Abstract-The IRNSS (Indian Regional Navigation Satellite System) user receiver calculates its position using the timing information embedded in the navigation signal, transmitted from the IRNSS satellites. The timing information broadcasted in the navigation signal is derived from Atomic clock onboard the IRNSS satellite. A perfect and high stability frequency source is required for precise time measurement. Highly accurate time synchronization between satellite time and ground system time is also required which is synchronized through uplink broadcast navigation parameters. But there is a shift in the frequency of the atomic clock when it is transported from ground to the satellite orbit. Since the atomic clock frequency onboard the IRNSS spacecraft has a very profound impact on user position accuracy, it is appropriate to know what should be the exact frequency of atomic clock to be set before satellite launch. This frequency has to be computed by taking into account the various relativistic effects on the satellite clocks. In this paper, we compute this offset (Factory offset) for the IRNSS satellite clocks that will be set before launch of the IRNSS satellites.

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Pulmonary auscultation is a vastly using diagnosis method over centuries. Together with the breath sound, there is a possibility of hearing heart sound, since both sounds are originated from the human chest. For the electronic analysis of the breath sound, separation of heart sound (HS) is important. In this paper, the separation of HS is achieved by using a modified Singular Spectrum Analysis (SSA) method, by introducing a provision for adaptive selection of SSA parameters. The advantage is Eigen triple grouping in the reconstruction stage of SSA is adaptive, that reduces the human effort. The performance of the new method is evaluated using synthetically mixed data and the real respiratory data and compared the results with the Advanced Line Enhancer (ALE) method which is an established single channel adaptive method. This method can also be useful for localizing the HS interferences in respiratory data, in some heart sound cancellation technique, where the localization is a fundamental preprocessing step. The comparative results suggest that the proposed method is more suitable for both separation and localization of heart sounds than the original ALE.

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Abstract-In this paper a modified Kalman based normalized least mean square algorithm is proposed for system identification. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) and original Kalman based normalized least mean square (KLMS) algorithm using standard IEEE sentence (SP23) of NOIZEUS database with different types of real world noises at different SNR. The proposed algorithm shows better output SNR and speed of convergence compared with NLMS and KLMS algorithm