Reconstructing conductivities with boundary corrected D-bar method

The aim of electrical impedance tomography is to form an image of the conductivity distribution inside an unknown body using electric boundary measurements. The computation of the image from measurement data is a non-linear ill-posed inverse problem and calls for a special regularized algorithm. One such algorithm, the so-called D-bar method, is improved in this work by introducing new computational steps that remove the so far necessary requirement that the conductivity should be constant near the boundary. The numerical experiments presented suggest two conclusions. First, for most conductivities arising in medical imaging, it seems the previous approach of using a best possible constant near the boundary is sufficient. Second, for conductivities that have high contrast features at the boundary, the new approach produces reconstructions with smaller quantitative error and with better visual quality

Positive-energy D-bar method for acoustic tomography: a computational study

A new computational method for reconstructing a potential from the Dirichlet-to-Neumann map at positive energy is developed. The method is based on D-bar techniques and it works in absence of exceptional points -- in particular, if the potential is small enough compared to the energy. Numerical tests reveal exceptional points for perturbed, radial potentials. Reconstructions for several potentials are computed using simulated Dirichlet-to-Neumann maps with and without added noise. The new reconstruction method is shown to work well for energy values between $10^{-5}$ and $5$, smaller values giving better results

Co-designing the knowledge management model

This work-in-progress study reviews co-designing processes through the lens of possibility-driven design (PDD). A knowledge management model (KMM) is co-designed by facilitating the development work of senior and regional innovation actors who share ideas, experience and information in the development of smart products and services for an age-friendly smart living environment. The empirical part is divided into three stages: an orientation workshop, two panel meetings and three co-design and validation workshops where an appropriate knowledge management model is co-designed through iteration rounds. The first stage maps the regional innovation actors, relevant organisations in the region and data flows between all the parties. Ideas of suitable ways to manage knowledge are gathered from the panel meetings of the second stage and are methodologically supported by the strategic options development and analysis (SODA) approach. At the time of writing this paper, the third stage consisting of three workshops with appropriate iteration rounds is on-going. The findings of the study provide insights regarding the use of PDD activities with an inclusion of the SODA approach when facilitating the co-design of a KMM with a multi-professional group of experts. The study contributes to the theory of PDD by integrating systematic methodological aspects to it when working on complex problems.info:eu-repo/semantics/publishedVersio

Quantification of uncertainty in aerosol optical thickness retrieval arising from aerosol microphysical model and other sources, applied to Ozone Monitoring Instrument (OMI) measurements

Satellite instruments are nowadays successfully utilised for measuring atmospheric aerosol in many applications as well as in research. Therefore, there is a growing need for rigorous error characterisation of the measurements. Here, we introduce a methodology for quantifying the uncertainty in the retrieval of aerosol optical thickness (AOT). In particular, we concentrate on two aspects: uncertainty due to aerosol microphysical model selection and uncertainty due to imperfect forward modelling. We apply the introduced methodology for aerosol optical thickness retrieval of the Ozone Monitoring Instrument (OMI) on board NASA's Earth Observing System (EOS) Aura satellite, launched in 2004. We apply statistical methodologies that improve the uncertainty estimates of the aerosol optical thickness retrieval by propagating aerosol microphysical model selection and forward model error more realistically. For the microphysical model selection problem, we utilise Bayesian model selection and model averaging methods. Gaussian processes are utilised to characterise the smooth systematic discrepancies between the measured and modelled reflectances (i.e. residuals). The spectral correlation is composed empirically by exploring a set of residuals. The operational OMI multi-wavelength aerosol retrieval algorithm OMAERO is used for cloud-free, over-land pixels of the OMI instrument with the additional Bayesian model selection and model discrepancy techniques introduced here. The method and improved uncertainty characterisation is demonstrated by several examples with different aerosol properties: weakly absorbing aerosols, forest fires over Greece and Russia, and Sahara desert dust. The statistical methodology presented is general; it is not restricted to this particular satellite retrieval application

mm-Wave DRW Antenna Phase Centre Determination

This document presents an approach to the phase centre determination of a dielectric rod waveguide (DRW) antenna by means of measurements obtained with a planar measuring system at millimeter wave lengths. Phase centre determination by the least squares fit technique is described in this document for different DRW antennas (silicon and sapphire). Results at different operating frequencies are offered

The impact of sleep on eyewitness identifications

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.Peer reviewe