UKFexamples

CODE是寫利用FUZZY的方式來做分群,也就是有歸屬度的程度分別 ,個人覺得比硬式分群結果來的較好,最後會畫圖來顯示結果.(CODE is written using FUZZY way to do grouping, which is a degree of attribution degrees, respectively, than the rigid grouping personally feel better the results will finally drawing to show the results,)

matlab下实现研究VSC-HVDC的次同步振荡特性 模型很不好找哦(matlab research under the VSC-HVDC model of sub-synchronous oscillation is 不好找 Oh)

AD/DA转换器的设计及MATLAB仿真实现(matlab A/D)

用前推回代法编写了一个算8节点配网潮流的程序，解压后就可以直接运行(With forward and backward substitution method to write an 8-node distribution network power flow calculation program, named after the needs of the non-alphanumeric characters removed after decompression, it can be run directly)

用子空间分解法求出时延估计，这种方法具有较强的抗噪声性能(Calculated using subspace decomposition delay estimation, this method has strong anti-noise performance)

数字波束阵列处理，简单仿真了平面阵的MVDR波束形成。(Digital beam array processing, a simple simulation of a planar array of MVDR beamforming.)

高斯赛德尔消元法、Heun法等实现线性方程组的求解、另附一份MATLAB之GUI设计基础(High Sisaideer elimination method, Heun method to achieve linear equations, attach a MATLAB GUI design basis)

小波分析用于FSK信号的频率检测，非常好用的！(Wavelet analysis of the frequency of detection for the FSK signal, very nice!)

这个是自己编写的MOD算法的程序，用于字典学习，具体的应用有图像融合，图像去噪，丢失点补充等。(this is a dictionary learning method, and the reader can use this algorithm to do image fusion and image denosing.)

“Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。(In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.)