Frequency Domain Analysis Class Help
This class contains functions that perform transformations between the time domain and the frequency domain. These functions also perform analysis in the frequency domain. These functions are based on the discrete implementation of the Fourier Transform. Several rely on frequency domain transformations to obtain the results indirectly.
When the size of the input signal is a power of 2, these functions use the split–radix algorithm for computing the Fast Fourier Transform. This algorithm computes the Fourier Transform in place and is highly memory efficient. Examples of data sizes for which you can use this technique are 256, 512, and 1024.
Another optimized algorithm is used to compute short Fourier Transforms of sizes 2, 3, 4, 5, 8, and 10. This optimized algorithm is a variation of the split–radix technique. As a result, when the size of the input signal is not a power of 2, but can be factored as N = (2m)(3k)(5j) for m, k, j – 0, 1, 2, 3..., the DFT can be computed with speeds comparable to the radix–2 FFT, but requires more memory. Examples of data sizes for which you can use this technique are 480, 640, 1000, and so on.
Finally, when the input signal cannot be factored into sizes that are of short DFT, a chirp Z implementation of the DFT is used. This is much faster than the direct evaluation of the DFT expression. This algorithm uses more memory than the prime–factor algorithms because it must allocate additional buffers for storing intermediate results during processing.
Library: Advanced Analysis Library