SemaphoreAndBuffer-P1

very good in fact. looking forward to see more codes

利用随机共振进行信号检测，本程序主要针对单频信号利用4节龙哥库塔实现共振检测(Stochastic resonance signal is detected, the program focused on the use of single-frequency signal section 4 Long Ge Kuta achieve resonance detection)

matlab采用半色调技术对图像进行显示：用模板近似表示10个灰度级，每一个灰度级用3*3 的模板来表示，并对如下图所示的256*256的渐变图像进行半色调输出的检验。(matlab using halftoning image display: template approximated 10 gray levels, a gray level of each template used to represent the 3* 3, and the gradation of the image as shown in Figure 256* 256 halftone inspection of the output.)

这是一本经典的移动通信方面的教程。涉及到ASK，PSK,FSK等调制方式，对想从事于通信方面的学生以及专业人士都很有帮助。(This is a classic tutorial for mobile communications. Related to ASK, PSK, FSK modulation, etc., on to the students engaged in the communications and professionals are very helpful.)

matlab.math.handbook是matlab数学手册，介绍了一些数学函数(matlab.math.handbook is Matlab mathematical manual, introduced the Math)

计算信号的魏格纳分布。 用法见函数说明。(Calculate the signal Wigner. Usage See function description.)

matlab code for AWGN channel

本文采用小波神经网络进行交通流量预测，短时交通流量存在随机性和非线性因素，影响预测的准确性。传统预测模型难以反映交通流量变化特点，同时传统神经网络易陷入局部极小值，泛化能力差，交通流量预测精度低。为了提高短时交通流量预测精度，提出一种小波神经网络的短时交通流量预测模型。小波神经网络可以对短时交通流量随机性、不确定性进行局部分析，并进行非线性预测，验证了模型的有效性，进行了对比试验。验证结果表明，小波神经网络提高了短时交通流量预精度，预测结果更具应用价值。(In this paper, wavelet neural network is used to forecast traffic flow. There are random and nonlinear factors in short-term traffic flow, affecting the accuracy of prediction. The traditional prediction model is difficult to reflect the characteristics of traffic flow changes. At the same time, the traditional neural network is easy to fall into the local minimum, the generalization ability is poor, and the prediction accuracy of traffic flow is low. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow prediction model based on wavelet neural network is proposed. The wavelet neural network can analyze the randomness and uncertainty of short-term traffic flow and perform nonlinear prediction. The validity of the model is verified and a comparative experiment is conducted. The verification results show that the wavelet neural network improves the short-term traffic flow pre-precision, and the prediction result has more application value.)

说明：  基于最大信噪比的盲源分离算法，源信号为混沌信号。(Based on the maximum signal to noise ratio Blind source separation algorithm, the source signal for the chaotic signal.)

equalizers LMS and RLS alghoritm