Synthetic generation of 2D data records based on Autoencoders

Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a dual-separation analytical technique widely used for identifying components in gaseous samples by separating and analysing the arrival times of their constituent species. Data generated by GC-IMS is typically represented as two-dimensional spectra, providing rich information but posing challenges for data-driven analysis due to limited labelled datasets. This study introduces a novel method for generating synthetic 2D spectra using a deep learning framework based on Autoencoders. Although applied here to GC-IMS data, the approach is broadly applicable to any two-dimensional spectral measurements where labelled data are scarce. While performing component classification over a labelled dataset of GC-IMS records, the addition of synthesized records significantly has improved the classification performance, demonstrating the method's potential for overcoming dataset limitations in machine learning frameworks.

Development of Ultra-Portable 3D Mapping Systems for Emergency Services

Miniaturization of cameras and LiDAR sensors has enabled the development of wearable 3D mapping systems for emergency responders. These systems have the potential to revolutionize response capabilities by providing real-time, high-fidelity maps of dynamic and hazardous environments. We present our recent efforts towards the development of such ultra-portable 3D mapping systems. We review four different sensor configurations, either helmet-mounted or body-worn, with two different mapping algorithms that were implemented and evaluated during field trials. The paper discusses the experimental results with the aim to stimulate further discussion within the portable 3D mapping research community.