resample2

simulink diode in matlab simulink

改进的RSSI算法的源代码，有一定的可行性，帮助学习算法(The RSSI algorithm to improve the source code , there is a certain possibility , to help learning algorithm )

DCT变换：变换域系数选取采用阈值编码，：阈值越小，图像恢复效果越好，但压缩效果差，一般取在5-10 即可很好的获得恢复图像信息。(DCT transform: transform domain coefficients selected using the threshold coding: the smaller the threshold value, the better the image restoration, but the compression is poor, and generally in the 5-10 to get a good recovery image information.)

这是一个单相并网逆变器滑模变结构控制，采用滑模变结构控制方法对其进行稳定性控制，并实现有源阻尼控制。(This is a single-phase grid-connected inverter sliding mode variable structure control, using sliding mode variable structure control method to its stability control, and to achieve active damping control.)

可以广泛的应用于数据预测及数据分析,计算晶粒的生长，入门级别程序,包含特征值与特征向量的提取、训练样本以及最后的识别。( Can be widely used in data analysis and forecast data, Calculation of growth, entry-level program grains Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition.)

output switching between 2 input switch

初略介绍k-means，初学者可通过此文档认识什么是k-means算法(Slightly introduced at the beginning of k- means, beginners can pass this document to know what is k- means algorithm)

Adaptive Filters 10

用于观察脑电信号全通道数据和多个单通道数据，并且输出相对颅内压参数。函数通过读取nt后缀脑电图文件，并生成GUI界面，可选择全通道观察所选文件脑电信号，也可以选择包含原始EEG脑电信号，40hzEEG，80hz肌电干扰和提取出的40hzEEG信号的多个单通道观察，并且生成颅内压参数。(Used to observe all channels of EEG data and multiple-channel data, and output parameters relative intracranial pressure. 40hzEEG signal function by reading nt suffix EEG file and generates a GUI interface, you can all channels of EEG observe the selected file, or you can choose to include original EEG EEG, 40hzEEG, 80hz EMG interference and extracted a plurality of single-channel observation, and generates an intracranial pressure parameters.)

说明：  每个传感器具有一定的范围和精度，而且周围环境可能对它们产生干扰。即单传感器只能提供部分不精确的信息，从而不能排除对外界环境描述的歧义性。鉴于各传感器性能上的差异表现出的互补性，如果综合各个传感器的信息，就能得到比单一传感器所能提供的更准确可靠的信息。简言之，与单一传感器相比，多传感器数据融合由于信息的冗余性和互补性存在很多方面的优势。 多传感器融合(Each sensor has a certain range and accuracy, and the surrounding environment may interfere with them. That is to say, a single sensor can only provide some inaccurate information, so it can not rule out the ambiguity of the description of the external environment. In view of the complementarity of the differences in the performance of each sensor, if we synthesize the information of each sensor, we can get more accurate and reliable information than a single sensor can provide. In short, compared with a single sensor, multi-sensor data fusion has many advantages due to the redundancy and complementarity of information.)