DPC: the subspace approach, which estimates prediction [...] Read more.
Data–driven predictive control (DPC) is becoming an attractive alternative to model predictive control as it requires less system knowledge for implementation and reliable data is increasingly available in smart engineering systems. Two main approaches exist within DPC: the subspace approach, which estimates prediction matrices (unbiased for large data) and the behavioral, data-enabled approach, which uses Hankel data matrices for prediction (allows for optimizing the bias/variance trade–off). In this paper we develop a novel, generalized DPC (GDPC) algorithm by merging subspace and Hankel predictors. The predicted input sequence is defined as the sum of a known, baseline input sequence, and an optimized input sequence. The corresponding baseline output sequence is computed using an unbiased, subspace predictor, while the optimized predicted output sequence is computed using a Hankel matrix predictor. By combining these two types of predictors, GDPC can achieve high performance for noisy data even when using a small Hankel matrix, which is computationally more efficient. Simulation results for a benchmark example from the literature show that GDPC with a reduced size Hankel matrix can match the performance of data–enabled predictive control with a larger Hankel matrix in the presence of noisy data. Full article
(This article belongs to the Special Issue Information Theory Applied in Scientific Computing)
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32 pages, 6748 KiB  
Article
Resources Relocation Support Strategy Based on a Modified Genetic Algorithm for Bike-Sharing Systems
by Horațiu Florian, Camelia Avram, Mihai Pop, Dan Radu and Adina Aștilean
Mathematics 2023, 11(8), 1816; https://doi.org/10.3390/math11081816 - 11 Apr 2023
Viewed by 1421
Abstract
In recent decades, special attention has been given to the adverse effects of traffic congestion. Bike-sharing systems, as a part of the broader category of shared transportation systems, are seen as viable solutions to these problems. Even if the quality of service in [...] Read more.
In recent decades, special attention has been given to the adverse effects of traffic congestion. Bike-sharing systems, as a part of the broader category of shared transportation systems, are seen as viable solutions to these problems. Even if the quality of service in bike-sharing service systems were permanently improved, there would still be some issues that needed new and more efficient solutions. One of these refers to the rebalancing operations that follow the bike depletion phenomenon that affects most stations during shorter or longer time periods. Current work develops a two-step method to perform effective rebalancing operations in bike-sharing. The core elements of the method are a fuzzy logic-controlled genetic algorithm for bike station prioritization and an inference mechanism aiming to do the assignment between the stations and trucks. The solution was tested on traffic data collected from the Citi Bike New York bike-sharing system. The proposed method shows overall superior performance compared to other algorithms that are specific to capacitated vehicle routing problems: standard genetic algorithm, ant colony optimization, Tabu search algorithm, and improved performance compared to Harris Hawks optimization for some scenarios. Since the algorithm is independent of past traffic measurements, it applies to any other potential bike-sharing system. Full article
(This article belongs to the Special Issue Information Theory Applied in Scientific Computing)
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34 pages, 2417 KiB  
Article
Development of Evolutionary Systems Based on Quantum Petri Nets
by Tiberiu Stefan Letia, Elenita Maria Durla-Pasca, Dahlia Al-Janabi and Octavian Petru Cuibus
Mathematics 2022, 10(23), 4404; https://doi.org/10.3390/math10234404 - 22 Nov 2022
Viewed by 1091
Abstract
Evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The mappings of these genomes lead to individuals that correspond [...] Read more.
Evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The mappings of these genomes lead to individuals that correspond to the searched solutions. The individual evaluations by simulations serve for the improvement of their genotypes. Quantum computations, unlike the classical computations, can describe and simulate a large set of individuals simultaneously. This feature is used to diminish the time for finding the solutions. Quantum Petri Nets (QPNs) can model dynamical systems with probabilistic features that make them appropriate for the development of ES. Some examples of ES applications using the QPNs are given to show the benefits of the current approach. The current research solves quantum evolutionary problems using quantum genetic algorithms conceived and improved based on QPN. They were tested on a dynamic system using a Quantum Discrete Controlled Walker (QDCW). Full article
(This article belongs to the Special Issue Information Theory Applied in Scientific Computing)
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19 pages, 13615 KiB  
Article
Speeding Up Semantic Instance Segmentation by Using Motion Information
by Otilia Zvorișteanu, Simona Caraiman and Vasile-Ion Manta
Mathematics 2022, 10(14), 2365; https://doi.org/10.3390/math10142365 - 06 Jul 2022
Viewed by 1602
Abstract
Environment perception and understanding represent critical aspects in most computer vision systems and/or applications. State-of-the-art techniques to solve this vision task (e.g., semantic instance segmentation) require either dedicated hardware resources to run or a longer execution time. Generally, the main efforts were to [...] Read more.
Environment perception and understanding represent critical aspects in most computer vision systems and/or applications. State-of-the-art techniques to solve this vision task (e.g., semantic instance segmentation) require either dedicated hardware resources to run or a longer execution time. Generally, the main efforts were to improve the accuracy of these methods rather than make them faster. This paper presents a novel solution to speed up the semantic instance segmentation task. The solution combines two state-of-the-art methods from semantic instance segmentation and optical flow fields. To reduce the inference time, the proposed framework (i) runs the inference on every 5th frame, and (ii) for the remaining four frames, it uses the motion map computed by optical flow to warp the instance segmentation output. Using this strategy, the execution time is strongly reduced while preserving the accuracy at state-of-the-art levels. We evaluate our solution on two datasets using available benchmarks. Then, we conclude on the results obtained, highlighting the accuracy of the solution and the real-time operation capability. Full article
(This article belongs to the Special Issue Information Theory Applied in Scientific Computing)
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