Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Power Management of PV-Battery-Based Low Voltage Microgrid Under Dynamic Loading Conditions

Abstract: The PV power plants (PVPP) integrated with the low voltage microgrid’s (LVMG) is one of the significant non-conventional sources of energy. In this article, power management of two PVPPs which are PVPP1 and PVPP2 along with the battery storage plant (BSP) is presented, and these are integrated with the LVMG. Each PVPP consists of an incremental conductance maximum power point tracking (InC MPPT) controller in order to maximize the power extraction and minimize the power losses under wide-varying irradiance (G) and temperature (T) conditions. A voltage source converter (VSC) is used to control the charging and discharging states of the BSP, and it is used to connect the BSP with the LVMG. This work deals with the frequency and voltage regulation of the LVMG by considering the dynamic loading conditions. The proposed PVPP’s along with BSP will control power flow by load leveling to maintain the active power balance which will mitigate the current and voltage harmonics. The proposed system is designed and verified by the real-time simulations using MATLAB/Simulink software.

An Efficient Multicast Routing Protocol Based on Ant with Improved Pheromone Updating Rule in Manet

Abstract: Multicast routing in Mobile Ad‐Hoc Network (MANET) is s great challenge due to limitations like energy of the node and bandwidth. Routing protocols play important role in selecting path between source and destination nodes. Multicast protocols provide a bandwidth‐efficient support for group‐oriented applications. Even though many multicast routing protocols are developed for MANETs, security is still challenging issue. So, in this work, an Efficient Multicast Routing Protocol (EMRP) is proposed by using Ant with Improved Pheromone Updating Rule (AIPUR) for MANETs. In the improved pheromone updating phase, worst ants are identified by using modified TOPSIS method and it is removed. Initially, clustering is done to make authentication process easy and the optimal Cluster Head (CH) selection is done with the concern of their security level where the selected cluster head should be trustable and should contain increased energy and bandwidth availability. Here optimal CH selection is done by using AIPUR. CH is responsible for generating the secret key for its cluster members by using which cluster member would generate signature in their data which is to be shared with other nodes before transmission and these CH will perform authentication by using the key generated based on which authentication of the data would be guaranteed. The proposed optimal and reliable routing is guaranteed by establishing routing with nodes that satisfied the QoS conditions like increased energy and bandwidth.

A Novel Statistical Feature Selection Measure for Decision Tree Models on Microarray Cancer Detection

Abstract: Recently, machine learning techniques have become popular and widely accepted for cancer detection and classification. Prediction of cancer disease focuses on three main objectives: susceptibility prediction, recurrence prediction, and survivability prediction. Most of the conventional classification techniques deal with limited attributes and small datasets. Random forest classifier is one of the ensemble learning models, which is capable to handle datasets with a large number of attributes. Machine learning algorithms used for cancer prediction are supervised learning with high prediction rate. In this paper, a novel statistical attribute selection measure was implemented for cancer disease prediction. In this work, we have used different decision tree models such as random tree, random forest, Hoeffding tree to evaluate the performance of cancer disease prediction using proposed attribute selection measure. Experimental results are evaluated on different types of microarray cancer datasets including lung cancer, ovarian, lung cancer, and DLBCL-Stanford. The performance of each model is compared in order to find the most efficient and optimized algorithm. Experimental results show that proposed model has high computational efficiency in terms of accuracy and true positive rate.

Hybrid framework for face recognition with expression & illumination variations

Abstract: In this paper, a hybrid framework is proposed to improve the performance of face recognition by combining global descriptors and local appearance descriptors and proved that their complementary nature makes them good candidates in the better recognition of faces. The proposed face recognition method can handle facial appearance variations which are caused by facial expression and illumination under controlled capture conditions. Different from traditional face recognition methods, the proposed method uses multiple features which are extracted using Global and Local feature extraction algorithms like Principal Component Analysis (PCA) & Local Binary Pattern (LBP). Wavelet fused feature vector has richer information than feature vector extracted using unifeature extraction algorithms. Radial Basis Function (RBF) is used to classify feature vectors. The proposed method has been extensively evaluated on the standard benchmark databases like ORL and Grimace. It is found that significant results obtained in comparison with well-known generic face recognition methods.