Java程序辅导

University of Pennsylvania 
Department of Electrical and Systems Engineering 
ESE 111 – Intro to Elec/Comp/Sys Engineering 
 
Created by Nick Howarth (EE ’13) and Sam Wolfson (EE ’13) 
Last updated:  October 8, 2012  
Lab 5 – Introduction to Data Acquisition and Processing 
 
Introduction: 
In previous labs, you used MATLAB to analyze data and the Arduino with various sensors to 
gather information from the environment.  In this lab, you will learn how to interface the 
Arduino with MATLAB.   
One of the most useful things about the Arduino is how widely it is used.  The Arduino platform 
is so popular that even companies such as MathWorks, which produces MATLAB, have 
developed software to support it.  In the first part of the lab, you will use the MATLAB Arduino 
support package to connect the Arduino to MATLAB, allowing you to obtain and plot 
accelerometer data directly from within the MATLAB command window.   
In the second part of the lab, you will use MATLAB to do some simple, real-time processing on 
the accelerometer data that you gather.  This will have important applications for the final project 
for the lab, as well as other classes you will take in the future.  
Procedure: 
1. Interfacing Arduino with MATLAB and plotting accelerometer data 
 
- Go to http://www.mathworks.com/matlabcentral/fileexchange/32374 to download the 
MATLAB support package for Arduino.  Click the blue “Download Submission” button 
on the right.   
- Extract the “ArduinoIO” folder and save to C:/user/MATLAB.   
- Open MATLAB:  Start  All Programs  Mathematics  MATLAB R2012a 
- On the left in the Current Folder tab, right click “ArduinoIO” and select Add to Path  
Selected Folders and Subfolders (Figure 1).  This makes the files accessible to 
MATLAB.   
 Figure 1: Adding folder to path in MATLAB (note that your folder will be called ArduinoIO, not MATLAB_Arduino) 
- Connect the Arduino to the computer and open the Arduino IDE.  Check which COM 
port the Arduino is using and select that option (Tools  Serial Port).  Also select the 
correct board (Tools  Board).   
- Go to Files  Open  C:/user/MATLAB/ArduinoIO/pde/adiosrv/adiosrv.pde to open 
the .pde file.  This program tells the Arduino to send data to and receive data from the 
computer such that it can be controlled by MATLAB.  Upload this code to the Arduino.   
- In the MATLAB command window, create a new Arduino object a by entering  
a = arduino(‘COMX’); 
where X should be replaced by the number of the COM port that the Arduino is using.  
Once the Arduino has successfully connected, you should see the following text:   
Attempting connection ............. 
Basic I/O Script detected ! 
Arduino successfully connected ! 
This object can now utilize all of the functions specified by the arduino.m library so that you 
can perform digital and analog I/O functions.   
Now you will write a script to read and plot analog values from an accelerometer.  A MATLAB 
script file (also known as an m-file because MATLAB script files always have the file extension 
.m) is a method for executing a series of MATLAB instructions without typing all of the 
commands into the Command Window.   
- To create a new script, go to File  New  Script, or press Ctrl + N.   
First, you must specify which I/O pins are inputs and which are outputs, just as you would in a 
regular Arduino script.  Recall that we can refer to analog pins 0-5 as pins 14-19.  If your 
accelerometer is connected as in Figure 2, then you will need to configure pins 18 and 19 as 
outputs and pins 15-17 as inputs (z-axis on pin 15, y-axis on pin 16, and x-axis on pin 17).   
 
Figure 2: Acclerometer connection to Arduino analog pins 
- To configure pins 16 (y-axis) and 17 (x-axis) as inputs (we will not use the z-axis), type 
the following into your script:   
a.pinMode(16,’input’); 
a.pinMode(17,’input’); 
- To configure pins 18 and 19 as outputs, type the following lines in your script: 
a.pinMode(18,’output’); 
a.pinMode(19,’output’); 
- Since pin 18 corresponds to the ground pin on the accelerometer, we must have it output 
0V.  Likewise, we must output 5V on pin 19, which corresponds to Vcc.   
a.digitalWrite(18,0);   
a.digitalWrite(19,1); 
You will now record data from the x-axis and y-axis readings of the accelerometer into dynamic 
vectors and plot this data in real time.  Dynamic vectors are vectors that do not have a fixed 
length; they grow as more data is added to them.   
First create two dynamic vectors to store the data from the x- and y-axes:   
xval = zeros(); 
yval = zeros(); 
 
Now you will create a for-loop that will read values from the analog pins and plot this data in 
real-time.  Begin a for-loop that increments an integer i from 1 to 500 by typing:   
 
for i = 1:500 
 
At each instance of i, you must read the accelerometer values and store them into the vectors.  
 
xval(i) = a.analogRead(3); 
yval(i) = a.analogRead(2); 
Now you can create two separate plots to show this data using the subplot function in 
MATLAB.  Copy the following code into your script (within the for-loop):   
  
subplot(2,1,1) 
plot(xval, 'b'); 
title('x'); 
     
subplot(2,1,2) 
plot(yval, 'r'); 
title('y'); 
 
In order to plot this data in real-time, you need to add a pause (similar to the Arduino delay 
function) between success iterations of the for-loop.  Add the following line to pause for five 
milliseconds between iterations:   
 
pause(.005); 
 
Finally, end the for-loop by typing:   
End 
 
- Now, click the “Run” button (green arrow on toolbar) or go to Debug  Save File and 
Run.  Name the file “aplot.m” and save it under the MATLAB folder 
(C:/user/MATLAB/).  A plot should now pop up and you should see the x- and y-values 
vary as you tilt and shake the accelerometer.   
 
2.  Using a moving average filter to smooth the signal 
Signals from sensors are often messy—they contain spurious peaks that can make it difficult to 
isolate the event that you are actually interested in.  One way to smooth out a signal is to use 
what is known as a moving average filter.  (You will learn more about this technique in ESE 
210.) 
The moving average works by creating a new signal composed of the average of the points of the 
original signal.  The simplest implementation is a “two-step” moving average (MA2).   
Imagine a signal A which is a vector of n data points, a1…an:     
                    
After applying a moving average filter, the resulting signal B is a vector of n data points:   
      
     
 
  
         
 
 
       
 
  
The first entry in B is the same as the first entry in A.  The second entry in B becomes the 
average of the first and second entries in A.  In general, the k
th
 entry of B is the average of the k
th
 
and the (k-1)
th
 entries of A; that is,  
   
       
 
 
For example, suppose we have a signal A with the values: 
              
If we apply a two-step moving average filter to this signal, the result B will look like: 
                  
Now let’s adapt the MATLAB program that we used to take accelerometer data so that it also 
plots a smoothed version of the data by running it through a two-step moving average filter.  
- Create a new script (call it MAtest.m) and copy the following code:   
 
 
a.pinMode(15,'input'); 
a.pinMode(16,'input'); 
a.pinMode(17,'input'); 
a.pinMode(18,'output'); 
a.pinMode(19,'output'); 
a.digitalWrite(18,0); 
a.digitalWrite(19,1); 
xval = zeros(); 
xave = zeros(); 
xval(1) = a.analogRead(3); 
xave(1) = xval(1); 
pause(.001); 
  
for i = 2:500 
 
    xval(i) = a.analogRead(3); 
    xave(i) = (xval(i)+xval(i-1))/2; 
     
    subplot(2,1,1) 
    plot(xval, 'b'); 
    title('x'); 
     
    subplot(2,1,2) 
    plot(xave) 
    title('x average') 
     
    pause(.001); 
end 
 
Note that this code is very similar to the one you used earlier, with the addition of a new vector 
xave instead of the yval vector.  Every i
th
 point in xave is generated by averaging the i
th
 and 
(i-1)
th
 point in xval.  Note also that the FOR loop runs from 2 to 500 instead of 1 to 500.  In 
MATLAB, the first entry in a vector is entry 1, not entry 0 (as in Java or C).  Since each entry of 
xave relies on both the current entry and the previous entry of xval, if the FOR loop went 
from 1:500 xval would have to calculate the average of xval(1) and xval(0).  xval(0) 
does not exist in MATLAB, so there would be an error.  To avoid this, we calculate xval(1) 
and xave(1) outside of the FOR loop, and loop from 2 to 500.    
- Run this program, and observe the differences between xave and xval as you move the 
accelerometer around.   
- What happens if you process xave with another moving average filter?  Hint:  you will 
need plot xval on subplot(3,1,1), xave on subplot(3,1,2), and xave2 
on subplot(3,1,3).   
- What happens if you use more than 2 points in your moving average filter?  (Try a 3-step 
moving average.)  Note that for an m-step moving average,