Hi, 1. There is a Nyquist–Shannon sampling theorem which describes properties of quantized analog signal. So it needs to double the frequency range to avoid aliasing (parasitic frequency noise). The result of FFT transform usually is mirror-like spectrum representation of non-aligned amplitude data for each frequency in real part of complex number and the phase data in imaginary part of complex number. So FFT is special computer-optimized implementation form of Fourier theorem, which says that any signal can be represented as a sum of given fundamental sinusoidal frequency and its [in]finite harmonics. In such way any spectral data can be restored to its analog sampling signal state using inversed FFT. 2. Without understanding basics of signal processing use of DSP library is useless in custom scenarios. And this is one of the most complicated topic in computer programming, which generally involves knowledge of advanced algebra and wave physics. Alex
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