china-character-font-modle-

1 设计一个ＦＩＲ数字低通滤波器， 满足一组模拟滤波器的指标参数；分别采用三角窗、hamming窗、blackman窗和kaiser窗进行设计。比较不同窗的设计效果。要求描绘滤波器的脉冲响应、窗函数以及滤波器的幅频和相频响应曲线。 2. 用上述设计滤波器完成几组给定信号的滤波。 3.设计数字高通、带通、带阻滤波器，要求描绘滤波器的脉冲响应以及滤波器的幅频和相频响应曲线。 (1、A design of a FIR digital low-pass filter, analog filters to meet the target of a group of parameters were used triangular window, hamming window, blackman windows and kaiser window design. Window to compare different design effect. Requirements described impulse response filter, window function and the filter amplitude-frequency and phase frequency response curve. 2. Using the above filter was designed to complete several sets of a given signal filtering. 3. Design of digital high-pass, band pass, band-reject filter, the filter impulse response described demand as well as the filter amplitude-frequency and phase frequency response curve.)

matlab codes for segmentation

卡尔曼滤波器模型及程序，一次作业做的，还可以希望对大家有帮助(Kalman filter model and procedures, once the job done, you can also hope to help everyone)

通过导入后缀为.wav的噪声音频信号通过matlab进行频谱分析(Frequency spectrum analysis by importing noise audio signal suffixed with. wav through MATLAB)

(2,1,3)卷积码编码，软判决译码；matlab语言编码；verilog语言译码；((2,1,3) convolutional code encoding, soft-decision decoding matlab coding verilog decoder )

用matlab实现的多站定位系统性能仿真(Using matlab to achieve multi-station location system performance simulation)

《有限单元法》一书中的相关程序，matlab变形，欢迎做有限元的人参考。("The finite element method," the relevant procedures, matlab deformation, welcome do finite element reference.)

Hybrid vehicle system modelling through simuliknk/Maltab

建立simulink模型进行波图像的仿真分析(Establishing Simulink model to simulate and analyze wave image)

说明：  多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性。（消除了共享参数）。(Multi-objective genetic algorithm is nsga-ii [1] (improved non-dominant sorting algorithm), which has the following advantages compared with other multi-objective genetic algorithms: the complexity of the traditional non-dominant sorting algorithm is, while the complexity of nsga-ii is, where M is the number of objective functions and N is the number of individuals in the population.The introduction of elite strategy to ensure that some good individuals in the evolutionary process will not be discarded, thus improving the accuracy of the optimization results.The comparison operator of crowding degree and crowding degree not only overcomes the defect that NSGA needs to specify the Shared parameter artificially, but also takes it as the comparison standard between individuals in the population, so that individuals in the quasi-pareto domain can uniformly expand to the whole Pareto domain, ensuring the diversity of the population.(eliminating Shared parameters).)