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A Fourier based algorithm to estimate the period of a sampled signal

dc.creatorRairán Antolines, José Danilo
dc.date2017-12-31
dc.date.accessioned2026-08-04T22:02:00Z
dc.date.available2026-08-04T22:02:00Z
dc.descriptionGiven a sampled signal, in general, is not possible to compute its period, but just an approximation. We propose an algorithm to approximate the period, based on the Discrete Fourier Transform. If that transformation uses data length for multiples of the true period, some of its harmonics have null value. Thus, the best candidate to be a multiple of the period minimizes the value of those harmonics. The validation for noiseless data shows an upper bound in the error equal to a quarter of the time between two consecutive samples, whereas the result for noisy data demonstrates robustness. As application, the algorithm estimates the period of physiological signals, and tracks the frequency of the power grid in real time, which evidence its versatilityen-US
dc.descriptionDada una se˜nal muestreada, en general, no es posible calcular su periodo, sino solo una aproximaci´on. En este art´ıculo se propone un algoritmo para aproximar el periodo, basado en la Transformada Discreta de Fourier. Si esa transformaci´on utiliza datos por un m´ultiplo del n´umero de periodos, algunos de sus arm´onicos resultan nulos. As´ı, el mejor candidato a ser un m´ultiplo del periodo es el que minimiza el valor de esos arm´onicos. La validaci´on para datos sin ruido muestra un l´ımite m´aximo para el error de un cuarto del tiempo entre dos muestras consecutivas, mientras que el resultado para se˜nales con ruido demuestra robustez. Como aplicaci´on, el algoritmo es utilizado para estimar el periodo de una se˜nal fisiol´ogica, y el seguimiento de la frecuencia de un sistema de potencia, en tiempo real, lo cual evidencia la versatilidad del algoritmo.es-ES
dc.formatapplication/pdf
dc.identifierhttps://revistas.udistrital.edu.co/index.php/visele/article/view/14624
dc.identifier10.14483/22484728.14624
dc.identifier.urihttp://hdl.handle.net/11349/103124
dc.languageeng
dc.publisherUniversidad Distrital Francisco José de Caldases-ES
dc.relationhttps://revistas.udistrital.edu.co/index.php/visele/article/view/14624/14651
dc.relation/*ref*/R. G. McKilliam, B. G. Quinn, I.V.L Clarkson, and B. Moran, “Frequency estimation by phase unwrapping,” in IEEE Transactions on Signal Processing, Vol. 58, no. 6, 2010, pp. 2953–2963, https://doi.org/10.1109/TSP.2010.2045786
dc.relation/*ref*/S. Provencher, “Estimation of complex single-tone parameters in the DFT domain,” in IEEE Transactions on Signal Processing, Vol. 58, no. 7, 2010, pp. 3879– 3883, https: //doi.org/10.1109/TSP.2010.2046693
dc.relation/*ref*/H. C. So, F. K. W. Chan, and S. Weize, “Subspace approach for fast and accurate single-tone frequency estimation”, in IEEE Transactions on Signal Processing, Vol. 59, no. 2, 2010, pp. 827–831, https://doi.org/10.1109/TSP.2010.2090875
dc.relation/*ref*/R. Chudamani, K. Vasudevan, C. S. Ramalingam, “Real-time estimation of power system frequency using nonlinear least squares,” in IEEE Transactions on Power Delivery, Vol. 24, no. 3, 2009, pp. 1021–1028, https://doi.org/10.1109/TPWRD.2009.2021047
dc.relation/*ref*/Y. Pantazis, O. Rosec, Y. Stylianou, “Iterative estimation of sinusoidal signal parameters,” in IEEE Signal Processing Letters, Vol. 17, no. 5, 2010, pp. 461–464, https://doi.org/10.1109/LSP.2010.2043153
dc.relation/*ref*/C. Candan, “A method for fine resolution frequency estimation from three DFT samples,” in IEEE Signal Processing Letters, Vol. 18, no. 6, 2011, pp. 351–354, https://doi.org/10.1109/LSP.2011.2136378
dc.relation/*ref*/C. Yang, and G. Wei, “A noniterative frequency estimator with rational combination of three spectrum lines,” in IEEE Transactions on Signal Processing, Vol. 59, no. 10, 2011, pp. 5065–5070, https://doi.org/10.1109/TSP.2011.2160257
dc.relation/*ref*/J. D. Rairan, “Two Algorithms for Estimating the Period of a Discrete Signal,” in Ingenieria e investigaci´on Journal, Vol. 34, no. 3, 2014, pp. 57–63, doi: dx.doi.org/10.15446/ing.investig. v34n3.41943
dc.relation/*ref*/Physionet, “PhysioBank ATM”. 2016 [Online] Available: http: physionet.org/cgi- bin/atm/ATM
dc.relation/*ref*/R. Llinares, and J. Igual, “Exploiting periodicity to extract the atrial activity in atrial arrhythmias”, in EURASIP Journal on Advances in Signal Processing, 2011, pp. 1–16, https://doi.org/10.1186/1687-6180-2011-134
dc.sourceVisión electrónica; Vol. 11 No. 2 (2017); 152-160en-US
dc.sourceVisión electrónica; Vol. 11 Núm. 2 (2017); 152-160es-ES
dc.source2248-4728
dc.source1909-9746
dc.subjectPeriod estimationen-US
dc.subjectPhysiological signalsen-US
dc.subjectPower grid frequencyen-US
dc.subjectDiscrete Fourier Transformen-US
dc.subjectEstimación del periodoes-ES
dc.subjectseñales fisiológicases-ES
dc.subjectFrecuencia de un sistema de potenciaes-ES
dc.subjectTransformada Discreta de Fourieres-ES
dc.titleA Fourier based algorithm to estimate the period of a sampled signalen-US
dc.titleUn algoritmo para estimar el periodo de una señal muestreada basado en la transformada de Fourieres-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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