fitness_function2

传统的近似三角形内点测试( APIT),即近似三角形内点测试定位算法,广泛应用于静态节点定位.结合粒子滤波提出改进算法,将APIT算法推广到节点动态定位.(Traditional approximate point inside the triangle test (APIT), namely the approximate point within the triangle test positioning algorithm, widely used in the static node localization. Combined with particle filter algorithm is proposed to improve the APIT algorithm is extended to the node dynamic positioning.)

光学双稳特性曲线 调制作用:It=Ii*T(phi) 反馈作用:phi=phi_0+K*It 得透射率T(phi)与相移phi的反馈关系是 T(phi)=[phi-phi_0]/[K*Ii] 式中phi_0为初始相移 对于多干涉(F-P干涉)有: T(phi)=1/[1+F*(sin(phi/2))^2](Optical bistable characteristic curve modulation role: It = Ii* T (phi) feedback: phi = phi_0+ K* It may transmittance T (phi) and phase-shifting relationship between phi feedback T (phi) = [phi-phi_0 ]/[K* Ii] where phi_0 for the initial phase shift for the multi-interference (FP interference) are: T (phi) = 1/[1+ F* (sin (phi/2)) ^ 2])

robot locator using the mat lab code

数学建模源代码，灰色系统理论，包括数学建模网站(Mathematical modeling of the source code, the grey system theory, including the mathematical modeling website)

pe to snr for bpsk when we use rectangular modulation on bw=0.025,0.05,0.1

说明：  windows下用来画曲线的小程序，看看好不好用(windows under the curve for painting small procedures to see is really handy)

震动信号的预警和过零率计算，需要有自己的原始震动信号(seismic signal processing zero_-pass rate)

基于MIMO-OFDM最优导频设计和带阻滤波算法的仿真程序，对信道估计研究有一定帮助(Optimal pilot design and band-stop filter algorithm simulation program based on MIMO-OFDM channel estimation study)

matlab仿真的ofdm系统程序，非常简单的系统，适合新手学习(OFDM simulation of the MATLAB system, the system is very simple, suitable for beginners to learn)

半监督学习(Semi-Supervised Learning，SSL)是模式识别和机器学习领域研究的重点问题，是监督学习与无监督学习相结合的一种学习方法。半监督学习使用大量的未标记数据，以及同时使用标记数据，来进行模式识别工作。当使用半监督学习时，将会要求尽量少的人员来从事工作，同时，又能够带来比较高的准确性，因此，半监督学习目前正越来越受到人们的重视。(Semi-Supervised Learning (SSL) is a key issue in the field of pattern recognition and machine learning. It is a learning method combining supervised learning with unsupervised learning. Semi-supervised learning uses a large number of unlabeled data, as well as labeled data, for pattern recognition. When using semi-supervised learning, it will require as few people as possible to work, and at the same time, it can bring relatively high accuracy. Therefore, semi-supervised learning is receiving more and more attention.)