DFT componente k=1 (mag)

|Σx·e^(-j2πkn/N)|.
Created by
Renato Passos, Eng. de Software
Reviewed by
Renato Passos, Eng. de Software

Last updated: Apr 18, 2026

|X[1]|
2,0000

About this calculator

The DFT k=1 component (magnitude) calculator computes the amplitude of the first Fourier component in a frequency spectrum. This component represents the fundamental frequency of a sampled signal. The formula used is |Σx·e^(-j2πkn/N)|, where N is the sample count, n is the index, and k=1 corresponds to the first harmonic. This tool is useful for signal analysis in engineering, audio processing, and periodic wave studies.

To use the calculator, input the time-domain sequence (samples), and it will return the magnitude of the k=1 component. The formula weights each sample by a complex exponential factor corresponding to the frequency of interest. The output is the absolute value of this sum, indicating the strength of the fundamental frequency in the signal. The result is given in relative amplitude units compared to the original signal.

This tool is ideal for scenarios focusing on the lowest frequency (fundamental) such as sensor testing, noise filtering, or mechanical vibration analysis. Note that this calculator provides only the magnitude, not the phase of the component. For full frequency spectra, use DFT/FFT calculators that process all components (k=0, 1, ..., N-1).

Frequently asked questions

What is the magnitude of the k=1 component?

It is the amplitude of the signal's fundamental frequency, calculated using the formula |Σx·e^(-j2πkn/N)|. It represents the strength of the first harmonic (lowest frequency) in the signal's spectrum.

Why is k=1 specifically calculated?

The k=1 component corresponds to the fundamental frequency of the signal. For full spectra, use DFT/FFT calculators that process all k values (0 to N-1).

How is the magnitude interpreted?

The magnitude indicates the relative amplitude of the fundamental frequency in the signal. Higher values suggest a stronger presence of this frequency in the spectrum.

Can this tool handle non-uniform samples?

No, DFT requires evenly spaced time samples. For irregular samples, use Non-Uniform FFT (NFFT) methods.

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