2-layers-simple-ANN

npc 3-level inverter spwm matlab

 产生长度为1000的标准正太分布的随机信号，画出时域波形及频谱；  采用u=255的非均匀PCM编码，每符号为8bit，画输入-输出关系图；  计算信号量化噪声比（SQNR）；  信道误码率为10-3；  解码，并画出u律反变换后的信号时域波形及频谱。 扩展要求：  采用均匀PCM、量化级数可变、信道误码率可变。 ( have a standard length of 1000 is too random signal distribution, draw time-domain waveform and frequency spectrum  u = 255 using the non-uniform PCM encoding, each symbol for 8bit, drawing input- output relationship diagram  calculated quantization noise signal than (SQNR)  channel bit error rate of 10-3  decoder, and law to draw u the signal after the inverse transform time-domain waveform and frequency spectrum. Expansion of the requirements:  uniform PCM, quantization level variable, a variable channel bit error rate.)

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.

脉冲噪声下跳频信号的时频分析，低阶SPWVD,抑制噪声并且得到其参数估计。(Impulse noise time-frequency analysis, frequency hopping signals under low SPWVD and suppress noise and get its parameter estimation.)

蝙蝠优化的极限学习机,提升极限学习机的效率(BA-ELM to improve effection of ELM)

说明：  MNIST手势识别数据库进行softmax回归分类，直接运行softmaxExercise.m文件即可运行。(The MNIST gesture recognition database is classified by soft Max regression and can be run directly by running the softmaxExercise. m file.)

说明：  matlab 电机控制仿真源代码 matlab 电机控制仿真源代码 matlab 电机控制仿真源代码 matlab 电机控制仿真源代码(matlab simulation of motor control motor control simulation matlab source code source code source code matlab simulation of motor control motor control simulation matlab source code)

image segmentation, new normalized cut

这是一部练习MATLAb编程的书籍，共有50道题目，没道题目都有详细的解释(This is a practice MATLAb programming books, a total of 50 questions, did not have a detailed explanation of the title track)

冈萨雷斯数字图像处理全书源代码，包含.m文件以及函数，应用matlab编写(Gonzalez Digital Image Processing book source code, including .m files and functions, applications written in matlab)