Report #1 - Data Acquisition, Sampling and Aliasing...

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Unformatted text preview: Data Acquisition, Sampling and Aliasing Analysis of Wave Form Data A report on an experiment performed for ME 120 Experimental Methods Laboratory San Jose State University Department of Mechanical and Aerospace Engineering Report by: Laboratory Date: February 23, 2009 Report Date: March 2, 2009 Abstract Using Lab View and the Data Acquisition System we sampled a waveform signal and observed how under-sampling causes the effect of aliasing. The analog signal was simulated by the DAQ signal accessory and varied its frequency to cause aliasing. The sampling signal was kept at a fix frequency and it was sampled at a fix sampling rate and number of samples. Using Lab View we constructed the front panel for user input controls and the block diagram to perform the sampling and aliasing analysis. From the results obtained we found out that as we increase the frequency of the analog signal to half the frequency of the sampling signal the effect of sampling occurred, in this case the aliasing started to be noticed at a frequency of 467Hz. 2 Background and Objectives This experiment was performed with the intent to gain experience and understanding of the following objectives. Understand the theory of sampling Understand the occurrence of aliasing when sampling Gain experience with the Lab View software and virtual instruments (VI) Understand Data Acquisition concepts Data acquisition was done through Lab View using a high level virtual instrument (VI) which simulated the analog input signal. A discrete sampling procedure was performed on the analog signal obtained from the VI in order to represent the analog signal accurately as a digital signal. The Data acquired from the analysis was saved as a spreadsheet from lab view for later analysis. By the Nyquist criterion it is understood that in order to represent any analog signal accurately aliasing must be avoided. In order to represent any analog signal in digital form a constraint needs to be placed in the frequency of the sampling signal to accurately match the analog signal. This constraint states that an analog signal can be properly represented only if the sampling frequency is greater than twice the maximum frequency ( f max ) of the analog signal, Equation 1 represents this statement mathematically. max 2 f f s (Equation 1, Mysore, 2009) If this criterion is not met and the sampling frequency ( f s ) happens to be half the maximum frequency of the given signal, then aliasing will occurred and the digital signal will be inadequately represented....
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This note was uploaded on 09/08/2010 for the course ME 120 at San Jose State University .

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Report #1 - Data Acquisition, Sampling and Aliasing...

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