A5039385219- Advanced Chemical Sensor Technologies
- Spectroscopy and Chemometric Analyses
- Identification and Quantification in Food
- Advanced Chemical Physics Studies
- Spectroscopy Techniques in Biomedical and Chemical Research
- Spectroscopy and Laser Applications
- Catalysis and Oxidation Reactions
- Advanced Physical and Chemical Molecular Interactions
- Advanced Database Systems and Queries
- Analytical chemistry methods development
- Rough Sets and Fuzzy Logic
- Data Management and Algorithms
- Semiconductor Quantum Structures and Devices
- Boron and Carbon Nanomaterials Research
- Magnetism in coordination complexes
- Catalytic Processes in Materials Science
- Image and Signal Denoising Methods
- Mobile Crowdsensing and Crowdsourcing
- Air Quality Monitoring and Forecasting
- Chemical and Physical Properties of Materials
- Gas Sensing Nanomaterials and Sensors
- Context-Aware Activity Recognition Systems
- Quantum Dots Synthesis And Properties
- Silicon Nanostructures and Photoluminescence
- IoT and Edge/Fog Computing
University of Ulster
2018-2024
University of Illinois Urbana-Champaign
2007-2018
QMCPACK is an open source quantum Monte Carlo package for ab initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, some model Hamiltonians. Implemented real space algorithms include variational, diffusion, reptation Carlo. uses Slater–Jastrow type trial wavefunctions in conjunction with a sophisticated optimizer capable optimizing tens thousands parameters. The orbital auxiliary-field method also implemented, enabling cross...
Food fraud, the sale of goods that have in some way been mislabelled or tampered with, is an increasing concern, with a number high profile documented incidents recent years. These and their scope show there are gaps food chain where authentication methods not applied otherwise sufficient more accessible detection would be beneficial. This paper investigates utility affordable portable visible range spectroscopy hardware partial least squares discriminant analysis (PLS-DA) when to...
Abstract Machine learning has been extensively used for analyzing spectral data in food quality management. However, collecting high-quality from miniature spectrometers outside the laboratory is challenging due to various factors such as distortions, noise, high dimensionality, and collinearity. This paper presents an in-depth analysis of datasets collected evaluate characteristics, by focusing on a case study olive oil check, where machine models were applied differentiate pure adulterated...
Abstract Trace methane detection in the parts per million range is reported using a novel scheme based on optical emission spectra from low temperature atmospheric pressure microplasmas. These bright low-cost plasma sources were operated under non-equilibrium conditions, producing with complex and variable sensitivity to trace levels of added gases. A data-driven machine learning approach partial least squares discriminant analysis was implemented for CH 4 concentrations up 100 ppm He,...
We report the results of a study ground-state and excitation energies Ge atoms, molecules, clusters as large ${\mathrm{Ge}}_{29}{\mathrm{H}}_{36}$, using quantum Monte Carlo (QMC) for valence electrons. QMC is one most accurate many-body methods; however, its accuracy limited by way core treated, which especially important atoms with shallow cores such Ge. Here we treat relativistic Hartree-Fock pseudopotential plus polarization potential (CPP) to take into account core-valence correlation...
Food authentication and quality checks can be carried out by applying machine learning algorithms on spectral data acquired from miniature spectrometers. This is a very appealing solution as the cost-effectiveness of spectrometers extends range consumer electronics available for ordinary citizens in fight against food fraud, widens their applications shortens processing time any in-situ scenario. In this paper, study olive oil purity check feasibility spectrometer presented. The aim to gauge...
The well-known and extensively studied Linear Discriminant Analysis (LDA) can have its performance lowered in scenarios where data is not homoscedastic or Gaussian. That is, the classical assumptions when LDA models are built applicable, consequently projections would be able to extract needed features explain intrinsic structure of for classes separated. As with many real word sets, obtained using miniature spectrometers suffer from such drawbacks which limit deployment technology food...
This paper presents a low-cost sensor system for food authentication based on computer vision and pattern recognition. The uses smartphone to record video of sample under gradient coloured illumination transforms the into data vector analysis. is evaluated tasks detecting olive oil adulteration milk fat content, achieving 100% test accuracies. Since built in without any additional hardware, it has potential serve as simple effective solution authentication.
A common challenge in smart environments is tracking individuals throughout different environments. Reliable solutions to this problem often involve cameras, which pose significant privacy issues, or trackable tags such as RFID require that be ‘prepared’ for the environment. In paper an exploratory study presented investigates utility of portable visible range spectroscopy hardware purposes identifying based on spectral pattern their clothing. This done by assessing accuracy a data-driven...
The detection of trace levels molecular gases has gained increasing attention in many fields from atmospheric pollution and climate change monitoring to industrial safety breath analysis for clinical diagnosis. Established techniques e.g. mass spectrometry, gas chromatography, electrochemical offer accuracy but are bulky expensive. Apart improving limits (LOD) the number target species, there is a major drive towards system miniaturisation cost reduction order enhance field deployment rapid...