7 research outputs found
Antibacterial and Antimutagenic activities of the extracts from the flowers of Peltophorum ferrugineum
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
Enhancing power conversion efficiency in five-level multilevel inverters using reduced switch topology
No full textThis paper presents extensive research on improving the power conversion efficiency of five-level multilevel inverters (MLIs) by utilizing a reduced switch topology. MLIs have received an abundance of focus because of their ability to generate high-quality output waveforms and have better harmonic outcomes than traditional two-level inverters. The high number of switches in MLIs, on the other hand, can result in increased power losses and lower overall efficiency. In this paper, a novel reduced switch topology for five-level MLIs, which is having five switches is proposed with the aim of minimizing power losses while preserving superior performance due to lesser number of switches. To achieve efficient power conversion, the proposed topology employs advanced pulse width modulation control strategies and optimized switching patterns. The simulation results show that the minimized switch topology improves the power conversion efficiency of the five-level MLI, resulting in lower losses and better overall system performance. The total harmonic distortion (THD) value of the output current has been reduced to 7.12% and the efficiency has been achieved to 96.92%. The findings of this investigation help to advance MLI technology, allowing for more efficient and reliable power conversion in a variety of applications such as renewable energy systems, electric vehicles, and industrial drives
Optimized Neural Network Based Location Prediction Along with Multiple Features in Communication Network
No full textBy the advances in wireless communication networks and the exponential rise in the number of UEs, the data usage is increasing due to time consumption, necessitating lot denser deployment. Extra common handoffs result in superior latency and reduced throughput that have a deleterious impact on network and user acceptance. In this study, we have proposed a ResNet-based Convolutional Neural Network with Tasmanian Devil Optimization (TDO) algorithm for the prediction of location in the communication network. The ResNet-based CNN model with TDO algorithm is used for location prediction, taking into account of wireless quantification findings from service access point and neighbouring core network, and introducing a direction gradient descent to allow the prototype to recognize data on the direction of the UE motion. Comprehensive simulations proved that the proposed model, which is derived from various characteristics and communication flows, performed best result on the prediction of location
Optimizing Edge Computing for Big Data Processing in Smart Cities
No full textThe surge of big data and IoT in smart cities requires effective computational models to process massive amounts of real-time data. Edge computing emerges as an innovative solution by minimizing latency, improving security, and maximizing energy efficiency. This paper investigates the convergence of AI-based edge computing for big data processing through a study of four sophisticated algorithms: Federated Learning, TinyML, Edge-Optimized CNNs, and Adaptive Data Compression. Experimental analysis proved a decrease of 37% in latency, 42% increase in computational performance, and 29% decrease in energy usage than that of common cloud-based computation. In addition, a multilayered data fusion mechanism increased data quality by 21%, facilitating smart city decision-making. The analysis also compares contemporary techniques and expounds on how cloud-edge interaction could be a boon for improving the infrastructure in smart cities. Findings validate that edge computing improves real-time analytics, transportation safety, and sustainable resource management. Yet, security threats and scalability challenges need more investigation. Future research should concentrate on blockchain-based edge security models and energy-aware AI architectures to provide hassle-free smart city deployment. This research concludes that edge computing is the key to the next generation of smart urban infrastructure, encouraging efficiency, sustainability, and intelligent automation
