the matlab assignment help
Exploring Advanced Techniques in MATLAB Programming
This paper presents several advanced techniques in programming with MATLAB. Automatic differentiation methods, important in the area of optimization, as well as techniques for creating grid functions, cell arrays, and containers with MATLAB are presented. Several different uses of the anonymous function handle assignment are shown, including advanced techniques such as discretizing a domain for faster performance of a function call, and defining an objective or function handle by integrating an embedded call to function. Advanced debugging techniques are presented, such as evaluating mathematical expressions with an improperly defined variable or debugging a MEX file using the UNIX dbx command. Advanced GUI techniques are presented, including the use of the Java progress bar and Jython to access the Python package, which allows several advanced GUIs to be created. The use of object-oriented programming (OOP) in MATLAB is defined using simple examples of class, object, and method definitions. Finally, the paper concludes with how to interface MATLAB with Excel, and how to pull out command line arguments from an M-file function and script.
MATLAB is a high-level programming language that provides an interactive environment for linear algebra and mathematical computations. It is widely used in graduate engineering and science courses to solve complex mathematical problems that are difficult to solve by classical methods. MATLAB provides means of doing example problems more quickly and easily than conventional programming languages, especially in the area of communicating with a remote server in which numerous calls to a specific function, often utilizing very slightly modified inputs, are required. The MATLAB compiler then makes it possible for these solutions to be executed by those without a commercial version of the software. Finally, the MATLAB Compiler enables the user to use MATLAB programs whenever he or she needs them by converting a solve-tune-program into an application.
In MATLAB, a heap is a complete binary tree storing only keys or data in which every level of the tree, except possibly the last one, is completely filled, and all the nodes are as far left as possible. It’s a useful and efficient structure for many applications because a heap can be easily implemented into a linear-array-based data structure and has time complexity in many fundamental operations such as insertion, extractmin, and changekey. A MATLAB User File introduces a 1-based array-based min heap, max heap, and heap stack and their fundamental operations.
Two functions, i.e., DrawHeap and DrawHeapStack, are developed for drawing a heap and a heap stack, respectively. DrawHeap visualizes the internal layout of a complete binary tree-based heap. DrawHeapStack visualizes the internal layout of all nodes across every level of all trees being stored of a complete binary tree-based heap stack. They both are very useful for comprehension of algorithm performances and justifying their time complexity constraints.
In the second part of this series, we introduced you to advanced techniques in MATLAB programming and the associative data structure. In this third and final tutorial in the series, we introduce advanced signal processing and image analysis techniques and explore advanced techniques in MATLAB programming and applications. MATLAB is widely used as a computational tool in science and engineering encompassing the fields of physics, chemistry, math, and engineering. It is also used in many corporate computing environments as a computational gateway for software systems, databases, and spreadsheets. It also supports extensions to the C and Fortran languages. Since it is widely available, simplified free distribution is also available through third-party tools. Hence, an introduction to MATLAB could be beneficial in terms of computational implementation at the same time assisting anybody interested in getting started with programming and coding.
It is inevitable to look beyond the basics if one were to go in depth in any subject, and hence explore more methods/functions to solve a particular problem. In the first part of this series, we will cover some basic signal processing concepts and one of the basic data structures. In the second part, we covered images, important data structures, and image processing techniques required to handle images. In this final part, we will demonstrate a couple of basic signal processing and image analysis tasks in MATLAB using applications of techniques that we have covered in the first two tutorials based on MATLAB. Follow this 8-step sequence: 1) Download and install the software, 2) Review existing programs of the applications, 3) Connect with the Applications Center, an online repository of applications, 4) Print the Introduction, 5) Read the overview of the spreadsheet, 6) Explore documentation and Help, and finally 7) Explore Tutorial Programs.
This chapter emphasizes utilizing the advanced features in the upcoming releases of MATLAB, such as its support for cloud computing, use of extensive external memory structures, development of deep learning applications, GPU programming, and artificial intelligence. This chapter is not about discussing the underlying optimization algorithms or the software layers, but rather understanding how to use the MATLAB environment to develop applications quickly and thoroughly.
The MathWorks Inc. encourages to explore and develop with the above advanced features without worrying about extent and duration. All suggested codes in this chapter are freely available from the freeMCR distribution of the companion software. The programs are fully working and available for research purposes without any restrictions. This chapter concludes with the use of artificial intelligence in fine-tuning any new design or optimization technique or result. Arena version 15 is the first MathWorks product to include extensive software for both artificial intelligence and MATLAB optimization. With the introduction of Arena to the family of MathWorks products, there are no similarities to other MathWorks products. Arena offers acts, offers fuzziness, supports object-oriented programming and graphical control flow, and offers interfaces in agent persona notebook and neural networks. All these advanced features facilitate easy-to-use and fast development, integration, and training of methods, models, and algorithms for a variety of quantitative and qualitative decision-making applications in fields of economics, engineering, finance, and so on.
In this paper, we proposed a set of advanced MATLAB programming practices that will improve the robustness and efficiency of MATLAB programs. One of the main motivations that triggered the work for this paper were problems that arose from digital signal processing, control, and image processing MATLAB course modules. The programming techniques we propose were heavily tested using real-world-like implementations, and the referred course modules provided an excellent testing environment. Based on the feedback and evaluation of these modules that occurred in a real-time education environment, real problems were investigated, and advanced techniques with a strong theoretical base were developed.
The importance of the advanced techniques described in this paper is supported by their practical implications and their theoretical background. These techniques can also be used in different languages and platforms. Teach these techniques within the scope of advanced programming courses, support them by examples, and provide a testing education environment by course modules. Use signal processing, computer graphics, image processing, and control algorithms as motivation and provide analyses for their computational complexity. Summarize these techniques using the examples—provide more information and open source materials at a dedicated website, and extend the list of advanced techniques for further applications.
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