Higher order spectral estimation techniques have been applied to the backscattered signals received from the troposphere and lower stratosphere by the Gadanki mesosphere-stratosphere-troposphere (MST) radar. These techniques allow identification of signals that have non-Gaussian probability distribution. To understand these processes and their effect on estimation of the atmospheric parameters, power spectrum, and bispectrum analyses have been performed on the signals received in both vertical and off-vertical directions. The results show that the backscattered echoes received in the vertical direction are significantly non-Gaussian, while those received in the off-vertical direction are inferred to have predominant Gaussian component. It is demonstrated that the bispectral analysis technique has capability for estimating the vertical wind component with greater accuracy than that derived from the commonly employed fast Fourier transform based power spectral technique.

Atmospheric signal processing is of interest to many scientists, where there is scope for the development of new and efficient tools for cleaning the spectrum, detection, and estimation of parameters like zonal (U), meridional (V), wind speed (W), etc. This letter deals with a signal processing technique for the estimation of the aforementioned parameters, based on the wavelets, by analyzing the mesosphere-stratosphere-troposphere radar data that are backscattered from the atmosphere at high altitudes and severe weather conditions with low signal-to-noise ratio. The proposed algorithm is self-consistent in detecting wind speeds up to a height of 18 km, in contrast to the existing method which estimates the Doppler manually and fails at higher altitudes. The results have been validated using the Global Positioning System sonde data.

Multitaper spectral analysis using sinusoidal taper has been carried out on the backscattered signals received from the troposphere and lower stratosphere by the Gadanki Mesosphere-Stratosphere-Troposphere (MST) radar under various conditions of the signal-to-noise ratio. Comparison of study is made with sinusoidal taper of the order of three and single tapers of Hanning and rectangular tapers, to understand the relative merits of processing under the scheme. Power spectra plots show that echoes are better identified in the case of multitaper estimation, especially in the region of a weak signal-to-noise ratio. Further analysis is carried out to obtain three lower order moments from three estimation techniques. The results show that multitaper analysis gives a better signal-to-noise ratio or higher detectability. The spectral analysis through multitaper and single tapers is subjected to study of consistency in measurements. Results show that the multitaper estimate is better consistent in Doppler measurements compared to single taper estimates. Doppler width measurements with different approaches were studied and the results show that the estimation was better in the multitaper technique in terms of temporal resolution and estimation accuracy.

MRI scanning has shown significant growth in the detection of brain tumors in the recent decade among various methods such as MRA, X-ray, CT, PET, SPECT, etc. Brain tumor identification requires high exactness because a minor error can be life-threatening. Brain tumor disclosure remains a challenging job in medical image processing. This paper targets to explicate a method that is more precise and accurate in brain tumor detection and focuses on tumors in neonatal brains. The infant brain varies from the adult brain in some aspects, and proper preprocessing technique proves to be fruitful to avoid miscues in results. This paper is divided into two parts: In the first half, preprocessing was accomplished using HE, CLAHE, and BPDFHE enhancement techniques. An analysis is the sequel to the above methods to check for the best method based on performance metrics, i.e., MSE, PSNR, RMSE, and AMBE. The second half deals with the segmentation process. We propose a novel ARKFCM to use for segmentation. Finally, the trends in the performance metrics (dice similarity and Jaccard similarity) as well as the segmentation results are discussed in comparison with the conventional FCM method.