用于动作识别的3D ResNet 更新（2020/4/13） 我们在arXiv上发表了一篇论文。 我们上载了本文所述的预训练模型，包括在结合了Kinetics-700和Moments in Time的数据集中预训练的ResNet-50。 更新（2020/4/10） 我们极大地更新了脚本。 如果要使用旧版本来复制我们的CVPR2018论文，则应使用CVPR2018分支中的脚本。 此更新包括： 重构整个项目 支持更新的PyTorch版本 支持分布式培训 支持对“时刻”数据集的培训和测试。 添加R（2 + 1）D模型 上载经过Kinetics-700，时刻矩和STAIR-Actions数

Solutions to some exercises from Bayesian Data Analysis, third edition, by Gelman, Carlin, Stern, and Rubin 22 Aug 2014

ldpc matlab代码用于 5G 无线通信的信道编码架构使用高级综合 动机和目标 在当今瞬息万变的世界中，对移动互联网的需求与日俱增。 第四代 (4G) 系统现已在全球范围内使用。 由于移动互联网用户数量的急剧增加，当今的 4G LTE 还存在一些挑战，例如更高的数据速率和频谱效率。 这导致我们将 4G 系统的 Turbo 码替换为承诺更高吞吐量的信道码。 从那时起，3GPP 就接受了 LDPC 码作为 5G 无线通信的信道编码方案，正在进行大量的研究来优化解码器。 在5G中，LDPC码和极性码分别用于数据通道和控制通道的纠错。 第五代系统的主要目标是更高的数据速率、更高的频谱效率、更高的吞吐量、更高的带宽和更高的能效，同时在更低的延迟下也是如此。 信道编码在任何无线通信系统中都起着至关重要的作用。 我们的项目为第五代无线通信的低密度奇偶校验码提供了一种新颖的高效高吞吐量编码器和解码器。 这项工作提出了实现 LDPC 码高吞吐量信道编码架构的策略。 所提议的设计以较低的延迟实现峰值吞吐量，满足 5G NR 标准的吞吐量和延迟要求。 建议的设计首先在 Matlab 中实现。 经过验证

Mastering OpenCV 3 - Second Edition by Daniel Lélis Baggio English | 4 May 2017 | ASIN: B01N7G0BKE | 250 Pages | AZW3 | 4.82 MB Key Features Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3 Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications Each chapter is a tutorial for an entire project from start to finish, showing you how to apply OpenCV to solve complete problems Book Description As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. This is all powered by Computer Vision. This book will put you straight to work in creating powerful and unique computer vision applications. Each chapter is structured around a central project and deep dives into an important aspect of OpenCV such as facial recognition, image target tracking, making augmented reality applications, the 3D visualization framework, and machine learning. You’ll learn how to make AI that can remember and use neural networks to help your applications learn. By the end of the book, you will have created various working prototypes with the projects in the book and will be well versed with the new features of OpenCV3. What you will learn Execute basic image processing operations and cartoonify an image Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video Use OpenCV 3’s new 3D visualization framework to illustrate the 3D scene geometry Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks Train and predict pattern-recognition algorithms to decide whether an image is a number plate Use POSIT for the six degrees of freedom head pose Train a face recognition database using deep learning and recognize faces from that database

开放可再生科学：工作流程结构

Mastering OpenCV 3, Second Edition, Get hands-on with practical Computer Vision using OpenCV 3. Second edition: April 2017

OpenCV-3 编程应用手册，英文原版。 OpenCV-3 编程应用手册，英文原版。

Sarah Harris and David Harris ARM Edition

Computer Systems- A Programmer’s Perspective, 1st,2nd,3rd, Global Edition

Martin大叔Clean Code三件套，包括《A handbook of Agile Software Craftsmanship》，《A Code of Conduct for Professional Programmers》，《Clean Architecture》。