OFDM Systems Resource Allocation using Multi- Objective Particle Swarm Optimization

Abstract Orthogonal Frequency Division Multiplexing (OFDM) has the inherent properties of being robust to interference and frequency selective fading and is de facto the adopted multiplexing techniques for the 4 th generation wireless network systems. In wireless system, resources such as bandwidth and power are limited, intelligent allocation of these resources to users are crucial for delivering the best possible quality of services. In this paper the problem of resource allocation in multiuser OFDM system is tackled using multi objective particle swarm optimization. Simulation results are presented for 3GPP-LTE system

Semi-Automatic Integrated Segmentation Approaches and Contour Extraction Applied to Computed Tomography Scan Images

We propose to segment two-dimensional CT scans traumatic brain injuries with various methods. These methods are hybrid, feature extraction, level sets, region growing, and watershed which are analysed based upon their parametric and nonparametric arguments. The pixel intensities, gradient magnitude, affinity map, and catchment basins of these methods are validated based upon various constraints evaluations. In this article, we also develop a new methodology for a computational pipeline that uses bilateral filtering, diffusion properties, watershed, and filtering with mathematical morphology operators for the contour extraction of the lesion in the feature available based mainly on the gradient function. The evaluations of the classification of these lesions are very briefly outlined in this context and are being undertaken by pattern recognition in another paper work

Optimization of AVR Parameters of a Multi-machine Power System Using Particle Swarm Optimization

In this paper, a method for optimizing the parameters of Automatic Voltage Regulation (AVR) system installed on the generators of a multi-machine power system using Artificial Intelligence (AI) techniques is presented. Each AVR system is equipped with a PID (Proportional, Integral and Derivative) controller and a Power System Stabilizer (PSS). Two methods are presented, which are the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The robustness of the AI algorithms is examined by studying the time-domain behavior of the system following different disturbances. The AI techniques provide a much simpler way to solve this non-linear system compared to classical techniques.Keywords: multi-machine power system stability, AVR system, power system stabilizer, PID controller, particle swarm optimization, genetic algorithm