p3x

说明：  DSP课程例程： ① 为频率特性加频域容差图； ② 在对数幅频特性上加要求的指标标识； ③ 量化子程序的调用。(DSP courses routines: ① to increase the frequency characteristics of frequency-domain tolerances map ② in logarithmic amplitude-frequency characteristics of the indicators require additional identification ③ quantify subroutine calls.)

说明：  EM最大期望算法：算法是在概率模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐藏变量。最大期望经常用在机器学习和计算机视觉的数据聚类领域。(EM algorithm)

采用滑模控制对LORENZE混沌系统进行抑制(Sliding Control To Lorenze Chaos system)

image watermarking method

说明：  边缘检测算法 椭圆检测算法 运动检测算法(edge detection,Elliptic Detection)

In any power system, stability is always a important parameter. To check the stability of a synchronous generator which is connected with the grid is the power angle and freq need to know. With the help of this simulation model one can easily find Power angle and Freq.

演示Hopfield网络，具有两个稳定平衡点，其期望向量T如下。(Demo Hopfield network with two stable equilibrium point, the expectation vector T as follows.)

LBM方法的shan-chen两相流分离模拟(LBM method shan-chen two-phase separation simulation)

局部频谱细化算法的实时效率 比较zoom-fft各种算法(Local spectrum thinning algorithm more efficient, real-time zoom-fft algorithms)

第9章共振峰的估算方法259 9.1预加重和端点检测259 9.1.1预加重259 9.1.2端点检测260 9.2倒谱法对共振峰的估算260 9.2.1倒谱法共振峰估算的原理260 9.2.2倒谱法共振峰估算的MATLAB程序261 9.3LPC法对共振峰的估算262 9.3.1LPC法共振峰估算的原理262 9.3.2LPC内插法共振峰的估算263 9.3.3LPC求根法共振峰的估算266 9.4连续语音LPC法共振峰的检测268 9.4.1简单LPC共振峰检测268 9.4.2改进的LPC共振峰检测270 9.5基于HilbertHuang变换(HHT)的共振峰检测274 9.5.1希尔伯特变换275 9.5.2语音信号的另一种模型——AMFM模型278 9.5.3对AMFM模型的分析279 9.5.4语音信号共振峰特征参数提取的HHT方法279 9.5.5基于HilbertHuang变换的共振峰检测步骤和MATLAB程序280(Estimation Chapter 9 259 9.1 formant pre-emphasis and pre-emphasis endpoint detection 259 259 9.1.1 9.1.2 9.2 endpoint detection principle cepstrum estimate 260 to 260 9.2.1 formant formant estimation cepstrum 260 9.2.2 cepstrum estimate estimate estimate formant MATLAB program 261 9.3LPC method formant 262 9.3.1LPC law principle of formant estimation interpolation within 262 9.3.2LPC formants 263 9.3.3LPC Root Law resonance estimate peak 266 9.4 Continuous Speech LPC formant detection method is simple LPC formant 268 9.4.1 268 9.4.2 Improved detection LPC 270 9.5 formant detection based HilbertHuang transform (HHT) 274 9.5.1 formant detection Hill Another model 275 9.5.2 Hilbert transform voice signals- AMFM model 278 9.5.3 Analysis of the AMFM model HHT Method 279 9.5.4 formant speech signal feature extraction based HilbertHuang transform 279 9.5.5 Resonance peak detection step and MATLAB program 280)