3 years ago

Experiment # 2 Filters

Experiment # 2 Filters

• Set your input

• Set your input signal to a 1KHz, 1V pp , square wave. Draw the time domain output andexplain what it represents.You have two options to visualize the signal in the frequency domain: one is to attach aspectrum analyzer to the output of the target board; the second is to use the digital signalprocessor to send data back into the Matlab workspace. F rom this point on, even thoughyou may have the spectrum analyzer attached to your target board, you will save the datain Matlab and manipulate it to generate plots to answer questions. The data retrieved willbe the same data sent to the DAC, which eventually will be seen in the time domain on the(real) oscilloscope. Now you will request that data be read from the DSP memory, and havethat data written into a Matlab variable.Since the data comes from a limited space in memory, the Matlab variable will be a vector oflimited length. In your case, the length is pre-determined to be 1024 samples. After the datais acquired, you can manipulate it mathematically to visualize it. You can also repeat theprocedure as many times as you wish, to visualize 1024 points of data at a time. Sometimesyou may observe a “discontinuity” on the signal retrieved. That is due to Matlab capturingthe data while the buffer (the space allocated in memory) is being written with new data. Ifthat happens, just capture the data once again. The length of 1024 is conveniently chosen6

for you to utilize a 1024-point FFT.From this point on to the end of this section, utilize a square wave input. You shouldbe aware of the fact that you have this signal input to two channels, and only one of thechannels will actually have a filter in it.The procedure to be followed is presented below (you are to modify it to produce meaningfulplots for the report):• Create a “.m” file with the following contents:close all, cc=CCS_Obj;x=read(cc,address(cc,’channel_a’),’double’,1024);xx=read(cc,address(cc,’channel_b’),’double’,1024);z=fft(x,1024);zz=fft(xx,1024);figuresubplot(2,1,1), plot(abs(z)), axis([0 512 0 300])subplot(2,1,2), plot(abs(zz)),axis([0 512 0 300])figuresubplot(2,1,1), plot(x), axis([0 150 -3 3])subplot(2,1,2), plot(xx), axis([0 150 -3 3])% channel A% channel B% using 1Vpp input• Run the file• Modify the file and run again as you wish, to visualize the data in the frequency domain.This program reads directly from the memory on the target hardware. All samples thatare produced on two separate channels are stored in two buffers prior to being sent to thedigital-to-analog converter (DAC). These buffers are called ’channel a’ and ’channel b’.In general lines, the program• closes all previous graphs to avoid multiple plots on your workspace;• assigns all hardware communication parameters to variable cc; it reads 1024 samples offormat ’double’ from address ’channel a’ on the target defined by variable cc and assignsthese values to variable x;• does the same for ’channel b’ and assigns to variable xx;• takes a 1024 point FFT on the 1024-sample long variable x, and assigns the results tovariable z;• does the same for variable xx, assigning to variable zz;• opens a new figure (a window which will hold the plot);• creates the first (of two) plot, which will display the absolute value of the first 512 valuesof z (as defined by the axis command);• creates the second plot, which will display the absolute value of the first 512 values ofzz;7

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