ABSTRACT:Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians' lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.
Abstract. This paper proposed a new algorithm master Image Temporal Spatial baseline, Doppler centroid frequency difference (MITSD) to select the PS-InSAR common master image (CMI), by using the sum of temporal baselines, spatial baselines, and Doppler centroid frequency differences as a reference. The existing persistent scatterer interferometric synthetic aperture radar (PS-InSAR) common master images election method is affected by three baseline factors: temporal baseline, spatial baseline, and Doppler centroid frequency differences, then one single baseline factor in the three baselines being too large or above the baseline threshold will cause the decoherence. This method normalizes the temporal baseline, spatial baseline, and Doppler centroid frequency baseline to the same order of magnitude, and then the results of baseline optimization are summed up as the minimum coherence. Simultaneously,the algorithm in this paper sets each limit the average value of each baseline as a threshold to reduce the influence of a single baseline. The C-band Sentinel-1A single-look complex (SLC) image data (VV-polarization) in the study area was used as experimental data to compare with the MITSD, the current MSTB (minimum sum of three baselines), and CCCM (comprehensive correlation coefficient method). The results showed that (a) the baseline optimization method was more reasonable and reliable in the selection of the master image in PS-InSAR technology; and (b) in this method, the calculation steps were reduced into the calculation process, and the model was more concise than other algorithms.
SETTING: Dhulikhel Hospital, Kathmandu University Hospital, Kathmandu, Nepal.OBJECTIVES: 1) To report the incidence of health-care-associated infections (HAIs), 2) to compare demographic, clinical characteristics and hospital outcomes in those with and without HAIs;
and 3) to verify bacterial types in HAI and community-acquired infections (CAIs) among inpatients with invasive devices and/or surgical procedures.DESIGN: This was a cohort study using secondary data (December 2017 to April 2018).RESULTS: Of 1,310 inpatients, 908 (69.3%)
had surgical procedures, 125 (9.5%) had invasive devices and 277 (21.1%) both. Sixty-six developed HAIs (incidence = 5/100 patient admissions, 95% CI 3.9–6.3). Individuals with HAIs had a 5.5-fold higher risk of longer hospital stays (7 days) and a 6.9-fold risk of being in intensive
care compared to the surgical ward. Unfavourable hospital exit outcomes were higher in those with HAIs (4.5%) than in those without (0.9%, P = 0.02). The most common HAI bacteria (n = 70) were Escherichia coli (44.3%), Enterococcus spp. (22.9%) and Klebsiella
spp. (11.4%). Of 98 CAIs with 41 isolates, E. coli (36.6%), Staphylococcus aureus (22.0%) and methicillin-resistant S. aureus (14.6%) were common.CONCLUSION: We found relatively low incidence of HAIs, which reflects good infection prevention and control standards.
This study serves as a baseline for future monitoring and action.
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