tidian

说明：  用于人造视觉 256*256的图像缩小到25*25 用saliency提取显著性区域 并根据设定的阈值剪裁(256* 256 for artificial visual image reduced to 25* 25 with saliency extraction of significant regional and in accordance with thresholds set tailoring)

先对语音信号进行采集、播放，分析其频谱，对该信号源添加噪声，再播放音频并分析频谱，然后分别通过IIR低通、带通滤波器和FIR低通、带通滤波器，分别播放音频和得到滤波后的频谱。(Firstly， voice signal acquisition, play, analyze its spectrum to add noise to the signal source, and then play the audio spectrum analysis, and then were by IIR low-pass, band-pass filter and FIR low-pass, band-pass filters, respectively, play audio and get the spectrum after filtering.)

应用MATLAB编写的三维时域有限差分程序。是初学FDTD的好帮手。建议初学者参考。(Application of MATLAB to prepare three-dimensional finite difference time domain procedure. FDTD are learning a good helper. Recommended reference for beginners.)

matlab图像的直线处理lineChange(matlab image of a straight line treatment lineChange)

用matlab 实现离散时间傅里叶变换的例程，方便初接触者使用。这是我dsp课的大作业(Using matlab discrete-time Fourier transform routines to facilitate early contact to use. This is my big job dsp lesson)

对风电功率的预测 时间序列预测 卡尔曼滤波预测 (Prediction of wind power time series prediction Kalman filter prediction)

PSO和GA的结合算法，对PSO中效果较差的粒子进行交叉和变异操作。(The combination of PSO and GA algorithm, PSO particles in the less effective cross and mutation.)

Basic models to build induction motor in IRFO vector control

cost function calculation for vertical handoff in heterogenous network

汽车高斯曲面拟合 --- 2程序，以适应到表面二维高斯： 子= A *的进出口（ -（（西为X0）^2/2/sigmax^2 +（艺Y0的）^2/2/sigmay^ 2）。。）+ b的 这些例程是自动在某种意义上说，他们并不需要出发对模型参数的猜测规范。 autoGaussianSurfML（十一，彝，子）适合通过对模型参数的最大似然（最小二乘）。它首先计算了该模型在许多可能的参数值，然后选择最佳质量设置和细化与lsqcurvefit它。 autoGaussianSurfGS（十一，彝，紫）的估计，通过指定数据的贝叶斯生成模型，然后采取通过从模型吉布斯抽样样本后ofthis模型参数。这种(Auto Gaussian Surface fit --- 2 routines to fit a 2D Gaussian to a surface: zi = a*exp(-((xi-x0).^2/2/sigmax^2+ (yi-y0).^2/2/sigmay^2))+ b The routines are automatic in the sense that they do not require the specification of starting guesses for the model parameters. autoGaussianSurfML(xi,yi,zi) fits the model parameters through maximum likelihood(least-squares). It first evaluates the quality of the model at many possible values of the parameters then chooses the best set and refines it with lsqcurvefit. autoGaussianSurfGS(xi,yi,zi) estimates the model parameters by specifying a Bayesian generative model for the data, then taking samples from the posterior ofthis model through Gibbs sampling. This method is insensitive to local minimain posterior and gives meaningful error bars (Bayesian confidence intervals))