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The article describes the application of peak picking (PP), complex mode indication function (CMIF), and Hilbert transform techniques to the measured output data from a typical instrumented school building in Istanbul, Turkey. Results reveal that PP gave good results because the frequencies were well separated and damping was low. The CMIF technique is said to be a singular value decomposition (SVD) enhancement of the classical PP technique. It can adequately pinpoint closely spaced modes.

Predictions of wind energy potential in a given region are based on on-location observations. The time series of these observations would later be analysed and modelled either by a probability density function (pdf) such as a Weibull curve to represent the data or recently by soft computing techniques, such as neural networks (NNs). In this paper, discrete Hilbert transform has been applied to characterize the wind sample data measured on İzmir Institute of Technology campus area which is located in Urla, İzmir, Turkey, in March 2001 and 2002. By applying discrete Hilbert transform filter, the instantaneous amplitude, phase and frequency are found, and characterization of wind speed is accomplished. Authors have also tried to estimate the hourly wind data using daily sequence by Hilbert transform technique. Results are varying. [ABSTRACT FROM AUTHOR]