Multivariable-Frequency-Domain-Toolbox

智能优化算法，可以用matlab平台实现。 关于优化方面比较好的书籍。(Intelligent optimization algorithm, can be used matlab platform. On the optimization better books.)

this is source code about extracting cars number

说明：  跳频参数估计论文，通过各种时频分析方法进行处理(The hopping parameter estimates thesis, mainly handled by a variety of time-frequency analysis methods)

book for clustering based method for edge detection in hyperspectral image.

这是一个人工免疫算法的matlab工具箱。该工具箱可以根据不同的优化函数形式设置来用人工免疫算法来寻优(This is an artificial immune algorithm matlab toolbox. The toolkit is based on different settings to optimize the function of the form of artificial immune algorithm with excellent来寻)

fuelcell model燃料电池模型，可直接套用。(fuel cell model_files)

An extra file fdct_usfft_path.m is included in fdct_usfft_matlab directory. This script needs to be called to append several subdirectories into the searching path.

fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!. Given the samples of a log-normal distribution, the PDF parameter is found fits data to the probability of the form: p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2)) with parameters: m,s format: result = fit_ML_log_normal( x,hAx ) input: x - vector, samples with log-normal distribution to be parameterized hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields m,s - fitted parameters CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML

基于Matlab的原子外层电子可能分布的3D球谐分布(3D Spherical Harmonic of atom s electrons scatter map based on Matlab)

录制汉语拼音 a1（阴平）的发音。分别提交你所能录制的基音频率最高和最低的两个 录音文件。文件名为: 学号_a_h.wav（高音文件）和学号_a_l.wav（低音文件）。（用MathLab 编程） 1）分别对这两个文件进行观察分析。找出最高和最低基音频率位置，分别显示此时它们的 局部波形和短时频谱（按照最大值为100 归一化显示）。 要求：在时域波形上标出基音周期，并算出数值（精确到一个采样点）。在频域图上标出 基音频率。注意：标出分析波形的起始位置（精确到采样点位置），说明谱分析时所采用 的窗函数。 2）用倒谱分析法，估计语音产生模型的声道传输函数H(w)的模|H(w)|。在1)的频谱图上叠 加显示H(w), （按照最大值为100 归一化显示）。 3）用线性预测分析方法，估计语音产生模型的声道传输函数H( )的模| H( ) |。并在 1) 的频谱图上叠加显示H( ) 。(no no non )