power_system_project_report

模拟电荷法计算程序,由模拟电荷的布置和场域电场的求解(csm)

说明：  pn序列多普勒估计 pn序列多普勒扩展 抗干扰技术应用能够 simulink的仿真分析(pn_dopple_estimation pn_dopple_despreading anti_interference simulink system)

说明：  对初学MATLAB的人员，可有作用，也很管用，特此共享。( To personnel who begins studies MATLAB, may have the function, is also effective very much, shares specially )

多个图像分割程序，都已经验证过，可以使用，适合初学者(thank)

matlab 两种写出泊松分布序列的程序(matlab possion code)

matlab pso toolbox 工具箱。 pso算法的matlab实现。 其中有几个经典的测试函数。(toolbox matlab pso toolbox. PSO algorithm matlab realize. Several of the classic test function.)

MATLAB7.1的潮流计算关于遗传算法的所有源程序详解【matlab】(MATLAB7.1 flow calculation for all the source code on the genetic algorithm matlab】 【Detailed )

MATLAB地震工具箱，包括地震勘探领域的常用算法，数据读取，剖面显示等(Seislab by matlab)

Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) is very sensitive to the carrier frequency offset (CFO), which not only affects the channel estimation but also the selection of a minimum error probability based precoder for peak-to-average power ratio (PAPR) reduction such that the system performance will degrade. This paper presents the joint scheme of CFO) and channel estimation in the MIMO-OFDM systems with the minimum error probability based precoder for reducing PAPR. We provide the solution to eliminate the effects of ICI due to CFO mismatch for OFDM with space frequency block codes and spatial multiplexing, drive a generalized linear model and propose a non-iterative reduced complexity model. Our proposed scheme is effective in estimating and compensating for frequency-independent and frequency dependent transmit and receive ICI in the presence of a carrier frequency offset.

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle s position and velocity. Each particle s movement is influenced by its local best known position but, is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.