microsoft office home and student 2007 download gratis baixakihidden and dangerous 2 download completemacromedia studio mx download completologixpro download keygen
Explore products for MATLAB, which of technical computing, and Simulink, for simulation and Model-Based Design.
Updates to MATLAB, Simulink, and 83 Other Products
Learn MATLAB basics and programming techniques from a desk.
On-demand use of MATLAB training.
Choose your country for getting translated content where available and find out local events and supplies. Based on your physical location, we recommend you end up picking United States from your following list:
MathWorks may be the leading developer of mathematical computing software for engineers and scientists.
1994-2015 The MathWorks, Inc.
Explore products for MATLAB, the word what of technical computing, and Simulink, for simulation and Model-Based Design.
Updates to MATLAB, Simulink, and 83 Other Products
Learn MATLAB basics and programming techniques from a desk.
On-demand usage of MATLAB training.
MathWorks could be the leading developer of mathematical computing software for engineers and scientists.
may be the high-level language and interactive environment employed by millions of engineers and scientists worldwide. It enables you to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
You will use MATLAB in projects for instance modeling energy consumption to construct smart power grids, developing control algorithms for hypersonic vehicles, analyzing weather data to visualize the track and power of hurricanes, and running a lot of simulations to pinpoint optimal dosing for antibiotics.
Explore, visualize, and model your data
Develop and share applications as code, executables, or software components
Discover more details on MATLAB by exploring these resources.
Explore documentation for MATLAB features, including release notes and code examples.
Browse their email list of available MATLAB functions.
Connect MATLAB to hardware platforms.
Learn MATLAB interactively possibly at your own pace.
View system requirements with the latest discharge of MATLAB.
Find strategies to questions and explore troubleshooting resources.
Test drive MATLAB and Simulink products.
Purchase MATLAB and explore add-on products.
MATLAB could be the foundation for everyone products, including Simulink
Explore add-on products and pay attention to how you are able to extend MATLAB capabilities.
Analyze and model data, find optimal solutions, and perform symbolic math computations.
Create, train, and simulate neural networks
Solve linear, quadratic, integer, and nonlinear optimization problems
Solve multiple maxima, multiple minima, and nonsmooth optimization problems
Fit curves and surfaces to data using regression, interpolation, and smoothing
Analyze, design, and implement audio, video, communications, radar, along with signal processing-intensive systems.
Design, model, and analyze networks of RF components
Design, analyze, and visualize antenna elements and antenna arrays
Design, test, and implement control systems, from plant modeling to deployment through automatic code generation.
Aerospace reference standards, environmental models, and aerodynamic coefficient importing
Gain clues about your image and video data, develop algorithms, and explore implementation tradeoffs.
Perform image processing, analysis, and algorithm development
Design image processing, video, and computer vision systems for FPGAs and ASICs
Develop and deploy quantitative applications to chart and model data, solve optimization problems, and minimize risk.
Design, price, and hedge complex financial instruments
Scale your workflow using multicore desktops, GPUs, clusters, grids, and clouds.
Perform parallel computations on multicore computers, GPUs, and computer clusters
Perform MATLAB and Simulink computations on clusters, clouds, and grids
Share the project you do in MATLAB with other people.
Run MATLAB analytics began this morning web, database, and enterprise applications
GPU Computing with MATLAB Learn how MATLAB users can leverage NVIDIA GPUs to accelerate computationally intensive applications in areas for example image processing, signal processing, and computational finance.
MATLAB Programming Techniques Training This course provides hands-on experience utilizing the features inside the MATLAB language to post efficient, robust, and well-organized code.
Use MATLAB for Big Data, Machine Learning and Production Analytics Systems.
Choose your country to acquire translated content where available and find out local events while offering. Based on your local area, we recommend you ultimately choose United States through the following list:
MathWorks would be the leading developer of mathematical computing software for engineers and scientists.
1994-2015 The MathWorks, Inc.
Program and Documentation, unused, to The MathWorks, Inc. Trademarks MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See to get a list of additional trademarks. Other product or brand /trademarks names could possibly be trademarks or registered trademarks with their respective holders.
Revision History April 1996 First printing Version 1.0 May 1997 Second printing Revised for Version 1.1 MATLAB 5.0 September 2000 Third printing Revised for Version 2.0 Release 12 May 2001 Online only Revised for Version 2.0.1 Release 12.1 July 2002 Fourth printing Revised for Version 2.1 Release 13
Contents Getting Product Overview.
Signal Gaussian Symbols.
Curve Fitting on an Error Rate Plot 3-15 Diagrams 3-20.
Getting the Current Simulation Sweep Value of Test Test Data into a Registered Test Probe.
Section Overview Example: Using a MATLAB Simulation with BERTool Varying the Stopping Criteria 5-25.
Source Quantizing a Overview.
Selected Bibliography for Source Coding 6-19 Error Detection and Coding.
Block Overview.
Modem Objects Overview 9-20.
Equalizers Equalizer Features of Communications 12-2.
Bibliography for Equalizers 12-36 Galois Field Field Terminology 13-3.
Processing Operations in Galois Fields Overview 13-29.
Elements of Galois Overview.
Algorithms Algorithms Used to Decode BCH and Codes.
This chapter first gives a brief review of the Communications Toolbox product and after that uses several examples to provide you started utilizing the toolbox. This chapter assumes little or no about your prior knowledge from the MATLAB technical computing environment, eventhough it does believe that you have a basic know-how about communications material.
Section Overview on-page 1-2 Expected Background on-page 1-2 Section Overview Communications Toolbox software extends the MATLAB technical computing environment with functions, plots, as well as a graphical user interface for exploring, designing, analyzing, and simulating algorithms for that physical layer of communication systems. The toolbox assists you to create algorithms for commercial and defense wireless or wireline systems.
Product Overview you find out which functions you would like to use, make reference to the online reference pages that describe those functions. For Experienced Users The online reference descriptions are the most relevant areas of this guide in your case. Each reference description includes the function s syntax together with a complete explanation of their options and operation.
This section also shows how Communications Toolbox functionalities build upon the computational and visualization tools from the underlying MATLAB environment. Modulating a Random Signal This first example addresses these problem: Problem Process a binary data stream by using a communication system that has a baseband modulator, channel, and demodulator.
MATLAB Command Window. edit commdocmod 1. Generate a Random Binary Data Stream. The conventional format for representing an indication in MATLAB is really a vector or matrix. This example uses function to produce a column vector that lists the successive values randint of the binary data stream.
Getting Started MATLAB to pick a portion from the vector. For more information concerning this syntax, see The Colon Operator within the MATLAB documentation set. %% Setup % Define parameters. M 16; % Size of signal constellation k
MATLAB. For more information about it and the similar operator, see Reshaping a Matrix from the MATLAB documentation set. %% Bit-to-Symbol Mapping % Convert the bits in x into k-bit symbols.
Getting Started 3. Modulate Using 16-QAM. Having looked as a column vector xsym containing integers between 0 and 15, you may use the method in the modulate mind modulate utilizing the baseband representation. xsym Recall that is certainly 16, the alphabet size. %% Modulation y
The code below also uses the, and procedures title legend axis in MATLAB a personalized plot. %% Scatter Plot % Create scatter plot of noisy signal and transmitted % signal on the very same axes. h scatterplotyrx1:nsamp5e3, nsamp, 0, g.; hold
Getting Started To learn much more about, see Scatter Plots on-page 3-21. scatterplot 6. Demodulate Using 16-QAM. Applying the method in the demodulate resist the received signal demodulates it. The result is usually a column vector containing integers between 0 and 15. %% Demodulation % Demodulate signal using 16-QAM.
Compare x and z to search for the number of errors and % the bit error rate. numberoferrors, biterrorrate biterrx, z The statistics appear within the MATLAB Command Window. Your results might vary for the reason that example uses random numbers. numberoferrors biterrorrate
Getting Started Solution of Problem To notice a completed M-file for it, enter edit commdocconst the MATLAB Command Window. 1. Find All Points from the 16-QAM Signal Constellation. The property on the object contains all points within the Constellation
The text from the annotation comes in the binary representation of. The function in MATLAB mapping dec2bin makes a string of digit characters, while
Getting Started By contrast, the constellation below is but one example of the Gray-coded 16-QAM signal constellation. Gray-Coded 16-QAM Signal Constellation The only difference, in comparison to the previous example, is you configure resist use a Gray-coded constellation. %% Modified Plot, With Gray Coding M
Solution of Problem This solution modifies the code from. To view an original commdocgray.m code in a editor window, enter these command inside MATLAB Command Window. edit commdocgray To experience a completed M-file for this situation,
Getting Started 2. Create a Square Root Raised Cosine Filter. To design the filter and plot its impulse response, insert the next commands following your commands you added from the previous step. % Create a square root raised cosine filter. rrcfilter
Studying Components of the Communication System command creates a watch diagram for part from the filtered eyediagram noiseless signal. This diagram illustrates the effect in the pulse shaping. Note that the signal shows significant intersymbol interference ISI for the reason that filter is really a square root raised cosine filter, not only a full raised cosine filter. To learn more details on, see Eye
Getting Started The last command removes the very first symbols along with the last 2delay 2delay symbols within the downsampled signal given that they represent the cumulative delay with the two filtering operations. Now, which will be the input to your demodulator, and, which may be the output through the modulator, share the same vector size.
This solution modifies the code from Pulse Shaping Using a Raised Cosine Filter onpage 1-15. To view an original code in a editor window, enter these command inside the MATLAB Command Window. edit commdocrrc To watch a completed M-file for it,
Getting Started 2. Encode the Binary Data. To encode the binary data before mapping it to integers for modulation, insert the next after the section Signal Source from the example and prior to the section. Bit-to-Symbol Mapping %% Encoder % Define a convolutional coding trellis and then use it % to encode the binary data.
Studying Components of the Communication System 5. Decode the Convolutional Code. To decode the convolutional code before computing the big mistake rate, insert the next after the entire section and just prior to the Symbol-to-Bit Mapping BER Computation section. %% Decoder % Decode the convolutional code. tb
Getting Started More About Delays The decoding operation within this example incurs a delay, so that the output in the decoder lags the input. Timing information isn't going to appear explicitly inside the example, along with the duration on the delay depends upon the specific operations being performed.
Simulating a Communication System Simulating a Communication System In this Section Overview on-page 1-23 Using BERTool to Run Simulations on-page 1-23 Varying Parameters and Managing a Set of Simulations onpage 1-31 Section Overview The examples to date have performed tasks linked to various components of an communication system.
BERTool. To view the initial code in a editor window, enter these commands inside MATLAB Command Window. edit commdocgray edit bertooltemplate To notice a completed M-file for this situation, enter edit commdocbertool inside the MATLAB Command Window.
Simulating a Communication System % - - Set up parameters. - - % - - INSERT YOUR CODE HERE. with the subsequent setup code adapted from your example in commdocgray.m % Setup % Define parameters. M 16; % Size of signal constellation k
is complete. Save the file to ensure BERTool can make use of it. mycommdocbertool 8. Open BERTool and Enter Parameters. To open BERTool, enter bertool inside the MATLAB Command Window. Then click on the Monte Carlo tab and enter parameters as shown below.
Simulating a Communication System These parameters tell BERTool to own your simulation function,, for every value of inside vector which is, the mycommdocbertool EbNo 2:10 vector. Each time the simulation runs, it continues 2 3 4 5 6 7 8 9 10 processing data until it detects 100 bit errors or processes a full of 1e8 bits, whichever occurs first.
Getting Started To compare these BER results with theoretical results, leave BERTool open and make use of the procedure below. Comparing with Theoretical Results To check if the results from your solution above are correct, use BERTool again. This time, use its Theoretical panel to plot theoretical BER results within the same window because simulation is a result of before.
Simulating a Communication System The parameters tell BERTool to compute theoretical BER recent results for 16-QAM over an AWGN channel, for E values within the vector 2:10 Click the Plot button. The resulting plot shows a good curve to the theoretical BER results and plotting markers to the earlier simulation results.
Getting Started Random Signal on-page 1-4. The theoretical performance results assume a Gray-coded signal constellation. To continue exploring BERTool, it is possible to select the Fit check box to match a curve on the simulation data, or set Confidence Level into a numerical value to add confidence intervals from the plot.
This solution modifies the code from Modulating a Random Signal onpage 1-4 by introducing and exploiting a nested loop structure. To view an original code inside an editor window, enter the next command inside MATLAB Command Window. edit
Getting Started To watch a completed M-file for it, enter edit commdocmcurves from the MATLAB Command Window. 1. Define the Set of Values with the Parameter. At the beginning with the script, introduce variables that list all of the values
Note An earlier step preallocated space for that matrices numberoferrors. While not strictly necessary, this is often a better MATLAB biterrorrate programming practice than expanding the matrices size in each iteration. To learn more, see Preallocating Arrays inside MATLAB documentation set.
Getting Started legendM 4, M 8, M 16, M Location, SouthWest; 6. Run the Entire Script. The script results in a plot such as the one shown inside following figure.
The Error Rate Test Console is usually a simulation tool for obtaining error rate results. The MATLAB software features a data file for use with all the Error Rate Test Console. You use this data file while performing the steps with this tutorial.
In this situation, you employ to compare simulation run time. Run the simulation, while using commands to measure simulation time. At the MATLAB command line, enter: tic; runtestConsole; toc MATLAB returns output similar to the next: Running Elapsed time is 275.671536 seconds.
The x-axis displays the home and property of, which TestParameter1 grayResults contains EbNo values. Generate the figure by entering this at the MATLAB command line: semilogygrayResults This script generates these figure.
TestParameter1 grayResults contains EbNo values. Although the simulation ran for multiple M values, this run contains data for M2. Plot multiple error rate curves by entering these at the MATLAB command line. semilogygrayResults This script generates these figure.
Running Simulations Using the Error Rate Test Console Running the Simulation Using Parallel Computing Toolbox Software If you then have a Parallel Computing Toolbox user license and also you create a matlabpool, the exam console runs the simulation in parallel. This approach cuts down on the processing time.
Getting Started If you then have a multicore computer, then a default matlabpool uses the cores as workers. Using the workers, run the simulation. At the MATLAB command line, enter: tic; runtestConsole; toc MATLAB returns output similar to the next: 4 workers designed for parallel computing., will likely be distributed among these workers.
Running Simulations Using the Error Rate Test Console %SystemBasicTemplate Template for building a system using these lines: classdef MyGrayCodedModulation %MyGrayCodedModulation Gray coded modulation system Rename the constructor by replacing: function obj SystemBasicTemplate %SystemBasicTemplate Construct a system with function obj MyGrayCodedModulation %MyGrayCodedModulation Construct a Gray coded modulation system Enter an outline for your system.
Getting Started M 16; % Size of signal constellation Replace it with: M getTestParameterobj, M; Register test parameters to your test console. Declare EbNo like a test parameter by placing the next line of code within the body from the method: register registerTestParameterobj, EbNo, 0, -50
Running Simulations Using the Error Rate Test Console % Create a binary data stream to be a column vector. x randi0 1, n, 1; % Random binary data stream Add a probe, TxBits, following your random binary data stream creation: % Create a binary data stream to be a column vector. x randi0
Getting Started Create a Gray coded modulation system. At the MATLAB command line, enter: mySystem MyGrayCodedModulation MATLAB returns the subsequent output: mySystem Description: Gray coded modulation EbNo: 0 M: 16 Create an Error Rate Test Console by entering the subsequent at the MATLAB command line: testConsole
Configure the Error Rate Test Console thus it uses the demodulator bit error test point for determining the amount of transmitted bits. DemodBitErrors Run the simulation. At the MATLAB command line, enter: runtestConsole Obtain the results in the simulation. At the MATLAB command line, enter: grayResults getResultstestConsole
To obtain more accurate results, run the simulations for the given minimum volume of errors. In this situation, moreover, you may limit the amount of simulation bits in order that the simulations tend not to run indefinitely. At the MATLAB command line, enter: Number of
Running Simulations Using the Error Rate Test Console Optimizing Your System for Faster Simulations In the first sort example, it only utilizes the technique. Every time the article calls the process, that's every 3e4 bits just for this simulation, the article sets the M and SNR values. This time interval includes: obtaining numbers in the test console, calculating intermediate values, and setting other variables.
Getting Started Then, modify its properties inside the setup method with the system to speed in the simulations. Save the file MyGrayCodedModulation as MyGrayCodedModulationOptimized. In the MyGrayCodedModulationOptimized file, replace the constructor name along with the class definition name. Locate this lines of code: classdef MyGrayCodedModulation
Running Simulations Using the Error Rate Test Console In the strategy, replace M together with the object property M. setup obj.M getTestParameterobj, M; k log2obj.M; % Number of bits per symbol This change provides having access to the M value on the method.
Getting Started Notice that the strategy creates the QAM modulator and demodulator. Move the QAM modulator and demodulator creation out with the method. Move following lines in the method for the constructor i.e the technique named MyGrayCodedModulationOptimized %% Create Modulator and Demodulator hMod
obj.M; obj.M; Save the le. Create an optimized system. At the MATLAB command line, enter: myOptimSystem MyGrayCodedModulationOptimized Create an Error Rate Test Console and fasten the system towards the test console. At the MATLAB command line, type: testConsole myOptimSystem