hetro1

对一个调查样本绘制计数的频率分布图.要求输入的样本事先由小到大排好序。(to make a frequency distribution table for a sample)

采用神经网络控制的方法。利用人工神经网络的自学习这一特性，并结合传统的PID控制理论，构造神经网络PID控制器，实现控制器参数的自动调整(Control method using neural network. Artificial neural network to learn the characteristics of the self, combined with the traditional PID control theory, structural neural network PID controller, automatically adjust the controller parameters )

e book for edge detection

实现极化码的编码和译码算法，其中译码部分采用的是SCL算法来实现译码的。(Polarization code coding and decoding algorithm, including decoding part adopts the SCL algorithm to realize the decoding.)

主要功能作用電腦做錄音跟放音的功能適合初學者學習(Primary function of the role of the computer to do recording with playback function for beginners to learn)

这个资料，集中系统的介绍了MATLAB在通信系统仿真中的应用，分享(This information, focus on the system introduced in the communication system MATLAB simulation, sharing)

Huffman编码算法是一种基于信源符号概率的变长码编码算法 (Huffman coding algorithm is based on the probability of source symbol variable-length coding algorithm)

一1000 MW水电站（机组M1）通过一条500 kV, 700 km的输电线路与负载中心连接。负载中心模型是5000 MW的阻抗负载，由遥远的1000 MW电站和当地的5000 MW (机组 M2)电站供电。系统已经初始化，因此线路传送950 MW，接近它的潮流阻抗负载（SIL = 977 MW）。这两台机相当于一台水轮机和管理(HTG)，励磁系统和电力系统稳定器(PSS)。这三个模块位于“涡轮和调速”子系统中。通过此例，在500 kV系统中制造故障，观察PSS对系统稳定性的影响。(a 1,000 MW hydropower station (crew M1) through a 500 kV, 700 km of transmission line and load center. Load center model is 5,000 MW of load impedance. from the distant 1000 MW power station and the local 5,000 MW (Unit M2) power supply. Is initialized, it transmits 950 MW. closer to its trend of load impedance (SIL = 977 MW). This machine is equivalent to a two hydraulic turbines and Management (HTG), excitation system and the power system stabilizer (PSS). This module is located at 3 "and turbine speed" subsystem. Through this cases, the 500 kV system manufacturing fault, PSS observation of the system stability.)

matlab编程的模拟地质雷达探测的复杂岩体模型的图片(the matlab programming simulation of complex geological radar detection pluton model pictures)

analyze data association algorithms (Nearest Neighbour and JCBB) in SLAM. Data association is basically a combinatorial optimization problem whose goal is to find the “best” set of associations between the observations and predictions according to a particular criterion (e.g. individual compatibility). Form this point of view, near- est neighbour (NN, a.k.a. ML) is simply a “greedy” algorithm in which the “compatibility” of individual associations with each other is not examined. On the contrary, JCBB, by considering both the individual and joint compatibility, is able to yield much better results. JCBB uses the branch and bound technique in order to prune the search tree