Semi-parametric geolocation estimation in NLOS environments

The position of a stationary target can be determined using triangulation in combination with time of arrival measurements at several sensors. In urban environments, none-line-of-sight (NLOS) propagation leads to biased time estimation and thus to inaccurate position estimates. Here, a semi-parametric approach is proposed to mitigate the effects of NLOS propagation. The degree of contamination by NLOS components in the observations, which result in asymmetric noise statistics, is determined and incorporated into the estimator. The proposed method is adequate for environments where the NLOS error plays a dominant role and outperforms previous approaches that assume a symmetric noise statistic

Estimation semi-paramétrique par minimisation de l'entropie des résidus, application en traitement d'images

Nous considérons un problème d'estimation semi-paramétrique en régression non linéaire, où le paramètre de nuisance (de dimension infinie) est la densité f du bruit additif, dont on suppose uniquement qu'elle est symétrique en 0. Nous proposons ici une extension au cas multivariable de l'estimateur présenté dans [7] qui minimise l'entropie de l'échantillon symétrisé des résidus. Des résultats en traitement d'images illustrent les bonnes propriétés de cette méthode d'estimation

Public perception of offshore wind in Ireland

Public attitude towards onshore wind farm development in Ireland has been extensively investigated. Prior to this study, there was little or no understanding of the perception of the Irish public of offshore wind farms (OSWFs). At this critical juncture in the development of the sector, it is necessary to gauge public opinion regarding offshore wind farms. Data was collected using an online survey (n = 1154) between May and June 2019. Results detail the opinions and attitudes of the Irish public toward the development of renewable energy projects in Irish waters. Demographics showed a 49% male, 51% female split. Education levels and age ranges roughly follow the same distribution levels as seen in the 2016 census of Ireland. Results indicate that attitudes to planned offshore wind farms change significantly with education levels. The evidence suggests that the link between climate change mitigation by energy emissions reduction and offshore wind farms is an important aspect of public perception that supports the development of the sector in Ireland. Most of those questioned believed that Ireland is too reliant on foreign energy and agreed that Ireland is running out of its limited fossil fuel reserves. The majority of people also believed that the government is not doing enough to reduce carbon emissions and should invest in offshore wind farms. Sixty-three percent of those surveyed believed that offshore wind farms will increase Ireland’s job creation potential. A clear majority of those who took part in the survey were in favour of offshore wind farms both on a local and national level. Just over half of the participants believed that offshore wind farms are the best solution to our energy situation. Thirty-seven percent of respondents trust offshore wind farm developers and 34% indicate that they were neutral on the subject. Fifteen percent of those who took part in the survey indicated that they mistrust developers. Approximately half of respondents had previous experience of offshore wind farms (the majority of whom had experienced offshore wind farms on holiday). A minority group had experience of offshore wind farms as a result of their daily commute or had an offshore wind farm in the vicinity of their homes. The data confirmed the hypothesis that experience of offshore wind farms has a significant effect on attitudes towards them. Results show that those with experience of offshore wind farms are more positive towards offshore wind farm development in Irish waters, than those with no experience of offshore wind farms. To further investigate the perception of those who are regularly exposed to offshore wind farms, a focus group involving five members of the public with regular exposure to Ireland’s only wind farm, Arklow Bank Wind Park, was held. The scope of sentiment expressed towards the offshore turbines ranged from benign to extremely positive. Returning to the results of the national survey; in terms of the effect on wildlife, tourism and aesthetics, respondents found offshore wind farms to be relatively unobtrusive and in general a positive addition to the sea scape. This report provides a resource for the offshore wind industry and policy makers alike. The data would suggest that an opportunity exists to create a public awareness campaign as a next step, to build on the favourable national mood and public understanding of the role of offshore wind in decarbonising the economy

Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma

Intratumoral heterogeneity biomarkers derived from positron emission tomography (PET) imaging with fluorodeoxyglucose (FDG) are of interest for a number of cancers, including sarcoma. A range of radiomic texture variables, adapted from general methodologies for image analysis, has shown promise in the setting. In the context of sarcoma, our group introduced an alternative model-based approach to the measurement of heterogeneity. In this approach, the heterogeneity of a tumor is characterized by the extent to which the 3-D FDG uptake pattern deviates from a simple elliptically contoured structure. By using a nonparametric analysis of the uptake profile obtained from this spatial model, a variable assessing the metabolic gradient of the tumor is developed. The work explores the prognostic potential of this new variable in the context of FDG-PET imaging of sarcoma. A mature clinical series involving 197 patients, 88 of whom have complete time-to-death information, is used. Texture variables based on the imaging data are also evaluated in this series and a range of appropriate machine learning methodologies are then used to explore the complementary prognostic roles for structure and texture variables. We conclude that both texture-based and model-based variables can be combined to achieve enhanced prognostic assessments of outcome for patients with sarcoma based on FDG-PET imaging information

Statistical assessment of treatment response in a cancer patient based on pre-therapy and post-therapy FDG-PET scans

This work arises from consideration of sarcoma patients in which fluorodeoxyglucose positron emission tomography (FDG-PET) imaging pre-therapy and post-chemotherapy is used to assess treatment response. Our focus is on methods for evaluation of the statistical uncertainty in the measured response for an individual patient. The gamma distribution is often used to describe data with constant coefficient of variation, but it can be adapted to describe the pseudo-Poisson character of PET measurements. We propose co-registering the pre-therapy and post- therapy images and modeling the approximately paired voxel-level data using the gamma statistics. Expressions for the estimation of the treatment effect and its variability are provided. Simulation studies explore the performance in the context of testing for a treatment effect. The impact of misregistration errors and how test power is affected by estimation of variability using simplified sampling assumptions, as might be produced by direct bootstrapping, is also clarified. The results illustrate a marked benefit in using a properly constructed paired approach. Remarkably, the power of the paired analysis is maintained even if the pre-image and post- image data are poorly registered. A theoretical explanation for this is indicated. The methodology is further illustrated in the context of a series of fluorodeoxyglucose-PET sarcoma patient studies. These data demonstrate the additional prognostic value of the proposed treatment effect test statistic