Dop-Net Data

https://dop-net.com/<b>DopNet</b> is a large Radar database organised in a hierarchy in which each node represents the data of a person which is divided into different gestures recorded from that person. The data here was measured with FMCW and CW Radars.DopNet‘s structure makes it a useful tool for Machine Learning Gesture Recognition software and Image Processing for the spectrograms.The shared data was generated by <b>Dr. Matthew Ritchie</b> (<b>University College London (UCL)</b>, London, United Kingdom) and <b>Richard Capraru</b> (<b>Nanyang Technological University (NTU)</b> and <b>Singapore Agency for Science, Technology and Research (A*STAR)</b>, Singapore) within the <b>UCL Radar Research Group</b> in collaboration with <b>Dr. Francesco Fioranelli</b> (<b>Delft University of Technology (TU Delft)</b>, Delft, Netherlands). Furthermore, it started as a <b>Laidlaw Scholarship project</b>.https://dop-net.com/ **DopNet**（DopNet）是一款采用层级化结构搭建的大型雷达数据库，其每个节点均对应一名个体的雷达数据，该数据被划分为该个体录制的各类手势数据。本数据集通过调频连续波（FMCW）与连续波（CW）雷达采集所得。**DopNet**的结构特性使其成为机器学习手势识别软件与频谱图图像处理领域的实用工具。该共享数据集由英国伦敦大学学院（UCL）的**马修·里奇博士（Dr. Matthew Ritchie）**、新加坡南洋理工大学（NTU）及新加坡科技研究局（A*STAR）的理查德·卡普拉鲁（Richard Capraru），在UCL雷达研究组框架下，与荷兰代尔夫特理工大学（TU Delft）的弗朗西斯科·菲奥拉内利博士（Dr. Francesco Fioranelli）合作研发完成。此外，该项目最初起源于**莱德劳奖学金项目（Laidlaw Scholarship project）**。