Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis

Devising and analysing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph convolution operation offers a...

Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow

Short-term forecasting of passenger flow is critical for transit management and crowd regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven by other latent factors, and exogenous factors bring challenges to the short-term forecasts of passenger flow of urban rail transit networks. An innovative deep...

FMCW Radar-Based Hand Gesture Recognition using Spatiotemporal Deformable and Context-Aware Convolutional 5D Feature Representation

Recently, frequency-modulated continuous-wave (FMCW) radar-based hand gesture recognition (HGR) using deep learning has achieved favorable performance. However, many existing methods use extracted features separately, i.e., using one of the range, Doppler, azimuth, or elevation angle information, or a combination of any two, to train...