fusion

Canny算子，用Canny提取数字图像的边缘(Canny operator, extract the digital image with Canny edge)

estimating shape of object using shading information

CDMA多用户检测的Simulink仿真研究(CDMA multi-user detection of Simulink Simulation)

盲信号分离(BSS)是指在对彼此独立的源信号混合过程及各源信号本身均未知的情况下，从混合信号中分离出这些源信号的方法。BSS可以用来从多个麦克风混合语音信号中提炼出单个语音信号。本文简要阐述LMS、RLS算法，并通过仿真实验来分析比较这两类方法的性能，并利用此方法对一实际的语音信号进行分离。(Blind Signal Separation (BSS) is defined as independent of each other mixed-signal process and the source of the signal itself are unknown circumstances, from the mixed-signal to isolate the source of these signals. BSS can be used from multiple microphones mixed voice signal to extract a single speech signal. This paper briefly described LMS, RLS algorithm, and through simulation experiments to analyze the comparative performance of these two types of methods and take advantage of this method on a real speech signal separation.)

说明：  FFT 与LZW编码算法相结合，实现压缩功能，MATLAB仿真实现(FFT LZW compress)

短路仿真，可仿真接地故障、相间短路、相间短路接地。(Short circuit simulation, three-phase transformer three-phase short-circuit, single-phase short circuit, two-phase short-circuit and two-phase short-circuit ground)

数值分析matlab源码，并带有详细设计说明，执行效率较高(Numerical analysis matlab source, with the detailed design shows that the implementation of more efficient)

说明：  matlab编程，用于计算电力系统短路潮流，源代码提供在这里，希望供大家参考(Matlab programming used in the calculation of the power system short-circuit the trend, the source code available here, for your reference)

时频分析工具箱中提供了计算各种线性时频表示和双线性时频分布的函数(Time-frequency analysis toolkit provides the calculation of linear time-frequency representation and bilinear time-frequency distribution function)

说明：  实现多尺度熵的算法 多尺度熵（Multiscale entropy, MSE）将样本熵扩展到多个时间尺度，以便在时间尺度不确定时提供额外的观察视角。样本熵的问题在于它没有很好地考虑到时间序列中可能存在的不同时间尺度。为了计算不同时间尺度下信号的复杂性，Costa等人（2002,2005）提出了多尺度熵。(MultiScale Entropy There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.)