Pinned Repositories
2015-Application-of-US-Chemistry-Grad-Schools
帮助女朋友整理的2015年申请美国化学方向博士生的情况，基本涵盖美国化学排名前50的各大院校。资料包括Excel中汇总的申请截止日期、官网网址、成绩要求和其他信息，以及一些重要的文件作为附件。
315MHz-ASK-Transmission-Circuit
315MHz的ASK发射电路设计，包含所用的TH72002芯片datasheet，测试电路的电路图和参数等。
AutoEncoder-Based-Communication-System
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
autoencoder-for-the-Physical-Layer
Using Keras to validate the simulation results according to Paper : "An Introduction to Deep Learning for the Physical Layer"
Blind-Channel-Identification
Blind channel identification and source signal recovery. The source codes will be make public after acceptance of the paper.
English-Exercise
General-Knowledge-of-Stochastic-Processes
Interview-Questions-of-Computer-Network
清华大学网络中心2015年研究生推研面试真题
Review-of-Solid-State-Physics
以下所有内容整理自电子系往年同学的面试经验以及作者的期末复习资料。本文旨在帮助准备电子系推研面试的同学从实际问答的角度复习固体物理。过于深入的内容暂不涉及，面试中也不太可能进行考察。Q&A部分为真题解析，其余资料包含总体复习PPT等。希望对大家复习固体物理有所帮助。
ReviewofCPP
The review of cpp course in THU EE.
LiuRuiQi's Repositories
LiuRuiQi/Blind-Channel-Identification
Blind channel identification and source signal recovery. The source codes will be make public after acceptance of the paper.
LiuRuiQi/General-Knowledge-of-Stochastic-Processes
LiuRuiQi/Review-of-Solid-State-Physics
以下所有内容整理自电子系往年同学的面试经验以及作者的期末复习资料。本文旨在帮助准备电子系推研面试的同学从实际问答的角度复习固体物理。过于深入的内容暂不涉及，面试中也不太可能进行考察。Q&A部分为真题解析，其余资料包含总体复习PPT等。希望对大家复习固体物理有所帮助。
LiuRuiQi/2015-Application-of-US-Chemistry-Grad-Schools
帮助女朋友整理的2015年申请美国化学方向博士生的情况，基本涵盖美国化学排名前50的各大院校。资料包括Excel中汇总的申请截止日期、官网网址、成绩要求和其他信息，以及一些重要的文件作为附件。
LiuRuiQi/Interview-Questions-of-Computer-Network
清华大学网络中心2015年研究生推研面试真题
LiuRuiQi/ReviewofCPP
The review of cpp course in THU EE.
LiuRuiQi/315MHz-ASK-Transmission-Circuit
315MHz的ASK发射电路设计，包含所用的TH72002芯片datasheet，测试电路的电路图和参数等。
LiuRuiQi/AutoEncoder-Based-Communication-System
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
LiuRuiQi/English-Exercise
LiuRuiQi/autoencoder-for-the-Physical-Layer
Using Keras to validate the simulation results according to Paper : "An Introduction to Deep Learning for the Physical Layer"
LiuRuiQi/Deep-Learning-for-the-Physical-Layer
PyTorch implementation for part of paper "An Introduction to Deep Learning for the Physical Layer" by Kenta Iwasaki on behalf of Gram.AI.
LiuRuiQi/Find-Mark-and-Remove-Duplicated-Strings
LiuRuiQi/iva
IVA: Independent Vector Analysis implementation
LiuRuiQi/radio-transformer-networks
A PyTorch implementation of Radio Transformer Networks from the paper "An Introduction to Deep Learning for the Physical Layer".
LiuRuiQi/SketchofD-A
Sketch of Data Structure and Algorithm