rf-energy-analysis-in-WSN

控制工具箱与SIMULINK软件应用：系统对象为G(s)=4/s(s+2) PI补偿器K1(s)=(s+1)/s K2(s)=(s+0.5)/s a 若由K1(s)与 G(s)串联组成的单位反馈系统，称为系统A。 b 若由K1(s) 放置在G(s)的反馈通道称为系统B。 c 若由K2(s)与G(s)串联组成的单位称为系统C，绘制上述每一种情况的阶跃响应与斜坡响应，求每一种情况下的系统阶跃响应误差与斜坡响应误差，并与理论分析结果进行比较。 (Control toolbox and SIMULINK software application: system object for G (s) = 4/s (s+ 2) PI compensator K1 (s) = (s+ 1)/s K2 (s) = (s+ 0.5)/s If a by K1 (s) and G (s) series of feedback system of the unit, known as a system a. B if the K1 (s) placed in G (s) feedback channel called system b. If c by K2 (s) and G (s) series of unit is called system c, draw the above each kind of condition of step response and slope response, for each of these cases system step response error and slope response error, and the results were compared with theoretical analysis.)

利用遗传优化算法设计磁体线圈的程序包，matlab环境编写(The use of genetic optimization algorithm design magnet coil package, matlab environment for the preparation of)

说明：  som算法 组织神经网络用于图像分割matlab实现(som-organizing neural network algorithm for image segmentation matlab achieve)

实现了一个复高斯信道，以及收和发端的相关矩阵(Implements a complex Gaussian channel, as well as the originator of the correlation matrix of received and)

计算方法，曲线插值，普通插值法。自编程序。附带图片。(Calculation methods, curve interpolation, ordinary interpolation. Self programming. With pictures.)

mri分割，基于MATLAB,很不错，住一起的一个朋友用过的，(mri segmentation, based on MATLAB, a very good living with a friend of mine used,)

声波方程的正演程序，注释非常清晰，并且可以显示正演波场快照。(Acoustic wave equation forward procedure, comments are very clear, and can display the forward wave field snapshot.)

Stock quote query (m file)

A color image is DCT transformed watermark embedded into the intermediate frequency coefficients to the effect of a good algorithm is the classic reference value has a strong image of yourself for the

采用神经网络实现手写识别的一种方法，建立Bp神经网络，采用快速训练方法，可快速完成一类相关手写字体的模式识别，识别率较高，当字体变化较大识别率降低时，可重新训练具有较强的适应性。实验证实本方法较好实现了手写字符识别，但也存在识别速度较慢，有时训练不收敛等缺点(Handwriting recognition using neural network is a way to establish Bp neural network, using fast training methods, and can be related to the completion of a class of handwriting pattern recognition, recognition rate higher recognition rate when the font changed greatly reduced, they can re- Training has strong adaptability. Experiments confirmed that this method achieved a better handwritten character recognition, but there are also identified slow, and sometimes non-convergence of the training deficiencies)