Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Time-History Analysis of Soil Slope Subjected to Seismic Loadings

Abstract: In the present paper, the seismic response of cohesive-frictional soil slope has been investigated in the time-domain by using the 1995 Kobe earthquake records. A 2-dimensional dynamic finite element model has been developed in the plain-strain condition by considering the elastic perfectly plastic Mohr-Coulomb soil model criterion. The analysis simulates maximum horizontal and vertical seismic accelerations simultaneously at the base of the slope. The seismic response of the slope under combined seismic accelerations has been reported in terms of the displacement induced during the earthquake shaking period. The results show that the induced displacement of slope under combined seismic loadings is greater compared to the case of horizontal loading only. Further, the slope is expected to fail at the toe level first rather than at the crest level.

A miniaturized triple band ACS-fed monopole printed antenna with meandered and circular ring shape resonators for WLAN/WiMAX applications

Abstract: In this research article, a novel tunable multi band antenna is proposed for WLAN and WiMAX applications. For generating three simultaneous operating bands, a circular ring integrated with two stair case shaped radiating strips are used in the antenna design. One stair case branch is attached inside the circular ring patch and another one is attached to the 50 Ω feed line. Design and analysis of the proposed structure is carried out by using CST MWS software tools followed by analysis. The simulated results of the antenna is shown under the condition of S 11 ≤ 10 dB with three distinct bandwidths obtained from 2.3–2.6 GHz, 3.4–3.6 GHz and 5–6 GHz respectively. The achieved wide impedance bandwidth with compact size (16 × 27 mm2), omnidirectional radiation patterns with constant peak gain (2 dBi) features enables best element for latest communication applications.

Nonlinear Acoustic Echo Cancellation Based on Multichannel Adaptive Filters: A Novel Approach

Abstract: This paper presented a new class of non-linear adaptive filters, whose structure is based on Hammerstein model. Such filters designed from the functional link adaptive filter (FLAF) model, described by a non-linear input expansion which develops the illustration of the input signal through a projection in a higher dimensional space and a subsequent adaptive filtering. In order to give toughness against different degrees of non-linearity, a collaborative FLAF is proposed based on the adaptive combination of filters. Such architecture provided the best performance regardless of the nonlinearity degree in the echo path. Particularly this architecture can accomplish the linear and non-linear filters. Such as Normalized least mean square algorithm using FDAF, Affine projection and Lattice filters are used. When comparing their Echo return loss enhancement performance, Affine projection algorithm gives 20 dB more than others in this work.

Dementia detection using the deep convolution neural network method

Abstract: Dementia is a persistent and dynamic intellectual decline of mental capacity due to weakness Accurate identification of the problem and timely diagnosis are the most crucial aspects of dementia To handle this problem, neuroimaging with computer-aided mechanisms has gained significant advances in recent years A particular variety of dementia is the next most regular sort of gradually increasing disease that arises with Alzheimer's It is due to abnormal deposits of proteins in and around the cells of the brain An example protein is amyloid, which generates plaques around the brain cells Another protein labeled tau produces tangles in the brain cells The indications of dementia are problems related to memory decline, increasing confusion, reduced concentration, changes in behavior, etc Although there is an improvement in diagnosis, the ability to recognize dementia in clinical practice remains low Neuroimaging with computer-aided algorithms has made striking advances in the discovery of dementia Analysis of neuroimages by utilizing machine learning and deep learning mechanisms has promising outcomes in the detection and diagnosis of dementia This chapter explores a model based on deep convolution neural networks to identify dementia using magnetic resonance imaging (MRI) scan images The pretrained model, Inception-V3, is retrained for that purpose Experimentation is performed by considering the brain MRI dataset called OASIS-1 The retrained network model achieves excellent results in detecting whether a person has dementia or not