Delineation of Road Networks from Remote Sensor Data with Deep Learning

In this thesis we address the problem of semantic segmentation in geospatial data. We investigate different deep neural network architectures and present a complete pipeline for extracting road network vector data from satellite RGB orthophotos of urban areas. Firstly, we present a network based on the SegNeXt architecture for the semantic segmentation of the roads. A novel loss function is introduced for training the network. The results show that the proposed network produces on average better results than other state-of-the-art semantic segmentation techniques. Secondly, we propose a fast post-processing technique for vectorizing the rasterized segmentation result, removing erroneous lines, and refining the road network. The result is a set of vectors representing the road network. We have extensively tested the proposed pipeline and provide quantitative comparisons with other state-of-the-art based on a number of known metrics. This work has been published and presented at the 14 th International Symposium on Visual Computing, 2019. Finally, we present an altogether different approach to road extraction. We reformulate the task of extracting vectorized road networks as a deep reinforcement learning problem with partially observable state-space and present our preliminary results and future work

Improvement of local budget filling

This chapter describes the biological removal of sulphur compounds from gas streams. First, an overview is given of the toxicity of sulphur compounds to animals and humans whereafter biological and industrial formation routes for (organic) sulphur compounds are given. Microbial degradation routes of volatile organic sulphur compounds under both aerobic and anaerobic conditions are presented. Finally, the most commonly applied processes for sulphur removal from gaseous streams are discussed and an overview is given of operating experiences for biological gas treatment systems. The chapter concludes with some remarks on future developments

Predictive value of contrast-enhanced carotid ultrasound features for stroke risk: a systematic review and meta-analysis

ObjectiveTo elucidate the contrast-enhanced ultrasound (CEUS) features of carotid artery plaques in patients who have experienced an ischemic stroke (IS).MethodsA computerized search was conducted in databases such as Pub-Med, EMSCO, and Ovid to identify studies reporting CEUS findings of carotid artery plaques. Patients were categorized as IS and non-IS based on clinical and radiological diagnosis, and the quantitative and semi-quantitative CEUS data were analyzed for differences between the two groups.ResultsAfter the computerized search, a total of 13 eligible studies, comprising 3,092 participants (1,953 with stroke), were included for analysis. IS patients exhibited significantly higher plaque enhancement intensity versus control group (SMD = 0.71, 95% CI: 0.32, 1.11). The positive rate of plaque enhancement within the plaques was significantly higher in IS patients versus non-IS patients (OR = 3.25, 95% CI: 1.86, 5.68). The sensitivity of hyperintense lesion-based diagnosis of stroke was 0.68 (95% CI: 0.54, 0.80), and the specificity was 0.61 (95% CI: 0.47, 0.73), with an area under the curve (AUC) of 0.697.ConclusionThere are significant differences in CEUS characteristics of carotid artery plaques between IS and non-IS patients. IS patients display markedly augmented plaque enhancement intensity and a higher rate of positive enhancement compared to non-stroke individuals. These noteworthy findings have critical implications in enhancing the accuracy of IS diagnosis and improving the stratification of stroke risk for patients.Systematic review registrationThis study is registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY), 202540006

The role of porosity and 3D cross-stent configuration of multiple overlapping uncovered stents in the management of complex aortic aneurysms – insights from haemodynamics

Background: Multiple overlapping uncovered stents (MOUS) have been introduced to manage complex aortic aneurysms with vital branches involvement. It has been shown that the porosity is a key determinant of the treatment outcome. However, the role of 3D cross-stent configuration remains unclear. Methods: One patient with a complex aortic aneurysm judged not suitable for open surgery nor endovascular repair was invited to participate this study. In total, four bare metal stents were deployed. 3D lesion geometry was reconstructed based on pre- and post-operative CTA, and the zero-pressure configuration was recovered using an inverse procedure. Local haemodynamic parameters, including wall shear stress (WSS), oscillatory shear index (OSI), and particle relative resident time (RRT), as well as the vessel structural stress (VSS), were quantified using one-way fluid-structure interaction (FSI) analysis. In comparison to MOUS, a corresponding compact model was reconstructed by projecting inner layer stents to the most outer layer to form a single layer to eliminate the 3D cross-stent configuration and one-way FSI analysis was performed. Results: Results obtained showed that the porosity decreased linearly with the number of stents. When the 1st stent was deployed, the mean velocity decreased 36.4% and further reduction of 49.3%, 59.8%, and 62.8% were observed when the 2nd, 3rd and 4th stents were deployed. WSS also decreased with the number of stents deployed, and both OSI and RRT increased, but the increase was very minor with the 4th stent. MOUS deployment induced high VSS concentration in the landing zone while the VSS and pressure in the sac remained nearly unchanged. The compact model yielded a small difference in the value of flow-related parameters and 10%-20% reduction in VSS. Conclusion: Compared with porosity, the 3D cross-stent of MOUS configuration plays a minor role in the modulation of local haemodynamics. A compact model does not reduce high VSS concentration in the diseased region significantly.This study was supported by British Heart Foundation (BHF) (PG/18/14/33562), Engineering and Physical Sciences Research Council (EPSRC) (EP/P021654/1) and National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre

Biofiltration of reduced sulphur compounds and community analysis of sulphur-oxidizing bacteria.

http://dx.doi.org/10.1016/j.biortech.2010.12.018The present work aims to use a two-stage biotrickling filters for simultaneous treatment of hydrogen sulphide (H2S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl disulphide (DMDS). The first biofilter was inoculated with Acidithiobacillus thiooxidans (BAT) and the second one with Thiobacillus thioparus (BTT). For separate feeds of reduced sulphur compounds (RSC), the elimination capacity (EC) order was DMDS > DMS > MM. The EC values were 9.8 gMM-S/m3/h (BTT; 78% removal efficiency (RE); empty bed residence time (EBRT) 58 s), 36 gDMDS-S/m3/h (BTT; 94.4% RE; EBRT 76 s) and 57.5 gH2S-S/m3/h (BAT; 92% RE; EBRT 59 s). For the simultaneous removal of RSC in BTT, an increase in the H2S concentration from 23 to 293 ppmv (EBRT of 59 s) inhibited the RE of DMS (97–84% RE), DMDS (86–76% RE) and MM (83–67% RE). In the two-stage biofiltration, the RE did not decrease on increasing the H2S concentration from 75 to 432 ppmv