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.

Enhancement of noisy speech using sub-band harmonic regeneration and speech presence uncertainty estimator

Abstract: This paper presents a noise reduction technique based on sub-band harmonic regeneration with Speech Presence Uncertainty (SPU). Harmonic Regeneration (HRNR) overcomes the harmonic distortion, which is significant at low SNR values by preserving harmonics. For further noise suppression at low SNRs, Sub-band modification of HRNR gain with weights calculated using speech presence uncertainty is proposed. Objective (SNR, SegSNR, LLR, WSS) performance measures show that the proposed method provides superiority in noise reduction and measures like SNR and SegSNR shows better speech preservation at high SNRs, better noise suppression at low SNRs.

Detection and isolation of black hole attack in mobile ad hoc networks: a review

Abstract: Mobile Ad hoc Network or MANET is a wireless network that allows communication between the nodes that are in range of each other and are self-configuring. The distributed administration and dynamic nature of MANET makes it vulnerable to many kind of security attacks. One such attack is Black hole attack which is a well known security threat. A node drops all packets which it should forward, by claiming that it has the shortest path to the destination. Intrusion Detection system identifies the unauthorized users in the system. An IDS collects and analyses audit data to detect unauthorized users of computer systems. This paper aims in identifying Black-Hole attack against AODV with Intrusion Detection System, to analyze the attack and find its countermeasure.

Deep Residual Convolutional Neural Network Based Detection of Covid-19 from Chest-X-Ray Images

Abstract: The Coronavirus or 2019-nCoV (COVID-19) is a contagious disease. This new strain outbreak pressuring the health community in the world because this virus not identified early and also spreading to a large number of countries and territories, here some of them are on the edge of failing to control the spread of this virus. This virus mostly affects the respiratory system. So, it is possible to diagnose the virus, infected in the respiratory system using CT scan and chest-x-ray imaging approaches. Chest-X-ray images are amply available and swift imaging time than CT scan. The coronavirus pneumonia infected chest-x-rays has taken as the input data. Convolutional Neural Network (CNN) pre-trained ResNet-50 model is the proposed method, and it uses cross-validation for analysis. However, this model is employed to get high performance with more accurate result.

Secret image sharing using eigenvalues and eigenvectors

Abstract: Steganography is one of the technique used for hiding secret information into another media safely. The main purpose of steganography is establishing covert communication between two author...

Optimization of Hybrid off Grid Power System Using HOMER software

Abstract: The main aim of this paper is to design, modelling and optimization of Solar, Wind and Diesel generator in power system by using HOMER software. The objective of this paper is to meet the electric demand for house hold purpose with these sources at the city of Vijayawada. By all these sources it reduces the cost and emission of the system. The COE (cost of energy), operating cost, and carbon emission are compared between all these sources. These sources generation is optimized by using the HOMER software.