NR 5G协议标准解读

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泊松盘采样 任意维度的泊松盘采样。 安装 用做： npm install poisson-disk-sampling 用做： yarn add poisson-disk-sampling CDN 上也提供了 Web 浏览器的编译版本： < script src =" https://cdn.jsdelivr.net/gh/kchapelier/poisson-disk-sampling@2.2.2/build/poisson-disk-sampling.min.js " > </ script > 特征 可用于任何维度（1D、2D、3D 等）。 可与自定义 RNG 函数一起使用。 允许配置最大尝试次数、最小距离和每个点之间的最大距离。 允许使用自定义函数来驱动分布的密度。 基本示例 var p = new PoissonDiskSampling ( { sha

matlab模拟poisson过程原始码欢迎来到PackingGeneration项目 该程序允许硬球包装生成和包装后处理（请参阅和Wikipedia页面）。 它支持Lubachevsky–Stillinger，Jodrey–Tory和受力生成算法。 它可以计算粒子插入概率，Steinhardt Q6全局和局部顺序测度，非响尾蛇粒子的配位数，对相关函数，结构因子以及压力平衡后的减压。 它不需要任何预装的库，并且是多平台（Windows / nix）。 它是由我（Vasili Baranau）在2012-2013年对德国马尔堡小组中的硬球包装进行研究时开发的。 它是根据MIT许可分发的（请参阅参考资料）。 这段代码（v1.0.1.28版）有一个DOI ：。 如果您在研究项目中使用此程序，请引用Baranau和Tallarek（2014）单分散和多分散硬球的随机密堆积极限值。 或者， Baranau等人。 （2013）随机硬球填料的Kong径熵； 。 样品产生的填料看起来像这样： 左：10000个颗粒的单分散堆积； 取自。 右：10000个颗粒的多分散填料； 取自。 有关程序选项和基本用法

美国国家航天局（NASA）的Simulink 生成c代码的培训教材

派罗 PyAero是用Python编写的开源机翼轮廓分析和CFD网格划分工具。 图形用户界面使用Qt for Python（PySide2）编写。 产品特点 加载并显示机翼轮廓文件 机翼花键和精炼 获得平滑的轮廓和足够的轮廓点 优化前缘和后缘分辨率 准备网格轮廓 自动计算前缘半径 样条线上的点分布用作网格分布 自动生成块结构网格 目前单件C型网 机翼附近严格正交的网格 机翼，前缘，后缘和风洞的网格分辨率控制 后缘尖锐或钝化 网格平滑（有待改进） 网格导出 （.flma） （.su2） （.msh） 自动定义边界元素（边缘，面） 翼型，入口，出口，对称 使用简单的空气动力学分析

Chapter 1, Introduction to Deep Learning, speaks all about refreshing general concepts and terminology associated with deep learning in a simple way without too much math and equations. Also, it will show how deep learning network has evolved throughout the years and how they are making an inroad in the unsupervised domain with the emergence of generative models. Chapter 2, Unsupervised Learning with GAN, shows how Generative Adversarial Networks work and speaks about the building blocks of GANs. It will show how deep learning networks can be used on semi-supervised domains, and how you can apply them to image generation and creativity. GANs are hard to train. This chapter looks at some techniques to improve the training/learning process. Chapter 3, Transfer Image Style Across Various Domains, speaks about being very creative with simple but powerful CGAN and CycleGAN models. It explains the use of Conditional GAN to create images based on certain characteristics or conditions. This chapter also discusses how to overcome model collapse problems by stabilizing your network training using BEGAN. And finally, it covers transferring styles across different domains (apple to orange; horse to zebra) using CycleGAN. Chapter 4, Building Realistic Images from Your Text, presents the latest approach of stacking Generative Adversarial Networks into multiple stages to decompose the problem of text to image synthesis into two more manageable subproblems with StackGAN. The chapter also shows how DiscoGAN successfully transfers styles across multiple domains to generate output images of handbags from the given input of shoe images or to perform gender transformations of celebrity images. Chapter 5, Using Various Generative Models to Generate Images, introduces the concept of a pretrained model and discusses techniques for running deep learning and generative models over large distributed systems using Apache Spark. We will then enhance the resolution of low quality images using pr

lstm-text-generation 文本生成(Word2Vec + RNN/LSTM) 目录： input : 输入文件数据 1.char_LSTM.py : 以字母为维度 预测下一个字母是什么 2.word_LSTM.py : 以单词为维度，预测下一个单词是是什么 char_LSTM.py 用RNN做文本生成，我们这里用温斯顿丘吉尔的任务传记作为我们的学习语料。 英文的小说语料可以从古登堡计划网站下载txt平文本：) 这里我们采用keras简单的搭建深度学习模型进行学习。 word_LSTM.py 跟上一个模型一样，只不过使用的word2vec对语料构建词向量，预测下一个单词。

Introduction The purpose of this application note is to provide a hardware and firmware solution to STR7 and STR9 microcontroller users for audio playback of a .WAV file. The approach is optimized in that it uses a minimal number of components external to the microcontroller, and offers a high degree of flexibility to the end-user for use with their own .WAV files. There are two .WAV file parameters that can be controlled by the user; the sample rate and the file size which depends on the application requirements. The actual content of the .WAV file is irrelevant and may consist of speech, music, etc., and the only limitation is the audio format. In fact, this application assumes that the .WAV file format must be: PCM (no compression), 8000/11025/22050/44100 Hz sample rate, 8-bit and mono. This document is structured as follows: a brief description of the .WAV file format in Section 1. Section 2 provides a detailed description of the basics of audio playback. Finally, Section 3 presents in detail an example of an application built around an STR711F microcontroller and that can be easily tailored to any other STR7/STR9 microcontroller

DCGAN-TensorFlow-面生成 使用深度卷积生成对抗网络生成的人脸图像