A novel approach to peak detection using sequential learning algorithm

Sumukha, B. N.; Kumar, Ranjeet; Bharadwaj, Skanda; George, Koshy

doi:10.1109/ic3i.2016.7918803

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…As the environment noises can affect the signals somehow, a robust peak detection, in this case, is a challenging topic. To obtain a successful peak detection method, several methods have been proposed, including automatic multiscale-based peak detection [1], window-threshold techniques [2] [3] [4], wavelet transform [5]- [11], techniques using entropy [12], and artificial neural networks [13] [14]. Particu-larly, each method was investigated in terms of the detection method employed and the detection performance achieved.…”

Section: Introductionmentioning

confidence: 99%

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

Çolak¹,

Manabe²,

Shibata³

et al. 2018

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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.

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