Introduction to MATLAB
Matrix Laboratory or MATLAB provided by company Mathworks is a programming environment used for performing mathematical computation, programming, and visualization. It is a powerful tool that includes its own high-level language and functions for performing math-related tasks faster. It is used in many organizations to perform real-world problems which reduce the complexity and helps in explaining the results to others more easily.
Features and Benefits of MATLAB Versions
There are more than 50 Matlab versions released from 1984 to till date. Matlab 1.0 was the first version released in The Year 1984. However recent versions are written in C, C++, and Java. Mathworks recently released Matlab 9.7 R2019b version, Which includes the Ai skills and tools to make the decisions easier.
- MATLAB 1.0: It was released in the year 1984 by Mathworks. It was written in C and worked across various machines.
- MATLAB 2: It was released in 1986.
- MATLAB 3: It was released in 1987.
- MATLAB 3.5: It was released in the year 1990. It was compatible with MS-DOS.
- MATLAB 4. It was released the year 1992. It ran on Windows 3 and MAC.
- MATLAB 4.2c: It was released in the year 1994.
- MATLAB 5.0: It was released in the year 1996. Released for all platforms.
- MATLAB 5.1: It was released in the year 1997.
- MATLAB 5.1.1 R9.1: It was released in the year 1997.
- MATLAB 5.2 R10: It was released in the year 1998. This was the last version working on ancient Macs.
- MATLAB 5.2.1 1: It was released in the year 1998.
- MATLAB 5.3 R11: It was released in the year 1999.
- MATLAB 5.3.1 R11.1: It was released in the year 1999.
- MATLAB 6.0 R12: It was released in the year 2000. This was the first release for Java Virtual Machine.
- MATLAB 6.1 R12.1: It was released in the year 2001.
- MATLAB 6.5 R13: It was released in the year 2002. It includes a new graphical user interface for importing data and provides a graphical view of the metadata.
- MATLAB 6.5.1 R13SP1: It was released in the year 2003
- MATLAB 6.5.2 R13SP2: It was released in the year 2003.
- MATLAB 7 R14: It was released in the year 2004. It introduced nested functions and reintroduced MAC.
- MATLAB 7.0.1 R14SP1: It was released in the year 2004
- MATLAB 7.0.4 R14SP2: It was released on March 2005.
- MATLAB 7.1 R14SP3: It was released on September 2005. It was available for Windows XP-64 bit
- MATLAB 7.2 R2006a: It was released on March 2006.
- MATLAB 7.3 R2006b: It was released on September 2006.
- MATLAB 7.4 R2007a: It was released on March 2007. Bsxfun function was introduced to apply the binary operation.
- MATLAB 7.5 R2007b: It was released on September 2007.
- MATLAB 7.6 R2008a: It was released on March 2008. Enhancements related to object-oriented programming abilities were done.
- MATLAB 7.7 R2008b: It was released on October 2008. There was an upgrade to Random Number Generators.
- MATLAB 7.8 R2009a: It was released in March 2009. This was the first release for Microsoft 32 and 64 bit Windows 7, interface with the .NET framework was done.
- MATLAB 7.9 R2009b: It was released in September 2009. There was the first release in Intel 64-bit MAC and a new operator (~) was introduced.
- MATLAB 7.9.1 R2009bSP1: It was released in April 2010. There were bug fixes for this upgrade.
- MATLAB 7.10 R2010a: It was released in March 2010. This was the last release for Intel 32-bit Mac
- MATLAB 7.11 R2010b: It was released in September 2010. There was help added for enumerations.
- MATLAB 7.11.1 R2010bSP1: It was released in March 2011. Bug fixes and updates.
- MATLAB 7.11.2 R2010bSP2: It was released in April 2011. Bug fixes and update
- MATLAB 7.12 R2011a: It was released in April 2011.rng function was introduced for random number generation.
- MATLAB 7.13 R2011b: It was released in September 2011. Changed parts of variables and increased the maximum of local workers.
- MATLAB 7.14 R2012a: It was released in the year 2012. This was the last version for 32- bit Linux support.
- MATLAB 8 R2012b: It was released in the year 2012. Tool strip interface was released and the documentation system was redesigned.
- MATLAB 8.1 R2013a: It was released in March 2013. The unit testing framework was added.
- MATLAB 8.2 R2013b: It was released in September 2013. Table data type was added and Java Runtime Environment was updated to version 7.
- MATLAB 8.3 R2014a: It was released in March 2014.USB webcam support in core MATLAB and the number of local workers was not limited to 12.
- MATLAB 8.4 R2014b: It was released in October 2014. Improved user Toolbox, new functions and packages like py (for using Python), web count, histograms, TCP client and others were added.
- MATLAB 8.5 R2015a: It was released in March 2015. This was the last release supporting Windows XP and Vista.
- MATLAB 8.5 R2015aSP1: It was released on October 2015.
- MATLAB 8.6 R2015b: It was released in 2015. A new execution engine(LXE) and new classes like graphs and digraphs were added to work with the graphs.
- MATLAB 9.0 R2016a: It was released in March 2016. App Designer was added to build and design apps. The new pause button was introduced to pause the run during the execution.
- MATLAB 9.1 R2016b: It was released in September 2016. Official MATLAB engine for JAVA, new encode and decode functions for JSON, new “string” data type was added.
- MATLAB 9.2 R2017a: It was released in March 2017. Cloud-based MATLAB (MATLAB Online), a framework for unit testing, operations on tall arrays functions for memorization and creating heat map charts were added.
- MATLAB 9.3 R2017b: It was released in September 2017. New functions like eigenfunction and new chart colors with new default colors were added.
- MATLAB 9.4 R2018a: It was released in March 2018. Different new functions were added for graphics and charts. The unit testing framework was modified.
- MATLAB 9.5 R2018a: It was released in September 2018.
- MATLAB 9.6 R2019a: It was released in March 2019. It contains enhancements for Artificial Intelligence and analytics along with the bug fixes.
- MATLAB 9.7 R2019b: It was released in September 2019. It includes updates on artificial Intelligence, new products in support of robotics and new resources for modeling.
Applications of MATLAB
Below are the following points:
1. Aerospace Industry
It is generally used during its engineering course period. We can evaluate the vehicle motion and orientation using built-in aerospace math operations and coordinate system and spatial transformations.
2. Digital Image Processing and Recognition
Image Processing Toolbox(IPT) apps in MATLAB help to perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
3. Data Science
It offers a wide variety of libraries related to machine learning and statistics and some advanced functions like nonlinear optimization, system identification. We can build predictive models using various algorithms logistic regression, classification trees, support vector, random forest methods, and use model refinement tools to create an accurate model.
4. Neural Networks
The aim of the Neural networks is to mimic the human brain that is how the brain performs. MATLAB includes Deep Learning tools and libraries which is used to build and train the neural networks and validate the results.
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Conclusion
There are numerous industries that use MATLAB where information analysis is the preferred task. If the job deals with some research study, then choosing MATLAB is the right option in almost every field as it has many outlining functions that provide better insights.
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