Priya Ranjan Sinha Mahapatra

Design of Fully Digital Controlled Reconfigurable Dual-Beam Concentric Ring Array Antenna Using Gravitational Search Algorithm

In this paper, we propose an optimization method based on Gravitational Search Algorithm (GSA) for design of reconflgurable dual-beam concentric ring array of isotropic elements with phase only control of flve-bit digital phase shifters. The problem is to flnd a common radial amplitude distribution using four-bit digital attenuator that will generate three difierent types of broadsided beam pair in vertical plane: a pencil/pencil beam pair, a pencil/∞at-top beam pair and a ∞at-top/∞at-top beam pair (sector pattern) with desired value of side lobe level, flrst null beam width and ripple. The two patterns difier only in radial discrete phase distribution while sharing a common discrete radial amplitude distribution. The optimum sets of four- bit discrete radial amplitude distribution generated by four-bit digital attenuators and flve-bit discrete radial phase distribution generated by flve-bit digital phase shifters for obtaining dual radiation patterns are computed by Gravitational Search Algorithm.

Generation of phase-only pencil-beam pair from concentric ring array antenna using Gravitational Search Algorithm

A pattern synthesis method based on Gravitational Search Algorithm (GSA) is presented to generate a dual pencil beam patterns with two different pre-defined sidelobe levels from a concentric ring array of isotropic antennas. Both the patterns are generated by sharing a common optimum amplitude distribution of the array elements. The pattern with sidelobe level lower than the other is generated by switching phase distribution of the array elements from zero to the optimum value. The amplitude and the phase of the array elements are varied radially along the array using GSA to generate a dual pencil beam of two different sidelobe levels.

Minimum-width rectangular annulus

In this paper, we identify a minimum width rectangular annulus that encloses a given set of n points in a plane. We propose an O(n^2logn) time and O(n) space algorithm for this problem. To the best of our knowledge this is the first sub-cubic algorithm for a rectangular annulus for arbitrary orientation.

k-enclosing axis-parallel square

Let P be a set of n points in the plane. Here we present an efficient algorithm to compute the smallest square containing at least k points of P for large values of k. The worst case time complexity of the algorithm is O(n + (n - k) log2(n - k)) using O(n) space which is the best known bound for worst case time complexity.

A Hierarchical Convex Polygonal Decomposition Framework for Automated Shape Retrieval

With the increasing number of images generated every day, textual annotation of images becomes impractical and inefficient. Thus, content-based image retrieval (CBIR) has received considerable interest in recent years. Keeping it as the primary motivational focus, we propose a method which exploits different degrees of convexity of an object’s contour using a multi-level tree structured representation called Hierarchical Convex Polygonal Decomposition (HCPD) tree and the method also uses a special spiral-chain-code to encode the polygonal representation of decomposed shape at every node. The performance of the proposed scheme is reasonably good and comparable with existing state-of-the-art algorithms.

Optimizing Squares Covering a Set of Points

We investigate two kinds of optimization problems regarding points in the 2-dimensional plane that need to be enclosed by squares. (1) Given a set of \(n\) points, find a given number of squares that enclose all the points, minimizing the size of the largest square used. (2) Given a set of \(n\) points, enclose the maximum number of points, using a specified number of squares of a fixed size. We provide different techniques to solve the above problems in cases where squares are axis-parallel or of arbitrary orientation, disjoint or overlapping. All the algorithms we use run in time that is a low-order polynomial in \(n\).

Genetic Algorithm Based Approaches to Install Different Types of Facilities

Given a set P of n-points (customers) on the plane and a positive integer k (1 ≤ k ≤ n), the objective is to find a placement of k circles (facilities) such that the union of k circles contains all the points of P and the sum of the radii of the circles is minimized. We have proposed a Genetic Algorithm (GA) to solve this problem. In this context, we have also proposed two different algorithms for k=1 and 2. Finally, we have proposed a GA to solve another optimization problem to compute a placement of fixed number of facilities where the facilities are hazardous in nature and the range of each such facility is circular.

Design of phase-differentiated dual-beam concentric ring array antenna using differential evolution algorithm

In this paper, the authors propose a pattern synthesis method to generate a dual radiation pattern of pencil/sector beam pair of a concentric ring array of isotropic elements with desired sidelobe levels by switching between the radial phase distribution among the elements which shares a common amplitude distribution. The optimum set of radial amplitude and radial phase distributions for generating dual radiation patterns with lower sidelobe levels are computed using Differential Evolution algorithm. The array with optimum radial amplitude and zero radial phase distributions generate a pencil beam while the array with same amplitude but optimum radial phase generates a sector beam in the vertical plane.

Optimum ring spacing and interelement distance for sidelobe reduction of a uniform concentric ring array antenna using differential evolution algorithm

Reduction of sidelobe level in a concentric ring array results in wide first null beamwidth. The authors propose a pattern synthesis method based on the Differential Evolution (DE) algorithm to reduce the sidelobe level of a concentric ring array of isotropic antennas while keeping the first null beamwidth (FNBW) fixed and variable by optimizing both ring spacing and interelement distance of each ring.

Solving maximal covering location problem using genetic algorithm with local refinement

The maximal covering location problem (MCLP) deals with the problem of finding an optimal placement of a given number of facilities within a set of customers. Each customer has a specific demand and the facilities are to be placed in such a way that the total demand of the customers served by the facilities is maximized. In this article an improved genetic algorithm (GA)-based approach, which utilizes a local refinement strategy for faster convergence, is proposed to solve MCLP. The proposed algorithm is applied on several MCLP instances from literature and it is demonstrated that the proposed GA with local refinement gives better results in terms of percentage of coverage and computation time to find the solutions in almost all the cases. The proposed GA-based approach with local refinement is also found to outperform the other existing methods for most of the small as well as large instances of MCLP.