A Performance Evaluation of Periodic Signal Analysis by ARS Compared with Frequency Analysis by FFT

Abstract

Accumulation for real-time serial-to-parallel converter (ARS) has been proposed as a computationally-efficient method for signal analysis. Since it consists of additions and a few divisions only, it was shown that the computational load is drastically reduced compared with the fast Fourier transform (FFT). In this paper, we clarify that ARS achieves a higher resolution in low-frequency bands comparing with FFT. In addition, selection criteria between ARS and FFT are clarified theoretically in terms of the resolution. Through the performance analysis of ARS in low-frequency bands, it is expected that ARS is a powerful tool for signal analysis in the Internet of Things realizing the edge computing.

Keywords

• accumulation for real-time serial-to-parallel converter
• fast fourier transform
• internet of things
• edge computing
• frequency analysis