VLSI Implementation of Optimized 2D SIMM Chaotic Map for Image Encryption

The current research work proposed a novel optimization-based 2D-SIMM(Two-Dimensional Sine Iterative chaotic map with infinite collapse Mod-ulation Map)model for image encryption.The proposed 2D-SIMM model is derived out of sine map and Iterative Chaotic Map with Infinite Collapse(ICMIC).In this technique,scrambling effect is achieved with the help of Chaotic Shift Transform(CST).Chaotic Shift Transform is used to change the value of pixels in the input image while the substituted value is cyclically shifted according to the chaotic sequence generated by 2D-SIMM model.These chaotic sequences,generated using 2D-SIMM model,are sensitive to initial conditions.In the proposed algorithm,these initial conditions are optimized using JAYA optimization algorithm.Correlation coefficient and entropy are considered asfitness functions in this study to evaluate the best solution for initial conditions.The simulation results clearly shows that the proposed algorithm achieved a better performance over existing algorithms.In addition,the VLSI implementation of the proposed algorithm was also carried out using Xilinx system generator.With optimization,the correlation coefficient was-0.014096 and without optimization,it was 0.002585.

Analytical Estimation of Radar Cross Section of Infinitely Long Conducting Cylinder Coated with Metamaterial

Aerospace structures can be approximately modeled as a combination of canonical structures such as cylinder,cone and ellipsoid.Thus the RCS estimation of such canonical structures is of prime interest.Furthermore metamaterials possess peculiar electromagnetic properties which can be useful in modifying the RCS of structures.This paper is aimed at calculating the RCS of an infinitely long PEC circular cylinder coated with one or two layers of metamaterial.The incident and scattered fields of coated cylinder are expressed in terms of series summation of Bessel and Hankel functions.The unknown coefficients of summation are obtained by applying appropriate boundary conditions.The computations are carried out for both principal polarizations.The computed results are validated against the numerical-based method of moments.Further,the variation of RCS of the metamaterial coated PEC cylinder with material parameters,frequency,aspect angle and polarization is analyzed.

Modeling of Surface Waves in a Fluid Saturated Poro-Elastic Medium under Initial Stress Using Time-Space Domain Higher Order Finite Difference Method

In this present context, mathematical modeling of the propagation of surface waves in a fluid saturated poro-elastic medium under the influence of initial stress has been considered using time dependent higher order finite difference method (FDM). We have proved that the accuracy of this finite-difference scheme is 2M when we use 2nd order time domain finite-difference and 2M-th order space domain finite-difference. It also has been shown that the dispersion curves of Love waves are less dispersed for higher order FDM than of lower order FDM. The effect of initial stress, porosity and anisotropy of the layer in the propagation of Love waves has been studied here. The numerical results have been shown graphically. As a particular case, the phase velocity in a non porous elastic solid layer derived in this paper is in perfect agreement with that of Liu et al. (2009).

An Internet of Things Platform with Google Eddystone Beacons

Consumer adoption of Internet of Things devices is increasing rapidly. About 66% of consumers mean to buy an associated home gadget by 2019. Beacon technology helps in providing actionable insights to businesses through visual heat maps generated by the consumers and these maps are further processed by machine learning algorithms. Google’s open source Eddystone beacon format released in 2015, mitigates the problem of high cost and provides an open source alternative for developers. The aim of the proposed work is to provide a low cost, reliable, flexible, scalable and open source alternative for small and medium scale enterprises. In the proposed work, an Internet of Things platform is configured and developed. The Raspberry Pi is configured as an Eddystone beacon through a NodeJs server. An android app is developed which is the front end of the platform and web services are deployed on the cloud.

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

Heterogeneous Ensemble Feature Selection Model(HEFSM)for Big Data Analytics

Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.

DC–DC Converter with Pi Controller for BLDC Motor Fuzzy Drive System

The Brushless DC Motor drive systems are used widely with renewable energy resources.The power converter controlling technique increases the performance by novel techniques and algorithms.Conventional approaches are mostly focused on buck converter,Fuzzy logic control with various switching activity.In this proposed research work,the QPSO(Quantum Particle Swarm Optimization algorithm)is used on the switching state of converter from the generation unit of solar module.Through the duty cycle pulse from optimization function,the MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor)of the Boost converter gets switched when BLDC(Brushless Direct Current Motor)motor drive system requires power.Voltage Source three phase inverter and Boost converter is controlled by proportional-integral(PI)controller.Based on the BLDC drive,the load utilized from the solar generating module.Experimental results analyzed every module of the proposed grid system,which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics(PV)power is generated and the QPSO with Duty cycle switching state is determined.The Boost converter module is boost stage based on generation and load is obtained.Single Ended Primary Inductor Converter(SEPIC)and Zeta converter model is compared with the proposed logic;the proposed boost converter achieves the results.Three phase inverter control,PI,and BLDC motor drive results.Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures.Overall design model is done by using MATLAB/Simulink 2020a.

2D matrix based indexing with color spectralhistogram for efficient image retrieval

A novel content based image retrieval （CBIR） algorithmusing relevant feedback is presented. The proposed frameworkhas three major contributions： a novel feature descriptor calledcolor spectral histogram （CSH） to measure the similarity betweenimages; two-dimensional matrix based indexing approach proposedfor short-term learning （STL）; and long-term learning （LTL）.In general, image similarities are measured from feature representationwhich includes color quantization, texture, color, shapeand edges. However, CSH can describe the image feature onlywith the histogram. Typically the image retrieval process starts byfinding the similarity between the query image and the imagesin the database; the major computation involved here is that theselection of top ranking images requires a sorting algorithm to beemployed at least with the lower bound of O（n log n）. A 2D matrixbased indexing of images can enormously reduce the searchtime in STL. The same structure is used for LTL with an aim toreduce the amount of log to be maintained. The performance ofthe proposed framework is analyzed and compared with the existingapproaches, the quantified results indicates that the proposedfeature descriptor is more effectual than the existing feature descriptorsthat were originally developed for CBIR. In terms of STL,the proposed 2D matrix based indexing minimizes the computationeffort for retrieving similar images and for LTL, the proposed algorithmtakes minimum log information than the existing approaches.

Supply Side Management for Cost Optimization in Developing Countries Suffering with Power Outage

In this increasingly growing population especially in the developing countries, it is almost impossible to solve the energy crisis. This has led to the extreme growth of the back up power generation industry such as Diesel generators and home invertors. From the last couple of years the solar PV technology has started to penetrate in these geographies. This paper discusses on the supply side management of the electricity sources available to a consumer to manage his outage and also the over all cost optimization, as the cost of energy from every source is different and it also depends on the load curve, last but not the least also as to what time of the day what source or combination of sources have been used.

Effect of process parameters on springback behaviour during air bending of electrogalvanised steel sheet

This work aims to study the springback behaviour of electrogalvanised (EG) steel sheets during the air bending process.Experiments have been conducted to analyse the influence of various parameters such as coating thickness,orientation of the sheet,punch radius,die radius,die opening,punch velocity,and punch travel on springback behaviour.It is established that the springback increases with increasing coating thickness,punch radius,punch travel,die radius,die opening,and punch velocity.The 90° orientation exhibits higher springback than 0° orientation.

Optimization of cloud load balancing using fitness function and duopoly theory

Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.