Hat-trick's starred repositories
tensorflow-generative-model-collections
Collection of generative models in Tensorflow
Dive-into-DL-TensorFlow2.0
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的认可
openwifi-hw
open-source IEEE 802.11 WiFi baseband FPGA (chip) design: FPGA, hardware
Signals-and-Systems-course
浙江大学信电学院2022信号与系统课程资料
Awesome-WiFi-CSI-Sensing
A list of awesome papers and cool resources on WiFi CSI sensing.
Awesome-WiFi-CSI-Research
Accelerate your WiFi CSI research progress by sharing and cooperation!
Lidar_IMU_Localization
Lidar-IMU Localization System with Prior Map Constraint and Lio Constraint
WIFI_CSI_based_HAR
Human Activity Recognition based on WiFi Channel State Information
indoor_localization
Research on indoor localization
Satellite-Open-Source
This is the repository for the collection of open source code and data for satellite communication.
WiFi-movement-identification
设置一个WiFi发射器，一个WiFi接收器，通过发射和接受CSI信号，进行空间内变化检测，当进行不同的动作（如蹲下、站起、跌倒等）使，会引起WiFi信号的变化，使用机器学习模型对不同的信号进行分类，以达到动作识别（movement identification）任务
Localization_via_WiFi_Fingerprinting
Multi-Floor Indoor Localization based on Wi-Fi Fingerprinting using various Machine Learning models on the UJIIndoorLoc dataset.
indoor-locationing
Indoor locationing using Wi-Fi fingerprints, machine learning, and deep learning.
Predicting_Indoor_Location_Using_WiFi_Fingerprinting
In this project, I predict Indoor Location of users using Wifi fingerprints with a combination of Principal Component Analysis (PCA) and Multi-label Classification using skmultilearn
indoor-location-using-naive-bayes
The scripts were intended to describe a Naive-Bayes-based approach to indoor location via WiFi signals.
Chinese-Resume-Template
中文简历模板，可添加照片，详细注释
RSSIIndoorLocation
This repository is dedicated to test a dataset of RSSI Indoor Location Algorithm, currently using Trilateration and KNN.
CNN-Indoor-Localization
Scalable Representation of RSSIs for Multi-Building and Multi-Floor Indoor Localization Based on Deep Neural Networks
WiFi_Locating
We use the data set from Kaggle database UjiIndoorLoc. The dataset consists of wifi signal strength from 520 access points. They are used as inputs for a model to locate the mobile device in a building complex. The outputs of the model are longitude, latitude, building number and floor number.