FFT Processing with Spectrum Plotting, RMS Spectrum, Power Spectrum, and IFFT Spectrum Diagrams

This document provides a comprehensive overview of FFT processing and spectrum visualization techniques using MATLAB. We begin by explaining the fundamental principles of Fast Fourier Transform (FFT), detailing how it converts time-domain signals into frequency-domain representations through efficient algorithm implementation. The MATLAB code implementation typically involves using built-in functions like fft() with proper signal windowing and zero-padding techniques to minimize spectral leakage. We then demonstrate MATLAB programming approaches for creating various spectrum plots, including amplitude spectrum visualizations using plot() or stem() functions with frequency axis calibration. The discussion extends to Root Mean Square (RMS) spectrum plots, which provide more accurate amplitude measurements by calculating sqrt(2)*abs(FFT)/N, where N represents the signal length. Power spectrum analysis is covered using periodogram() or pwelch() functions, highlighting their applications in signal power distribution analysis across frequency components. Finally, we examine Inverse FFT (IFFT) spectrum processing through the ifft() function, which reconstructs time-domain signals from frequency-domain data while maintaining phase information. Each section includes practical MATLAB code snippets showing parameter configuration, data normalization techniques, and visualization best practices. This expanded coverage enables readers to gain deeper insights into complete spectral analysis workflows using MATLAB, from fundamental transformations to advanced spectrum interpretation.