ResearchPaper

matlab模拟的圆孔衍射 平面波 衍射间距可调(Matlab simulation using circular aperture diffraction images, the incident light for the plane wave)

EKF，UKF以及自适应UKF性能的比较，里面给出了详细的仿真场景及结果说明(EKF, UKF and adaptive UKF performance comparison, which gives a detailed simulation scenario and the results show that)

常微分方程-初值问题【matlab例程】经典算法直接应用方便快捷(ODE- convenient initial value problem [] classical algorithm matlab routines direct application)

用于广义预测自适应控制的M文件。根据模型可以自动匹配进行预测控制；(Used for generalized predictive adaptive control of M files. According to the model can forecast the automatic matching of control )

applies soft-threshold t to all elements of x and computes SURE

用于一维信号的小波包去噪，选取特定的阈值，采用最优小波基(For one-dimensional signal denoising using wavelet packet , select a specific threshold, the optimal wavelet)

最小均方误差（MMSE）的算法，是信号处理的基础，合成孔径雷达（SAR)目标成像仿真，一种流形学习算法（很好用），使用起来非常方便，可实现对二维数据的聚类，MIMO OFDM matlab仿真。( Minimum mean square error (MMSE) algorithm, Is the basis of the signal processing, Synthetic Aperture Radar (SAR) imaging simulation target, A fluid manifold learning algorithm (good use), Very convenient to use, Can realize the two-dimensional data clustering, MIMO OFDM matlab simulation.)

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)

this is about matlab 2009

blind syncronisation