In the first part of this paper a Variable rate sampling, algorithm for prediction has been described. Variable rate sampling systems were used for reducing power demand, by reducing the number of sent samples. In the second part is performed an experiment where is tested algorithm for prediction of data for transmission and transmitted data. For the purpose of this analyze transmission and predictions are performed for temperatures, air pressure, wind speed, and visibility. Prediction on the side of the transmission is performed using three different extrapolations (mam, mab, and ng). At the receiving side reconstruction of the received signal is performed applying Linear, Cubic, Makima, and Spline extrapolations, built into Matlab. A simple reconstruction is also performed, using the last known value for prediction the next value (this prediction is in the paper named Step extrapolation). Objective quality measures SR (correlation coefficient of reducing the number of samples between a number of measured and transmitted samples), and MAE (mean absolute error between the measured and predicted values of temperatures, pressures and visibility values) were calculated. The results are presented in the table and graphically.
The first part of this paper describes an algorithm for estimating the fundamental frequency F0 of a speech signal, using an autocorrelation algorithm. After that, it was shown that, due to the discrete structure of the autocorrelation function, the accuracy of the fundamental frequency estimate largely depends on the sampling period TS. Then, in order to increase the accuracy of the estimation, an interpolation of the correlation function is performed. Interpolation is performed using a one parameter (1P) Keys interpolation kernel. The second part of the paper presents an experiment in which the optimization of the 1P Keys kernel parameter was performed. The experiment was performed on test Sine and Speech signals, in the conditions of ambient disturbances (N8 Babble noise, SNR = 5 to -10 dB). MSE was used as a measure of the accuracy of the fundamental frequency estimate. Kernel parameter optimization was performed on the basis of the MSE minimum. The results are presented graphically and tabularly. Finally, a comparative analysis of the results was performed. Based on the comparative analysis, the window function, in which the smallest estimation error was achieved for all ambient noise conditions, was chosen.
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