Analysis of Bifurcations in a Wind Turbine System Based on DFIG

This main aim of this study is investigation of the dynamic stability in a grid-connected wing turbine system based on Double Feed Induction Generator (DFIG) using the bifurcation theory. Regarding the overview of stability by Cardenas et. al. [1]. In our research, the proposed system model is simulated based on bifurcation theory in MATLAB software. In each step, one of the controlling or non-controlling parameters is selected. Eigenvalues of system are traced permanently during simulation. According to the change of the eigenvalues of system, due to the change of bifurcation parameter, stability of the equilibrium point and special bifurcations including saddle-node and Hopf bifurcations in the system are determined

Power and Time Slot Allocation in Cognitive Relay Networks Using Particle Swarm Optimization

The two main problems in cognitive radio networks are power and time slot allocation problems which require a precise analysis and guarantee the quality of service in both the primary and secondary users. In this paper, these two problems are considered and a method is proposed to solve the resulting optimization problem. Our proposed method provides an improved performance in solving the constrained nonlinear multiobject optimization for the power control and beamforming in order to reach the maximum capacity and proper adaption of time slots, and as a result a new scheme for joint power and time slot allocation in cognitive relay networks is proposed. We adopt space diversity access as the secondary users access scheme and divide the time between multiple secondary users according to their contribution to primary user's transmission. Helping primary users provides more opportunities for secondary users to access the channel since the primary users can release the channel sooner. In contrast, primary network leases portion of channel access time to the secondary users for their transmission using particle swarm optimization (PSO). Numerical studies show good performance of the proposed scheme with a dynamic cost function in a nonstationary environment

Enhancement in Medical Image Processing for Breast Calcifications and Tumor Detection

Abstract: Xray image processing is a computerized system which enhances the amount of detail visible on a digitalized x-ray image. The effect of this technique in the diagnoses of breast cancer, where the detection of early malignant tumors is essential for effective treatment, is reviewed in this study. The mammograms, as normally viewed, display a small percentage of the information they detect and that is due to the minor difference in x-ray attenuation between normal glandular tissues and malignant disease. This makes the detection of small malignancies difficult. The digital medical image processing uses denoising and image enhancement techniques so as to reveal any tumors that may not be obvious and help the oncologist decide. The idea is to transform the data into the wavelet basis, in which the large coefficients are mainly the signal and the smaller ones represent the noise. By suitably modifying these coefficients, the noise can be removed from the data. In this study we employ wavelet method of image enhancement and the conclusion would be satisfied