SUITABILITY ASSESSMENT OF DIFFERENT SENSORS TO DETECT HIDDEN INSTALLATIONS FOR AS-BUILT BIM

Knowledge on the utilities hidden in the wall, e.g., electric lines or water pipes, is indispensable for work safety and valuable for planning. Since most of the existing building stock originates from the pre-digital era, no models as understood for Building Information Modeling (BIM) exist. To generate these models often labor-intensive procedures are necessary; however, recent research has dealt with the efficient generation and verification of a building’s electric network. In this context, a reliable measurement method is a necessity. In this paper we test different measurement techniques, such as point-wise measurements with hand-held devices or area-based techniques utilizing thermal imaging. For this purpose, we designed and built a simulation environment that allows various parameters to be manipulated under controlled conditions. In this scenario the low-cost handheld devices show promising results, with a precision between 92% and 100% and a recall between 89% and 100%. The expensive thermal imaging camera is also able to detect electric lines and pipes if there is enough power on the line or if the temperature of the water in the pipe and the environment’s temperature are sufficiently different. Nevertheless, while point-wise measurements can directly yield results, the thermal camera requires post-processing in specific analysis software. The results reinforce the idea of using reasoning methods in both the do-it-yourself and commercial sector, to rapidly gather information about hidden installations in a building without prior technical knowledge. This paves the way for, e.g., exploring the possibilities of an implementation and presentation in augmented reality (AR)

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method

The size of the population potentially in need of palliative care in Germany - an estimation based on death registration data

BACKGROUND: No data exist on the size of the population potentially in need of palliative care in Germany. The aim of this study is to estimate the size of the German population that may benefit from palliative care. METHOD: Based on existing population-based methods (Rosenwax and Murtagh), German death registration data were analyzed and contrasted with international results. The data include all death cases in 2013 in Germany. RESULTS: According to the method Rosenwax defined, between 40.7 % (minimal estimate) and 96.1 % (maximal estimate) of death cases could benefit from palliative care. The estimation, based on Murtagh’s refined method, results in 78.0 % of death cases potentially being eligible for palliative care. The percentage of potential palliative care candidates is conditioned by age. Based on the Murtagh Method, in the age category between 30 and 39 years, a potential demand for palliative care can be found for 40.4 % percent of all deaths occurring in this age category, with this number increasing to 80.3 % in the age bracket of 80 years and over. CONCLUSION: An estimation of the size of the population in need is essential for healthcare planning. Therefore, our data serve as a guide and starting point for further research

Non-response in a survey of physicians on end-of-life care for the elderly

<p>Abstract</p> <p>Background</p> <p>Physicians are quite often surveyed with the aim to investigate their opinions regarding provision and improvement of health care. However, in many cases response rates tend to be rather low. The aim of the study is to reflect methodological aspects regarding survey conduction and to analyse factors that cause physicians to take part in a study on delivering end-of-life care for the elderly.</p> <p>Methods</p> <p>N = 4,727 physicians in Lower Saxony, Germany, received a standardised questionnaire on their attitudes about end-of-life care for the elderly. Non-responders were asked to state the reasons for non-participation. Comparison of the sociodemographic characteristics between responders and non-responders, and evaluation of the reasons for non-participation were made.</p> <p>Results</p> <p>The response rate to the questionnaire on end-of-life care for the elderly was 40% (n = 1,892). Of the non-responders to the questionnaire, 12.8% (n = 364) stated the reasons for non-participation. Overall, the response rate to the questionnaire varied with specialty and location of the practice: radiotherapists answered significantly more frequently than other categories of physician (e.g. general practitioners) and physicians in rural areas significantly more frequently than their colleagues in urban areas. The reasons most frequently given for non-participation were "Not concerned with the subject" and "No time".</p> <p>Conclusions</p> <p>The varying rates of response indicate that the survey was not sufficiently relevant to all groups of physicians, or that the awareness of the topic may be partly underdeveloped.</p

Ordinal classification for efficient plant stress prediction in hyperspectral data

Detection of crop stress from hyperspectral images is of high importance for breeding and precision crop protection. However, the continuous monitoring of stress in phenotyping facilities by hyperspectral imagers produces huge amounts of uninterpreted data. In order to derive a stress description from the images, interpreting algorithms with high prediction performance are required. Based on a static model, the local stress state of each pixel has to be predicted. Due to the low computational complexity, linear models are preferable. In this paper, we focus on drought-induced stress which is represented by discrete stages of ordinal order. We present and compare five methods which are able to derive stress levels from hyperspectral images: One-vs.-one Support Vector Machine (SVM), one-vs.-all SVM, Support Vector Regression (SVR), Support Vector Ordinal Regression (SVORIM) and Linear Ordinal SVM classification. The methods are applied on two data sets - a real world set of drought stress in single barley plants and a simulated data set. It is shown, that Linear Ordinal SVM is a powerful tool for applications which require high prediction performance under limited resources. It is significantly more efficient than the one-vs.-one SVM and even more efficient than the less accurate one-vs.-all SVM. Compared to the very compact SVORIM model, it represents the senescence process much more accurate

IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS

Abstract. Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system. </jats:p