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MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications.
Find the advanced features here:
- Data Analytics
- This functionality is used by organizations worldwide to be more profitable and efficient by increasing quality and reducing costs. Examples being the automation industry, where the failure of machinery is predicted even before it happens. Power companies make use of analytics when they need to make multiple plants work with maximum power generation but at low-cost. When it comes to the scenario of big data, cleaning up messy data is very important. This is done quickly by high-level functions of the program. The second step involves the creation of predictive models.
- Used in commercial, business, and scientific applications to see if a hypothesis work or not. It makes use of the available data pool to draw conclusions.
- Machine learning and AI
- We have too many data from too many sources, which make it difficult to build human-driven models. This could be tackled using computers available to us if they can be taught using machine learning techniques. Learning can be supervised and unsupervised.
- Statistics and Machine Learning toolbox and Neural Network toolbox can be put to use. Training the machine to learn on its own, making them look at patterns and perform a task without human intervention.
- Test and measurement analysis
- Simulink is the platform generally used for the purpose. Four key toolboxes used to acquire, analyze and explore data and automate tests are Data Acquisition Toolbox, Image Acquisition Toolbox, Instrument Control Toolbox, Vehicle Network toolbox. Data is acquired from fixed instruments, plug-in boards, cameras, and CAN buses. Analysis of test data is followed by automating tests and building test applications.
- Helps in network design, distribution, and maintenance. Cost-effective networks can be developed, both hardware and software components can be tested for performance.
And here are the areas where it finds application:
- Signal processing
- MathWorks Matlab software is a significant tool used in a wide variety of applications, namely aerospace communications, robotics, biomedical industry, and scientific research. The processes attached with it are signal pre-processing, the design of digital filters, the transformation of signals, performing measurements and detecting patterns and events. Digital filter toolbox helps in sampling a non-uniform signal into a uniform sample. It reconstructs signals with missing samples, detects change points, find signal similarities, and estimate power spectra of uniformly and non-uniformly sampled signals, finds the signal to noise ratio, performs time-frequency analysis and modal analysis for vibrating structures.
- Signals from various sources (analog or digital) can be acquired, analyzed and processed.
- Image processing
- Several basic functions are covered using commands available in the Image Processing Toolbox. ‘imread()' function reads an image file as an array of pixel values. ‘imshow()' displays the image. ‘rgb2gray()' converts the jpeg image into grayscale. ‘imhist()' is used to obtain specific intensity which is displayed as a histogram. ‘imadjust()' adjusts the contrast, ‘im2bw()' changes the image from grayscale to black and white. Some other functions are ‘subplot()', ‘imrotate()', ‘imresize()', ‘imcrop()', etc. Color separation, inversion, filtering, quantization, normalization, histogram equalization, convolution, masking, blurring, image compression, optical character recognition, the introduction of color spaces, edge detection, frequency domain analysis, Fourier series, and Fourier transform are the large set of processes for which the computations are widely done.
- For this purpose, there is even an Image Processing toolbox available. The original purpose of the software was to work upon arrays and matrices. Images are two-dimensional arrays of pixels. Feature extraction and measurement are made possible using the appropriate algorithms.
- Embedded systems
- Embedded Matlab function in Simulink is a useful tool to code, design and verify the system from the time of prototype production to product development. A signal generator and scope is to be linked to the function block. Scripts are saved into the editor, which is then ‘run' and deployed to obtain the result. The design can evolve by itself avoiding the need for us to generate code manually in the future. This makes more iterations possible and speeds up the process.
- FPGA models can be made, specific functionalities are given by coding, and performance is studied before launching new integrated chips.
- Perception, control, prototyping, and implementation are the classification of tasks while incorporating Simulink into Robotics. The system gets the information through perception, the design part of which involves the choice of sensors for capturing data (for example, a camera, if the data is an object). Actuator control, planning a path, obstacle avoidance and supervisory control like the use of switches. Prototyping helps in breaking the system into manageable pieces. Performance of these individual blocks is studied, and tested before assembling. Simulation is a vital part of prototype development. Implementation phase shows how the system looks and works efficiently in an ideal environment.
- Uses in the automation industry deals with doing target specific tasks, teaching the consoles make a move according to the input data.
- Deep learning
- This area makes use of MathWorks Matlab as it helps in identifying and dealing with problems in a limited time. The steps range from preprocessing to deployment. Many great models developed by experts are available for users to go through. It may take days to train your model. Once trained, the code can be deployed into the web, phone or embedded GPU. Some areas, which make use of deep learning, are speech recognition, text analytics, automated driving, defense, medical research, industrial automation, and electronic applications. Python is an open source programming language that can be used in collaboration to help with deep learning.
- Building neural networks by making the machines learn from the surroundings, acquire data from the surroundings and perform classification tasks.
- Control systems
- There is a Control System Toolbox to carry out operations such as model creation, manipulation, analysis, and design. Step response, filter designing, plotting root locus, Bode plots, Nyquist plots, stability analysis are the major parameter-based operations that need to be done. The open loop control system and its degraded performance in comparison to feedback control systems can be better understood by building models and seeing how the components interact with each other. Disturbances can be introduced into the plant and the toolbox shows how the closed loop system compensates for all these disturbances.
- Analysis and design of control systems by specifying parameters like state space diagram, frequency response and/or transfer function.
- Data science
- Data stored in files and databases are accessed, cleaned and preprocessed. Preprocessing step saves a lot of time here. Live editor, as well as a graphical platform, is made use for data analysis. Domain-specific engineering is done for different kinds of data depending on whether they are text, image, speech, video or signals from a sensor. Machine learning or deep learning is employed to develop models.
- Huge amounts of data are being handled in the world every day, which are in different forms, from different sources, and at different speeds. Business and financial sectors thrive on the profits they obtain in the long run. Big data is used to predict customer behavior and psychology. Demand patterns are recorded, and this is studied with the use of statistical tools to predict the future. This helps in deciding what customer-centric products to offer, how to deal with competition and how to create an impact on the organization.