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R eferences [1] Chen, K., Wang, J., Zhang, S. (2008). Spectrum correction based on the complex ratio of discrete spectrum around the main-lobe. Journal of Vibration Engineering , 21 (3), 314-318. [2] Wang, X., Xu, K. (2005). Fundamental wave extraction and frequency measurement base on wavelet transform. Chinese Journal of Scientific Instrument, 26 (2), 146-151. [3] Ksibi, R.E., Besbes, H., Valcarce, R.L., Cherif, S. (2010). Frequency estimation of real-valued single-tone in colored noise using multiple autocorrelation lags. Signal Processing , 90 (7), 2303

Abstract

Signal processing precision of Coriolis mass flowmeters affects the measurement accuracy directly. To improve the measurement accuracy of Coriolis mass flowmeters, a phase and frequency matching-based signal processing method for Coriolis mass flowmeters is proposed. Estimated phase difference is obtained by means of frequency estimation, 90° phase shift, generating reference signals and cross-correlation. Simulated results demonstrate that the proposed method has better phase difference estimation and anti-interference performance than the Hilbert transform method, cross-correlation method, data extension-based correlation method, and quadrature delay estimator. Measurement results of Coriolis mass flowmeters verify the effectiveness and superiority of the proposed method in practice.

and Signal Processing, Atlanta, GA, USA, 7-10 May 1996, ICASSP’1996, Vol. 5 , pp. 2801-2804. 12. Cao, Y., G. Wei, F.-J. Chen. An Exact Analysis of Modified Covariance Frequency Estimation Algorithm Bas Ed on Correlation of Single-Tone. – Signal Processing, Vol. 92 , 2012, No 11, pp. 2785-2790. 13. Fu, H., P. Kam. Phase-Based, Time-Domain Estimation of the Frequency and Phase of a Single Sinusoid in AWGN – the Role and Applications of the Additive Observation Phase Noise Model. – IEEE Trans. on Inf. Theory., Vol. 59 , 2013, No 5, pp. 3175-3188. 14. Kay, S. A. Fast

Resources, 25, 1287–1304. Kendall, M.G., 1975. Multivariate Analysis. London, Griffin. Khaliq, M.N., Ouarda, T.B.M.J., Ondo, J.C., Gachon, P., Bobee, B., 2006. Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: a review. Journal of Hydrology, 329, 3–4, 534–552. Kjeldsen et al., 2014. Documentary evidence of past floods in Europe and their utility in flood frequency estimation. Journal of Hydrology, 517, 963–973. Kobold, M., Ulaga, F., Trcek, R., Lalic, B., Sušnik, M., Polajnar, J., Robic, M., 2005. High waters in August

References [1] LJ. Stankovi´c, I. Djurovi´c, S. Stankovi´c, M. Simeunovi´c and M. Dakovi´c, “Instantaneous Frequency Time-Frequency Anal- ysis: Enhanced Concepts and Performance of Estimation Algo- rithms”, Digital Signal Processing, vol. 35, Dec 2014, pp. 1-13. [2] B. Boashash, “Estimating and Interpreting the Instantaneous Frequency of a Signal. I. Fundamentals”, Proceedings of the IEEE, vol. 80, no. 4 pp. 520-538, Apr 1992. [3] I. Djurovi´c and LJ. Stankovi´c, “An Algorithm for the Wigner Distribution based Instantaneous Frequency Estimation a High Noise

References Acreman M. C., Wiltshire S. E., 1989: The regions are dead; long live the regions. Methods of identifying and dispensing with regions for flood frequency analysis. IAHS Publ. 187, 175-188. Alcoverro J., Corominas J., Gómez M., 1999: The Barranco de Arás flood of 7 August 1996 (Biescas, Central Pyrenees, Spain). Engineering Geology, 51, 237-255. Bayliss A. C., Reed D. W., 2001: The use of historical data in flood frequency estimation. Report to MAFF. CEH Wallingford, 87 p. Available at: http://nora.nerc.ac.uk/8060/1/BaylissRepN008060CR.pdf Beable M. E

(2009), 634-639. ZOLGHADR, A.—GOODARZI, E.—MOOSAVINEZHAD, M. : Real-Time Implementation of G.723.1 Speech Coder, Iranian Journal of Information Science and Technology 2 No. 1 (2004), 3-12. JOEN, B.—KANG, S.—BAEK, S.—SUNG, K. : Filtering of a Dissonant Frequency Based on Improved Fundamental Frequency Estimation for Speech Enhancement, IEICE Trans. Fundamentals E86-A No. 8 (Aug 2003), 2063-2064. MILIVOJEVIC, Z.—BALANESKOVIC, D. : Enhancement of the Perceptive Quality of the Noisy Speech Signal by Using of DFF-FBC Algorithm, Facta Universitatis, Ser.: Elec. Energ

R eferences [1] M. Bollen, “Understanding Power Quality Problems: Voltage Sags and Interruptions”, Wiley-IEEE Press, 2000. [2] M. Akke, “Frequency Estimation by Demodulation of Two Complex Signals”, IEEE Transactions on Power Delivery , vol. 12, no. 1, pp. 157–163, Jan 1997. [3] H. J. Jeon and T. G. Chang, “Iterative Frequency Estimation based on MVDR Spectrum”, IEEE Transactions on Power Delivery , vol. 25, no. 2, pp. 621–630, Apr 2010. [4] Y. Xia and D. P. Mandic, “Augmented MVDR Spectrum-Based Frequency Estimation for Unbalanced Power Systems”, IEEE

References [1] SEKHAR, S. C. : Auditory Motivated Level-Crossing Approach to Instantaneous Frequency Estimation, IEEE Trans. Sig. Process. 53 No. 4 (2005), 1450-1462. [2] PANTAZIS, Y.-ROSEC, O.-STYLIANOU, Y. : Iterative Estimation of Sinusoidal Signal Parameters, IEEE Signal Processing Letters 17 No. 5 (2010), 461-464. [3] WANG, F.-BOLLEN, M. : Frequency Response Characteristics and Error Estimation in RMS Measurement, IEEE Trans. Power Delivery 19 No. 4 (2004), 1569-1578. [4] WAND, M.-SUN, Y. : A Practical, Precise Method for Frequency Tracking and Phasor

power system frequency using a digital signal processing technique. [6] Arpaia, P., Cruz Serra, A., Daponte, P., Monteiro, C.L. (2001). A critical note to IEEE 1057-94 standard on hysteretic ADC dynamic testing. Meas. [7] Wu, J., Long, J., Wang, J. (2005). High-accuracy, wide-range frequency estimation methods for power system signals under nonsinusoidal conditions. Trans. Power Delivery [8] So, H.C., Chan, K.W., Chan, Y.T., Ho, K.C. (2005). Linear prediction approach for efficient frequency estimation of multiple real sinusoids: Algorithms and analyses. 2290