Certified In MATLAB Programming

MATLAB Programming course is offered by Nano Scientific Research Centre. It is operated by a group of highly qualified technocrats with sound business acumen and strong skills. The top management has hands-on experience in design and development, who are dedicated to taking the organization toward its business objective.

Content:

• MATLAB Programming
• Introduction
• Obtain a quick overview of The Math Works and MATLAB
• Discuss course set-up
• Evolution of the language
• Features of Mat lab
• Working with the MATLAB User Interface
• Command window
• Command History
• Workspace
• Reading and writing data
• Variables and Expressions
• Entering commands
• Creating variables
• Getting help
• Accessing and modifying values in variables
• Operators
• Operators classification
• Arithmetic operators
• Relational operators
• Logical operators
• Assignment operator
• Semicolon operator
• Colon operator
• Typecasting operator
• Flow Control
• Simple if
• If - else
• Else if
• Nested if
• Switch
• For loop
• While loop
• Writing Functions
• Creating functions
• Calling functions
• Sub functions
• Data Types
• MATLAB? data types
• Integers
• Structures
• Function handles
• Analysis and Visualization with Vectors
• Calculations with vectors
• Plotting vectors
• Basic plot options
• Annotating plots
• Analysis and Visualization with Matrices
• Size and dimensionality
• Calculations with matrices
• Multidimensional arrays
• Graphics
• 2Dimensional plots
• 3Dimensional plots
• Contour plots
• File I/O
• Opening and closing files
• Reading and writing text files
• Reading and writing binary files
• Graphical User Interfaces
• GUIDE introduction
• Designing the GUI
• Programming the GUI
• MATLAB Image Processing Training
• We provide hands-on experience with performing image analysis. Examples and exercises demonstrate the use of appropriate MATLAB? and Image Processing Toolbox? functionality throughout the analysis process. Topics include
• Importing and exporting images
• Analyzing images interactively
• Removing noise
• Aligning images and creating a panoramic scene
• Detecting lines and circles in an image
• Segmenting object edges
• Segmenting objects based on their color and texture
• Performing batch analysis over sets of images
• Segmenting objects based on their shape using morphological operations
• Measuring shape properties
• Course Objective
• Importing and Visualizing Images
• Objective: Import image or video frames into MATLAB and visualize them. Convert images to a format that is useful for analysis.
• Importing and displaying images
• Converting between image types
• Exporting images
• Importing and playing video files
• Interactive Exploration of Images
• Objective: Explore object details such as shape, texture, and color and create a custom image exploration tool.
• Obtaining pixel intensity values
• Extracting a region of interest
• Computing pixel statistics on a region of interest
• Measuring object sizes
• Creating a custom interactive tool
• Preprocessing Images
• Objective: Perform image preprocessing operations and apply custom functions to images.
• Adjusting image contrast
• Reducing noise in an image
• Using sliding neighborhood operations
• Using block processing operations
• Spatial Transformation and Image Registration
• Objective: Align images to use the same scale and orientation.
• Compare aligned images. Create a panoramic scene by stitching images.
• Geometric transformations
• Image registration using point mapping
• Creating a panoramic scene
• MATLAB Image Processing Training
• We shows how to analyze signals and design signal processing systems using MATLAB, Signal Processing Toolbox?, and DSP System Toolbox.
• Topics include:
• Creating and analyzing signals
• Performing spectral analysis
• Designing and analyzing filters
• Designing multi-rate filters
• Designing adaptive filters
• Course Objective: Signals in MATLAB
• Objective: Generate sampled and synthesized signals from the command line and visualize them. Create noise signals for a given specification. Perform signal processing operations like re-sampling, modulation, and correlation.
• Creating discrete signals
• Sampling and re-sampling
• Visualizing signals
• Modeling noise
• Performing re-sampling, modulation, and correlation
• Generating streaming signals
• Spectral Analysis
• Objective: Understand different spectral analysis techniques and the use of windowing and zero padding. Become familiar with the spectral analysis tools in MATLAB and explore nonparametric (direct) and parametric (model-based) techniques of spectral analysis.
• Discrete Fourier transform
• Windowing and zero padding
• Power spectral density estimation
• Time-varying spectra
• Using a spectrum analyzer in MATLAB
• Linear Time Invariant Systems
• Objective: Represent linear time-invariant (LTI) systems in MATLAB and compute and visualize different characterizations of LTI systems.
• LTI system representations
• z-transform
• Frequency and impulse response
• Visualizing filter properties
• Applying filters to finite and streaming signals
• MATLAB Simulink Training
• Simulink for System and algorithm Modeling
• we provide training in algorithm modeling and design validation in Simulink. It demonstrates how to apply basic modeling techniques and tools to develop Simulink block diagrams.
• Topics include:
• Creating and modifying Simulink models and simulating system dynamics Modeling continuous time, discrete time, and hybrid systems Modifying solver settings for simulation accuracy and speed Building hierarchy into a Simulink model Creating reusable model components using subsystems, libraries, and model references
• Creating and Simulating a Model Objective:
• Create a simple Simulink model, simulate it, and analyze the results. Define the potentiometer system Explore the Simulink environment interface
• Create a Simulink model of the potentiometer system Simulate the model and analyze results Modeling
• Programming Constructs Objective:
• Model and simulate basic programming constructs in Simulink. Comparisons and decision statements Zero crossings MATLAB Function block
• Modeling Discrete Systems Objective:
• Model and simulate discrete systems in Simulink. Define discrete states
• Create a model of a PI controller
• Model discrete transfer functions and state space systems Model multirate discrete systems
• Modeling Continuous Systems Objective:
• Model and simulate continuous systems in Simulink. Create a model of a throttle system Define continuous states Run simulations and analyze results Model impact dynamics
• Solver Selection Objective:
• Select a solver that is appropriate for a given Simulink model. Solver behavior System dynamics Discontinuities Algebraic loops
• Developing Model Hierarchy Objective:
• Use subsystems to combine smaller systems into larger systems. Subsystems Bus signals Masks
• Modeling Conditionally Executed Algorithms Objective:
• Create subsystems that are executed based on a control signal input. Enabled subsystems Triggered subsystems Input validation model
• Combining Models into Diagrams Objective:
• Use model referencing to combine models. Model referencing and subsystems Model referencing workflow Setup a model reference Model reference simulation modes Store parameters in referenced models
• Creating Libraries Objective:
• Use libraries to create and distribute custom blocks. Create and populate libraries Manage library links Add a library to the Simulink Library Browser