pcm-coding-basic-on-matlab

orthogonal frequency division multiplexing matlab code-3

核磁共振的原理介绍，以及其相关领域的发展，介绍了脑功能成像的原理的处理技术(Introduce the principle of nuclear magnetic resonance, and its related fields of development, introduced the principle of functional brain imaging processing technology)

利用matlab/simulink进行电气系统仿真。提供说明与示例。(Using matlab/simulink simulation of the electrical system. Provides instructions and examples.)

说明：  harrisi 算法，用此算法可以快速在图像中寻找到角点，快速 准确(harrisi algorithm)

TOA定位算法，代码无错误，根据站点信息，闪电位置计算出接受信息，然后加误差反演出数据(TOA for matlab)

Considered a 16QAM communication system. The information rate is 2 Mb/s and the carrier frequency is 12 GHz. Assumed perfect synchronization. a) Simulated the system if the channel is AWGN and draw bit error rate of the system versus Eb/No. Compare the results with what you obtain in theory. b) Considered (15,11) Hamming code. Apply this code into the above system. Compare the results with what you obtain in theory. c) Simulated uncoded system if we have a Doppler Spread due to mobile movement in the channel. Simulate and draw the bit error rate of the system for maximum mobile speed 42.3 Km/Hour. No equalizer is used in the system. d) Applied coding of part (b) into the system of part (c). Simulate the system and discuss the results. e) Designed an interleaver for the system of part (d) to improve the coded performance results. Simulated the system.

How to use the ordinary Voronoi partition generator. The m-files also have their own help documentation that you can view in Matlab. This code creates a bounding polygon that is a square. Then it randomly creates 10 points and then creates and draws the partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(2, 10) regions = voronoi(bounds, points) drawRegions(bounds, regions) ================================ This is a brief set of instructions on how to use the Multiplicatively- weighted Voronoi partition generator. Each of the m-files has a help section, so in Matlab you can call: help mwvoronoi help drawRegions Here is an example that creates a bounding polygon, a square with side length 10. Then it randomly creates six points with random weights. Finally, the code draws the resulting partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(3, 6) regions = mwvoronoi(bounds, points) drawRegions(bounds, regions) Enjoy!

用软频率处理小区间的干扰，并将小区分成内环外环中环三个部分，仿真分析当三个环 的发射功率不同时的各个部分的吞吐量比较。(With a soft frequency interference among processing district, and residential area is divided into central, inner loop three parts, the simulation analysis when the transmission power of three ring does not throughput of each part of the comparison at the same time.)

说明：  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.)

color thresholding. this code very usefull