جستجوی مقالات مرتبط با کلیدواژه "signal analysis" در نشریات گروه "برق"

Statistical pattern recognition has recently emerged as promising and effective set of complementary methods in structural health monitoring to assess the global state of structures. The aim of this paper is to detect nonlinearity changes resulting from damage by some efficient signal analysis methods. The primary idea behind these methods is to use raw measured vibration time-domain data without applying any feature extraction technique associated with the statistical pattern recognition paradigm. Firstly, statistical moments and central tendency measurements are applied as damage indicators to determine their changes due to damage occurrence. Subsequently, cross correlation and convolution methods are used to measure the similarity between the vibration time-domain signals in the undamaged and damaged conditions. The main innovation of this study is the capability of proposed signal analysis methods for implementing the nonlinear damage identification without extracting damage-sensitive features. In the following, numerical and experimental benchmark models are employed to demonstrate the performance of proposed methods. Results show that nonlinearity changes lead to a reduction in the values of cross correlation and convolution methods caused by damage. Moreover, some of the statistical criteria on the basis of the exploratory data analysis are applicable tools for the global structural health monitoring.

To analyze speech signal, wavelet transform is suitable and powerful. Speech signal is non-stationary requiring both time and frequency domains data at processing. By wavelet transform, required data of both domains could be extracted from the signal. This paper studies which wavelet best suits for analyzing vowel phonemes of Persian speech signal by wavelet transform. When a wavelet is found proper to analyze speech signal, output of the method with more suitable wavelet is expected to have more optimized results than the other wavelets in speech processing applications based on wavelet transform.
According to the results, similarity between signals of Persian language vowel phonemes and approximation coefficient of signal analysis was measured by using a special wavelet and finally, by making some calculation, examining correlation coefficient, and comparing results of different wavelets, coif3 & Db5 were found best wavelets to analyze Persian language vowel phonemes. What follows is comparing this method with other methods by studying the effect of using a proper wavelet to decrease noise and audibly improve the signal, and this proves the efficiency of the wavelet used.