Impact-Cost

this gives code to load global variable in matlab

这是有关MATLAB学习的一些编程技巧和使用的一个程序示例(MATLAB STUDY)

The signal received by a user over the forward link in such a high data rate system contains interference from the neighboring base stations. we note that the strength of this interference is a function of the network load in the neighboring cells. we obtain an expression for the received SINR (Signal to Interference and Noise Ratio) as a function of the traffic load in the interfering cells. Using this result, we propose an improvement to the SINR estimation scheme that is implemented in the current cellular data network. The proposed scheme provides a more accurate estimate of the user SINR by taking better account of the contribution of inter-cell interference. It builds on top of the current SINR measurement scheme by using a combination of pilot measurement and traffic load measurement

说明：  计算四阶累积量 基于四阶累积量的DOA估计方法，同MUSIC-like方法一样，有很好的估计性能(Fourth order cumulant calculation of fourth-order cumulant based DOA estimation methods, with the MUSIC-like methods, have a good estimation performance)

Analysis, Modeling and Control of Doubly-Fed Induction Generators for Wind Turbines

下雨的falsh源码，简单高效的程序。一学就会。(Rain falsh source, simple and efficient procedure.)

主要详细的介绍了matlab的一些使用方法，这对于一个新手而言绝对很有帮助(Mainly a detailed description of some use matlab, which is definitely helpful for a novice)

This file compares three different versions of the Kalman filter. The Kalman filter is used for recursive parameter estimation. The Kalman filter can handle noisy measurements. The first implemented filter (fcn_KF) is the Kalman filter with standard update of the covariance matrix P. The covariance matrix reflects the uncertainties of the predictions. To improve the numerical stability Potter developed a square root update (fcn_KF_SRP) of the covariance matrix P. Another version is the square root covariance update via triangularization (fcn_KF_SRT). This file generates a model. Then the three Kalman filters perform an estimation of the model parameter. At the end the results are compared. Sources: Simon, D. (2006): Optimal state estimation Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques Golub, G. (1996): Matrix Computations

本代码是关于Multi-Dimensional Scaling（MDS）的代码，可以用于特征提取、特征选择，或是矩阵降维。(This file is part of the Matlab Toolbox for Dimensionality Reduction v0.4b. You are free to use, change, or However, )

通信原理实验生成软件及相关代码，调制解调（C生成）(Communication principle experiment generation software and related codes, modulation and demodulation (C generated))