FastNN

实现字符识别功能，实验报告，详细，容易理解(To achieve character recognition function, experimental report, detailed, easy to understand)

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

this is used to extract the leaf vein

PPG data, photoplethysmographic data containing PPG of subjects doing various exercises

利用matlab 制作GUI系统 模拟并分析LTE 基站的功效与多种调制方式之间的配合 从而得出最优化方案。 (Produced using matlab GUI system simulation and analysis of the effectiveness of LTE base station with a variety of co-ordination between the modulation and thus finding the most optimal solution. discription: 1.At the beginning ,the Base Station covers 20 UEs ,each uses 5 RBs 2.the new UE inserts to the system would also use 5RB 3.the total resource for the system : 100 RBs)

惯导轨迹仿真，对于初学惯导的人有很大的帮助(INS trajectory simulation, inertial navigation for beginners who have a lot of help)

利用行像素点、列像素点识别分割字符成单个字符图像----letercog.m(The row of pixels, pixel columns into a single character recognition segmentation, image---- letercog. M)

MATLAB中经典的风速建模，用于风电机组的动态仿真分析等(MATLAB classic wind modeling for dynamic simulation analysis of the wind turbine)

精通matlab科学计算书本上的一些例题(Proficient in matlab scientific computing books on some of the examples)

说明：  最小均方濾波器是一類可通過最小化誤差訊號之均方值而修正濾波器係數，以類比所需理想濾波器的自適應濾波器，其中作為修正依據的誤差訊號為理想參考訊號與實際輸出訊號之差。該種濾波器所用之最小均方法只以當前之訊號誤差值為準進行修正，是一種隨機梯度下降法。最小均方法係由斯坦福大學的Bernard Widrow教授及他的首位博士班學生Marcian Hoff於1960年提出。最小均方濾波器屬於常見的自適應濾波器種類之一。(The minimum mean square filter is an adaptive filter that can correct the filter coefficients by minimizing the mean square value of the error signal to analogize the desired ideal filter. The error signal used as the correction reference is the ideal reference signal and the actual output. The difference between the signals. The minimum averaging method used in this kind of filter is only corrected based on the current signal error value, which is a stochastic gradient descent method. The minimum mean method was proposed in 1960 by Professor Bernard Widrow of Stanford University and his first PhD student, Marcian Hoff. The least mean square filter is one of the common types of adaptive filters.)