فهرست مطالب ahamd akbari

Climate change is crucially important in Iran given its arid and semiarid climate. It influences water resources, thereby affecting many activities and even threatening human life seriously. The assessment of the extent to which climate change can influence water use and resources can play a decisive role in macro-policymaking. Present study uses positive mathematical programming (PMP) in the context of four climatic scenarios (normal climate change, climate change, climate variability, and concurrent climate change) to explore impacts of these changes on agricultural water use and water resources of Iran in a 20-year period. The results showed that in the context of concurrent climate change and also climate change, in spite of the decrease in water use by the agricultural sector, water resources of Iran are reduced with much higher rate than that under other climatic conditions. According to results, average annual agricultural water use under four studied scenarios will be 35,103.65, 26,533.8, 35,216 and 26,510.69 million m3, and average rate of annual loss of water resources will be 4,422.2, 11,165.6, 4,438.24, and 11,267.45 million m3, respectively. Accordingly, changes should be applied in approach to water demand and supply management. Also, policies should be adopted to cope with climate change.

In recent years, sub-band speech recognition has been found useful in addressing the need for robustness in speech recognition, especially for the speech contaminated by band-limited noise. In sub-band speech recognition, the full band speech is divided into several frequency sub-bands, with the result of the recognition task given by the combination of the sub-band feature vectors or their likelihoods as generated by the corresponding sub-band recognizers. In this paper, we draw on the notion of discrete wavelet transform to divide the speech signal into sub-bands. We also make use of the robust features in sub-bands in order to obtain a higher sub-band speech recognition rate. In addition, we propose a likelihood weighting and fusion method based on the wavelet thresholding technique. The experimental results indicate that the proposed weighting methods for likelihood combination and classifiers fusion improve the sub-band speech recognition rate in noisy conditions.