A new algorithm is proposed to compress speech signals using wavelet transform and linear predictive coding. Signal compression based on the concept of selecting a small number of approximation coefficients after they are compressed by the wavelet decomposition (Haar and db4) at a suitable chosen level and ignored details coefficients, and then approximation coefficients are windowed by a rectangular window and fed to the linear predictor. Levinson Durbin algorithm is used to compute LP coefficients, reflection coefficients and predictor error. The compress files contain LP coefficients and previous sample. These files are very small in size compared to the size of the original signals. Compression ratio is calculated from the size of the compressed signal relative to the size of the uncompressed signal. The proposed algorithms where fulfilled with the use of Matlab package
This study aims to derive a general relation between line loads that acting on two-way slab system and the equivalent uniformly distributed loads. This relation will be so useful to structural designer that are used to working with a uniformly distributed load and enable them to use the traditional methods for analysis of two-way systems (e.g. Direct Design Method). Two types of slab systems, Slab System with Beams and Flat Slab Systems, have been considered in this study to include the effect of aspect ratio and type of slab on the proposed relation. Five aspect ratios, l2/l1 of 0.5, 0.75, 1.0, 1.5 and 2.0, have been considered for both types of two-way systems.
All necessary finite element analyses have been executed with SAFE Soft
The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin
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