From the temporal spectra of vertical wind obtained from MST radar observations, the Brunt‐Vaisala frequency is identified. Altitude profile of temperature in troposphere and lower stratosphere is derived from the altitude profile of the Brunt‐Vaisala frequency thus obtained.

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Automatic animal sound classification and retrieval is very helpful for bioacoustic and audio retrieval applications. In this paper we propose a system to define and extract a set of acoustic features from all archived wild animal sound recordings that is used in subsequent feature selection, classification and retrieval tasks. The database consisted of sounds of six wild animals. The Fractal Dimension analysis based segmentation was selected due to its ability to select the right portion of signal for extracting the features. The feature vectors of the proposed algorithm consist of spectral, temporal and perceptual features of the animal vocalizations. The minimal Redundancy, Maximal Relevance (mRMR) feature selection analysis was exploited to increase the classification accuracy at a compact set of features. These features were used as the inputs of two neural networks, the k-Nearest Neighbor (kNN), the Multi-Layer Perceptron (MLP) and its fusion. The proposed system provides quite robust approach for classification and retrieval purposes, especially for the wild animal sounds.

The paper proposes an improved fast and efficient decision-based algorithm for the restoration of images that are highly corrupted by Salt-and-Pepper noise. The new algorithm utilizes previously processed neighboring pixel values to get better image quality than the one utilizing only the just previously processed pixel value. The proposed algorithm is faster and also produces better result than a Standard Median Filter (SMF), Adaptive Median Filters (AMF), Cascade and Recursive non-linear filters. The proposed method removes only the noisy pixel either by the median value or by the mean of the previously processed neighboring pixel values. Different images have been tested by using the proposed algorithm (PA) and found to produce better PSNR and SSIM values.