FFT Transformation of Sine Wave Signals

In this documentation, we perform FFT transformation on sine wave signals and generate spectrum diagrams. Through spectral analysis, we can calculate the signal-to-noise ratio (SNR). The implementation typically involves using MATLAB's fft() function to convert time-domain signals to frequency-domain representations, followed by plotting the magnitude spectrum using plot() or stem() functions. Additionally, we can employ other digital signal processing techniques to further analyze signal characteristics and extract more useful information. SNR calculation can be achieved by comparing the power of fundamental frequency components against noise components in the frequency spectrum.

These analytical approaches provide deeper insights into signal frequency components and noise levels present in the signal. Such information is crucial for research and applications across various fields including communication system design, audio processing, and image processing. Therefore, spectrum analysis and SNR calculation represent common tasks in digital signal processing, serving as valuable techniques for signal processing engineers and researchers. The process typically involves proper windowing functions (like Hanning window) to reduce spectral leakage, zero-padding for frequency resolution enhancement, and logarithmic scaling for better visualization of dynamic range.