FFT stands for Fast Fourier Transform. It is a mathematical algorithm used to efficiently calculate the discrete Fourier Transform (DFT) of a sequence or signal. It is commonly used in signal processing and data analysis.
The main properties of FFT include:
FFT can be used for any frequency, as long as the input signal is periodic. However, it is most commonly used for signals with high frequencies or a large number of data points. The more data points there are, the more accurate the FFT results will be.
FFT works by breaking down a signal into its individual frequency components, using a divide-and-conquer approach. It divides the signal into smaller segments, calculates their DFTs, and then combines them to get the final DFT of the entire signal. This process is repeated recursively until the desired accuracy is achieved.
No, FFT can only be used for periodic signals, as it assumes that the input signal is repeating infinitely. For non-periodic signals, other methods such as the Discrete Cosine Transform (DCT) or Wavelet Transform can be used.