ClusterCoeff

Visual C++ MATLAB图像处理与识别实用案例精选 pdg格式(tha visual C+++ Matlab)

自己写的关于信号完整性的文章，拿出来与大家分享一下，适合初学者(Write your own articles on signal integrity, to share with everyone out, suitable for beginners)

MATLAB CODE FOR CHANNEL EQULIZATION USING MATCHED FILTER

通信原理方面的一些基础matlab程序,包含数字基带传输系统中升余弦滚降系统,最佳基带系统,基带信号眼图,OOK,2PSK,2FS,MPSk,DSB,SSB,VSB(Some basic matlab programs of communication principle, including up-cosine roll-down system in digital baseband transmission system, optimal baseband system, baseband signal eye diagram, OOK, 2PSK, 2FS, MPSk, DSB, SSB, VSB)

深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

LTE信道模型，包括发送，接收，调制，等等(LTE channel models, including the transmission, reception, modulation, etc.)

一个用PCA和LDA降维，并用knn分类的人脸识别例程(A dimension reduction using PCA and LDA, and face recognition with the knn classification routines)

研究表明，构成K一L变换矩阵的向量也就是ToePhtz矩阵的特征向量。与此同时，离散 余弦变换矩阵逼近于ToePutz矩阵的特征向量矩阵，所以离散余弦变换矩阵与自然图像的 K一L变换矩阵十分相似。经过离散余弦变换后的变换系数块的协方差矩阵Cy非常接近对角 阵，即除了对角线元素以外，其它很多元素都近似为0，并且在左上角集中了主要能量。这 反映了自然图像大部分区域变化不大，亮度突变只占少数，即图像能量以低频成分为主的特 性。通过变换后的量化，舍弃对视觉效果影响较小的次要信息，可达到进一步的压缩效果。 虽然从去相关性能的意义上讲，DCT是一种次于K一L变换的准最佳变换，但是从算法实现的角度来看，DCT则远远优于K一L变换。首先，当图像的分块大小确定后，DCT的变换矩阵也就随之确定了，不随输入信号的统计特性变化而变化 其次，二维DCT能够分解成两次一维DCT，有利于硬件实现。 (Studies have shown that constitute a K L transformation matrix vector is ToePhtz eigenvector of the matrix. At the same time, discrete The cosine transform matrix approximation in ToePutz matrix eigenvector matrix, so the discrete cosine transform matrix and natural images Is very similar to a K L transformation matrix. After discrete cosine transform, the transform coefficient block of Cy is very close to the diagonal of covariance matrix Matrix, namely except diagonal elements, many other elements are approximate to 0, and focused the main energy in the upper left corner. this Reflects the natural images most area changed little, brightness mutations accounted for only a few, the energy is given priority to with low frequency components of image Sex. Through quantitative after transformation, abandoning smaller effect on the visual performance of secondary information, can achieve further compression effect. Although from related to the performance of the sense, DCT is a kind o)

Phased array antenna pattern (Chebyshev weights), In matlab R2009b debugging through, Algorithm for researchers to do reference.

按部州描述很详细，精确到每一行.按部州描述很详细，精确到每一行(an bu zhou shu ru hen xiang xi qing que dao mei yi hang hao dong xi a ge men ye shi mei zhe le ta ma de fei dei xie )