Anup Kumar Bhattacharjee

Compact Dual-Band Microstrip Antenna for IEEE 802.11a WLAN Application

A compact dual-band rectangular microstrip antenna (RMSA) is realized by two different single-slotted single-band rectangular microstrip antennas with slotted ground plane. Each open-ended slot in the single-slotted antenna is responsible to generate a wide impedance band that is shifted to lower frequencies by the effect of the ground slot. The length and position of each open-ended slot is varied to operate the antenna in a suitable resonant band (5.15-5.35 and 5.725-5.825 GHz). The proposed antenna meets the required impedance bandwidth, necessary for dual-band IEEE 802.11a WLAN application (5.125-5.395 and 5.725-5.985 GHz). The dimension of the antenna (12 × 8 × 1.5875 mm3) shows an average compactness of about 53.73% with respect to a conventional unslotted rectangular microstrip patch antenna.

Optimized radii and excitations with concentric circular antenna array for maximum sidelobe level reduction using wavelet mutation based particle swarm optimization techniques

In this paper, a Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach with Wavelet Mutation (PSOCFIWA-WM) is applied to the process of synthesizing three-ring Concentric Circular Antenna Arrays (CCAA) without and with central element feeding, focused on maximum sidelobe level reductions. Sidelobe level (SLL) is a critical radiation pattern parameter in the task of reducing background noise and interference in the most recent wireless communication systems. To improve the radiation pattern with maximum SLL reduction, an optimum set of antenna element parameters as excitation weights and radii of the rings are to be developed. The extensive computational results show that the method of PSOCFIWA-WM provides a maximum sidelobe level reduction of 96.06% with respect to the uniformly excited case for the particular CCAA containing 4, 6, and 8 numbers of elements in the three successive rings along with central element feeding.

Linear antenna array synthesis using Novel Particle Swarm Optimization

In this paper the synthesis of linear array geometry with minimum sidelobe level using a new class of Particle Swarm Optimization technique namely Novel Particle Swarm Optimization (NPSO) is described. The NPSO algorithm is a newly proposed, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the NPSO algorithm is much easier to understand and implement and requires the least of mathematical pre-processing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression, and then solved by the NPSO algorithm for the optimum element locations and current excitations. Five design examples are presented that illustrate the use of the NPSO algorithm, and the optimization goal in each example is easily achieved. Standard Particle Swarm Optimization (PSO) is adopted to validate the results of the NPSO algorithm.

Effect on Resonant Frequency for E-Plane Mutually Coupled Microstrip Antennas

The resonant frequency of a microstrip patch (rectangular, square, and circular) changes as soon as an identical patch is brought closer than 0.2λ. Closed form expressions are presented for the E-plane coupled configuration from which this altered resonant frequency may be easily computed. These expressions may be readily used in the computer aided design of microstrip antenna arrays.

Wide null control of linear antenna arrays using Particle Swarm Optimization

In this paper, the method of nullifying the radiation pattern of a symmetrical linear antenna array in a particular direction is propounded using Particle Swarm Optimization with Constriction Factor Approach (PSOCFA). Single or multiple wide nulls are achieved by optimum perturbations of elements current amplitude weights to have either symmetric nulls about the main beam. Different numerical examples are presented to illustrate the capability of PSOCFA for pattern synthesis with a prescribed wide nulls locations and depths.

Optimal sidelobe reduction of symmetric linear antenna array using Genetic Algorithm

In this paper synthesis of symmetric linear antenna arrays is described using Genetic Algorithm (GA). Genetic Algorithm has many advantages over other conventional optimization techniques. Real coded GA (RGA) is a high performance evolutionary optimization algorithm. It is used in this paper to find optimum inter-element spacing and excitation coefficients for the symmetric linear antenna array in order to minimize the maximum relative sidelobe level (SLL) in the radiation pattern of the array for minimum possible First Null Beamwidth (BWFN) increment.

Grammatical Swarm Based-Adaptable Velocity Update Equations in Particle Swarm Optimizer

In this work, a new method for creating diversity in Particle Swarm Optimization is devised. The key feature of this method is to derive velocity update equation for each particle in Particle Swarm Optimizer using Grammatical Swarm algorithm. Grammatical Swarm is a Grammatical Evolution algorithm based on Particle Swarm Optimizer. Each particle updates its position by updating velocity. In classical Particle Swarm Optimizer, same velocity update equation for all particles is responsible for creating diversity in the population. Particle Swarm Optimizer has quick convergence but suffers from premature convergence in local optima due to lack in diversity. In the proposed method, different velocity update equations are evolved using Grammatical Swarm for each particles to create the diversity in the population. The proposed method is applied on 8 well-known benchmark unconstrained optimization problems and compared with Comprehensive Learning Particle Swarm Optimizer. Experimental results show that the proposed method performed better than Comprehensive Learning Particle Swarm Optimizer.

Masking Based Segmentation of Diseased MRI Images

We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The rectangular mask is grown using an algorithm provided in the subsequent sections using similarity of neighbourhood pixels. Tests on various diseased MRI images show that small diseased objects are successfully extracted irrespective of the complexity of the background and difference in intensity levels and class sizes. Previous works are based on bimodal images whereas our work is based on multimodal images.

Improved swarm intelligence based optimal design of concentric circular antenna array

In this paper the maximum sidelobe level (SLL) reductions for various designs without and with central element feeding in three-ring concentric circular antenna arrays (CCAA) are examined using an improved particle swarm optimization algorithm (IPSO) to finally determine the global optimal three-ring CCAA design. Real coded Genetic Algorithm is also employed for comparative optimization but it proves to be suboptimal. Among the various CCAA designs, the design containing central element and 4, 6 and 8 elements in three successive concentric rings proves to be such global optimal design with global minimum SLL (−18.40 dB) determined by IPSO.

Quantum pinning-transition due to charge defects in ferroelectrics

We investigate the pinning of domain walls in ferroelectrics on the basis of the two phonon bound state (TPBS) or discrete breather state due to impurity energy levels above the phonon continua in ferroelectrics such as LiNbO3 in order to show the pinning transition, which indicates the point of easiest switching. We predict, with the help of our quantum calculations, that every ferroelectric material has such a critical point of easy switching. Here we describe the quantum origin of pinning through the findings of analytical and numerical calculations, as interpreted by a TPBS concept by such impurity or nonlinearity induced modes, by plotting various TPBS parameters against the Landau coefficient and percent impurity content. This new approach might lead to many interesting applications for device manufacturing.