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.
References
[1]
Scholkmann, F., Boss, J. and Wolf, M. (2012) An Efficient Algorithm for Automatic Peak Detection in Noisy periodic and Quasi-Periodic Signals. Algorithms 2012, 5, 588-603. https://doi.org/10.3390/a5040588
[2]
Jacobson, M. (2001) Auto-Threshold Peak Detection in Physiological Signals. Technical Report, DTIC Document.
[3]
Ding, F., Booth, C.D. and Roscoe, A.J. (2016) Peak-Ratio Analysis Method for Enhancement of LOM Protection Using M-Class PMUs. IEEE Transactions on Smart Grid, 7, 291-299. https://doi.org/10.1109/TSG.2015.2439512
[4]
Belkaid, A., Gaubert, J.P. and Gherbi, A. (2017) Design and Implementation of a High-Performance Technique for Tracking PV Peak Power. IET Renewable Power Generation, 11, 92-99. https://doi.org/10.1049/iet-rpg.2016.0023
[5]
Du, P., Kibbe, W.A. and Lin, S. (2006) Improved Peak Detection in Mass Spectrum by Incorporating Continuous Wavelet Transform-Based Pattern Matching. Bioinformatics, 22, 2059-2065. https://doi.org/10.1093/bioinformatics/btl355
[6]
Nenadic, Z. and Burdick, J.W. (2005) Spike Detection Using the Continuous Wavelet Transform. IEEE Transactions on Biomedical Engineering, 52, 74-87.
https://doi.org/10.1109/TBME.2004.839800
[7]
Goodfellow, J., Escalona, O.J., Kodoth, V., Manoharan, G. and Bosnjak, A. (2016) Denoising and Automated R-Peak Detection in the ECG Using Discrete Wavelet Transform. Computing in Cardiology Conference (CinC), Vancouver, 1045-1048.
[8]
Das, S., Mukherjee, S., Chatterjee, S. and Chatterjee, H.K. (2016) Noise Elimination and ECG R Peak Detection Using Wavelet Transform. Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual, New York, 1-5. https://doi.org/10.1109/UEMCON.2016.7777876
[9]
Thiamchoo, N. and Phukpattaranont, P. (2016) Application of Wavelet Transform and Shannon Energy on R Peak Detection Algorithm. 13th International Conference on Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Mai, 1-5. https://doi.org/10.1109/ECTICon.2016.7561280
[10]
Li, Q., Zhang, W., Li, M., Niu, J. and Wu, Q.M.J. (2017) Automatic Detection of Ship Targets Based on Wavelet Transform for HF Surface Wavelet Radar. IEEE Geoscience and Remote Sensing Letters, 14, 714-718.
https://doi.org/10.1109/LGRS.2017.2673806
[11]
Khan, G.K. and Sawant, A.G. (2016) Spartan 6 FPGA Implementation of 2D-Discrete Wavelet Transform in Verilog HDL. 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), Pune, 139-143. https://doi.org/10.1109/ICAECCT.2016.7942570
[12]
Palshikar, G. (2009) Simple Algorithms for Peak Detection in Time Series. Proc. 1st Int. Conf. Advanced Data Analysis, Business Analytics and Intelligence, Ahmedabad.
[13]
Khatri, K.L. and Tamil, L. (2017) Early Detection of Peak Demand Days of Chronic Respiratory Diseases Emergency Department Visits Using Artificial Neural Networks. IEEE Journal of Biomedical and Health Informatics, 22, 285-290.
https://doi.org/10.1109/JBHI.2017.2698418
[14]
Sumukha, B.N., Kumar, R.C., Bharadwai, S.S. and George, K. (2016) A Novel Approach to Peak Detection Using Sequential Learning Algorithm. 2nd International Conference on Contemporary Computing and Informatics (IC3I), IEEE, Noida, 1-6.
[15]
Crema, C., Depari, A., Flammini, A. and Vezzoli, A. (2016) Efficient R-Peak Detection Algorithm for Real-Time Analysis of ECG in Portable Devices. Sensors Applications Symposium (SAS), IEEE, Catania, 1-6.
[16]
Dora, C. and Biswal, P.K. (2016) Robust ECG Artifact Removal from EEG Using Continuous Wavelet Transformation and Linear Regression. 2016 International Conference on Signal Processing and Communications (SPCOM), IEEE, Bangalore, 1-5. https://doi.org/10.1109/SPCOM.2016.7746620
[17]
Annam, J.R. and Surampudi, B.R. (2016) Inter-Patient Heart-Beat Classification Using Complete ECG Beat Time Series by Alignment of R-Peaks Using SVM and Decision Rule. International Conference on Signal and Information Processing (IConSIP), IEEE, Vishnupuri, 1-5. https://doi.org/10.1109/ICONSIP.2016.7857480
[18]
Aqil, M., Jbari, A. and Bourouhou, A. (2016) Adaptive ECG Wavelet Analysis for R-Peaks Detection. 2016 International Conference on Electrical and Information Technologies (ICEIT), IEEE, Tangiers, 164-167.
https://doi.org/10.1109/EITech.2016.7519582
[19]
Park, J.S., Lee, S.W. and Park, U. (2017) R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope. Journal of Healthcare Engineering, 2017, 1-14. https://doi.org/10.1155/2017/4901017
[20]
Qin, Q., Li, J., Yue, Y. and Liu, C. (2017) An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm. Journal of Healthcare Engineering, 2017, 1-14.
https://doi.org/10.1155/2017/5980541
[21]
Singh, A., Dubey, S. and Bhatia, M. (2013) Design and Simulation of FPGA Based Digital System For Peak Detection and Counting. IJARCSSE, 3, 804-807.
[22]
Ma, M. (2005) Developing and Implementing Phase Normalization and Peak Detection for Real-Time Image Registration. Ph.D. Thesis, Master’s Thesis, Delft University of Technology.
[23]
Colak, A.M., Shibata, Y. and Kurokawa, F. (2016) FPGA Implementation of the Automatic Multiscale Based Peak Detection for Real-Time Signal Analysis on Renewable Energy Systems. 5th International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, 379-384.
