Asymmetric Stencil Approach for Latency Reduction of Real-Time Peak Detection Using AMPD Algorithm and FPGA Technology

Alperen Mustafa COLAK, Taito MANABE, Yuichiro SHIBATA, Fujio KUROKAWA

Abstract


In many signal processing applications, the detection of peaks is a substantial stage. However, the high false-positive peak identification rate is a crucial problem because of the complexity of the signals and multiple noise sources. For this reason, a modified Automatic Multiscale Peak Detection (AMPD) algorithm of any time serial data based on Field-Programmable Gate Array (FPGA) has been implemented by these authors. In addition, a kind of approximation with an asymmetric stencil is proposed to reduce the pipeline latency. In this paper, it is focused on evaluating the trade-off relationship between latency reduction effects and accuracy of peak point detection on a real-time peak detection method developed in the previous study using the AMPD algorithm and FPGA technology.


Keywords


automatic multiscale-based peak detection, latency reduction, FPGA

Full Text:

PDF

References


J. Li, Y. Li, W. Zhao and M. Jiang, “Diffusion

enhancement model and its application in peak

detection”, Chemometrics and Intelligent Laboratory

Systems, vol. 189, pp. 130-137, 2019.

Y. Zheng, R. Fan, C. Qiua, Z. Liu and D. Tian, “An

improved algorithm for peak detection in mass spectra

based on continuous wavelet transform”, International

Journal of Mass Spectrometry, vol. 409, pp. 53-58,

S.S. Kumar, N. Mohan, P. Prabaharan and K.P. Soman,

“Total variation denoising based approach for R-peak

detection in ECG signals”, Procedia Computer Science,

vol. 93, pp. 697-705, 2016.

J. Rahul, M. Sora and L.D. Sharma, “A novel and

lightweight P, QRS, and T peaks detector using adaptive

thresholding and template waveform”, Computers in

Biology and Medicine, 2021.

S. Vadrevu and M.S. Manikandan, “Effective systolic

peak detection algorithm using variational mode

decomposition and center of gravity”, IEEE Region 10

Conference, 22-25 November 2016, Singapore.

F. Scholkmann, J. Boss and M. Wolf, “An Efficient

Algorithm for Automatic Peak Detection in Noisy

periodic and Quasi-Periodic Signals,” Algorithms,

vol.5, pp.588-603, 2012.

MN. Schmidt, T.S. Alstrøm, M. Svendstorp and J.

Larsen, “Peak detection and baseline correction using a

convolutional neural network”, International

Conference on Acoustics, Speech and Signal

Processing, 12-17 May 2019, Brighton, UK.

F. Liu, X. Tong, C. Zhang, C. Deng, Q. Xiong, Z. Zheng,

P. Wang, “Multi-peak detection algorithm based on the

Hilbert transform for optical FBG sensing”, Optical

Fiber Technology, vol. 45 pp. 47-52, 2018.

T. Bodendorfer, M.S. Muller, F. Hirth and A.W. Koch,

“Comparison of different peak detection algorithms

with regards to spectrometic fiber Bragg grating

interrogation systems”, International Symposium on

Optomechatronic Technologies, 21-23 September 2009,

Istanbul, Turkey.

G. Tolt, C. Grönwall and M. Henriksson, “Peak

detection approaches for timecorrelated single-photon

counting three-dimensional lidar systems”, Optical

Engineering vol. 57, no. 3, 031306, 2018.

H. Guo, S. Cui and X. Xu, “Design and implementation

of voltage peak detection based on Fourier analysis”,

Advances in Computer Science Research, vol. 94, pp.

-102, 2019.

Q. Wu, S. Wang, C. Liao, Z. Tang, H. Luo, S. Huang,

and L. Deng, “A mV-level real-time peak-voltage

detection circuit based on differential structure”, Review

of Scientific Instruments, vol. 92, 034713, 2021.

P.V. Manitha, M.G. Nair and T. Thakur, “Fundamental

voltage peak detection controller for series active

filters”, Electric Power Systems Research, vol. 184,

, 2020.

A. Ahmad, A. Khandelwal and P. Samuel, “Golden

band search for rapid global peak detection under partial

shading condition in photovoltaic system”, Solar

Energy, vol. 157, pp. 979-987, 2017.

W.C. Lee and T.K. Lee, “Peak detection method using

two-delta operation for single voltage sag”, International

Power Electronics Conference, 18-21 May 2014,

Hiroshima, Japan.

N. Kumar, I. Hussain, B. Singh and B.K. Panigrahi,

“Peak power detection of PS solar PV panel by using

WPSCO”, IET Renewable Power Generation, vol. 11,

no. 4, pp. 480-489, May 2017.

Z. Shi and H. Liu, “STM32F4 based real-time peak

detection of FBG”, 15th International Conference on

Optical Communications and Networks, pp. 1-3, 24-27

September 2016, Hangzhou, China.

M. Schirmer, F. Stradolini, S. Carrara and E. Chicca,

“FPGA-based approach for automatic peak detection in

cyclic voltammetry”, IEEE International Conference on

Electronics, Circuits and Systems, pp. 65-68, 11-14

December 2016, Monte Carlo, Monaco.

R. Ghozzi, S. Lahouar, C. Souani and K. Besbes, “Peak

detection of GPR data with lifting wavelet transform

(LWT)”, International Conference on Advanced

Systems and Electric Technologies pp. 34-37, 14-17

January 2017, Hammamet, Tunisia.

W.C. Lee, K.N. Sung and T.K. Lee, “Fast detection

algorithm for voltage sags and swells based on delta

square operation for a single-phase inverter system”,

Journal of Electrical Engineering and Technology, vol.

, no. 1, pp. 157-166, January 2016.

A.T. Tzallas, V.P. Oikonomou and D.I. Fotiadis,

“Epileptic spike detection using a Kalman filter based

approach”, International Conference of the IEEE

Engineering in Medicine and Biology, pp. 501-504, 30

August-3 September 2006, New York, USA.

A. M. Colak, T. Manabe, Y. Shibata, and F. Kurokawa,

“Peak Detection Implementation for Real-Time Signal

Analysis Based on FPGA,” Journal of Circuits and

Systems, vol. 9, no. 10, pp. 148-167, 2018, October 31.

A.M. Colak, Y. Shibata and F. Kurokawa, “Peak Point

Detection of Phase-to-Phase Effective Voltages for

Smart Grids: A Comparative Study,” IEEE 6th

International Conference on Renewable Energy

Research and Applications (ICRERA), San Diego, CA,

, pp.1149-1153.




DOI (PDF): https://doi.org/10.20508/ijrer.v11i1.10523.g8158

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2024; 

h=33,

Average citation per item=6.17

Impact Factor=(1749+1867+1731)/(201+146+171)=10.32

Category Quartile:Q4