6小時學會labview,
labview Six Hour Course – Instructor Notes
This zip file contains material designed to give students a working knowledge of labview in a 6 hour timeframe. The contents are:
Instructor Notes.doc – this document.
labviewIntroduction-SixHour.ppt – a PowerPoint presentation containing screenshots and notes on the topics covered by the course.
Convert C to F (Ex1).vi – Exercise 1 solution VI.
Convert C to F (Ex2).vi – Exercise 2 solution subVI.
Thermometer-DAQ (Ex2).vi – Exercise 2 solution VI.
Temperature Monitor (Ex3).vi – Exercise 3 solution VI.
Thermometer (Ex4).vi – Exercise 4 solution subVI.
Convert C to F (Ex4).vi – Exercise 4 solution subVI.
Temperature Logger (Ex4).vi – Exercise 4 solution VI.
Multiplot Graph (Ex5).vi – Exercise 5 solution VI.
Square Root (Ex6).vi – Exercise 6 solution VI.
State Machine 1 (Ex7).vi – Exercise 7 solution VI.
The slides can be presented in two three hour labs, or six one hour lectures. Depending on the time and resources available in class, you can choose whether to assign the exercises as homework or to be done in class. If you decide to assign the exercises in class, it is best to assign them in order with the presentation. This way the students can create VI’s while the relevant information is still fresh. The notes associated with the exercise slide should be sufficient to guide the students to a solution. The solution files included are one possible solution, but by no means the only solution.
This program will ask how many numbers you want to find the average of, then it will allow you to enter your numbers(yes they can even be decimals) then it will calculate the mean, median, mode and range of what you enter.
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
The goal with this project was to make it possible for almost any mobile-phone to use ICQ and be able to communicate with other users!
One other goal with this project was to lower the GPRS-traffic in the phone and make the ICQ-ing cheaper.
A third goal was to make this service as easy to log-in to as possible. Anyone tried to fill a log-in screen with a WAP-browser should know what I mean.
With Wapmess all you have to do is to write your login-url ONCE and then bookmark it in your phone, to make it available fast. :)
This project is created using the Keil ARM CA Compiler.
The Logic Analyzer built into the simulator may be used to monitor and display any variable or peripheral I/O register. It is already configured to show the PWM output signal on PORT3.0 and PORT3.1
This ARM Example may be debugged using only the uVision Simulator and your PC--no additional hardware or evaluation boards are required. The Simulator provides cycle-accurate simulation of all on-chip peripherals of the ADuC7000 device series.
You may create various input signals like digital pulses, sine waves, sawtooth waves, and square waves using signal functions which you write in C. Signal functions run in the background in the simulator within timing constraints you configure. In this example, several signal functions are defined in the included Startup_SIM.INI file.
The module LSQ is for unconstrained linear least-squares fitting. It is
based upon Applied Statistics algorithm AS 274 (see comments at the start
of the module). A planar-rotation algorithm is used to update the QR-
factorization. This makes it suitable for updating regressions as more
data become available. The module contains a test for singularities which
is simpler and quicker than calculating the singular-value decomposition.
An important feature of the algorithm is that it does not square the condition
number. The matrix X X is not formed. Hence it is suitable for ill-
conditioned problems, such as fitting polynomials.
By taking advantage of the MODULE facility, it has been possible to remove
many of the arguments to routines. Apart from the new function VARPRD,
and a back-substitution routine BKSUB2 which it calls, the routines behave
as in AS 274.
bayeserr - Computes the Bayesian risk for optimal classifier.
％ bayescln - Classifier based on Bayes decision rule for Gaussians.
％ bayesnd - Discrim. function, dichotomy, max aposteriori probability.
％ bhattach - Bhattacharya s upper limit of mean class. error.
％ pbayescln - Plots discriminat function of Bayes classifier.
