Modified__second_review_31_10_11

基于最小二乘的解扩重扩波束成形算法matlab实现(the implementation of beamforming algorithm based on least squares spread despread algorithm in matlab.)

Multi-Instance Learning matlab code

k ua gi aegt ae gtaert .a wg.

通过该例子熟悉雷诺方程，仔细体会雷诺方程的求解过程，仅供参考(Through this example, familiar with the Reynolds equation, carefully understand the solution process of the Reynolds equation, for reference only.)

1.录制一段自己的语音信号,对录制的信号进行采样 画出采样后语音信号的时域波形和频谱图 给定滤波器的性能指标,采matlab设计数字滤波器,并画出滤波器的频率响应 然后用自己设计的滤波器对采集的信号进行滤波,画出滤波后信号的时域波形和频谱,并对滤波前后的信号进行对比,分析信号的变化 回放语音信号。2.(1. Recorded his voice for some signal, the signal is sampled on the record draw sampled speech signal time-domain waveform and frequency spectrum performance for a given filter, mining matlab digital filter design, and draw the filter frequency response then use their own filters designed to filter the collected signals, to draw the filtered time domain waveform and frequency spectrum, and comparing the signal before and after filtering, analysis of signal changes playback of audio signals. 2.)

国外matlab相关实验资料，包括源码及相关说明文档(matlab source code)

EEMD是针对EMD方法的不足,提出了一种噪声辅助数据分析方法。EEMD分解原理为:当附加的白噪声均匀分布在整个时频空间时,该时频空间就由滤波器组分割成的不同尺度成分组成。当信号加上均匀分布的白噪声背景时,不同尺度的信号区域将自动映射到与背景白噪声相关的适当尺度上去。当然,每个独立的测试都可能会产生非常嘈杂的结果,这是因为每个附加噪声的成分都包括了信号和附加的白噪声。既然在每个独立的测试中噪声是不同的,当使用足够测试的全体均值时,噪声将会被消除。全体的均值最后将会被认为是真正的结果,唯一持久稳固的部分是信号本身,所 加入的多次测试是为了消除附加的噪声。(The EEMD collection of experience decomposition is improved on the basis of the EMD more given a data processing method!)

用matlab实现了数字信号处理中的多采样样转换，并依此原理进行采样转换滤波器的设计。 (Multi-sampling samples converted digital signal processing using matlab, and so the principle of sampling conversion filter design.)

利用鱼群算法优化BP神经网络权值，该代码可以直接运行。(Fish swarm algorithm is used to optimize the weight of BP neural network, and the code can run directly.)

说明：  希尔伯特黄变换，经验模态分解，边际谱，超级全代码，可以出图可以自由更换信号(Hilbert Huang transform, empirical mode decomposition, marginal spectrum, super full code, mapping and free signal replacement)