a-quadrotor-of-modeing

只是一个关于短时平均过零率的matlab代码(Just a short-time average zero-crossing rate on the matlab code)

IEEE1394的相关介绍，是1394入门的首选！强烈推荐朋友们(IEEE1394 relevant, the portal is the first choice for 1394! Strongly recommend friends to s)

Batch Relaxation : Linear Discriminants

求解一幅图像内连接任意两点的变化最平缓曲线上最大值与最小值的差值(Solution to connect any two points within an image change in the most gentle curve of the difference between maximum and minimum)

非线性SVM分类器，可以很好的将两类划分开(Non-linear SVM classifier, can be very good by the two open)

matlab数字信号处理实例；MATLAB下的数字信号处理实现示例(matlab digital signal processing examples MATLAB under the sample implementation of digital signal processing)

在DCT域，利用8*8分块DM方法，嵌入隐藏信息。可设置量化步长、选择各种经典的攻击方式和控制攻击强弱。返回误码率和峰值信噪比。利用Matlab编写。(In the DCT domain, the use of 8* 8 block DM methods, embedding hidden information. Quantization step size can be set to choose a variety of classic attacks and control the strength of the attack. Return to the bit error rate and peak signal to noise ratio. Prepared using Matlab.)

vector controle of the induction machine

数据仿真，简单的回波信号时域滤波，动目标显示(echo simualtions, time filtering, GMTI)

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.