Asymmetric Stencil Approach for Latency Reduction of Real-Time Peak Detection Using AMPD Algorithm and FPGA Technology
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
Full Text:
PDFReferences
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 2025;
h=35,
Average citation per item=6.59
Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43
Category Quartile:Q4