Energy transfers and magnetic energy growth in small-scale dynamo

In this letter we investigate the dynamics of magnetic energy growth in small-scale dynamo by studying energy transfers, mainly energy fluxes and shell-to-shell energy transfers. We perform dynamo simulations for magnetic Prandtl number $\mathrm{Pm}=20$ on $1024^3$ grid using pseudospectral method. We demonstrate that the magnetic energy growth is caused by nonlocal energy transfers from the large-scale or forcing-scale velocity field to small-scale magnetic field. The peak of these energy transfers move towards lower wavenumbers as dynamo evolves, which is the reason why the integral scale of the magnetic field increases with time. The energy transfers $U2U$ (velocity to velocity) and $B2B$ (magnetic to magnetic) are forward and local.Comment: 6 pages, 8 figure

Study Of Gaussian & Impulsive Noise Suppression Schemes In Images

Noise is introduced into images usually while transferring and acquiring them.The main type of noise added while image acquisition is called Gaussian noise while Impulsive noise is generally introduced while transmitting image data over an unsecure communication channel , while it can also be added by acquiring. Gaussian noise is a set of values taken from a zero mean Gaussian distribution which are added to each pixel value. Impulsive noise involves changing a part of the pixel values with random ones. Various techniques are employed for the removal of these types of noise based on the properties of their respective noise models. Impulse Noise removal algorithms popularly use ordered statistics based ¯lters. The ¯rst one is an adaptive ¯lter using center-weighted median. In this method, the di®erence of the center weighted mean of a neighborhood with the central pixel under consideration is compared with a set of thresholds. Another method which takes into account the presence of the noise free pixels has been implemented.It convolutes the median of each neighborhood with a set of convolution kernels which are oriented according to all possible con¯gurations of edges that contain the central pixel,if it lies on an edge. A third method which deals with the detection of noisy pixels on the binary slices of an image is implemented. It is based on threshold Boolean ¯ltering. The ¯lter inverts the value of the central pixel if the number of pixels with values opposite to it is more than the threshold. The fourth method has an e±cient double derivative detector, which gives a de- cision based on the value of the double derivative. The substitution is done with the average gray scale value of the neighborhood. Gaussian Noise removal algorithms ideally should smooth the distinct parts of the image without blurring the edges.A universal noise removing scheme is implemented which weighs each pixel with respect to its neighborhood and deals with Gaussian and impulse noise pixels di®erently based on parameter values for spatial, radiometric and impulsive weight of the central pixel. The aforementioned techniques are implemented and their results are compared subjectively as well as objectively

Neuromyelitis optica and liver cirrhosis: an association or co-incidence

Neuromyelitis optica (NMO) is a rare central nervous system demyelination syndrome predominantly targeting optic nerves and the spinal cord. Here we present a middle-aged female presenting with new onset quadriparesis and a history of persistent splenomegaly and pancytopenia, eventually being diagnosed as NMO along with autoimmune cirrhosis. The association of NMO spectrum disorders (NMOSD) with chronic liver disease has not been previously described in the literature. The purpose of this case report is to bring forward an unusual presentation and to ascertain whether it could be part of a heterogenous spectrum of an autoimmune disorder, or merely a co-incidence

Progressive-Regressive Strategy for Biometrical Authentication

This chapter thoroughly investigates the use of the progressive–regressive strategy for biometrical authentication through the use of human gait and face images. A considerable amount of features were extracted and relevant parameters computed for such an investigation and a vast number of datasets developed. The datasets consist of features and computed parameters extracted from human gait and face images from various subjects of different ages. Soft-computing techniques, discrete wavelet transform (DWT), principal component analysis and the forward–backward dynamic programming method were applied for the best-fit selection of parameters and the complete matching process. The paretic and non-paretic characteristics were classified through Naïve Bayes’ classification theorem. Both classification and recognition were carried out in parallel with test and trained datasets and the whole process of investigation was successfully carried out through an algorithm developed in this chapter. The success rate of biometrical authentication is 89%