An Improved Fault Tolerant Technique of Median Filter

Acquisition noises in the digital image processing system basically made out of imprudent clamors, for example, hot and dead pixels, and for the most part expelled utilizing middle channels. The median filtering algorithm can be speedup by FPGA implementation. Configuration memory cells in SRAM based FPGAs are susceptible to radiation effects such as SEUs which leads to configuration memory bit flips and hence a protective measure is required for the proper operation of median filtering algorithm.The fault tolerant implementations of median filter provides a range for median value with which the calculated median value is checked and find out error if the median is out of the provided range. The main aim of the project is to fasten up the fault tolerant implementation of median filter in FPGAs by adding a few resources. Experimental results show that the proposed technique significantly reduces the latency of the fault tolerant median filtering process

Implementation of Gas Detection System using Unmanned Moving Vehicle

Abstract: Unmanned moving vehicles are nowadays largely used in environment monitoring system. In order to identify the leakage of gas in a housing area or an industry or in an agricultural area, it can be easily monitored and detected by the sensors that are embedded on a moving vehicle. A remote controlled vehicle is used in the proposed system. With help of camera attached to this the area where hazardous gas leakage can be identified. In case of emergency like fire explosion in some other area the vehicle can be manually moved to that location. The information about the gas leakage is transferred through ZIGBEE. GPS is used to trace the location where leakage has happened. The leakage of harmful gas in agricultural area, housing area and industrial area can be detected more accurately

Adaptive Marine Predator Optimization Algorithm (AOMA)–Deep Supervised Learning Classification (DSLC)based IDS framework for MANET security

Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MANET) is one of the difficult and challenging tasks today. In MANET, the Intrusion Detection System (IDS) is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation. Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET. However, it still has significant flaws, including increased algorithmic complexity, lower system performance, and a higher rate of misclassification. Therefore, the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models. Here, the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields, which increases the overall intrusion detection performance of classifier. Then, a novel Adaptive Marine Predator Optimization Algorithm (AOMA) is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier. Moreover, the Deep Supervise Learning Classification (DSLC) mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations. During evaluation, the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets

An Intelligent Computing Model for Wind Speed Prediction in Renewable Energy Systems

AbstractThis paper presents an intelligent computing model for wind speed prediction, which uses back propagation algorithm. Wind energy is inexhaustible unlimited clean energy. Wind power in the world has been rapidly growing. The prediction of wind speed has an important role in wind energy. The back-propagation algorithm (BPA) is used in the majority of neural networks application. The objective of this paper is to compute predicted output (wind speed) based on BP algorithm. The results are obtained using back propagation algorithm by training and testing methodologies. Simulation results show the performance of ANN for predicting wind speed in renewable energy system

Review on Methods to Fix Number of Hidden Neurons in Neural Networks

This paper reviews methods to fix a number of hidden neurons in neural networks for the past 20 years. And it also proposes a new method to fix the hidden neurons in Elman networks for wind speed prediction in renewable energy systems. The random selection of a number of hidden neurons might cause either overfitting or underfitting problems. This paper proposes the solution of these problems. To fix hidden neurons, 101 various criteria are tested based on the statistical errors. The results show that proposed model improves the accuracy and minimal error. The perfect design of the neural network based on the selection criteria is substantiated using convergence theorem. To verify the effectiveness of the model, simulations were conducted on real-time wind data. The experimental results show that with minimum errors the proposed approach can be used for wind speed prediction. The survey has been made for the fixation of hidden neurons in neural networks. The proposed model is simple, with minimal error, and efficient for fixation of hidden neurons in Elman networks