gorupdelay_L_revise

说明：  通过对图像的二值化以及矩阵计算 识别汽车的车牌(Through the binary image and the vehicle license plate recognition Matrix)

脉冲编码调制(PCM)实现 编程实现PCM技术的三个过程：采样、量化与编码。 采样：低通连续信号采样，以 x=sin(200*t) m=x./(200*t) m=m.*m 为例说明低通采样定理，绘出信号时、频图形；带通连续信号采样，以x=sin(20*t) m=x./t 为例说明带通采样定理，绘出信号时、频图形。 量化：均匀量化，以幅度 的正弦信号为例实现为64级电平的均匀量化；非均匀量化，输入A律PCM编码器的正弦信号 ，采样序列为 ，将其进行PCM编码，给出编码器的输出码组序列 编码：以上述信号为例，实现A律的13折线近似法及国际标准PCM对数A律量化编码。(Pulse code modulation (PCM) to achieve PCM technology programming three processes: sampling, quantization and coding. Sample: low-pass continuous signal sampling to x = sin (200* t) m = x./(200* t) m = m.* m an example low-pass sampling theorem, draw the signal, frequency graphics bandpass sampling continuous signals to x = sin (20* t) m = x./t an example bandpass sampling theorem, draw the signal, frequency graph. Quantization: uniform quantization, in order to realize the magnitude of sinusoidal signal as an example for the 64 level uniform quantization non-uniform quantization, input A law PCM encoder sine signal, the sampling sequence, to be PCM encoded, the encoder is given The output code sequence Code: A Case Study of the signal to achieve the 13 A line approximation law and international standards on the number of A law PCM coding quantization.)

Motion estimation algorithm - New three step search

说明：  今天刚照的matlab源程序的学习案例，方便大家学习，欢迎下载！！(According matlab source case study, to facilitate learning, welcome to download! !)

Program to view FFT Spectrum from WAV file

matlab中用于信号处理的工具箱，可添加到其toolbox中，并在Matlab中设置路径，便可以在Matlab中调用。(used matlab signal processing toolbox, can add to their toolbox, and set in the Matlab path, they can call in Matlab.)

matlab编程通过MUSIC算法定位声源方向（DOA），图形最高点对应横坐标即为声源方向(MUSIC algorithm matlab programming by positioning the sound source direction (DOA), the horizontal axis corresponds to the highest point of the graphic is the sound source direction)

Adaptive Filter Theory

deep learning一般是学习的层次结构，它也是有一定理论依据的，即模拟人脑的大脑皮层工作，因为大脑皮层的视觉区域也是分层次工作的，越底层的视觉皮层对那些底层特征就越敏感。综上所述，feature learning有这么多应用需求的驱动和生物神经理论上的支持，注定它能够在AI领域中发挥一定的作用。一些实验表明，有些feature learning学习到的特征几乎比所有其它的特征效果要好，比如本文中的ISA模型就是其中一个。(deep learning hierarchy of learning, it is also a certain theoretical basis that simulate the human brain' s cerebral cortex, visual areas of the cerebral cortex is hierarchical work, the more the bottom of the visual cortex is more sensitive to those underlying characteristics . In summary, feature learning so many application needs the support of the drive and biological neural theoretically doomed it to be able to play a certain role in the field of AI. Some experiments show that some feature learning learning to feature almost better than all the other features of the effect of the ISA model such as this is one.)

本实验是IIR滤波器设计-巴特沃斯滤波器设计，并通过实例检验得到了很好的滤波效果(IIR filter design, Butterworth filter has been designed and tested by example with excellent results)