Toolboxes in MATLAB

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Major Toolboxes in MATLAB

Y.Tyagi


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What are toolboxes MATLAB has a family of add-on application-

specific solutions called toolboxes. You can use these toolboxes to learnand apply

specialized technology.

Toolboxes are comprehensive collections ofMATLAB functions (M-files) that extend theMATLAB environment to solve particular classesof problems.

Examples toolboxes for signal processing, control systems,

neural networks, fuzzy logic, wavelets,simulation, and many other areas.


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Before you Start: use ver command to see installed toolboxes on

your system


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Communications Toolbox in MATLAB

You can use this toolbox for creating algorithms forcommercial or defense systems, such as mobile handsetsand base stations, wired and wireless local areanetworks.

You can also use it in research and education forcommunication systems engineering. Functions for designing the physical layer of communications links,

including source coding, channel coding, interleaving, modulation,channel models, and equalization.

Plots such as eye diagrams and constellations for visualizingcommunications signals

Graphical user interface (bertool)


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ControlSystem Toolbox

The Control System Toolbox has many functions designed forcontrol engineering. Complex arithmetic, eigenvalues, root-finding,matrix inversion, and fast Fourier transforms.

This toolbox has number of algorithms that implementscommon control system design, analysis, and modelingtechniques. Convenient graphical user interfaces (GUIs)simplify typical control engineering tasks.


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Curve Fitting ToolboxThis toolbox provides

Data preprocessing such as sectioning and smoothing

Data Fitting Parametric and nonparametric, polynomials, exponentials,rationals, sums of Gaussians and custom equations

For nonparametric fit using a smoothing spline or various interpolants.

Standard linear least squares, nonlinear least squares, weighted leastsquares, constrained least squares,

Fit statistics to assist you in determining the goodness of fit with analysiscapabilities such as extrapolation, differentiation, and integration

It has a graphical environment that allows you to explore and analyze datasets and fits visually and numerically.

You can explore the Curve Fitting Tool by typing cftool


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Data Acquisition Toolbox With this toolbox you can bring live, measured data

into MATLAB using PC-compatible, plug-in dataacquisition hardware analog input , analog outputand digital I/O .

Support for many popular hardware vendors/devices:like Advantech boards, Keithley boards, NationalInstruments boards.

You can also use the Data Acquisition Toolbox

Adaptor Kit to interface unsupported hardwaredevices to the toolbox.

Event-driven acquisitions


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Database Toolbox The Database Toolbox move data MATLAB and

popular relational databases.With the Database Toolbox, you can bring data from

an existing database into MATLAB, use any of theMATLAB computational and analytic tools, and store

the results back in the database or in anotherdatabase.

For example, a financial analyst working on a mutualfund could import a company's financial data into

MATLAB, run selected analyses, and store the resultsfor future tracking and then export result back indatabase.


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Instrument Control Toolbox

Communicate with instruments that support the GPIB interface (IEEE-

488), the VISA standard. Support for networked instruments using the TCP/IP and UDP

protocols.

It has functions for transferring data between MATLAB and instrument.

Simulink blocks for sending and receiving live data binary (numerical)or text between instruments and Simulink models.

Graphical user interface for identifying, configuring, andcommunicating with instruments

Event-based communication Functions for recording data and event

information to a text file


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Symbolic Math Toolboxes

You can do symbolic computation into the numericenvironment of MATLAB.

Calculus (Differentiation, integration, limits,summation).

Linear Algebra

Determinants, eigenvalues, singular valuedecomposition

Differential equations

Fourier, Laplace, z-transform, and corresponding

inverse transforms


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ImageAcquisitionToolbox

Image acquisition from many types of imageacquisition devices like frame grabbers cards to USB-based Webcams.

Viewing a preview of the live video stream.

Triggering acquisitions (includes external hardwaretriggers) Configuring callback functions that executewhen certain events occur.

You can get image data into the MATLABworkspace.


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The Image Processing Toolbox

The toolbox supports a wide range ofimage processing operations, includingSpatial image transformations

Morphological operations Neighborhood andblock operations

Linear filtering and filter design TransformsImage analysis and enhancement Image

registration Deblurring Region of interest operations


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The Fuzzy Logic Toolbox.

GUI-based tools for designing, simulating,and analyzing fuzzy inference systems.

Fuzzy logic provides an easilyunderstandable, yet powerful way to map

an input space to an output space witharbitrary complexity, with rules andrelationships specified in natural language.

Systems can be simulated in MATLAB orincorporated into a Simulink blockdiagram, with the ability to generate code

for stand-alone execution.


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Neural Networks toolbox in Matlab

You can Design and simulate neural networks.

The toolbox consists of a set of functions and structures

that handle neural networks, so we do not need to write

code for all activation functions, training algorithms, etc.that we want to use!

Neural networks are invaluable for applications where formal

analysis would be difficult or impossible, such as pattern

recognition and nonlinear system identification and control.


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Neural Networks toolbox in Matlab Cont.

Features in Toolbox

Command line function as well as GUI for creating, training, andsimulating neural networks.

Quick start wizards for fitting, pattern recognition, and clustering.

Support for the most commonly used supervised and

unsupervised network architectures.

Comprehensive set of training and learning functions.

Simulink blocks for building neural networks and advanced

blocks for control systems applications.


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Application of Neural Networks

Pattern Recognition

Trend prediction

Character recognition

Clustering data (grouping data by similarity )

Image Processing ex. Recognizing face

Typical application of neural network are in area of imageprocessing,, speech compression, banking, credit applicationevaluation, machine vision, robotics, etc

And many more


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Toolboxes in MATLAB

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