ZSWatch - the Open Source Zephyr™ based Smartwatch, including both HW and FW.
Bluetooth Low Energy (BLE) packet sniffer and transmitter for both standard and non standard (raw bit) based on Software Defined Radio (SDR).
The python library for working with the ESPARGOS WiFi channel sounder
Multilanguage (C#/Matlab/Rust) library for solving navigation (2D/3D) & geodetic problems: Multilateration (true range), Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival AOA (Direction-Of-Arrival, DOA); Direct & inverse geodetic problems: Vincenty equations, Haversine formula; Virtual Long Baseline navigation (VLBL) etc.
Project for finding beacon location using Angle of Arrival (AoA) signal. The Direction of Arrival estimation is based on the MUltiple SIgnal Classification (MUSIC) algorithm here.
Source Code for Paper "Augmenting Channel Charting with Classical Wireless Source Localization Techniques"
Parametric analysis of MUSIC algorithm for Angle of Arrival estimation in Python
Channel Charting demo for ESPARGOS datasets, related to paper "ESPARGOS: Phase-Coherent WiFi CSI Datasets for Wireless Sensing Research"
The team from University of Patras participating in the 2018 IEEE AP-S Student Design Contest: Radiolocation and Channel Sounding
Source code related to the paper "Passive Channel Charting: Locating Passive Targets using Wi-Fi Channel State Information"
course project of Introduction to Auditory-visual Information System of EE, Tsinghua University
"Bayesian Enhanced AoA Estimator: A Physics-Informed Machine Learning Approach for Accurate Angle of Arrival Estimation". This repository is an AoA estimator for passive UHF RFID based on Bayesian regression and classical antenna array signal processing. Combines physics-informed analysis with Pyro-based uncertainty quantification.
Jupyter Notebook associated with our submission for the 2023 ICASSP, "Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound"
Official implementation of our ICC 2025 paper on structured nonuniform pruning of deep learning models for TinyML wireless applications.
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