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线性加速度法计算单自由度体系地震时程反应(Single degree of freedom linear acceleration method Seismic Response System)

The windowed periodogram spectral estimator.用于经典谱估计方法，这是相应的matlab函数(The windowed periodogram spectral estimator)

电池模型，锂离子电池参数估计模型，matlab源代码(battery 1 RC model using in matlab soft)

与潘左击鼠标拖动，绘制一个右键拖动zoombox，并检视双击所有数据。这也保留任何现有的WindowButtonXFcn这个数字已，以及任何轴UIContextMenus。 我写了实时数据采集这一点，因为linkaxes套XLimMode至“手动“ -这意味着将不会自动调整轴的新数据。 panzoom（figureHandle，axesHandles） 添加到这个数字，只能到指定的坐标轴功能的处理。 panzoom（figureHandle，axesHandles，XLink的） panzoom（figureHandle，axesHandles，XLink的，雁联） 假设你有四个轴处理：1，2，3，4。你可以连接所有的X轴一起：XLink的= [1，2，3，4]，或者你可以对链接在一个单元格数组中：XLink的={[1，2][3，4]}。这同样适用于雁联。 XLink的或雁联也可以为空。(Pan with left click mouse drag, draw a zoombox with right click drag, and view all data with double-click. This also retains any existing WindowButtonXFcn the figure has, as well as any axes UIContextMenus. I wrote this for live data capture, as linkaxes sets XLimMode to "manual"- which means the axes wouldn t automatically resize for new data. panzoom(figureHandle, axesHandles) Adds the functionality to that figure, ONLY to the axes handles specified. panzoom(figureHandle, axesHandles, xLink) panzoom(figureHandle, axesHandles, xLink, yLink) Assuming you have four axes handles: 1, 2, 3, 4. You could link all of the x axes together: xLink = [1, 2, 3, 4], or you could link them in pairs in a cell array: xLink = {[1, 2], [3, 4]}. The same applies for yLink. xLink or yLink can also be empty.)

对脑电时域信号提取30Hz以内各个节律波能量比重(Extracting energy proportion of each rhythm within 30Hz for EEG time-domain signal)

Design and analysis of microstip antenna in terms of radiation pattern, by using different patches in matlab simulation software,

matlab在通信原理中的应用，信道编码循环码仿真，希望大家能用得上！(matlab in Communication Theory in the application of channel coding cycle simulation code, I hope everyone can useful!)

学习matlab的必备书籍,很好,我看过了,关于数据处理的 (a book of matlab)

通过学习BP神经网络技术，对手写数字进行识别，基于结构的识别法及模板匹配法来提高识别率。(Through the study of BP neural network technology, digital handwriting identification, structure-based identification method and template matching method to improve the recognition rate.)

对数据先进行经验模态分解得到不同特征尺度的信号分量，然后对所有的信号分量用AR模型进行预测得到预测后的分量，然后将预测后的分量进行重构得到最终的预测信号。(The data first empirical mode decomposition characteristics of the signal components of different scales, and then for all signal components obtained by AR model to predict the component forecasts, and then predict the components are reconstructed to obtain final predicted signal.)