Relevant bibliographies by topics / Matlab language

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Matlab language'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 June 2025

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Journal articles on the topic "Matlab language"

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Wirth, M. A., and P. Kovesi. "MATLAB as an introductory programming language." Computer Applications in Engineering Education 14, no. 1 (2006): 20–30. http://dx.doi.org/10.1002/cae.20064.

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Geete, Prof Nitin, and Prof Saurabh Verma. "MATLAB APPLICATIONS FOR MATHEMATICS STUDENTS." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 01 (2023): 3138–41. http://dx.doi.org/10.47191/ijmcr/v11i1.05.

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One of the oldest professions in human society is mathematics. It is closely tied to all stages of the development of human society's cognitive system. An essential tool for researching and resolving these real-world issues is mathematical modeling. A mathematical model is a structure that can be created mathematically by using the right tools and making the necessary, simplified assumptions about the underlying laws of a specific object and purpose in the real world. A mathematical model is an abstraction of real-world issues, to put it simply. Computer application technology has greatly influenced many different businesses as a result of computer progress. Among these, MATLAB is a piece of commercial math software that excels in applications for math in science and technology. One of the most widely used fourth-generation programming languages worldwide is MATLAB. It is one of the greatest environments for numerical analysis. Additionally, the most potent and effective language utilized in technical computing is MATLAB. It is designed to address mathematically notated challenges. It is employed in algorithm development and matrix calculations. Additionally, it enables us to run programmers written in any other programming language. It implies that MATLAB can make use of the advantages of other programming languages. It is extensively utilized in robotics, communications, control systems, signal processing, and image processing. This paper first examines MATLAB's benefits and discusses how to utilize it for mathematical operations and modeling.
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Prof., Nitin Geete, and Saurabh Verma Prof. "MATLAB APPLICATIONS FOR MATHEMATICS STUDENTS." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 01 (2023): 3138–41. https://doi.org/10.5281/zenodo.7524723.

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One of the oldest professions in human society is mathematics. It is closely tied to all stages of the development of human society's cognitive system. An essential tool for researching and resolving these real-world issues is mathematical modeling. A mathematical model is a structure that can be created mathematically by using the right tools and making the necessary, simplified assumptions about the underlying laws of a specific object and purpose in the real world. A mathematical model is an abstraction of real-world issues, to put it simply. Computer application technology has greatly influenced many different businesses as a result of computer progress. Among these, MATLAB is a piece of commercial math software that excels in applications for math in science and technology. One of the most widely used fourth-generation programming languages worldwide is MATLAB. It is one of the greatest environments for numerical analysis. Additionally, the most potent and effective language utilized in technical computing is MATLAB. It is designed to address mathematically notated challenges. It is employed in algorithm development and matrix calculations. Additionally, it enables us to run programmers written in any other programming language. It implies that MATLAB can make use of the advantages of other programming languages. It is extensively utilized in robotics, communications, control systems, signal processing, and image processing. This paper first examines MATLAB's benefits and discusses how to utilize it for mathematical operations and modeling.
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Sharma, Gaurav, and Jos Martin. "MATLAB®: A Language for Parallel Computing." International Journal of Parallel Programming 37, no. 1 (2008): 3–36. http://dx.doi.org/10.1007/s10766-008-0082-5.

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Joisha, Pramod G., and Prithviraj Banerjee. "A translator system for the MATLAB language." Software: Practice and Experience 37, no. 5 (2007): 535–78. http://dx.doi.org/10.1002/spe.781.

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Wang, Pin. "The status of MATLAB in the Software Development Market." ITM Web of Conferences 73 (2025): 03015. https://doi.org/10.1051/itmconf/20257303015.

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With the continuous development of technology, it is necessary to analyze the applicability of MATLAB in the field of software development according to the mainstream trend of the market. This article aims to outline the design purpose of MATLAB and its applicable user groups, and emphasizes the advantages of MATLAB in scientific computing and engineering applications. By comparing it with other programming languages such as Python, this article further highlights the unique advantages of MATLAB in computing performance and functions, thereby clarifying its importance and application value in related fields. This article concludes that as a programming language specializing in computing, MATLAB is not only loved by professionals in engineering, finance, physics, and other fields but also has a high status in education. Through the experiment of developing small games and comparing the rich functions of Python in software development, it points out the immaturity of MATLAB in UI design, code optimization, data transmission, and other aspects. The subsequent research provides some improvements that MATLAB can make in code performance optimization and loop performance. The significance of this study is to let people who are new to software development choose a more suitable programming language.
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Chauveau, Stéphane, and François Bodin. "Menhir: An Environment for High Performance Matlab." Scientific Programming 7, no. 3-4 (1999): 303–12. http://dx.doi.org/10.1155/1999/525690.

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In this paper we present Menhir a compiler for generating sequential or parallel code from the Matlab language. The compiler has been designed in the context of using Matlab as a specification language. One of the major features of Menhir is its retargetability to generate parallel and sequential C or Fortran code. We present the compilation process and the target system description for Menhir. Preliminary performances are given and compared with MCC, the MathWorks Matlab compiler.
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Szymczyk, Magdalena, and Piotr Szymczyk. "Matlab and Parallel Computing." Image Processing & Communications 17, no. 4 (2012): 207–16. http://dx.doi.org/10.2478/v10248-012-0048-5.

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Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.
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Cuevas, Erik Hombre, Daniel Zaldivar, and Marco Perez. "Impact of Programming Languages on Learning Performance." International Journal of Information and Communication Technology Education 21, no. 1 (2025): 1–17. https://doi.org/10.4018/ijicte.371419.

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The integration of various programming languages into the undergraduate engineering curriculum often occurs without adequate evaluation of their effectiveness within specific disciplines. Recently, Python and MATLAB have garnered significant attention as preferred languages for teaching subjects such as image processing and computer vision. Despite their popularity, few studies have evaluated their effectiveness in teaching these topics. This study aimed to determine which programming language, Python or MATLAB, facilitates a better understanding of image processing concepts. The analysis compared the learning performance of two groups, each comprising 40 students. One group utilized MATLAB as the programming tool, while the other implemented image processing algorithms using Python. To analyze the differences between these languages, a testing method of experimental design was employed. The results indicate that students who learned with MATLAB demonstrated superior learning performance compared to those who used Python.
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Mathews, John H., and Kurtis D. Fink. "Using MATLAB as a programming language for numerical analysis." International Journal of Mathematical Education in Science and Technology 25, no. 4 (1994): 481–90. http://dx.doi.org/10.1080/0020739940250402.

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Dissertations / Theses on the topic "Matlab language"

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Chevalier-Boisvert, Maxime. "McVM: An optimizing virtual machine for the MATLAB programming language." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86850.

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In recent years, there has been an increase in the popularity of dynamic languages such as Python, Ruby, PHP, JavaScript and MATLAB. Programmers appreciate the productivity gains and ease of use associated with such languages. However, most of them still run in virtual machines which provide no Just-In-Time (JIT) compilation support, and thus perform relatively poorly when compared to their statically compiled counterparts. While the reference MATLAB implementation does include a built-in compiler, this implementation is not open sourced and little is known abouts its internal workings. TheMcVMproject has focused on the design and implementation of an optimizing virtual machine for a subset of the MATLAB programming language.<br>Virtual machines and JIT compilers can benefit from advantages that static compilers do not have. It is possible for virtual machines to make use of more dynamic information than static compilers have access to, and thus, to implement optimization strategies that are more adapted to dynamic languages. Through theMcVMproject, some possible avenues to significantly improve the performance of dynamic languages have been explored. Namely, a just-in-time type-based program specialization scheme has been implemented in order to take advantage of dynamically available type information.<br>One of the main contributions of this project is to provide an alternative implementation of the MATLAB programming language. There is already an open source MATLAB interpreter (GNU Octave), but our implementation also includes an optimizing JIT compiler and will be open sourced under the BSD license. McVM aims to become a viable implementation for end-users, but could also see use in the compiler research community as a testbed for dynamic language optimizations. In addition to the contribution of the McVM framework itself, we also contribute the design and implementation of a novel just-in-time type-based program specialization system aimed at dynamic languages.<br>The novel specialization system implemented in McVM shows much promise in terms of potential speed improvements, yielding performance gains up to 3 orders of magnitude faster than competing implementations such as GNU Octave. It is also easily adaptable to other dynamic programming languages such as Python, Ruby and JavaScript. The investigation of performance issues we make in this thesis also suggests future research directions for the design of dynamic language compilers of the future.<br>Ces dernières années, il y a eu une augmentation de la popularité des langages dynamiques tels que Python, Ruby, PHP, JavaScript et MATLAB. Les programmeurs apprécient les gains de productivité et la facilité d'utilisation associée à ces langues. Cependant, la plupart de ces langages s'exécutent encore dans des machines virtuelles qui ne fournissent aucun support pour la compilation à la volée, et ont donc une performance inférieure si on les compare à leurs homologues compilés statiquement. Bien que l'implémentation de référence de MATLAB comprenne un compilateur intégré, cette application n'est pas open source et son fonctionnement interne demeure un secret industriel. Le projet McVM a mis l'accent sur la conception et l'implémentation d'une machine virtuelle optimisée pour un sous-ensemble du langage de programmation MATLAB.<br>Les machines virtuelles et les compilateurs à la volée peuvent bénéficier d'avantages que les compilateurs statiques n'ont pas. Il est possible pour les machines virtuelles de faire usage d'informations dynamique à laquelle les compilateurs statiques n'ont pas accès, et donc, de mettre en oeuvre des stratégies d'optimisation qui sont plus adaptées aux langages dynamiques. à travers le projet McVM, plusieurs avenues possibles pour améliorer considérablement la performance des langages dynamiques ont été explorées. Entre autre, un système de spécialisation de programmes à la volée permettant de profiter d'informations sur les types disponible dynamiquement a été implémenté.<br>L'une des principales contributions de ce projet est de fournir une implémentation alternative du langage de programmation MATLAB. Il existe déjà un interpréteur MATLAB open source (GNU Octave), mais notre application comprend également un compilateur à la volée optimisé et sera distribuée sous la licence open source BSD. McVM vise à devenir une implémentation viable pour les utilisateurs finaux, mais pourrait aussi être utilisée dans le milieu de la recherche sur les compilateurs comme outil d'expérimentation. En plus de la contribution du logiciel intégré McVM lui-même, nous avons également contribué à la conception et la réalisation d'un système de spécialisation de programme à la volée visant à l'optimisation des langages dynamiques.<br>Le système de spécialisation mis en oeuvre dans McVM se montre très prometteur en termes de potentiel d'améliorations de la vitesse d'exécution, permettant des gains de performance allant jusqu'à trois ordres de grandeur comparés aux implémentations concurrentes telles que GNU Octave. Il est également facilement adaptable à d'autres langages de programmation dynamique tels que Python, Ruby et JavaScript. L'examen des problèmes de performance que nous faisons dans cette thèse suggère aussi des pistes de recherche pour la conception des compilateurs de langages de programmation dynamiques de l'avenir.
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Doherty, Jesse. "McSAF: An extensible static analysis framework for the MATLAB language." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106412.

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MATLAB is a popular language for scientific and numerical programming. Despite its popularity, there are few active rojects providing open tools for MATLAB related compiler research. This thesis provides the McLAB Static Analysis Framework, McSAF, the goal of which is to simplify the development of new compiler tools for MATLAB. The McLAB project was started in order to develop such tools in the hopes of attracting further research. The goal of the project is to provide an extensible compiler toolkit for MATLAB and scientific programming. It is intended to explore the compilation challenges unique to MATLAB and to explore new language features that could help scientific programmers be more productive. One piece of functionality that is particularly important for compiler research is the ability to perform static analysis. Without the information provided by static analyses, program transformations and optimizations, and automated programmer feedback would not be possible. In order to make the development of static analyses simpler, this thesis contributes a framework for creating static analyses for the MATLAB language. This framework is intended to make writing analyses easier by providing core functionality and API for developing such analyses. It also aims to make analysis development easier by providing an intermediate representation called McLAST, which provides simpler syntax and explicitly exposes some of MATLAB's semantics. In order to give analysis writers a head start, some example analyses are provided. These include simple analyses intended to demonstrate the use of the framework, and some more complicated analyses that provide basic semantic information about MATLAB programs. In addition to the framework making development of analyses simpler, McSAF is also designed to be extended to new language features. Not only can the framework be extended, but existing analyses can also be extended. This allows work that was previously done for analyzing MATLAB code to be applied to future language extensions.<br>MATLAb est un langage de programmation science et numérique utilisé autant en industrie que dans le milieu académique. Malgré cette popularité, peu de project de rercherhe on été entreprise dans le but de produire une suite de compilation pour MATLAB. Cette thèse contribue le McLAB Static Analysis Framework, McSAF, qui a l'objectif de simplifier le développement des nouveaux outils de compilation pour MATLAB. Le projet McLAB fait suite à ce manque, dans l'espoir d'attiser les recherches sur ce sujet. L'objectif principale ce résume au développent d'une trousse de compilation extensible pour MATLAB et les langage de programmation pour science. Le projet est motivé par des défis de compilation unique à MATLAB, et par l'exploration de nouvelles structures syntaxical améliorant l'expérience de programmation scientifique. L'une des fonctionnalité cher au domaine de la compilation est l'habilité a performer des analyses statique de programme. Sans ces informations que nous procures l'analyse statique, une grande partie des transformations et autres optimisations désiré lors du processus de compilation ne serait pas possible. Pour rendre la développent des analyses statique plus simple, cette thèse contribue un cadre pour créer des analyses statique pour le langage MATLAB. L'objectif de ce cadre est de rendre la programmation des analyses plus simple en fournissant les fonctionnalités de base et une API pour développer de telles analyses. Un autre objectif est de rendre le développent des analyses plus simple en fournissant une représentation intermédiaire, \mclast, qui fourni une syntaxe plus simple est qui expose les sémantiques de MATLAB. Pour aider les écrivains d'analyse, quelques exemples d'analyse sont fournis. En plus, quelques analyses utiles sont également fournis. Ces analyses fournissent des informations de base reliée à les sémantiques de MATLAB. Ils ont des application partout dans le projet. L'objectif final du cadre est d'être extensible. Le framework doit fonctionner avec des nouvelles structure de langue. Sa veut dire que les programmeur peuvent créer des nouvelles analyses pour ces extension, et que les analyses qui existaient pour le langage de base, peut être adapter aux nouvelles structure.
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Willkomm, Johannes [Verfasser]. "The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation / Johannes Willkomm." München : Verlag Dr. Hut, 2021. http://d-nb.info/1238423108/34.

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Bäck, Carl. "Evaluation of high-level synthesis tools for generation of Verilog code from MATLAB based environments." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-78738.

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FPGAs are of interest in the signal processing domain as they provide the opportunity to run algorithms at very high speed. One possible use case is to sort incoming data in a measurement system, using e.g. a histogram method. Developing code for FPGA applications usually requires knowledge about special languages, which are not common knowledge in the signal processing domain. High-level synthesis is an approach where high-level languages, as MATLAB or C++, can be used together with a code generation tool, to directly generate an FPGA ready output. This thesis uses the development of a histogram as a test case to investigate the efficiency of three different tools, HDL Coder in MATLAB, HDL Coder in Simulink and System Generator for DSP in comparison to the direct development of the same histogram in Vivado using Verilog. How to write and structure code in these tools for proper functionality was also examined. It has been found that all tools deliver an operation frequency comparable to a direct implementation in Verilog, decreased resource usage, a development time which decreased by 27% (HDL Coder in MATLAB), 45% (System Generator) and 64% (HDL Coder in Simulink) but at the cost of increased power consumption. Instructions for how to use all three tools has been collected and summarised.<br>I ingångssteget på ett mätsystem är det av intresse att använda en FPGA för att uppnå höga hastigheter på de oundvikliga datafiltrering och sorterings algoritmer som körs. Ett problem med FPGAer är att utvecklingen ställer höga krav på specifik kunskap gällande utvecklingsspråk och miljöer vilket för en person specialiserad inom t.ex. signalbehandling kan saknas helt. HLS är en metodik där högnivåspråk kan användas för digital design genom att nyttja ett verktyg för automatgenerering av kod. I detta arbete har utveckling av ett histogram använts som testfall för att utvärdera effektivitet samt designmetodik av tre olika HLS verktyg, HDL Coder till MATLAB, HDL Coder till Simulink och System Generator for DSP. Utvecklingen i dessa verktyg har jämförts mot utvecklingen av samma histogram i Vivado, där språket Verilog använts. Arbetets slutsater är att samtliga verktyg som testats leverar en arbetsfrekvens som är jämförbar med att skriva histogrammet direkt i Verilog, en minskad resursanvändning, utvecklingstid som minskat med 27% (HDL Coder i MATLAB), 45% (System Generator) och 64% (HDL Coder i Simulink) men med en ökad strömförbrukning. En sammanställning av instruktioner för utveckling med hjälp av verktygen har även gjorts.
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Mach, Břetislav. "Využití virtuální reality v dynamice vozidel." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228626.

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The aim of this master’s thesis is to use virtual reality to analyze and display the vehicle and the quantities characterizing the dynamic properties of the vehicle. The simulation software tools are used Matlab/Simulink and Virtual Reality Toolbox. The measured or calculated data will the input for the simulation. The dissertation is aimed to create a virtual scene and a dynamic system for the movement of file.
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Kadeřábek, Jan. "Generátor biologických průběhů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218661.

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This semestral thesis describes signal EKG, it’s origin, evaluation and device for his generation. After identification with standard course of EKG signal, was such as signal created in program language Matlab. It’s consisting of 10 sinus courses, where each of them has it’s own frequence, start phase and amplitude. Each of this part of EKG signal is beat frequence variant. Rates of it’s durations and sizes were fixed from medicin literature. Such as created program were rewritten to program language C and is basic of main program, which control signal processor. Finally was propoused preparation, which will be generated this signal. Outcome of this thesis is teoretic treatise about signal EKG, created program for generation of this course include part control and machine doing realization of biological courses.
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Štefanec, Tomáš. "Vizualizace výsledků simulace jízdní dynamiky vozidla." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231198.

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The master thesis is focused on creation of a visualization environment that displays a movement of a vehicle. A connection of the virtual reality with Matlab/Simulink and its 3D Animation toolbox is realized. Basic parameters of the vehicle can be easily edited using application created in Matlab. Selected dynamic effects are rendered together with the movement of the vehicle. The visualization is managed by results of the simulation calculations or measurements.
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SHI, YU. "Enhanced SAR Image Processing Using A Heterogeneous Multiprocessor." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11517.

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<p>Synthetic antenna aperture (SAR) is a pulses focusing airborne radar which can achieve high resolution radar image. A number of image process algorithms have been developed for this kind of radar, but the calculation burden is still heavy. So the image processing of SAR is normally performed “off-line”.</p><p>The Fast Factorized Back Projection (FFBP) algorithm is considered as a computationally efficient algorithm for image formation in SAR, and several applications have been implemented which try to make the process “on-line”.</p><p>CELL Broadband Engine is one of the newest multi-core-processor jointly developed by Sony, Toshiba and IBM. CELL is good at parallel computation and floating point numbers, which all fit the demands of SAR image formation.</p><p>This thesis is going to implement FFBP algorithm on CELL Broadband Engine, and compare the results with pre-projects. In this project, we try to make it possible to perform SAR image formation in real-time.</p>
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Geridonmez, Fatih. "Simulation Of Motion Of An Underwater Vehicle." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608765/index.pdf.

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In this thesis, a simulation package for the Six Degrees of Freedom (6DOF) motion of an underwater vehicle is developed. Mathematical modeling of an underwater vehicle is done and the parameters needed to write such a simulation package are obtained from an existing underwater vehicle available in the literature. Basic equations of motion are developed to simulate the motion of the underwater vehicle and the parameters needed for the hydrodynamic modeling of the vehicle is obtained from the available literature. 6DOF simulation package prepared for the underwater vehicle was developed using the MATLAB environment. S-function hierarchy is developed using the same platform with C++ programming language. With the usage of S-functions the problems related to the speed of the platform have been eliminated. The use of Sfunction hierarchy brought out the opportunity of running the simulation package on other independent platforms and get results for the simulation.
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Amorosi, Davide. "Analog signal generation with Raspberry Pi boards for short-range communications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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The aim of this thesis is to realise a low-cost wireless application for short range underwater communications, using ultrasonic frequencies. The project can be described in four main steps. The first one is a detailed study of the serial peripheral interface. It has been carried out in order to understand how to physically and logically connect, through SPI, the hardware involved in this work: a Raspberry Pi 3 model B and the MCP4822 digital to analog converter embedded in the ADC-DAC Pi Zero module. The second phase concerns a Simulink model created in order to reduce the complexity of the problem. It has been useful as a guideline for the development of the software. Then, the implementation step includes the full execution of the code, which processes information data and digital samples of a sine wave and converts them in their respective analog signals. Configurable parameters, as the amplitude and the frequency of the sinusoidal carrier, provide more flexibility to the system. The last testing phase consists in several measurements on the hardware to assess the reliability of the system, varying some parameters and comparing these results with the simulations, run on Simulink. The overall performance respects the low-cost nature of the Raspberry Pi: the analog signals exhibit phase noise due to the not perfect periodicity of the SPI clock but they are still reliable and clear enough for project purposes.
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Books on the topic "Matlab language"

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Inc, Math works, ed. MATLAB: The language of technical computing. Math works Inc, 1998.

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Inc, Math Works, ed. Matlab version 5: The language of technical computing : Matlab 5 new features. Math Works Inc., 1996.

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Inc, Math Works, ed. Matlab version 5: The language of technical computing : Matlab 5 late-breaking news. Math Works Inc., 1996.

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Boutsen, Frank R. MATLAB primer for speech-language pathology and audiology. Plural, 2016.

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Jr, Raul Raymond Kapuno. Programming for Chemical Engineers: Using C, C++, and MATLAB. Infinity Science Press, 2008.

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David, Chancogne, ed. Introduction to engineering programming: In C, Matlab and Java. Wiley, 1999.

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Kapuno, Raul Raymond. Programming for chemical engineers: Using C, C++, and MATLAB®. Infinity Science Press, 2008.

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Spencer, Graves, Hooker Giles, and SpringerLink (Online service), eds. Functional Data Analysis with R and MATLAB. Springer-Verlag New York, 2009.

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Banchs, Rafael E. Text Mining with MATLAB®. Springer New York, 2013.

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Sticklen, Jon. Introduction to technical problem solving with MATLAB. 2nd ed. Great Lakes Press, 2006.

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Book chapters on the topic "Matlab language"

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Turk, Irfan. "Fundamentals of MATLAB Language." In Practical MATLAB. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5281-9_2.

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Danial, Albert. "Language Basics." In Python for MATLAB Development. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7223-7_3.

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Danial, Albert. "Language Pitfalls." In Python for MATLAB Development. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7223-7_15.

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López, César Pérez. "Matlab Language: Development Environment Features." In MATLAB Programming for Numerical Analysis. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0295-1_3.

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Banchs, Rafael E. "The Structure of Language." In Text Mining with MATLAB®. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87695-1_6.

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Webb, Cerian Ruth, and Mirela Domijan. "MATLAB® as a Programming Language." In Learning Materials in Biosciences. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21337-4_11.

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López, César Pérez. "MATLAB Language: Variables, Numbers, Operators and Functions." In MATLAB Programming for Numerical Analysis. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0295-1_2.

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Chen, Yi, and Long Huang. "Introduction." In MATLAB Roadmap to Applications. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8788-3_1.

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Abstract Explain the purpose and applications of MATLAB in various fields. Gain an overview of the MATLAB environment and user interface Understand the structure and organisation of this book. Appreciate the importance of MATLAB as a programming language and numerical computing tool. Describe the topics covered in this book. Discover the benefits of using MATLAB. Identify the different types of users who can benefit from using MATLAB.
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López, César Pérez. "MATLAB Language: M-Files, Scripts, Flow Control and Numerical Analysis Functions." In MATLAB Programming for Numerical Analysis. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0295-1_4.

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Feng, Xueqing, Nan Zhang, and Zhenhua Duan. "Implementation of Matlab matfun Toolkit Based on MSVL." In Structured Object-Oriented Formal Language and Method. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29476-1_4.

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Conference papers on the topic "Matlab language"

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Kulkarni, Abhishek v., Addanki Ranga Ravindra, Maheswarla Sai Sangeethi, G. Tejdeep Reddy, and Meena Belwal. "Cross Language Compiler Construction From Matlab To Python." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726222.

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Kiepas, Patryk, Jaroslaw Kozlak, Claude Tadonki, and Corinne Ancourt. "Profile-based vectorization for MATLAB." In PLDI '18: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2018. http://dx.doi.org/10.1145/3219753.3219756.

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Curry, Guy L., Amarnath Banerjee, Hiram Moya, and Harry L. Jones. "A modeling language generator for a discrete event simulation language in MATLAB." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822161.

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Bispo, João, Luís Reis, and João M. P. Cardoso. "Multi-Target C Code Generation from MATLAB." In PLDI '14: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2014. http://dx.doi.org/10.1145/2627373.2627389.

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Bispo, João, Luís Reis, and João M. P. Cardoso. "Techniques for efficient MATLAB-to-C compilation." In PLDI '15: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2015. http://dx.doi.org/10.1145/2774959.2774961.

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Reis, Luís, João Bispo, and João M. P. Cardoso. "SSA-based MATLAB-to-C compilation and optimization." In PLDI '16: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2016. http://dx.doi.org/10.1145/2935323.2935330.

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Schmalz, Mark S., Gerhard X. Ritter, Joseph N. Wilson, and Eric T. Hayden. "Supporting image algebra in the Matlab programming language for compression research." In SPIE Optical Engineering + Applications, edited by Mark S. Schmalz, Gerhard X. Ritter, Junior Barrera, Jaakko T. Astola, and Franklin T. Luk. SPIE, 2009. http://dx.doi.org/10.1117/12.829203.

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Hummel, Patrick, Joachim Grimstad, Yuchen Xia, and Andrey Morozov. "Generation of MATLAB Simscape Models Using Large Language Models for Training Reliability Evaluation Methods." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-144344.

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Abstract The approach presented in this paper makes use of the natural language processing capabilities of large language models in order to generate MATLAB Simscape models based on specification texts. This process is based on a well-defined state machine and begins with the creation of abstract system models that may be iterated on using natural language. The abstract models are then converted to more detailed models by adding parameters and blocks required for simulation. Fault tolerance may optionally be improved by automatically applying design patterns that increase redundancy using voter and comparator blocks, among others. The abstract models are then converted into MATLAB Simscape models using traditional algorithms. The generated models can be used to train artificial intelligence systems designed for reliability evaluation. A selection of publicly available large language models is systematically evaluated based on capability regarding this task. A software prototype demonstrates the feasibility of using generative artificial intelligence to assist with the modeling of systems in MATLAB Simscape as a proof-of-concept. Current limitations and future research potential is discussed.
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Ogbonnaya, Chibueze. "Teaching Statistics With MATLAB: How and Why?" In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t10e2.

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Teaching statistics with software is a practical approach for introducing students to real world problems and how to analyse data. There are different choices of software for teaching statistics, however R and SPSS have been the most common choices. This has often caused a dilemma for students and researchers who have an engineering background and may prefer to do most of their analysis within MATLAB instead of learning a new programming language they may not use often. This paper explores lesson learned from creating a new MATLAB short course and some useful tools for teaching statistics using MATLAB.
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Schmalz, Mark S., Gerhard X. Ritter, and Eric Hayden. "Image Algebra Matlab language version 2.3 for image processing and compression research." In SPIE Optical Engineering + Applications. SPIE, 2010. http://dx.doi.org/10.1117/12.864277.

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Reports on the topic "Matlab language"

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May, Trent, Thomas Clement, John Miller, Charles Carleton, Aaron Webber, and Samuel Williams. From research to production : lessons learned and best practices. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49197.

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This paper provides an overview of best practices to assist individuals and teams in transitioning software from a research product into a production environment. The information contained in this paper consists of best practices and lessons learned from an assignment consisting of transitioning a science-based research suite of programs into a more modern software format with appropriate preparations and considerations to be deployed in a production environment. The original software suite was written using both MATLAB and Python programming languages, and the new production version was written in the Python programming language.
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Yates, Timothy, and Kevin McNally. Uncertainty and sensitivity analysis of the HardSPEC environmental exposure model for pesticide regulatory assessments. HSE, 2024. http://dx.doi.org/10.69730/hse.24rr1204.

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HardSPEC is an environmental fate model that predicts concentrations of pesticides in surface and groundwater following application on hard surfaces, such as roads, pavements and railway tracks, and a subsequent series of rainfall events; such pesticide use is typically of herbicides used for weed control. HardSPEC is used by HSE to assess the risk of, and take decisions related to, herbicide application. In this project we implemented the model in Matlab, a scientific programming language, and verified that the results precisely matched the results from the spreadsheet implementation. We developed a sensitivity analysis framework that varies the values of any of the parameters automatically and records the predicted concentrations
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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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