Question

MATLAB

Answer 1

Introduction

What Is MATLAB?

MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include:

• Math and computation
• Algorithm development
• Modeling, simulation, and prototyping
• Data analysis, exploration, and visualization
• Scientific and engineering graphics
• Application development, including Graphical User Interface building

MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar noninteractive language such as C or Fortran.

The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access to matrix software developed by the LINPACK and EISPACK projects, which together represent the state-of-the-art in software for matrix computation.

MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-productivity research, development, and analysis.

MATLAB features a family of application-specific solutions called toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others.

The MATLAB System

The MATLAB system consists of five main parts:

The MATLAB language.

This is a high-level matrix/array language with control flow statements, functions, data structures, input/output, and object-oriented programming features. It allows both "programming in the small" to rapidly create quick and dirty throw-away programs, and "programming in the large" to create complete large and complex application programs.

The MATLAB working environment.

This is the set of tools and facilities that you work with as the MATLAB user or programmer. It includes facilities for managing the variables in your workspace and importing and exporting data. It also includes tools for developing, managing, debugging, and profiling M-files, MATLAB's applications.

Handle Graphics.

This is the MATLAB graphics system. It includes high-level commands for two-dimensional and three-dimensional data visualization, image processing, animation, and presentation graphics. It also includes low-level commands that allow you to fully customize the appearance of graphics as well as to build complete Graphical User Interfaces on your MATLAB applications.

The MATLAB mathematical function library.

This is a vast collection of computational algorithms ranging from elementary functions like sum, sine, cosine, and complex arithmetic, to more sophisticated functions like matrix inverse, matrix eigenvalues, Bessel functions, and fast Fourier transforms.

The MATLAB Application Program Interface (API).

This is a library that allows you to write C and Fortran programs that interact with MATLAB. It include facilities for calling routines from MATLAB (dynamic linking), calling MATLAB as a computational engine, and for reading and writing MAT-files.

Uses of Matlab

Matrix Laboratory or MATLAB for short is a multi-paradigm numerical computing environment and proprietary programming language developed by Mathworks. It combines computation, visualization, and programming in an easily usable environment and is all expressed in mathematical equations. Written in C, C++, and Java, Matlab was initially released in 1984. The latest version has been released on March 2018.

Applications of Matlab is built around the Matlab scripting language and revolves based on the following mathematical concepts:

• Variables
• Vectors and matrices
• Structures
• Functions
• Function handles
• Classes and object-oriented programming

Top Uses of Matlab

Matlab is a numerical computing environment. According to the Matlab website, the environment is designed for the following uses.

1. Embedded Systems

Embedded systems are computer systems consisting of hardware and software components and are designed to do a specific task. Some examples of embedded systems are washing machines, printers, automobiles, cameras, industrial machines, etc. With the push of one button, Matlab allows us to generate code and run it on hardware.

1. Control systems

Another most common reasons why the use of Matlab is important is that it provides control to device and system. A control system is responsible for managing, giving commands and regulating the behavior of other devices or systems. It is based on control loops. The devices or systems being controlled can range from simple home heaters to large industrial control systems that control the processes or the machines. The control system toolbox of Matlab provides algorithms and apps for systematically analyzing, designing and tuning linear control systems.

1. Digital signal processing

Digital signal processing is the usage of digital processing such as by computers or specialized digital signal processors to perform a range of signal processing operations. uses of Matlab products make it easy to use signal processing techniques to analyze time series data and also provides a unified workflow for the development of embedded systems and streaming applications.

1. Wireless communications

Wireless communication is a broad term used for connecting two devices using a wireless signal. Teams in wireless Engineering use Matlab too reduce development time, eliminate design problems early, and streamline testing and verification.

1. Image processing and computer vision

Image processing is mainly focused on the processing of raw images and making them ready for other tasks such as computer vision. Computer vision, on the other hand, looks at images like the human eye. It involves understanding and predicting the visual output. Algorithm building is vital for image processing and computer vision. Uses of Matlab provides a comprehensive environment to analyze images and build algorithms.

1. Internet of Things

Internet of Things is the network of devices, vehicles and home appliances and others embedded with electronics, software, sensors, actuators, and connectivity which enables the exchange of data. uses of Matlab help in the design, prototype, and deployment of IOT applications such as predictive maintenance, operations optimization, supervisory control etc.

1. FPGA Design and Codesign

Matlab enables hardware-software codesign by providing C/C++ and HDL code generation with targeted support for programmable SoC devices.

1. Mechatronics

Mechatronics is the technology combining electronics and mechanical engineering. Mechatronic systems require integrating mechanical, electrical, control, and embedded software subsystems. Uses of Matlab allows you to design and simulate all of this in a single environment.

1. Test and Measurement

Testing and Measurement is the process in which electronic products are subjected to a range of tests starting from physical tests to identify any physical defects to product level functional testing. Matlab provides tools that you need to acquire and automate tasks. Once you acquire data, you can explore it and perform live visualization and data analysis.

1. Computational Biology and Computational finance

Computational biology is the study of biological data for better understanding of biological systems and relationships. Computational finance on the other is the computer science study of financial data and financial modeling. Matlab helps by solving ordinary differential equations that model biological behavior. Also, the Matlab computational finance suite enables you to develop quantitative applications for risk management, investment management, insurance, and econometrics.

1. Robotics

Robotics is an interdisciplinary field of science and engineering. It involves mechanical engineering, electronic engineering, and computer science to name a few to create robots or human-like machines. Robotics researchers and engineers use MATLAB to design and tune algorithms, model real-world systems, and automatically generate code – all from one software environment.

1. Data Analytics

Data Analytics is the process of studying data in order to gain insights. It is mostly done with the help of other software and tools. Engineering and IT people are using Matlab to build the Big Data Analytics Systems.

1. Predictive Maintenance

Predictive maintenance techniques are designed to determine the condition of in-house equipment so that we can figure out when maintenance has to be done. The Matlab predictive maintenance toolbox provides tools for labeling data, designing condition indicators, and estimating the remaining useful life (RUL) of a machine.

1. Motor and power control

Motor control algorithms regulate speed and other performance characteristics. Matlab algorithms help with energy efficiency, precision control, and system protection. It reduces the time and cost of algorithm development before you commit to expensive hardware testing.

1. Deep Learning

Deep learning is a broader family of machine learning. Uses of Matlab is such that with just a few simple lines of coding one can build deep learning models without having to be an expert.

Conclusion

Matlab has a wide range of applications as we have already seen and is used in a number of industries covering

1. Automotive
2. Biological Sciences
3. Biotech and Pharmaceutical
4. Chemicals and Petrochemical
5. Communications
6. Electronics
7. Earth, Ocean, and Atmospheric Sciences
8. Energy Production
9. Financial Services
10. Industrial Automation and Machinery
11. Medical Devices
12. Metals
13. Materials, and Mining
14. Neuroscience
15. Railway Systems
16. Semiconductors
17. Software and Internet.