Shyam S. Pattnaik

A novel method of using artificial neural networks to calculate input impedance of circular microstrip antenna

A new method of using artificial neural networks(ANNS) for calculation of input impedance of rectangular microstrip patch antennas has been adopted in this paper. The results obtained using ANNs are compared with the experimental findings, theoretical values. The ANNs results are more in agreement with experimental findings.

Bacterial Foraging Optimization Technique Cascaded with Adaptive Filter to Enhance Peak Signal to Noise Ratio from Single Image

Abstract This paper proposes a new approach to enhance peak signal to noise ratio of highly corrupted image affected by impulse noise. The proposed technique is implemented using an adaptive median Alter and the bacterial foraging optimization (BFO) technique. The adaptive median Alter is used to identify pixels affected by noise and replace them with median value to keep the information uncorrupted. The BFO technique minimizes errors between adaptive median filter output image and noisy image to maintain an error percentage of 0.0001. It has been observed that results of the proposed method are superior to conventional methods in terms of perceptual image quality as well as clarity and smoothness in edge regions of the resultant image. This technique can remove the salt-and-pepper noise of highly corrupted images with a noise as high as 90%.

Small Population Based Modified Parallel Particle Swarm Optimization for Motion Estimation

In this paper, the authors propose a small population based modified parallel particle swarm optimization (SPMPPSO) and its application to reduce computational time for motion estimation in video sequence. In motion estimation, initial search, search space, matching criteria, search parameter and step size are important aspect to predict the position of the current macro block for which motion vector is to be found. In the proposed technique, the position equation of PPSO known as step size is modified to find best matching block in current frame. In the SPMPPSO, small population i.e. five swarms is used to find global optimum value. Due to neighbourhood search criteria (N4), the convergence is very fast. The limitations of existing methods like computational time, search parameter, initial search and search space are overcome by SPMPPSO. The suggested method saves computational time up to 94% when compared with other published method. The SPMPPSO can be used in adaptive network, self-managing system ubiquitous learning environment etc for efficiency improvement.

Memetic Algorithm with Local Search as Modified Swine Influenza Model-Based Optimization and Its Use in ECG Filtering

The Swine Influenza Model Based Optimization (SIMBO) family is a newly introduced speedy optimization technique having the adaptive features in its mechanism. In this paper, the authors modified the SIMBO to make the algorithm further quicker. As the SIMBO family is faster, it is a better option for searching the basin. Thus, it is utilized in local searches in developing the proposed memetic algorithms (MAs). The MA has a faster speed compared to SIMBO with the balance in exploration and exploitation. So, MAs have small tradeoffs in convergence velocity for comprehensively optimizing the numerical standard benchmark test bed having functions with different properties. The utilization of SIMBO in the local searching is inherently the exploitation of better characteristics of the algorithms employed for the hybridization. The developed MA is applied to eliminate the power line interference (PLI) from the biomedical signal ECG with the use of adaptive filter whose weights are optimized by the MA. The inference signal required for adaptive filter is obtained using the selective reconstruction of ECG from the intrinsic mode functions (IMFs) of empirical mode decomposition (EMD).

Initializing artificial neural networks by genetic algorithm to calculate the resonant frequency of single shorting post rectangular patch antenna

A simple and accurate method for calculating the resonant frequency of a rectangular microstrip patch antenna with a single shorting post by using the artificial neural networks is presented in this paper. Genetic algorithm is used to select the initial weights of artificial neural networks. The calculated resonant frequency is compared with experimental results. The results are in very good agreement with experimental results with decreased computational time.

Multimodal Function Optimization Using Synchronous Bacterial Foraging Optimization Technique

Abstract The periodic nonlinearity in nano-metrology systems based on heterodyne interferometers is the most important limitation to the accuracy of displacement measurement. It is mainly produced due to the polarization-mixing and frequency-mixing. In this paper, a new approach based on an ensemble of neural networks for modeling and compensation of nonlinearity in a high-resolution laser heterodyne interferometer is presented. We model the periodic nonlinearity arising from elliptical polarization and non-orthogonality of the laser polarized light based on the neural network approaches, including the multi-layer perceptrons and radial basis function as single neural networks and stacked generalization method as ensemble of neural networks. It is also shown that by using the stacked generalization method, the primary periodic nonlinearity of 1.3 nm is significantly compensated by a factor of 168.AbstractThe paper presents an improved variant of bacterial foraging optimization (BFO) named as Synchronous Bacterial Foraging Optimization (SBFO). The proposed SBFO is used to optimize multimodal and high dimensional functions. As all the bacteria update their information simultaneously, it has been named synchronous. A mutation operator proposed in this paper performs the global search. In SBFO, all bacteria process information independently in the same generation; hence parallel computers can be used to evaluate fitness values. The performance of SBFO is validated on a set of seven benchmark functions i.e. Sphere, Rosenbrock, Rastrigin, Griewank, Ackley, Schaffer’s, Shekel’s Foxholes. The results are compared with other published methods such as classical BFO, hybrid BFO (BSO), swarm-based algorithms, differential evaluation and Ashaker. The simulation results on benchmark functions show that the proposed optimization is capable of producing good quality global optima as compared to the above mentione...

Coupling of ANNN with GA for effective optimization of dimensions of rectangular patch antenna on thick substrate microstrip patch antenna on thick substrate

A novel method of optimization by coupling the genetic algorithm (GA) and artificial neural network (ANN) is presented in this paper. A trained artificial neural network is taken as objective function in GA for optimization. By utilizing this technique the optimized dimensions of patch antenna on thick substrate has been calculated. It is seen that the results obtained by this method are closer to experimental value compared to earlier results obtained by curve fitting method. To validate this, the results are compared with experimental values for five fabricated antenna. The results are in very good agreement with experimental findings.

Input Impedance of Circular Microstrip Antenna using Artificial Neural Networks

This paper deals with the use of artificial neural networks (ANNs) for calculation of input impedance of circular patch antennas. The results obtained using ANNs are compared with the experimental findings, theoretical values and with the simulation results obtained using IE3D Package. The ANNs results are in good agreement with experimental findings.

Memetic Algorithm with PSO or BFO's chemotaxis mechanism as local search for function optimization

Memetic Algorithm (MA) is a metaheuristic search method. In proposed memetic algorithm two memes are used in definite proportion out of total local calls and are applied with random selection. Genetic Algorithm due to its good exploration capability is used as main algorithm and Particle Swarm Optimization (PSO) as well as chemotaxis mechanism of Bacterial Foraging Optimization (BFO) are used as local searches. The memetic process is realized using global best fitness among particles of PSO and by imitating the nutrient information from the bacteria of the best fitness in BFO. The proposed variant of memetic algorithm is tested on the standard benchmark functions with unimodal and multimodal property. When the results are compared, the proposed memetic algorithm shows better performance than MA using PSO (pMA) and MA using BFO (bMA) both in terms of speed of convergence and quality of solutions.

Syncro Germ Co-operation by Twin Swarm Intelligence for multimodal function optimization

This paper proposes fusion of Synchronous Germ Computing (SGC) with Twin Swarm Intelligence (TSI) technique named as SGCTSI to enhance quality of global solutions with faster convergence of multimodal functions. In this paper, initially the authors tried to increase the speed of bacteria by updating bacteria positions synchronously, which is treated as SGC. In SGC, all the bacteria update their positions to attain global best position after completion of each generation, by adopting the feature of communication with each other. After each generation, current positions of bacteria are updated by co-operation of ePSO (Particle Swarm Optimization with extrapolation technique) and GLBestPSO (Global and Local Best PSO) called as mutation operator. The mutation operator brings about diversity in the population to avoid premature convergence or being trapped in some local optima. The SGCTSI has more global search ability at the beginning and improves the quality of solutions at the end of each run. The proposed technique is tested with eight standard benchmark functions and results are compared with ePSO GLBestPSO and canonical PSO (cPSO). The experimental results on bencmark functions validadte that, the proposed trifusion approch produces good quality solution with faster convergence compared to other techniques. The performance of the SGCTSI has been tested through various statistical parameters and analysis of variance (ANOVA) test.