Convolutional neural networks for automated targeted analysis of raw gas chromatography–mass spectrometry data

Through their breath, humans exhale hundreds of volatile organic compounds (VOCs) that can reveal pathologies, including many types of cancer at early stages. Gas chromatography-mass spectrometry (GC-MS) is an analytical method used to separate and detect compounds in the mixture contained in breath samples. The identification of VOCs is based on the recognition of their specific ion patterns in GC-MS data, which requires labour-intensive and time-consuming preprocessing and analysis by domain experts.

Ergonomics Systems Mapping for Professional Responder Inter-operability in Chemical, Biological, Radiological and Nuclear Events


A European consensus was developed as a concept of operations (CONOPS) for cross-border, multi-professional chemical, biological, radiological and nuclear (CBRN) responses. AcciMaps were co-designed with professional responders from military, fire, ambulance, and police services in UK, Finland and Greece. Data were collected using document analysis from both open and restricted sources to extract task and operator information, and through interviews with senior staff representatives (Gold or Silver Command level).

Sprayed liquid-gas extraction of semi-volatile organophosphate Malathion from air and contaminated surfaces


In this study, a new air sampling method termed sprayed liquid–gas extraction (SLGE) was developed for semi-volatile organic compounds. Water droplets with an average diameter of less than 10 μm were created, using a flow blurring nebulizer from distilled water and the gas-phase sample. This allowed the fast, simple and highly-efficient enrichment of trace levels of the widely used organophosphate insecticide malathion, which is also an accepted simulant for the potent nerve-agent VX.

Sprayed liquid-gas extraction in combination with ion mobility spectrometry: a novel approach for the fast determination of semi-volatile compounds in air and from contaminated surfaces

We developed a fast, simple and highly-efficient enrichment procedure for trace levels of semi volatile organic compounds from air and surfaces and combined it with ion mobility spectrometry as field-deployable and rapid analytical technique. Our new technique, the sprayed liquid-gas extraction, was developed and optimized to allow the enrichment of semi volatile organic compounds.

Robotic and autonomous countermeasures

Security and defence discussions are now filled with speculation about robot weapons and artificial intelligence (AI). The problem is, security cannot be built on robotic weapons, autonomous systems or AI alone. Autonomous systems are more like operating platforms and firing systems than weapons per se. For example, electro-optical weapons, firearms, missiles, and nuclear weapons are defined by their impact. Robotised features simply increase that impact.

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Discrimination of bacteria by rapid sensing their metabolic volatiles using an aspiration-type ion mobility spectrometer (a-IMS) and gas chromatography-mass


The objective of our study was to investigate whether one may quickly and reliably discriminate different microorganism strains by direct monitoring of the headspace atmosphere above their cultures. Headspace samples above a series of in vitro bacterial cultures were directly interrogated using an aspiration type ion mobility spectrometer (a-IMS), which produced distinct profiles (“fingerprints”) of ion currents generated simultaneously by the detectors present inside the ion mobility cell. Data processing and analysis using principal component analysis showed net differences in the responses produced by volatiles emitted by various bacterial strains. Fingerprint assignments were conferred on the basis of product ion mobilities; ions of differing size and mass were deflected in a different degree upon their introduction of a transverse electric field, impacting finally on a series of capacitors (denominated as detectors, or channels) placed in a manner analogous to sensor arrays.

A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders

Detecting anomalies from any image data, especially hyperspectral ones, is not a trivial task. When combined with the lack of apriori labels or detection targets, it grows even more complex. Detecting spectral anomalies can be done with numerous methods, but the detection of spatial ones is vastly more complicated affair. In this thesis a new way to detect both spatial and spectral anomalies at the same time is proposed.