llku

能够输出50kHZ,占空比50%的带死区的互补PWM(Complementary PWM with dead zone and 50% duty cycle can output 50kHZ)

Rtu MotherBoard containing slots for DI,DO,AI, Ethernet, Serial and GSM

不用外设控制，自己调试的程序，一段没有任何控制的程序，输入STM单片机，能自行控制电机，里面的定义引脚可自行改变。(A program without any control, input STM microcontroller, can control the motor itself, the definition pin can change itself.)

在DELPHI项目里，碰到SQL不要显示科学计数法(Do not display scientific counting method when encountering SQL in DELPHI project)

说明：  WordPress文件,好用的博客系统,最新版本(WordPressDocuments, Useful Blog System, Latest Version)

说明：  Abbyy Finereader v14 Crack

BFSK信号调制与解调，对加噪信号进行延迟差动处理，得到重构信号后，解调与初始信号对比。(BFSK Signal Modulation and Demodulation)

通过模型预测控制（MPC）对单相逆变器进行电流跟踪控制(Current tracking control of single-phase inverters by model predictive control (MPC))

说明：  使用2020，IGRF-13球谐系数，计算点位磁正常场。igrf.m，输入点位三维坐标(Use 2020, igrf-13 spherical harmonic coefficient to calculate point magnetic normal field,IGRF. M, input three-dimensional coordinates of point position)

说明：  独立成分分析(Independent Component Analysis，ICA)是近年来提出的非常有效的数据分析工具，它主要用来从混合数据中提取出原始的独立信号。它作为信号分离的一种有效方法而受到广泛的关注。近几年出现了一种快速ICA算法(Fast ICA)，该算法是基于定点递推算法得到的，它对任何类型的数据都适用，同时它的存在对运用ICA分析高维的数据成为可能。又称固定点(Fixed-Point)算法，是由芬兰赫尔辛基大学Hyvärinen等人提出来的。是一种快速寻优迭代算法，与普通的神经网络算法不同的是这种算法采用了批处理的方式，即在每一步迭代中有大量的样本数据参与运算。但是从分布式并行处理的观点看该算法仍可称之为是一种神经网络算法。(Independent component analysis (ICA) is a very effective data analysis tool proposed in recent years. It is mainly used to extract the original independent signals from the mixed data. As an effective method of signal separation, it has been widely concerned. In recent years, a fast ICA algorithm (fast ICA) has appeared. The algorithm is based on the fixed-point recursive algorithm, which is applicable to any type of data. At the same time, its existence makes it possible to use ICA to analyze high-dimensional data. Also known as fixed-point algorithm, it was proposed by HYV & auml; rinen et al. It is a fast optimization iterative algorithm. Different from the common neural network algorithm, this algorithm adopts the way of batch processing, that is, there are a large number of sample data in each iteration. But from the point of view of distributed parallel processing, this algorithm can still be called a neural network algorithm.)