Interval Active Devices Table A pie chart showing the percentages and total numbers of each device type for all active devices.

Basic Signal Analysis Computations The basic computations for analyzing signals include converting from a two-sided power spectrum to a single-sided power spectrum, adjusting frequency resolution and graphing the spectrum, using the FFT, and converting power and amplitude into logarithmic units.

The power spectrum returns an array that contains the two-sided power spectrum of a time-domain signal. The array values are proportional to the amplitude squared of each frequency component making up the time-domain signal.

A plot of the two-sided power spectrum shows negative and positive frequency components at a height where Ak is the peak amplitude of the sinusoidal component at frequency k.

Figure 1 shows the power spectrum result from a time-domain signal that consists of a 3 Vrms sine wave at Hz, a 3 Vrms sine wave at Hz, and a DC component of 2 VDC. A 3 Vrms sine wave has a peak voltage of 3. The power spectrum is computed from the basic FFT function. Refer to the Computations Using the FFT section later in this application note for an example this formula.

Converting from a Two-Sided Power Spectrum to a Single-Sided Power Spectrum Most real-world frequency analysis instruments display only the positive half of the frequency spectrum because the spectrum of a real-world signal is symmetrical around DC.

Thus, the negative frequency information is redundant.

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The two-sided results from the analysis functions include the positive half of the spectrum followed by the negative half of the spectrum, as shown in Figure 1. In a two-sided spectrum, half the energy is displayed at the positive frequency, and half the energy is displayed at the negative frequency.

Therefore, to convert from a two-sided spectrum to a single-sided spectrum, discard the second half of the array and multiply every point except for DC by two.

The remainder of the two-sided power spectrum SAA The non-DC values in the single-sided spectrum are then at a height of This is equivalent to where is the root mean square rms amplitude of the sinusoidal component at frequency k.

Thus, the units of a power spectrum are often referred to as quantity squared rms, where quantity is the unit of the time-domain signal. For example, the single-sided power spectrum of a voltage waveform is in volts rms squared. Figure 2 shows the single-sided spectrum of the signal whose two-sided spectrum Figure 1 shows.

In addition, the spectrum stops at half the frequency of that in Figure 1. Adjusting Frequency Resolution and Graphing the Spectrum Figures 1 and 2 show power versus frequency for a time-domain signal.

The frequency range and resolution on the x-axis of a spectrum plot depend on the sampling rate and the number of points acquired. The number of frequency points or lines in Figure 2 equals where N is the number of points in the acquired time-domain signal. The first frequency line is at 0 Hz, that is, DC.

The last frequency line is at where Fs is the frequency at which the acquired time-domain signal was sampled. The frequency lines occur at f intervals where Frequency lines also can be referred to as frequency bins or FFT bins because you can think of an FFT as a set of parallel filters of bandwidth f centered at each frequency increment from Alternatively you can compute where t is the sampling period.

Thus is the length of the time record that contains the acquired time-domain signal. The signal in Figures 1 and 2 contains 1, points sampled at 1.

The computations for the frequency axis demonstrate that the sampling frequency determines the frequency range or bandwidth of the spectrum and that for a given sampling frequency, the number of points acquired in the time-domain signal record determine the resolution frequency.

To increase the frequency resolution for a given frequency range, increase the number of points acquired at the same sampling frequency. For example, acquiring 2, points at 1. Alternatively, if the sampling rate had been Computations Using the FFT The power spectrum shows power as the mean squared amplitude at each frequency line but includes no phase information.

Because the power spectrum loses phase information, you may want to use the FFT to view both the frequency and the phase information of a signal. The phase information the FFT yields is the phase relative to the start of the time-domain signal.

For this reason, you must trigger from the same point in the signal to obtain consistent phase readings. In many cases, your concern is the relative phases between components, or the phase difference between two signals acquired simultaneously.

You can view the phase difference between two signals by using some of the advanced FFT functions. The FFT returns a two-sided spectrum in complex form real and imaginary partswhich you must scale and convert to polar form to obtain magnitude and phase.

The frequency axis is identical to that of the two-sided power spectrum. The amplitude of the FFT is related to the number of points in the time-domain signal. Use the following equation to compute the amplitude and phase versus frequency from the FFT.

The two-sided amplitude spectrum actually shows half the peak amplitude at the positive and negative frequencies.

To convert to the single-sided form, multiply each frequency other than DC by two, and discard the second half of the array. The units of the single-sided amplitude spectrum are then in quantity peak and give the peak amplitude of each sinusoidal component making up the time-domain signal.

For the single-sided phase spectrum, discard the second half of the array. To view the amplitude spectrum in volts or another quantity rms, divide the non-DC components by the square root of two after converting the spectrum to the single-sided form.Understanding Digital Signal Processing Third Edition Richard G.

Lyons Windows 89 DFT Scalloping Loss 96 DFT Resolution, Zero Padding, and Frequency-Domain 4 THE FAST FOURIER TRANSFORM Relationship of the FFT to the DFT Hints on Using FFTs in Practice Derivation of the Radix-2 FFT Algorithm one FFT frame, they will all appear to happen simultaneously.

One method of gaining shorter time-visibility is to use an alternate Discrete Time Transform method. Understanding FFTs and Windowing. and windowing as well as how you can use them to improve your understanding of a signal.

This white paper is part of our Instrument Fundamentals Series. Download the White Paper. Topics Covered. Understanding the time domain, frequency domain, and fast Fourier transform (FFT) All signals are the .

I need some help understanding the output of the DFT/FFT computation. I'm an experienced software engineer and need to interpret some smartphone accelerometer readings, such as finding the principal frequencies.

Fast Fourier Transform, or FFT, is an algorithm for computing the frequency spectrum of a time-varying input signal. This line chart shows the FFT duty cycle, which represents the percent of time a signal is broadcast on the specified channel or frequency.

Understanding FFTWindows APPLICATION NOTE AN Introduction FFT based measurements are subject to errors from an effect known as leakage. This effect occurs when the FFT is computed from of a block of data which is with Hanning and Flat Top windows is shown in Figure 7.

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An Interactive Guide To The Fourier Transform – BetterExplained