Peak Detection Implementation for Real-Time Signal Analysis Based on FPGA

Alperen Mustafa Colak,Fujio Kurokawa,Yuichiro Shibata,Taito Manabe

doi:10.4236/cs.2018.910016

Alperen Mustafa Colak, Fujio Kurokawa + Show 2 more

Open Access

https://doi.org/10.4236/cs.2018.910016

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Abstract

In this paper a real-time peak detection method based on modified Automatic Multiscale Field Detection (AMPD) algorithm and Field Programmable Gate Arrays (FPGA) technologies of a time series data is studied, and optimum scaling is highlighted after testing several scales. To validate the results obtained from modified algorithm, they are compared with the results of original AMPD method. As data of this study, three-phase voltage values of a power station are used. A detail detective sensitivity analysis of phase-to-phase voltage values is tried at different scales. Moreover, the original algorithm is tested regarding the off-line mode to obtain optimum scaling for real-time peak point detection. It is concluded that the peak detection of minimum and maximum points of data series achieved by modified algorithm is very close to the results of original AMPD algorithm.

Highlights

Peak detection of any time series data is always a hot topic in many engineering fields including chemistry, biology, biomedical, optics, astrophysics and energy systems
In this paper a real-time peak detection method based on modified Automatic Multiscale Field Detection (AMPD) algorithm and Field Programmable Gate Arrays (FPGA) technologies of a time series data is studied, and optimum scaling is highlighted after testing several scales
This paper introduces a novel approach for robust and real-time peak detection by using the automatic multiscale-based peak detection (AMPD) algorithm and the FPGA technology

Summary

Introduction

Peak detection of any time series data is always a hot topic in many engineering fields including chemistry, biology, biomedical, optics, astrophysics and energy systems. These fields often require real-time peak detection. As the environment noises can affect the signals somehow, a robust peak detection, in this case, is a challenging topic. The algorithms with fewer parameters are restricted for use in specific applications like the detection of R-peaks in electroencephalography (ECG) signals and to obtain an adaptive and time-efficient R-peak detection algorithm for ECG processing as well as reduce the size and noise of ECG signals [15]-[20]. Noise in analyzed signal is a challenge for many peak detection algorithms

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