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Abstract: It is important to use models developed specifically for the equatorial ionospheric estimation for real-time applications, particularly in Satellite Navigation. This work demonstrates a methodology for improved predictions of VTEC in real time using the model developed for the equatorial ionosphere by the authors. This work has been done using TEC data of the low solar activity period of 2005 obtained using dual frequency GPS receivers installed under the GAGAN project of ISRO. For the purpose, the model is first used in conjunction with Kriging technique. Improvement in accuracy is observed when compared with the estimations from the model alone using the measurements as true reference. Further improvement is obtained by Bayesian combination of these estimates with independent Neural Network based predictions. Statistical performance of improvement is provided. An improvement of ∼1m in confidence level of estimation of VTEC is obtained. [Copyright &y& Elsevier]

Abstract: Increasing demand of bandwidth in communication satellites has forced satellite links to be designed in Ku bands and above. But at these frequencies, rain and other tropospheric elements result in large attenuation. To mitigate the tropospheric attenuation of microwave satellite signals above 10GHz using any standard Fade Mitigation Technique (FMT), it is essential to have a priori knowledge about the level of attenuation. Hence, short-term rain attenuation prediction models play a key role in maintaining the link in which necessary compensation can be applied depending on the early information of attenuation. This paper presents a method of attenuation prediction using Adaptive Artificial Neural Network. Here In situ Learning Algorithm (ILA) has been used to enable the system to track the non-stationary nature of the attenuation. To validate this, Ku Band data, collected at three different sites in India have been used for the purpose of prediction. The performance of the algorithm is determined through the estimation of prediction accuracy by comparing the predicted values with the measured data. Results obtained using the mentioned technique shows considerably good accuracy even up to 20s of prediction interval with acceptable ratio between the under and over predictions. The prediction performance is evaluated for different prediction intervals. Furthermore the present model is also compared with the persistence model and the relative performance is quantified. [Copyright &y& Elsevier]

Abstract: The temporal variation of the equatorial electrojet is estimated utilising a suitably designed Kalman filter and using the established empirical relations between the anomaly component of equatorial TEC and the modified electrojet. TEC data obtained from dual frequency GPS receivers are used for the purpose. Estimation requires the a-priori knowledge of the peak electrojet value of the day and hence can be made in post temporal scenario only. Initial results obtained during a low solar activity time in an equinoctial month shows acceptable accuracy of the proposed algorithm. Limited analysis is done by segregating the results into temporal sessions of pre-attainment and post-attainment of the electrojet peak. [Copyright &y& Elsevier]

Abstract: The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results. [Copyright &y& Elsevier]

The effects of the herbicides 2,4-dichlorophenoxy acetic acid and isoproturon on 3 wheat (Triticum aestivum L.) varieties (HUW 234, HUW 468, and HUW 533) were studied with regards to mitotic abnormalities and chromosomal behavior. Pre-soaked seeds were treated with both herbicides at concentrations of 50-1200 ppm. Both 2,4-D and isoproturon were highly mito-inhibitory and induced chromosomal abnormalities, such as precocious movement, stickiness, and chromosome bridges, with and without laggards and fragments at the anaphase and telophase. The frequency of chromosomal anomalies in almost all the targets used was high at the maximum dose of both herbicides, individually as well as in combination. Isoproturon was more toxic, as it resulted in high-level chromosomal damage in all the varieties. Both herbicides had a dose-dependent impact on the mitotic index (MI) and relative abnormality rate (RAR). These parameters collectively indicate that variety HUW 468 was more susceptible to the mito-depressive and chromotoxic action of the 2 herbicides. [ABSTRACT FROM AUTHOR]