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**MATLAB** is an acronym for ‘Matrix Laboratory’ and it is a platform for programming and numeric computing developed by **MathWorks**. Matlab is a programming language that contains its own IDE (Integrated Development Environment) and is similar to other popular languages such as **Java**, C#, etc. Since it is a matrix-based language it enables the most natural expression of computational mathematics. Millions of engineers and scientists use it worldwide to develop algorithms, analyze data and create models.

Our Matlab programmers help with basic & advanced Matlab homework topics including Matlab image processing, digital signal processing, Matlab control systms, neural networks, control systems, numerical methods, statistics, finance, electrical engineering, electronics engineering, FFT and more. Our solutions are professional, accurate and relevant to the question discussed.

MATLAB was first developed by Cleve Moler, who was at that time the Chairman of Computer Science Department in the University of New Mexico. While teaching Linear Algebra and Numerical Analysis there he wanted his students to have easy access to EISPACK and LINPACK (Matrix Eigensystem Package and Linear Equation Package, respectively) without the hassle of writing Fortran programs. In order to achieve that he learned parse programming language and wrote his first MATLAB. The first MATLAB was written by him in **Fortran** with matrix as its only data type. And the first version of MATLAB was developed as an interactive matrix calculator with no programming language and it had no programs, toolboxes, and graphics in it.

While teaching the graduate course in Numerical Analysis in Stanford, Moler introduced his class to this matrix calculator. One of his students showed Jack Little, an engineering program graduate at Stanford, MATLAB and it was him who adopted it for his own work. It was Little who suggested that creation of commercial based product based on MATLAB was viable. Little believed in the potential of MATLAB to evolve and wrote a new and extended version of it in C.

PC-MATLAB was released on December, 1984 followed by Pro-MATLAB a year later. Both Little and Steve Bangert made significant modifications and improvements to MATLAB and created its new and extended versions including adding the features of functions, toolboxes, and graphics.

Engineers and scientists in diverse fields like predictive maintenance, finance, medical research, and climatology use MATLAB for organizing, analyzing, and cleaning complex data sets. There are multiple features in MATLAB to achieve these, some of them are listed below:

- Data visualizations are interactive and highly customizable.
- Reprocessing capabilities and data types are designed specifically for scientific and engineering data.
- Availability of documentation that is extensive and professionally written.
- Performance can be accelerated with additional hardware and simple code changes.
- Availability of prebuilt functions that can be used for machine learning, signal processing, and statistical analysis.
- Analysis of big data can be expanded without big code changes.
- Automatic generation of shareable reports from the analysis.

**Graphics:** Ready-to-use graphics along with customizable functions and interactions are available in MATLAB to plot and share the data.

**To create visualizations from built-in libraries:**Built-in plots can be used for visualization of data, to gain insights, and to identify patterns and trends. Function syntax and available chart options can be explored using the integrated documentation,**To explore and annotate visualization:**MATLAB visualizations can be explored and annotated without needing to write a lot of codes. There are options available to pan, zoom, or rotate graphs so that visual exploration to understand the data can be done more thoroughly. Titles, axis labels, and data tips can be annotated interactively.

**Data Analysis:** MATLAB can be used for data analysis including organizing and exploring data, analyzing data with less code, and to expand analysis by making few changes. Tools such as organizing of data using datatypes designed for time-series, categorical, text data, and tabular are available. Iterative tasks such as labeling data or training machine learning models can be done interactively through MATLAB apps.

**Programming:** MATLAB is a programming language that expresses both array mathematics and matrix directly. MATLAB can be used widely from simple interactive commands to developing applications on a large scale basis. Some of the features are elaborated below:

**To write reusable functions:**A complicated program can be broken down into smaller, reusable parts using functions. Code in scripts can also be automatically refactored into usable functions.**To author custom classes:**Using object-oriented programming, objects can be defined by combining data (properties) with functions that can operate on that data (methods). Objects can be used to model the behavior of both systems and devices in the real world as well as to organize code into components that are easier to extend and maintain.**Develop large-scale applications:**Large-scale applications can be developed and maintained using MATLAB via source control integration, continuous integration and deployment, projects, and tool packaging.**App Designer:**This feature can be used for creating software apps. It is user friendly as those who do not possess any professional software developing skills can also use it. Building an app integrates two principal tasks of app building; viz. to program app behavior and to lay out visual components of a graphical user interface. Coding problems can also be automatically checked in app designer by using the Code Analyzer.**External Language Interfaces:**Legacy code written in another programming language can be reused with MATLAB. Using this, responsive websites powered by MATLAB can be created or a program hardware that uses error free embedded C-code program can directly be generated. MATLAB algorithms can also be converted into C/C++ using MATLAB Coder. By using MATLAB Engine APIs, it can be used from within another programming environment. MATLAB Engine APIs are available for other programming languages such as Fortran, C/C++, Java, Python, COM components & applications.

**Hardware:** Through this feature, real-world inputs and outputs can be directly received and sent from MATLAB. C, HDL, or PLC code can be automatically generated from MATLAB algorithms and can be run on FPGAs, microprocessors, and more.

**Parallel Computing:** With parallel computing toolbox one can speed up their work by taking control of local multicore processors and GPUs. Parallelizing of MATLAB applications can be done by using high-level constructs without the use of CUDA and MPI programming.

**Desktop and Web Deployment:** Through this feature people in an organization who work in MATLAB can share their work with those who do not have access to it. MATLAB based programs that are used can be deployed as add-ins for Microsoft Excel, as standalone applications, and software components which can be integrated into enterprise and web applications. And to protect the intellectual property of the person sharing their work all his/her applications and components are encrypted. Sharing of such work can be done royalty free.

**MATLAB on the Cloud:** This feature enables those scientists and engineers using MATLAB to speed up their development processes. MATLAB can be accessed in the web browser using MATLAB Online. MATLAB models and MATLAB analytics can be deployed and incorporated into cloud-based application without recoding them into another language.

**Control Systems:**MATLAB can be used for designing, testing, and implementing control systems. Hence, it is used widely by control system engineers at all stages of development including the initial phases of planting modeling and designing to the later phases of validation and testing of system.**Machine Learning:**Engineers and domain experts have used MATLAB to utilize thousand of machine learning applications. It makes the hard features of machine learning easy through point-and-click apps to train and compare models, by presence of advanced feature extraction and signal processing techniques, and availability of the feature to automatically generate C/C++ code for high-performance and embedded applications, etc.**Signal Processing:**MATLAB helps in analyzing signals and time-series data as well as to model, design, and stimulate signal processing systems. It is widely used by Signal processing engineers for all stages of development including exploring algorithms, and for evaluating design implementation tradeoffs.**Deep Learning:**Through a few lines of MATLAB code, deep learning can be incorporated in one’s application whether one is preparing and labeling data, designing algorithms or generating code and deploying it to embedded systems.**Predictive Maintenance:**MATLAB is used by engineers for its Predictive Maintenance Toolbox feature to develop and utilize predictive maintenance software and condition monitoring to undertake IT and OT systems.**Test and Measurement:**MATLAB provides the tools that are needed to acquire, analyze, and explore data as well as to automate the tasks. In MATLAB, data can be controlled and acquired from test instruments, plug-in data acquisition boards, frame grabbers and web cameras. Once data is acquired it can be interactively explored and live visualization and data analysis can be performed.**Image Processing and Computer Vision:**Using MATLAB can lead to insightful understanding on developing of algorithms, exploring of implementation tradeoffs, and image and video data.**Robotics and Autonomous Systems:**MATLAB can be used for developing autonomous applications including perception and motion and to optimize system-level behavior. It is widely used by robotics researchers and engineers to stimulate, design, and verify different aspects of autonomous systems.**Wireless Communications:**MATLABS can be used for creating, designing, testing, and verifying wireless communication systems. It is widely used by Wireless engineering teams to reduce development time, to eliminate design problems early, and to streamline testing and verification.

Matlab is one of the best technologies available and is well-known for the mathematical operations that are performed on matrices and linear algebra. It also provides the fastest IDE for the functioning of mathematical computation, and contains one of the best mathematical package libraries where support is provided for all fields of mathematics. Flexibility is also provided for designing new interfaces as per individual needs.

- Monte-Carlo:If sample sizes are increasing generate largest size first in a vector and use increasingly larger portions for calculations. Saves time and memory.
- Trick: If you have a program that takes a long time to run and does not use all of the CPU/memory, split it into more programs and run using different workers (instances).
- Matlab is a column vector based language. Load memory columnwise first always. For faster code also preallocate memory for variables, Matlab requires contiguous memory usage. Matlab uses copy-on-write, so passing pointers (adresses) to a function will not speed it up. Change variable class to potentially save memory (Ram) using:int8, int16, int32, int64, double, char, logical, single
- You can turn the standard (mostly) Just-In-Time compilation off using: feature accel off. You can use compiled (c,c++,fortran) functions using MEX functions.
- Avoid global variables, they are user-error prone and compilers cant optimize them well.
- Functions defined in a .m file is only available there. Preface function names with initials to avoid clashes, eg.MrP function1.
- Graphic cards(GPU)’s have many (small) cores. If (1) program is computationally intensive (not spending much time transfering data) and (2) massively parallel, so computations can be independent. Consider using the GPU!
- Using multiple cores (parallel computing) is often easy to implement, just use parfor instead of for loops.
- Warnings: empty matrices are NOT overwritten ([] + 1 = []). Rows/columns are added without warning if you write in a nonexistent row/column. Good practise: Use 3i rather than 3*i for imaginary number calculations, because i might have been overwritten by earlier. 1/0 returns inf, not NaN. Dont use == for comparing doubles, they are floating point precision for example: 0.01 == (1 − 0.99) = 0.

Disclaimer:* All of the above information has been shared from publicly available sources for information only. All copyrights acknowledged. *

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clc | Clear command window |

clear | Clear system memory |

clear x | Clear x from memory |

commandwindow | open/select commandwindow |

whos | lists data structures |

whos x | size, bytes, class and attributes of x |

ans | Last result |

close all | closes all gures |

close(H) | closes gure H |

winopen(pwd) | Open current folder |

class(obj) | returns objects class |

save filename | saves all variables to .mat le |

save filename x,y | saves x,y variables to .mat le |

save -append filename x | appends x to .mat le |

load filename | loads all variables from .mat le |

ver | Lists version and toolboxes |

beep | Makes the beep sound |

doc function | Help/documentation for function |

docsearch string | search documentation |

web google.com | opens webadress |

inputdlg | Input dialog box |

methods(A) | list class methods for A |

distrnd | random numbers from dist |

distpdf | pdf from dist |

distcdf | cdf dist |

distrnd | random numbers from dist |

hist(x) | histogram of x |

histfit(x) | histogram and |

*Standard distributions (dist) | norm, t, f, gam, chi2, bino |

*Standard functions | mean,median,var,cov(x,y),corr(x, |

*quantile(x,p) is not textbook version. (It uses interpolation for missing quantiles. |

edit filename | Opens filename in editor |

Alt | Displays hotkeys |

F1 | Help/documentation for highlighted function |

F5 | Run code |

F9 | Run highlighted code |

F10 | Run code line |

F11 | Run code line, enter functions |

Shift + F5 | Leave debugger |

F12 | Insert break point |

Ctrl+Page up/down | Moves between tabs |

Ctrl+Shift | Moves between components |

Ctrl + C | Interrupts code |

Ctrl + D | Open highlighted codes le |

Ctrl + R/T+ | Comment/uncomment line |

Ctrl + N | New script |

Ctrl + W | Close script |

Ctrl+Shift+d | Docks window |

Ctrl+Shift+u | Undocks window |

Ctrl+Shift+m | max window/restore size |

format short Displays 4 digits after 0 format long Displays 15 digits after 0 disp(x) Displays the string x disp(x) Displays the string x num2str(x) Converts the number in x to string num2str([’nA is = ’ OFTEN USED! num2str(a)]) ! mat2str(x) Converts the matrix in x to string int2str(x) Converts the integer in x to string sprintf(x) formated data to a string

addpath(string) adds path to workspace genpath(string) gets strings for subfolders pwd Current directory mkdir Makes new directory tempdir Temporary directory inmem Functions in memory exit Close matlab dir list folder content ver lists toolboxes

xlsread/xlswrite Spreadsheets (.xls,.xlsm) readtable/writetable Spreadsheets (.xls,.xlsm) dlmread/dlmwrite text files (txt,csv) load/save -ascii text files (txt,csv) load/save matlab files (.m) imread/imwrite Image files

keyboard Pauses exceution return resumes exceution tic starts timer toc stops timer profile on starts profiler profile viewer Lets you see profiler output try/catch Great for finding where errors occur dbstop if error stops at first error inside try/catch block dbclear clears breakpoints dbcont resume execution lasterr Last error message lastwarn Last warning message break Terminates executiion of for/while loop waitbar Waiting bar

abs(x) | absolute value |

pi | 3:1415::: |

inf | ∞ |

epf | floating point accuracy |

1e6 | 10^{6} |

sum(x) | sums elements in x |

cumsum(x) | Cummulative sum |

prod | product of array elements |

cumprod(x) | cumulative product |

diff | difference of elements |

round/ceil/fix/floor | Standard functions. |

*Standard functions: sqrt, log, exp, max, min, Bessel

*Factorial(x) is only precise for x < 21

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