Manjeet Singh Patterh

Optimization of Yagi-Uda Antenna Using Simulated Annealing

Simulated annealing (SA) is a stochastic global optimization technique, which has been used successfully for optimization of antenna arrays. In this paper, the use of SA for optimization of gain, impedance and bandwidth of the Yagi-Uda antenna has been presented. To evaluate the performance of designs, a method of moments code NEC2 has been used. Comparative results indicate superiority of using SA over other methods.

A LINEAR ANTENNA ARRAY FAILURE CORRECTION USING FIREFLY ALGORITHM

The element failure of antenna arrays increases the sidelobe power level. In this paper, the problem of antenna array failure has been addressed using Fire∞y Algorithm (FA) by controlling only the amplitude excitation of array elements. A fltness function has been formulated to obtain the error between pre-failed (original) sidelobe pattern and measured sidelobe pattern and this function has been minimized using FA. Numerical example of large number of element failure correction is presented to show the capability of this ∞exible approach.

Wireless Sensor Network Localization Based on Cuckoo Search Algorithm

Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field to perform sensing and acting task. The goal of localization is to assign geographical coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, the cuckoo search algorithm is implemented to estimate the sensor’s position. The proposed approach has been compared in terms of localization error with particle swarm optimization (PSO) and various variants of biogeography based optimization (BBO). The results show that our method outperforms the PSO and BBO variants which are recently used in the literature.

Quality controlled ECG compression using Discrete Cosine transform (DCT) and Laplacian Pyramid (LP)

This paper introduces a new quality controlled Discrete Cosine transform (DCT) and Laplacian Pyramid based compression method for electrocardiogram (ECG) signal. The ECG signal is transformed using DCT and Laplacian Pyramid. The transformed coefficients are thresholded using the bisection algorithm in order to match the predefined user specified percentage root mean square difference (PRD) within the tolerance. Then, the binary lookup table is made to store the position map for zero and non-zero coefficients (NZC). The NZC are quantized by Max-Lloyd quantizer followed by Arithmetic coding. Lookup table is encoded by Huffman coding. The results are presented on different ECG signals of varying characteristic. The results show that DCT gives better performance at low PRD. The results show that Laplacian Pyramid provides better CR over 2.5 PRD value on the normal rhythm (MIT-BIH 117) as compared to DCT, whereas on abnormal rhythm (MIT-BIH 232) the behavior of the transforms is opposite.

Optimization of circular antenna arrays of isotropic radiators using simulated annealing

This paper presents the design optimization of circular antenna arrays of isotropic radiators using simulated annealing. The problem has been formulated to achieve a desired value of sidelobe level and a minimum possible value of beamwidth. This is accomplished by jointly optimizing the excitation amplitude and spacing between elements. Simulation examples have been given and comparison has been carried out with particle swarm optimization method.

Modified Bat Algorithm for Localization of Wireless Sensor Network

AbstractThe problem of node localization in wireless sensor networks aims to assign th e geographical coordinates to each device with unknown position, in the deployment area. In this paper the meta heuristic optimization algorithm known as bat algorithm is described in order to evaluate the precision of node localization problem in wireless sensor networks. Meanwhile the existing bat algorithm has also been modified by using the bacterial foraging strategies of bacterial foraging optimization algorithm. Compared with the existing bat algorithm, the proposed modified bat algorithm is shown through simulations to perform constantly better not only in increasing localization success ratios and fast convergence speed but also enhance its robustness.

DESIGN OF A LINEAR ARRAY OF HALF WAVE PARALLEL DIPOLES USING PARTICLE SWARM OPTIMIZATION

Particle Swarm Optimization (PSO) is a new, high performance evolutionary technique, which has recently been used for optimization problems in antennas and electromagnetics. It has been used for optimization of linear array of isotropic radiators, and found to give better results than other companion algorithms. A half wave dipole is a practical radiator, which closely resembles the behavior of an isotropic radiator and is most common array element. In this paper, a linear array of half wavelength parallel dipoles has been optimized for minimum side lobe level and null control using Particle Swarm Optimization. The results obtained show superiority of PSO over conventional method. 132 Rattan, Patterh, and Sohi

Optimization of Cognitive Radio System Using Simulated Annealing

Cognitive radio (CR) technology has introduced a revolution in wireless communication network and it is capable to operate in a continuously varying radio frequency environment that depends on multiple parameters. In this paper, optimization of CR system has been achieved using simulated annealing (SA) Technique. SA is a stochastic global optimization technique that exploits an analogy between the way in which a metal cools and freezes into a minimum energy crystalline structure. SA has been used to meet the quality of service (QoS) that is defined by the user in terms of minimum transmit power, minimum bit error rate, maximum throughput, minimum interference and maximum spectral efficiency. The results obtained by SA are compared with the genetic algorithm (GA) results for the various QoS parameters and it has been observed that SA is outperforming GA in CR system optimization.

Biogeography-based optimisation of Cognitive Radio system

Biogeography-based optimisation (BBO) is a novel population-based global optimisation algorithm that is stimulated by the science of biogeography. The mathematical models of biogeography describe how a species arises, migrates from one habitat (Island) to another or gets extinct. BBO searches for the global optimum mainly through two steps: migration and mutation. These steps are controlled by immigration and emigration rates of the species in the habitat which are also used to share information between the habitats. In this paper, BBO has been applied to Cognitive Radio (CR) system for optimising its various transmission parameters to meet the quality of service (QoS) that is defined by the user in terms of minimum transmit power, minimum bit error rate (BER), maximum throughput, minimum interference and maximum spectral efficiency. To confirm the capability of biogeography-based optimisation algorithm, the results obtained by BBO are compared with that obtained by using genetic algorithm (GA) for the various QoS parameters, and it has been observed that BBO outperforms GA in system optimisation.

Optimization of Gain, Impedance, and Bandwidth of Yagi-Uda Array Using Particle Swarm Optimization

Particle swarm optimization (PSO) is a new, high-performance evolutionary technique, which has recently been used for optimization problems in antennas and electromagnetics. It is a global optimization technique-like genetic algorithm (GA) but has less computational cost compared to GA. In this paper, PSO has been used to optimize the gain, impedance, and bandwidth of Yagi-Uda array. To evaluate the performance of designs, a method of moments code NEC2 has been used. The results are comparable to those obtained using GA.