Haoxiang Huang (haoxiang01)

haoxiang01

Geek Repo

Company:Imperial College London

Location:London

Github PK Tool:Github PK Tool

Haoxiang Huang's repositories

Imperial_MSc_CSP_Lab

This is a repository to store the my implementation of MSc CSP Lab at Imperial College London

Language:MATLABStargazers:3Issues:1Issues:0

Adaptive-Signal-Processing-and-Machine-Intelligence

Imperial Colleage London Adaptive Signal Processing and Machine Intelligence Coursework Repository

Language:HTMLStargazers:2Issues:0Issues:0

Multipath-Spatiotemporal-SIMO-Wireless-Comms

Imperial Colleage London MSc C&SP ACT CW Repository

Language:MATLABStargazers:2Issues:0Issues:0

awesome-snn-conference-paper

🔥 This repo compiles top conference papers and code for Spiking Neural Networks research. The project is actively evolving. 本仓库收集了脉冲神经网络领域的顶会顶刊论文和代码,正在持续更新中。

License:MITStargazers:0Issues:0Issues:0
Language:TypeScriptStargazers:0Issues:0Issues:0

Deep-JSCC-for-images-with-OFDM

Codes for "Deep Joint Source Channel Coding for Wireless Image Transmission with OFDM", accepted by ICC 2021

Language:PythonStargazers:0Issues:0Issues:0

End2End_GAN

Conditional GAN based End-to-End Communication System

Language:PythonLicense:BSD-2-ClauseStargazers:0Issues:0Issues:0

meta-demodulator

Code for the paper "Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning"

Language:PythonStargazers:0Issues:0Issues:0
Language:PythonStargazers:0Issues:0Issues:0

Paper-with-Code-of-Wireless-communication-Based-on-DL

无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL

Stargazers:0Issues:0Issues:0

pytorch-meta

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

SNN-Daily-Arxiv

Update arXiv papers about Spiking Neural Networks daily.

Language:PythonStargazers:0Issues:0Issues:0

spikingjelly

SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.

Language:PythonLicense:NOASSERTIONStargazers:0Issues:0Issues:0

SSCC

Traditional Codecs of Compression, also with Channel Coding

Language:PythonStargazers:0Issues:0Issues:0