chap15

matlab code for qpsk

Prediction of rainfall using past data

plot example in python

基于+MATLAB+的时域信号采样及频谱分析(+ MATLAB+ based on the time-domain signal sampling and spectral analysis)

使用matlab编程，实现FFT卷积算法，经过样机的实验，得到正确的结果，而且代码简单易懂。(Using matlab programming, the realization of FFT convolution algorithm, the prototype of the experiment, to obtain accurate results, and easy-to-read code.)

扩频系统sumlink仿真可以 将直接序列扩频(Spread spectrum the system sumlink simulation can direct sequence spread spectrum)

功率谱估计的各种方法的matlab代码 包括AR模型，ESPRIT,周期图谱，MUSIC，PISARENKO等方法(Power spectrum estimation method matlab code, including the method of AR model, ESPRIT, the cycle map, the MUSIC, PISARENKO etc.)

用短时平均能量值对干净的语音信号进行检测，可以区分语音和非语音部分(With short time average energy value to test the clean speech signal, can distinguish between voice and non-voice part )

提高风力机将风能转化为机械能的效率，根据我国西北地区的风频风能曲线，用最优设计攻角沿叶片轴线的非线性分布修正传统Wilson算法，优化设计大型水平轴风力机叶片的气动性 能．该优化设计利用Madab优化工具箱，优化速度干涉因子的迭代计算，提高计算效率；考虑多翼型和变攻角等因素对叶片外形优化的影响，从结构及加工工艺角度修正翼型．通过对1．2MW风力发电机组叶片外形的气动性能计算和优化设计，结果表明该优化设计的有效性和可行性，可为风力机叶片外形设计提供参考．(Improve wind turbine wind energy into mechanical energy efficiency, according to the Northwest China wind frequency wind power curve, optimum design angle of attack along the blade axis of traditional non-linear distribution correction Wilson algorithm to optimize the design of the aerodynamic performance of the large-scale horizontal axis wind turbine blades . The Optimal Design Optimization Toolbox Madab optimized for speed interference factor iterative calculation to improve computational efficiency consider multi-airfoil and change the angle of attack and other factors on the blade shape optimization, airfoil from the structure and processing angle correction. By 1.2MW wind turbine blade shape aerodynamic performance calculation and optimization design, the results show that the effectiveness and feasibility of the optimized design can provide a reference for the wind turbine blade shape design.)

经典著作 pattern classification的配套matlab版解题指导(Computer_manual_to_accompany_Pattern_Classification)