Implementation of fast Fourier transform in IoT data analysis, AI and audio signal processing

FFT has been a cornerstone in signal processing since its introduction in 1965. This research explores the recent applications of FFT in IoT data analysis, AI, and Audio Signal Processing. According to the analysis, this study reveals that in IoT, FFT significantly enhances data compression efficiency, reducing storage requirements by up to 60% while maintaining data integrity. In AI applications, FFT-based feature extraction improves speech recognition accuracy by 15% and reduces computational time in image processing tasks by 30%. For audio signal processing, FFT-driven noise reduction algorithms demonstrate a 20 dB improvement in signal-to-noise ratio. However, FFT shows limitations in processing non-stationary signals and faces challenges in real-time applications due to its computational complexity. Future research directions include developing adaptive FFT algorithms for non-stationary signals and optimizing FFT for edge computing in IoT devices. This study underscores the continuing relevance of FFT in modern digital signal processing and highlights its potential for driving innovations in emerging technologies.