elham horri

The present study from the target perspective is the type of applied research and based on the data collection method is a descriptive-correlational research. Data collection tools included, a researcher -made questionnaire consisting of 21 items extracted from valid questionnaires related to the effectiveness of virtual education. The random sampling method is clustered and includes 382 students of Isfahan Payam Noor University who were trained by LMS in the academic year 1401-1400 and answered the questionnaire. The content validity of the questionnaire was approved by experts, and its reliability was estimated at 94 % using Cronbach's alpha, indicating acceptable reliability. With the applied exploration analysis, 2 factors, including satisfaction with the quality of education and satisfaction with the technical quality of the system, were extracted. These two factors explain 53.42 % of the total variance. According to the results of the factors, they have a significant relationship with each other and the values of the indicators are within the acceptance range. Isfahan Payam Noor University Student Satisfaction Questionnaire with LMS E-learning Organization has a good fit. So this questionnaire can be used at this university.

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.