20.309: Biological Instrumentation and Measurement
The following two tables will pop up frequently in 20.309 for the rest of the semester. Table 8.0.1 describes the Fourier transform and many of its useful properties, while table 8.0.2 contains the transform pairs of many common functions. These two tables are useful because you can combine functions in table 8.0.2 using the properties in table 8.0.1 to figure out the transforms of an endless number of functions (without doing any math!).
Note: there is an error in the table below. The transform of $ u(t) $ is: $ \frac{1}{2}\left ( \frac{1}{i \pi f}+\delta (f) \right ) $.
Table 8.0.1: Short table of Fourier transform properties
Table 8.0.2: Short table of Fourier transform pairs

 Sketch each function in table 8.0.2 as well as the magnitude of its Fourier transform. Include any relevant constants in your sketch (for example: a, $ \alpha $, and $ f_0 $).

In class we found the Fourier transform of $ \cos^2(\omega_0 t) $. Use graphical convolution to determine the transform of $ \cos^4(\omega_0 t) $.

Using the transform pairs in table 8.0.2, sketch the fourier transform of $ e^{\alpha t} u(t) \times \cos(\omega_0 t) $. Assume that $ \alpha\ll\omega_0 $.
 Table 8.0.3 shows plots of eight timedomain signals AH. The table on the right includes magnitude plots of the Fourier transform of ten signals numbered 110. For each time domain signal AH, write the number 110 in the empty column of the matching frequencydomain signal. You may use a numbered plot more than once.
Some of the frequency plots are shown on loglog axes and some are linear, as indicated by the plot title.
Hint: Gaussian or white noise is a random signal with equal contributions from every frequency.

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