协同OFDM系统中一种改进的相位跟踪算法

与MIMO-OFDM系统相比,协同OFDM（CO-OFDM）系统的优势是适用于尺寸较小的用户终端,劣势是不同步的节点晶振和多普勒频移导致系统出现多个载波频偏（MCFO）,从而在接收端形成载波间干扰（ICI）。为了消除ICI对CO-OFDM系统性能的影响,将适用于1-2-1模型的＂相位跟踪＂算法推广至1-3-1模型,并通过重新设置导频序列和改变相位漂移计算方式,对已有的相位跟踪算法进行改进,使运算复杂度降低,有利于在实际系统中部署。仿真结果表明,改进后的算法在较高信噪比条件下进一步提高了系统性能。     

Optimal multi-scale geometric fusion based on non-subsampled contourlet transform and modified central force optimization

In the current era of technological development, medical imaging plays an important part in several applications of medical diagnosis and therapy. This requires more precise images with much more details and information for correct medical diagnosis and therapy. Medical image fusion is one of the solutions for obtaining much spatial and spectral information in a single image. This article presents an optimization-based contourlet image fusion approach in addition to a comparative study for the performance of both multi-resolution and multi-scale geometric effects on fusion quality. An optimized multi-scale fusion technique based on the Non-Subsampled Contourlet Transform (NSCT) using the Modified Central Force Optimization (MCFO) and local contrast enhancement techniques is presented. The first step in the proposed fusion approach is the histogram matching of one of the images to the other to allow the same dynamic range for both images. The NSCT is used after that to decompose the images to be fused into their coefficients. The MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. The proposed fusion framework is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation (STD), edge intensity, entropy, peak signal-to-noise ratio, Q ^ab/f, and processing time. Experimental results demonstrate that the proposed optimized NSCT medical image fusion approach based on the MCFO and histogram matching achieves a superior performance with higher image quality, average gradient, edge intensity, STD, better local contrast and entropy, a good quality factor, and much more details in images. These characteristics help for more accurate medical diagnosis in different medical applications.