[1] The standard auroral electrojet (AE) indices are based on magnetic disturbance data from 10 to 12 northern auroral observatories. Recently, Newell and Gjerloev (2011a) computed equivalent SuperMAG electrojet (SME) indices using data from around 100 mid latitude to high latitude observatories in the Northern Hemisphere. The SME indices certainly have advantage over the AE indices in terms of number as well as temporal resolution of substorm onsets due to better latitudinal and longitudinal coverage. The UT and seasonal variations of geomagnetic activity have been extensively examined in the past. However, particularly for the AE indices, these variations have remained elusive due to sparse distribution of the AE observatories. In this study, we examine what effect the inclusion of large number of stations would make on the UT and seasonal variations of the auroral electrojets activities. For this purpose, data for years 1997-2009 have been considered when consistently many stations (> 70) were available for the computation of the SME indices. We demonstrate that the SME indices exhibit grossly similar UT and seasonal variations as observed in the AE indices. However, there are subtle differences which arise due to difference in number of stations. Our study suggests that most of the UT and seasonal variations of the AE indices, reported earlier, were mainly not due the sparse distribution of stations, but rather to the actual physical processes that control them.
The presence of simultaneous head and neck squamous cell carcinoma and pulmonary malignancies should not be a deterrent to aggressive surgical therapy because a potentially satisfactory outcome can be expected in these patients.
Most important way of communication among humans is language and primary medium used for the said is speech. The speech recognizers make use of a parametric form of a signal to obtain the most important distinguishable features of speech signal for recognition purpose. In this paper, Linear Prediction Cepstral Coefficient (LPCC), Mel Frequency Cepstral Coefficient (MFCC) and Bark frequency Cepstral coefficient (BFCC) feature extraction techniques for recognition of Hindi Isolated, Paired and Hybrid words have been studied and the corresponding recognition rates are compared. Artifical Neural Network is used as back end processor. The experimental results show that the better recognition rate is obtained for MFCC as compared to LPCC and BFCC for all the three types of words.
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