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.

Testing using selenium web driver

Abstract: Software testing is considered to be the most important step in Software Development Life Cycle. The main objective of the testing process is to compare the obtained results with those of expected by the end user of the software. Test Automation simplifies the work of tester by automating the execution of test scripts with the use of a special software. This paper focuses on the use of Selenium Webdriver to test a web application and to demonstrate the use of this tool in combination with other tools like the Maven, TestNG, etc., for more easier approach to testing and to improve the quality of testing process.

A novel ACS fed multi band antenna loaded with mirrored S and L shaped strips for advanced portable wireless communication applications

Abstract: A compact (0.15λ × 0.33λ) tri-band printed antenna with Asymmetric Coplanar Strip (ACS) feeding is proposed. The proposed structure is consisting of a half wavelength (0.5λ) meandered mirrored S shaped strip to excite the first resonant frequency at 2.35 GHz, a 0.29λ electrical length mirrored L shaped radiating branch is chosen to generate second operating at 3.45 GHz and an quarter wavelength (0.25λ) ACS monopole has been used to achieve third operating band centered at 5.2 GHz. All the three radiating branches with various optimized lengths have been chosen strategically to fulfill compact size feature. A prototype of the proposed design has been printed on a 1.6 mm thickness low cost substrate (FR4) and verified its performance characteristics experimentally. The measured and simulated results shows that the triple operating bandwidth with −10 dB return loss is about 180 MHz from 2.28–2.46 GHz, 300 MHz from 3.33–3.63 GHz and 350 MHz from 5.05–5.4 GHz, respectively. The proposed ACS fed tri band antenna is able to cover LTE 2300/2.3 GHz WiBro/RFID/WiBree/Zigbee/2.4/5 GHz ISM/2.4/5.2 GHz WLAN and 3.5 WiMAX applications. Further, the design offers omnidirectional radiation patterns with a constant peak gain of 1.85 dBi in the three operating bands.

Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking

Abstract: One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. Four pre-trained DL models were considered: Visual Geometry Group (VGG 19), Residual Network (ResNet 101), Densely Connected Convolutional Networks (DenseNet 169) and Wide Residual Network (WideResNet 50 2). From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. The performance of the proposed model was compared with two other ensemble models, baseline pre-trained computer vision models and existing models for COVID-19 detection. The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. In relevance to COVID-19, as the recall is more important than precision, the trade-offs between recall and precision at different thresholds were explored. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset.

Intelligent medicine box for medication management using IoT

Abstract: Now a day's trend in healthcare is to move routine medical checks and other health care services from hospital to the home environment. A modern healthcare IoT platform with an intelligent medicine box along with sensors for health monitoring and diagnosis is proposed here. An intelligent home based medicine box with wireless connectivity along with an android application helps patients and doctors to be in more close communication. The proposed model has an intelligent medicine box that gives alerts to patients for their medication at right time. It is connected to internet to make timely updates about medicine to patient's Smartphone through notices in android application. The system automatically gives alarm to the patient to take the medicine at right time. Sms alerts are given to predefined guardian if there are any vital signs noticed.