Signal Processing

Overview of Fast Fourier Transform, phenomena, windowing, Filtering Frequency Response, functions, blocking, and Hilbert Transform.

Fast Fourier Transform

The first fundamental tool of signal processing is the Fourier Transform. The Fourier Transform is used to map time-domain data into the frequency domain. The frequency-domain representation of a curve (or signal) contains the same information as the time-domain representation, but in a different form. Each representation is useful in certain circumstances. The most basic use of the frequency-domain representation is to view the frequency content of a signal.

The equation for a Fourier Transform is:(1) Notice that the result is complex-valued. Normally, the complex-valued function is represented by a magnitude and a phase angle rather than real and imaginary components, although they are equivalent. This is because the magnitude and phase angle have physical meaning, whereas the real and imaginary components do not. The lower limit of the integral is usually set to zero, since time cannot be negative in a physical sense.

In order to perform a Fourier Transform on a computer, the signal to be transformed must be digitized. The basic requirement for all discrete Fourier Transforms is that the discrete input data be sampled at a constant frequency, such that all time intervals are identical. If the input data is not evenly sampled, the discrete Fourier transform will be incorrect.

A Fast Fourier Transform (FFT) is a computationally efficient algorithm for evaluating the Fourier Transform equation. The only additional requirement is that the input signal have a number of points equal to two raised to some integral power: i.e., 256 (28), 512 (29), and so on. If the input signal does not have a valid number of points, zeros are added at the end of the signal until a valid number of points is reached. This is called zero-padding.

Figure 1. Discrete Fourier Transform and Fast Fourier Transform

HyperView, MotionView, and HyperGraph have both a regular discrete Fourier Transform and a Fast Fourier Transform. The advantage of the discrete Fourier Transform is that no zero-padding occurs. This is important because zero-padding can distort the phase angle spectrum. The advantage of the Fast Fourier Transform is in computation speed, which varies with the number of points.

Normally, the frequency domain representation of a signal is shown in two plots: the magnitude, or amplitude, spectrum and the phase spectrum. At any given frequency, the magnitude and phase can be used to construct a sine wave of that frequency which is contained in the original signal, according to the following:(2) If the responses at every frequency are combined, the result is the original signal.
For example, in HyperGraph, create the following curve:
Now create a second curve in a different window on the same page. It should be:

Figure 2.

The result shows spikes at each of the frequencies present in the initial equation, that is: at 5 Hz, 15 Hz, 20 Hz, and 33 Hz.

Notice that there will also be other spikes at frequencies not present in the initial equation, at 67 Hz, 80 Hz, 85 Hz, 95 Hz. This is due to phenomena caused by sampling the data, and is discussed below.

Since the right half of the spectrum is essentially redundant, the left half is normally all that is plotted. HyperGraph accounts for this using the fold function. The right half of the spectrum is folded back onto the left half. In the previous example, replace the y equation with:
y = fold(FFTmag(p1w1c1.y))

Only the left half of the spectrum is shown.

FFT Phenomena

There are two separate and distinct phenomena introduced in digital signal processing. The first is called aliasing, and is due to the sampling of the data. The second phenomenon is known as leakage, and is due to discontinuities at the endpoints of the signal.
Due to the nature of sampled data, only a limited frequency range can be examined using discrete Fourier transforms. The valid frequency range is from 0 to half of the sampling frequency. This is known as the Nyquist frequency. Any frequencies outside of this range are erroneously attributed to frequencies inside the range. These frequencies will be "folded" back into the valid range.

Figure 3.
For example, a curve has a frequency content at 90 Hz and is sampled at 100 Hz. The Nyquist frequency is then 50 Hz. Since 90 Hz is greater than 50 Hz, it will be folded about the Nyquist frequency back into the valid range of 0-50 Hz. Therefore, it will be mapped to 10 Hz, as well as 90 Hz (and 110 Hz, 190 Hz, ...). The spectrum is always symmetric about the Nyquist frequency, as seen in the above example.
An assumption made by the Fourier Transform is that the signal being transformed is periodic, allowing the integral in Equation 1 to go from 0 to the signal duration rather than from 0 to infinity. This means that, in effect, the FT algorithm is concatenating the signal with itself indefinitely. At the endpoints of the signal, discontinuities often occur. These discontinuities are very difficult to map to periodic components. In fact, they require an infinite number of periodic components. This, in turn, introduces spurious frequency components into the resultant spectrum.

Figure 4.
The most common method used to reduce leakage is to use data windowing.


As described in the previous section, discontinuities at the end points of the signal introduce leakage. Leakage is undesirable because it distorts the true spectral characteristics of the signal. To eliminate leakage, not only must the end points of the signal have the same value, but they must also have the same slope.

The most common way to reduce leakage is by using windowing functions. A sample windowing function is shown below. The sample shows the Hamming window function.

Figure 5.

The value of the windowing function starts out very small, increases to a value of one in the middle, and then decreases symmetrically. When this function is multiplied by the input signal, it has the effect of damping out the ends of the signal. Thus, when the signal is concatenated with itself, no large discontinuities will be present since the end points should both have values and slopes close to zero.

Using a windowing function, however, alters the quantitative properties of a signal, such as the RMS. HyperGraph uses power correction in its windowing functions to eliminate this problem. HyperGraph also removes the mean (DC component) from the signal before windowing. This makes the windowing more effective and does not affect the frequency content of the signal.

A common way of viewing frequency domain representations of data is the power spectral density function or PSD. This function is the square of the magnitude spectrum scaled by the inverse of the duration of the signal. Assuming the input signal is in volts, the integral of the PSD with respect to frequency will give the RMS value, or total power, of the signal. By integrating only over a specific frequency range, the RMS of the signal due to frequency components in that frequency range can be determined.


A common practice in signal processing is to smooth data by eliminating high-frequency components. This is done using a low-pass filter. It is called a low-pass filter because it only keeps low-frequency components and eliminates high frequency components. Thus, it only lets low-frequency components pass. Other types of filters are high-pass, band-pass, and band-stop, or notch, filters.

In order to filter a signal, the application transforms the input data into the frequency domain using an FFT. All unwanted frequency components are eliminated by multiplying the FFT values at these frequencies by zero. The modified frequency domain data is then transformed back into the time-domain.

Figure 6. Low-Pass Filtered Signal

In order to specify which frequencies are to be removed, cutoff frequencies must be defined. For low- and high-pass filters, only a single cutoff frequency is necessary. For low-pass filters, frequencies below the cutoff frequency are passed, while the opposite is true for high-pass filters. Band-pass and band-stop filters require two cutoff frequencies, a low and a high. For a band-pass filter, only frequencies between the two cutoff frequencies are passed, while for a band-stop filter, only frequencies outside this range are passed.

One problem with this approach is that often the beginning and end of the filtered signal do not match well with the original data. This is due to the fact that data is missing on either side, making it more difficult to reconstruct the signal from the filtered frequency data. Also, if the signal does not have a number of points which are a valid number for an FFT, it will be zero-padded. In many cases, this causes the filtered signal to sharply tend toward zero at the end. In order to minimize this problem, often the input signal is concatenated with constant values equal to the last value, then filtered, and finally the added values are removed.

It is recommended that the mean be removed from a signal before it is filtered so the mean of the filtered curve is 0.00. Then, add the mean back after filtering to obtain the desired magnitudes. The mean may be left out altogether if the DC component is not desired.

Frequency Response Functions

A Frequency Response Function, FRF, is used to determine the frequency characteristics of a given system. The system must be quantified with an input signal and an output signal.

The result of the FRF is a complex-valued function. Normally this is also shown in magnitude/phase form. The magnitude spectrum gives information as to the system gain at various frequencies, while the phase spectrum gives delay information.

Figure 7.

For a system with a uniform gain and delay, the magnitude spectrum would be constant at the gain of the system and the phase spectrum would be linear with slope proportional to the system delay.


Random data theoretically has no Fourier Transform, because it is aperiodic. Therefore, in the Fourier Transform equation, , the upper limit cannot be changed to the period of the signal. This would mean an infinite amount of data would have to be collected in order to get the true Fourier Transform of a random signal. In practice, this point is ignored. However, two random signals from the same system will have vastly different spectral characteristics. In order to circumvent this problem, a process known as blocking is often used.

In blocking, a single random signal is subdivided into many shorter signals. The frequency characteristics of each of the smaller signals are calculated, and then averaged. This takes advantage of a statistical property which states that if the frequency spectra of a large enough number of signals are averaged, the results will tend toward the true spectrum of the system.

However, by using this technique, the duration of each individual signal is lessened, resulting in poorer frequency resolution. Since the sampling interval is the same for each sub-signal as the original signal, the Nyquist frequency is not changed by blocking.

Hilbert Transform

The Hilbert Transform is used to obtain the envelope of a curve. It is in fact guaranteed to be greater than or equal to the input signal for all data which is functional, i.e.: the X values are monotonic or strictly increasing. It is done using a complex algorithm incorporating FFTs. It tends to work very well with smooth curves, but for noisy data the results are not as good. Also, as with frequency-domain filtering, the beginning and end of the signal tend not to match the original data very closely.

Figure 8.