- What is Matlab is used and where.
- Introduction of Matlab.
- Matlab advantages, disadvantages, the concept of ToolBox.
- Double String, Cell, working with the Struct data type Logical data Uint8
- Realization of basic matrix operations in Matlab
- Realization of the basic mathematical formula in Matlab
- Mathematical and logical operators
- Conditional Branching. (if, switch command)
- Loops (for, while loops)
- Array and matrix operations
- Solution of algorithmic problems
- Programs written in m-file and type in MATLAB m-function
- Numerical and analytical integral derivative calculation
- Analytical and numerical solving differential equations
- Removing the statistical properties
- Txt, Excel binary file, audio file, image file, obtaining data from stationary sources such as database and data to be sent to those resources
- Microphone, Camera Serial Parallel port, connect to peripherals such as custom data acquisition card data is sent to the real-time data acquisition and output of equipment unit
- To draw the graph of the function
- 2-D and 3-D line graphics
- Surface Graphics
- Upgrade spouse, Bar, Pie, Graphics
- Moving to prepare animations

- Numerical Differentiation, coding methods defined in the literature for Numerical Integration and implementation of these methods are available in Matlab function
- Linear and Non-Linear defined in the literature of methods for the numerical solution of equations to encode these operations and the use of ready-made MATLAB functions
- One or several variables, ordinary differential equations and coding of Partial Differential Equations with literature-defined methods and use of available function makes operations
- Unconstrained and constrained optimization algorithms to encode these operations and the use of ready-made MATLAB functions
- Be adapted to the different characteristics of functions in the coding of collected data interpolation and extrapolation techniques, the use of ready-made MATLAB functions that actions.

- Concepts of fuzzy sets and fuzzy numbers
- Membership function varieties
- Fuzzy rule bases
- Rinsing operations
- Fuzzy ANFIS neural networks (Artificial Neural Fuzzy inteface System)
- Sample applications: the diagnosis of cancer ANFIS use of the collected data

- Creating user interaction windows
- Data entry screen, error screen, create a message screen.
- File open, create saving windows Listings
- Graphical interface development environment (GUIDE)
- GUI objects
- GUI program development
- Check boxes, list boxes, combo boxes use
- Push button, radio button, toggle button application
- ActiveX operations
- Create Menu
- Sample Applications

- Neuron models and architectures
- Artificial neural network models
- Learning algorithms
- Training, validation and test steps.
- MLP, RBF, GRNN, working with SOMA architecture
- Nntool, nftool, nprtool, nctool and use nntraintool
- Case Study: Collected training data using different neural networks and testing of real-world application
- Case Study: Character recognition application

- The average of the collected data, moments, median, mode, standard deviation, variance, skewness, kurtosis covariance, removal of features such as statistical correlation
- Discrete and continuous distributions and their properties
- Expressing the distribution of the collected data and tests.
- One-dimensional and multidimensional random number generation methods.
- Markov process and random walk algorithms.
- Monte Carlo Simulation
- Statistics connected estimation methods (Bayesian method womb)
- Garch method
- Historical Method in risk calculation and variance-Kovarsanys
- Retrospective Tests

- Introduction to Simulink
- Simulink modeling
- The introduction of Simulink library
- Creation of a block diagram of an input output system
- Implementation of feedback systems with Simulink
- Realization of PID control system with Simulink

- Acceptable solution can not be resolved in time for the introduction of a difficult problem (NP-hard)
- Blind search, cross search, deep search and A * algorithm
- The definition of intuitive methods
- Genetic Algorithm (Genetic Algorithm) Particle Swarm Optimization (Partial Swarm Optimization) and Ant Colony Algorithm (Ant Colony Algorithm)
- Sample applications: traveling salesman problem, course adjustment problem, solution with an intuitive method of work-bench scheduling problems)

- The production of programs that can run on systems that have Matlab
- M-file m-function transformation
- MCC and command mbuild
- P-code compilation
- C, C ++ and Java library for use in creating
- Exe, dll and create mex files

- Reading and writing of image files
- Reading and writing of video
- Real-time image taken from the camera
- The concept of color space and color conversion
- Converting a color image to gray levels
- Gray-level official binary file conversion (threshold transactions)
- Morphological operations on binary images.
- By applying a noisy image filter cleaning
- Image smoothing and sharpening operations
- There are specific points such as corners and edges and feature extraction
- Video segment operations (segmentation)
- Character Recognition application
- Conducting depth account with dual camera
- Wavelet Applications
- There is a still from a video background
- The presence of the moving object and to follow special
- Alarm application

- The production of programs that can run on systems that have Matlab
- M-file m-function transformation
- MCC and command mbuild
- P-code compilation
- C, C ++ and Java library for use in creating
- Exe, dll and create mex files

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