waveletthreshod

介绍matlab在控制过程中的应用。They are designed to help you learn how to use Matlab for the analysis and design of automatic control systems.(Matlab introduction in the control process applications. They are designed to help you learn how to use Matlab for the analysis and design of automatic control systems.)

本课件讲解了脉冲压缩雷达的概念和原理，简明清晰，适合于初学者。(The courseware explain the concepts and principles of pulse compression radar, concise and clear, suitable for beginners.)

multi device antenna in matlab

发动机运动仿真计算，连杆、活塞力运动过程的矢量分析。(Motion Simulation Engine)

克里金法广泛地应用于地下水模拟、土壤制图等领域，是一种很有用的地质统计格网化方法。它首先考虑的是空间属性在空间位置上的变异分布．确定对一个待插点值有影响的距离范围，然后用此范围内的采样点来估计待插点的属性值。该方法在数学上可对所研究的对象提供一种最佳线性无偏估计(某点处的确定值)的方法。它是考虑了信息样品的形状、大小及与待估计块段相互间的空间位置等几何特征以及品位的空间结构之后，为达到线性、无偏和最小估计方差的估计，而对每一个样品赋与一定的系数，最后　进行加权平均来估计块段品位的方法。(Kriging is a technique that provides the Best Linear Unbiased Estimator of the unknown fields (Journel and Huijbregts, 1978 Kitanidis, 1997). It is a local estimator that can provide the interpolation and extrapolation of the originally sparsely sampled data that are assumed to be reasonably characterized by the Intrinsic Statistical Model (ISM). An ISM does not require the quantity of interest to be stationary, i.e. its mean and standard deviation are independent of position, but rather that its covariance function depends on the separation of two data points)

In computer science and information theory, Huffman coding is an entropy encoding algorithm used for lossless data compression

本文提供了在不同信噪比条件下衰落信道上多分辨率MQAM同相比特的BER表达式(This article provides different SNR with fading channels compared to multi-resolution characteristics of MQAM BER expression)

一个GUI界面编辑 包括多种图像处理 方法 阈值 直方图均一化等(A GUI interface for editing include a variety of image processing methods, such as threshold histogram homogenization)

ceemd2011和2014可以对比看看有什么区别(ceemd2011 and 2014 can be compared to see any difference)

说明：  该工具包提供了一个用于通过算法、预训练模型和应用程序来设计和实现深度神经网络的框架。您可以使用卷积神经网络（ConvNet、CNN）和长短期记忆 (LSTM) 网络对图像、时序和文本数据执行分类和回归。应用程序和绘图可帮助您可视化激活值、编辑网络架构和监控训练进度。(The toolbox provides a framework for designing and implementing deep neural networks through algorithms, pre training models and applications. You can use convolutional neural networks (convnet, CNN) and long and short term memory (LSTM) networks to perform classification and regression on image, temporal, and text data. Applications and graphics help you visualize activation values, edit network architecture, and monitor training progress.)