aceps

实现9/7小波正反变换 注1： 采用标准正交方法，对行列采用不同矩阵（和matlab里不同） 注2： 为了保证正交，所有边界处理，全部采用循环处理 注3： 正交性验证，将单位阵带入函数，输出仍是单位阵（matlab不具有此性质） 注4： 此程序是矩阵实现，所以图像水平分量和垂直分量估计被交换位置 注5： 此程序实现的是类小波（wavelet-like）变换，是介于小波包变换与小波变换之间的变换 注6： 此程序每层变换相对原图像矩阵，产生的矩阵都是正交阵，这和小波包一致 注7： 但小波变换每层产生的矩阵，是相对每个待分解子块的正交矩阵，而不是原图像的正交矩阵 注8： 且小波变换产生的正交矩阵维数，随分解层数2分减少 注9: 提升系数可以在MATLAB7.0以上版本，用liftwave( 9.7 )获取，这里直接给出，考虑兼容性 注10：由于MATLAB数组下标从1开始，所以注意奇偶序列的变化 注11：d为对偶上升，即预测；p为原上升，即更新(Note 1: The standard orthogonal method, the ranks of the different matrices (and matlab where different) Note 2: In order to ensure orthogonality, all boundary treatment, all with recycling Note 3: Orthogonal validation, the identity matrix into the function, the output is still the unit matrix (matlab does not have this property) Note 4: This program is a matrix to achieve, so the image horizontal and vertical components is estimated to be swapped Note 5: This class implements the wavelet (wavelet-like) transformation, is between the wavelet packet transform and wavelet transform between Note 6: This procedure transforms each image relative to the original matrix, the resulting matrix are orthogonal matrix, consistent with this and wavelet packet Note 7: But each generation wavelet transform matrix, is to be broken down for each sub-block relative to the orthogonal matrix, rather than the original image of the orthogonal matrix Note 8: Wavelet transf)

线性卡尔曼滤波，二维状态空间模型一般的卡尔曼滤波(kalman fitering)

基于短时能量与过零率、倒谱特征和谱熵的三种语音端点检测(Endpoint detection)

一个介绍matlab的课件，介绍初等模型的，相信对大家都有用处的，很好的课件。(An introduction matlab courseware to introduce elementary model, I believe we have useful, well courseware.)

weight-based resampling: an application of multinomial resampling. Also a SIR particle filter

集合卡尔曼滤波的Matlab程序，enkf-matlab-0.30，最新版本(EnKF-Matlab is designed primarily as a research and study tool. As a consequence, speed and memory usage are given a lower priority than simplicity and ease of use)

实现白噪声、正弦信号和带有白噪声的正弦信号的自相关函数的分析。(Achieve white noise, white noise with a sinusoidal signal and a sinusoidal signal of the autocorrelation function.)

802.11n opnet model 11.0

时间序列的多步预测程序，希望对大家能都有所帮助(Time series of multi-step forecasting process)

Abstract—In this paper, a new lossy technique based on wavelet transform for compression of breast ultrasound images is presented. The experiments are performed on 16 different wavelet functions and the quality of reconstructed images is evaluated by using Compression Ratio (CR), Normalized Mean Square Error (NMSE), Normalized Absolute Error (NAE), and Peak Signal to Noise Ratio (PSNR) criterion. An ultrasonographer performed the subjective assessment. This study shows that among the utilized wavelets,