This programme is to control DC motor in a certain speed using PWM.
The target speed is "r", it is the speed in 1s.
The sample rate is 0.1s, so the actual speed target is "rc"=r/10.
The "r" and "rc" are integer, and the range of "r" is from 50 to 100.
Keep rc=r/10!!!
The array "speed1" and "speed2" are the control result, in 0.1s and 1s.
The length of "speed1" is 2400, and "speed2" is 240.
The "pw" and "nw" are the parameters of PWM.
The test will last 4 min.
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FFTW, a collection of fast C routines to compute the Discrete
Fourier Transform in one or more dimensions.The fftw/ directory contains the source code for the complex transforms,
and the rfftw/ directory contains the source code for the real transforms.
The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
approximate inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use "help inference" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some parameters in between.
Use "help gbp_preprocess" and "help gbp" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use "help simulatedAnnealing" from Matlab.
he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another.
KEYWORDS: Total least squares, generalized total least squares, software implementation.
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Java For Artists targets both the undergraduate computer science or information technology student and the practicing programmer. It is both an introductory-level textbook and trade book.
As a textbook it employs learning objectives, skill-building exercises, suggested projects, and self-test questions to reinforce the learning experience. The projects offered range from the easy to the extremely challenging. It covers all the topics you’d expect to find in an introductory Java programming textbook and then some.
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This application includes a demo server and client program. You could write your own server launcher and client application by using the SimpleScreenCaptureServer class and the SimpleRemoteScreenCaptureClient class.
Usage:
1. Type the following command to launch the server program.
java -jar SimpleCaptureScreenServerDemo.jar portnumber
2. Type the following command to launch the client program. It s a swing-based UI.
java -jar RemoteScreenCaptureClientUI.jar
3. Select Run/Connect menu. Input your server address and port number.
You could just double-click on the RemoteScreenCaptureClientUI.jar to launch the client program if you are using Windows.
This program is written just for fun. :lol: :lol: :lol: