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Monitoring tree growth processes is relevant for ecological research and understanding the intricate relationship between vegetation and the environment. Time series analyses have revealed a correlation between leaf emergence timing and climate change, with earlier leaf emergence attributed to global warming. While traditional forest inventory methods struggle to quantify growth processes on small scales, terrestrial laser scanning provides a powerful alternative for providing high-resolution 3D information. This study explores the use of high-frequency hyper-temporal terrestrial laser scanning data to quantitatively describe deciduous tree growth, tested on a pedunculate oak (Quercus robur). The research aims to address key questions about detecting leaf growth in hypertemporal terrestrial laser scanning data. Additionally, it explores how 3D tree parameters and point cloud comparisons capture leaf and tree growth throughout the year. Results from M3C2 point cloud analyses indicate that the temporary branch movements correlate with precipitation. Over the year, branch movements were detected to increase with growing distance from the trunk.

A common problem when imaging and measuring through moving water surfaces is the quasi-random refraction caused by waves. The article presents two strategies to overcome this problem by lowering the complexity down to a planer air/water interface problem. In general, the methods assume that the shape of the water surface changes randomly over time and that the water surface moves around an idle-state (calm planar water surface). Thus, moments at which the surface normal is orientated vertically should occur more frequently than others should. By analysing a sequence of images taken from a stable camera position these moments could be identified – this can be done in the image or object space. It will be shown, that a simple median filtering of grey values in each pixel position can provide a corrected image freed from wave and glint effects. This should have the geometry of an image taken through calm water surface. However, in case of multi camera setups, the problem can be analysed in object space. By tracking homological underwater features, sets of image rays hitting accidently horizontal orientated water surface areas can be identified. Both methods are described in depth and evaluated on real and simulated data.

LiDAR bathymetry provides an efficient and comprehensive way to capture the topography of water bodies in shallow water areas. However, the penetration depth of this measurement method into the water column is limited by the medium water and water turbidity, resulting in a limited detectability of the bottom topography in deeper waters. An increase of the analyzable water depth is possible by the use of extended evaluation methods, in detail full-waveform stacking methods. So far, however, this has only been investigated for water depths of up to 3.50 m due to water turbidity. In this article, the potential of these extended data processing methods is investigated on an alpine mountain lake with low water turbidity and thus high analyzable water depth. Compared to the standard data processing, the penetration depth could be significantly increased by 58%. In addition, methods for depth-resolved water turbidity parameter determination on the basis of LiDAR bathymetry data were successfully tested.

If multimedia-photogrammetry is used for the generation of point clouds of submerged objects or of the water bottom, Snell’s law has to be considered. When the images are taken from air, image rays are refracted at the air-water interface. This results in the collinearity equations being no longer valid. Bundle block adjustment can still be solved by adding additional terms considering Snell’s law. Existing approaches usually assume that the water surface is flat. Refractive indices and water height can either be measured separately or included as unknowns in the adjustment. However, when the water surface is not flat due to the presence of waves, assuming a planar water surface leads to large geometric errors. This work will analyze the significance of those errors and propose a way of including water surface parameters as unknowns into the bundle block adjustment, both based on simulated data. The simulation reproduces multiple images taken simultaneously, e.g. from synchronized UAV cameras or from cameras on tripods.

Bridge structures can be surveyed using a number of different methods. Established are image-based methods using structure from motion by an unmanned aerial vehicle (UAV), terrestrial laser scanning (TLS), or a combination of both methods. Beyond static terrestrial laser scanning, buildings can also be efficiently surveyed using personal laser scanner (PLS) systems. The advantage here is the greater flexibility and increased speed compared to the static method. On the other hand, the accuracy may be more critical, and the resulting point cloud will be more sensitive to systematic global or local deformations under unfavorable measurement conditions. For example, temporary influences can lead to local mapping errors. These include influences such as uneven measurement system motion or non-static, feature-sparse environments. This study investigates the acquisition of 3D point clouds representing the outer shell of a concrete bridge using a PLS system. We demonstrate a method for detecting possible deformations in PLS point clouds using the example of a bridge structure. For this purpose, the reference (TLS) and the PLS point clouds are segmented into individual clusters and a segment-based ICP fine registration is performed. Different RMSE values for the upper road section (0.061 m) and for the pillar segments (0.021 m) as well as different transformation parameters indicate slight displacements in the PLS point cloud. The analysis of the cloud-to-cloud distances showed that there were slight deformations in the Z direction in the area of the road surface. In the lateral direction, no significant residual deviations were found in the area of the bridge pillars.

The presented research investigates different strategies to acquire high-precision digital elevation models (DEMs) of complex and inaccessible terrain using Structure-from-Motion and Multi-View Stereo applied to data of an unoccupied aerial system (UAS) equipped with real-time-kinematic (RTK)-GNSS. The survey scenarios are taken from real-life situations and thus, in comparison to many previous studies, provide information on how to operate under challenging conditions in difficult terrain. Among others, the study examines the influence of different flight configurations (parallel axes and cross-grid), flight altitudes (relative to ellipsoid or terrain) and associated variations in ground sampling distance, image orientations (nadir and oblique), advanced camera self-calibration techniques and georeferencing strategies in image block processing (direct and integrated) on the overall accuracy of the resulting DEMs. Random and systematic errors, including spatial patterns such as doming and bowling, are quantified using check points and differences between DEM calculations and independently acquired surface data from laser scans. This comprehensive analysis contributes valuable insights for UAS-based analysis of complex terrain with improved accuracy in DEM generation and subsequent applications like change detection.

Robust and automated point cloud registration methods are required in many geoscience applications using multi-temporal and multi-modal 3D point clouds. Therefore, a 3D keypoint-based coarse registration workflow has been implemented, utilizing the ISS keypoint detector and 3DSmoothNet descriptor. This paper contributes to keypoint-based registration research through variations of the standard workflow proposed in the literature, applying a two-staged strategy of global and local keypoint matching as well as prototypical keypoint projection and fine registration based on ICP. Further, by testing the utilized detector and descriptor on unstructured, multi-temporal and multi-source point clouds with variations in point cloud density, generalization ability is tested outside benchmark data. Therefore, data of the Bøverbreen glacier in Jotunheimen, Norway has been acquired in 2022 and 2023, deploying UAV-based image matching and terrestrial laser scanning. The results show good performance of the implemented robust matching algorithm PROSAC, requiring fewer iterations than the well-known RANSAC approach, but solving the rigid body transformation with TEASER++ is faster and more robust to outliers without demanding pre-knowledge of the data. Further, the results identify the keypoint detection as most limiting factor in speed and accuracy. Summarizing, keypoint-based coarse registration on low density point clouds, applying a global and local matching strategy and transformation estimation using TEASER++ is recommended. Keypoint projection shows potential, increasing number and precision in low density clouds, but has to be more robust. Further research needs to be carried out, focusing on identifying a fast and robust keypoint detector.

Abstract Although antimicrobial dressings have proven to be crucial in the treatment of wounds, they may give rise to antibiotic‐resistant strains or result in the release of endotoxins after bacterial death, which in turn inhibits wound healing. This study highlights the efficacy of a novel alumina ceramic textile as dressing that utilizes the principles of bacteria capture from the wound bed and inhibition of bacterial infiltration into the wound bed to reduce the bacterial burden and to inhibit the spread of infection, without the involvement of active antimicrobial substances or functional nanoparticles. The alumina textiles are compared to commercial dressings like the non‐woven mesh Cutimed Sorbact and gauzes from LEINA WERKE and performed significantly better in capturing bacteria. They are found to be effective against both Gram‐negative Escherichia coli and Gram‐positive Bacillus subtilis and show promising results in the presence of simulated wound fluid and in artificial wound bed tests from which they can be easily lifted without leaving behind any visible residues. In summary, the alumina textiles exhibit a highly efficient bacterial binding activity, possibly due to the intrinsic material properties of their hierarchical structure including the tricot knit mesh, small fiber diameters, pronounced fiber surface microtopography, and high specific surface area.

This study investigates the performance of eight different deep learning architectures for voxel segmentation in volume images. The motivation is to segment carbon in carbon reinforced concrete (CRC) in micro-tomography (μ-CT) data. Although there are many 3D convolutional neural networks (CNNs) available, it is not yet clear which one works best for these specific tasks. In this study, the authors compare the following networks: DenseVoxNet, HighResNet, Med3D, Residual 3D U-Net, 3D SkipDenseSeg, 3D U-Net, V-Net, and LV-Net. To provide a more general recommendation for selecting a neural network, three medical datasets were added in addition to the three CRC datasets to facilitate the selection of an appropriate network based on the dataset characteristics. The experiments emphasize the importance of the initial random state, used for example to initialize the network weights. On average, the 3D U-Net is the best generalizing CNN, followed by the Residual 3D U-Net and the 3D SkipDenseSeg. While the 3D U-Net is a good architecture to start with, the experiments show that it does not perform best on all domains. To achieve optimal results, the authors propose recommendations for selecting a 3D neural network based on the dataset attributes.

The use of inertial measurement units (IMUs) as an alternative to optical marker-based systems has the potential to make gait analysis part of the clinical standard of care. Previously, an IMU-based system leveraging Rauch–Tung–Striebel smoothing to estimate knee angles was assessed using a six-degrees-of-freedom joint simulator. In a clinical setting, however, accurately measuring abduction/adduction and external/internal rotation of the knee joint is particularly challenging, especially in the presence of soft tissue artefacts. In this study, the in vivo IMU-based joint angles of 40 asymptomatic knees were assessed during level walking, under two distinct sensor placement configurations: (1) IMUs fixed to a rigid harness, and (2) IMUs mounted on the skin using elastic hook-and-loop bands (from here on referred to as “skin-mounted IMUs”). Estimates were compared against values obtained from a harness-mounted optical marker-based system. The comparison of these three sets of kinematic signals (IMUs on harness, IMUs on skin, and optical markers on harness) was performed before and after implementation of a REference FRame Alignment MEthod (REFRAME) to account for the effects of differences in coordinate system orientations. Prior to the implementation of REFRAME, in comparison to optical estimates, skin-mounted IMU-based angles displayed mean root-mean-square errors (RMSEs) up to 6.5°, while mean RMSEs for angles based on harness-mounted IMUs peaked at 5.1°. After REFRAME implementation, peak mean RMSEs were reduced to 4.1°, and 1.5°, respectively. The negligible differences between harness-mounted IMUs and the optical system after REFRAME revealed that the IMU-based system was capable of capturing the same underlying motion pattern as the optical reference. In contrast, obvious differences between the skin-mounted IMUs and the optical reference indicated that the use of a harness led to fundamentally different joint motion being measured, even after accounting for reference frame misalignments. Fluctuations in the kinematic signals associated with harness use suggested the rigid device oscillated upon heel strike, likely due to inertial effects from its additional mass. Our study proposes that optical systems can be successfully replaced by more cost-effective IMUs with similar accuracy, but further investigation (especially in vivo and upon heel strike) against moving videofluoroscopy is recommended.

As an extension to existing work on crack detection and subpixel accuracy crack width determination as a tool for civil engineering material testing, the paper shows an algorithmic approach to handle widening cracks with relative rotations between related crack borders. In the first time step under zero-load, a set of points to be tracked through consecutive frames of an image sequence is defined. Then, subpixel-precise displacement fields are computed for the image data of the following time steps using an 8-parameter least-squares matching approach. The points are triangulated into a mesh, and the changes of the inner geometry of the triangles are considered with a mathematical model assuming a split of each triangle. With this model, subpixel-precise deformation vectors are derived. Crack candidates are determined by a thresholding applied to the vectors’ lengths. After an estimation of the crack normal, a decomposition of the deformation vectors is applied, allowing to compute crack widths and shear movements. As a novel contribution to the technique, a model extension is proposed for the case of a relative rotation between the crack borders in order to reduce systematic errors. The model includes two separate rigid transformations for each crack side.

Abstract This work presents porous zirconia‐toughened alumina ceramics functionalized with Au@Pd/Au@Pt core–shell nanoparticle (NP) for in situ monitoring of catalytic reactions via surface‐enhanced Raman scattering (SERS) which is augmented by the open cell foam structure of the ceramic support. In this respect, the porous ceramic enables efficient light trapping and propagation onto the coated surface, which provides good accessibility of the catalyst, while the core–shell particles are equipped with a catalytically active shell and a plasmonic core which enables SERS sensing. The metallic hybrid core–shell NPs are synthesized by the Au‐seed mediated method and colloidally deposited onto the open porous ceramic matrix prepared via the polymer replica method. The Au@Pt NP functionalized porous ceramic show a Raman enhancement factor up to 106, which is significantly higher than that of non‐porous samples. In situ reaction monitoring via SERS is demonstrated by the Pt‐catalyzed reduction of 4‐nitrothiophenol to 4‐aminothiophenol, showing high specificity for analysis of reactants and products. This multifunctional material concept featuring ceramics‐augmented SERS and catalytic activity could be extended beyond real‐time, sensitive reaction monitoring toward high temperature reactions, photothermal catalysis, bioprocessing and ‐sensing, green energy conversion, and related applications.

3D concrete printing (3DCP) promises progress in the automation of the construction industry. The complexity and high quality requirements of 3DCP require automatic and digital control processes and systems that can continuously assess quality at any time and any place. In this paper, the relationship between the surface moisture of concrete and the degree of linear polarization (DolP) of light reflected from the concrete surface is investigated, which could serve as a basis for a camera-based control system. To proof this correlation, extensive practical investigations were carried out in which a mold-cast concrete specimen was illuminated with a white LED spotlight. The reflected light was recorded with a polarization camera, and the weight of the specimen served as a reference for the surface moisture. 14 experimental tests showed a high correlation between mass loss and DolP with a mean correlation coefficient of 0.994 in the last 60 minutes of the 180-minute observation period. The time series show an increasing DolP at the beginning of the observation period, which is probably due to the concrete bleeding and does not correlate with the mass loss. Further investigation into the factors influencing the DolP in the first part of the observation period needs to be carried out, either by testing influencing factors such as concrete bleeding or by finding a reliable method to measure surface moisture to correlate it with the DolP over the entire observation period. Nevertheless, the concept and methodology of data processing has been developed and prototyped, and a correlation has been demonstrated, that can provide a basis for the development of an operational camera-based concrete moisture monitoring system.

Accurate digital representation of indoor facilities is a key component for the generation of building twins. 3D indoor scenes are often reconstructed from 3D point clouds obtained by various measurement techniques, which usually show different accuracy characteristics. During the reconstruction process, the uncertainties of data and intermediate products propagate into the accuracy of the vectorized model. Although point clouds-based 3D building modeling has been a hot topic of research for at least two decades, a thorough analysis of error propagation for this problem from a geodetic point of view is still underrepresented. In this contribution, we propose an analytical approach to estimate the uncertainty of 3D modeling results using the analytic approach based on first-order Taylor-series expansion. A general model for the input data is established and the uncertainty expressions of all computed products are symbolically derived. We estimate the uncertainty of 3D data fitting, followed by the derivation of vectorized building parameters and their covariance matrices. The results of the theoretical approaches are tested on real data presenting an indoor scene. The practical example is illustrated, thoroughly analysed, and quantified.

Accurate information on turbidity in water bodies is relevant to numerous limnological and oceanological issues. However, the collection of turbidity parameters using conventional in-situ measurement methods is time-consuming and cost-intensive and therefore usually limited to very small study areas. The use of airborne LiDAR bathymetry data is a promising alternative. However, existing methods for deriving turbidity parameters from airborne LiDAR bathymetry data are limited to the determination of one single turbidity parameter per water column element. The paper presents a novel approach that overcomes the existing limitations enables the determination of 3D water turbidity fields. By volumetric data analysis, the vertical turbidity stratification in the water body can be determined. For validation purposes, the approach was applied to synthetic measurement data generated in a simulation as well as a real measurement data set of a shallow coastal water.

3D building modeling is a diverse field of research with a multitude of challenges, where data integration is an inherent component. The intensively growing market of BIM-related consumer applications requires methods and algorithms that enable efficient updates of existing 3D models without the need for cost-intensive data capturing and repetitive reconstruction processes. We propose a novel approach for semantic enrichment of existing indoor models by window objects, based on amateur camera RGB images with unknown exterior orientation parameters. The core idea of the approach is the parallel estimation of image camera poses with semantic recognition of target objects and their automatic mapping onto a 3D vector model. The presented solution goes beyond pure texture matching and links deep learning detection techniques with camera pose estimation and 3D reconstruction. To evaluate the performance of our procedure, we compare the estimated camera parameters with reference data, obtaining median values of 13.8 cm for the camera position and 1.1° for its orientation. Furthermore, a quality of 3D mapping is assessed based on the comparison to the reference 3D point cloud. All the windows presented in the data source were detected successfully, with a mean distance between both point sets equal to 3.6 cm. The experimental results prove that the presented approach achieves accurate integration of objects extracted from single images with an input 3D model, allowing for an effective increase of its semantic coverage.

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disease with a fast increasing prevalence. Several pharmacological and non-pharmacological interventions are available to alleviate symptoms. Technology can be used to improve the efficiency, accessibility and feasibility of these treatments. Although many technologies are available, only few are actually implemented in daily clinical practice. Aim: Here, we study the barriers and facilitators, as experienced by patients, caregivers and/or healthcare providers, to successful implement technology for PD management. Methods: We performed a systematic literature search in the PubMed and Embase databases until June 2022. Two independent raters screened the titles, abstracts and full texts on: 1) people with PD; 2) using technology for disease management; 3) qualitative research methods providing patients’, caregivers and/or healthcare providers’ perspective, and; 4) full text available in English or Dutch. Case studies, reviews and conference abstracts were excluded. Results: We found 5420 unique articles of which 34 were included in this study. Five categories were made: cueing (n = 3), exergaming (n = 3), remote monitoring using wearable sensors (n = 10), telerehabilitation (n = 8) and remote consultation (n = 10). The main barriers reported across categories were unfamiliarity with technology, high costs, technical issues and (motor) symptoms hampering the use of some technologies. Facilitators included good usability, experiencing beneficial effects and feeling safe whilst using the technology. Conclusion: Although only few articles presented a qualitative evaluation of technologies, we found some important barriers and facilitators that may help to bridge the gap between the fast developing technological world and actual implementation in day-to-day living with PD.

Abstract Due to the importance of gas-phase chemical reaction kinetics in low-emission combustion, stirred tank reactors have been used for decades as an experimental tool to study high- and low-temperature oxidation. A Jet-Stirred Reactor (JSR) setup is valuable to determine the evolution of species mole fractions. For the accuracy of the experimental results, it is important that a JSR is designed such that the concentration field is as homogeneous as possible in order to avoid disturbance of the chemical kinetics. In this work, numerical simulations were performed to investigate the mixing in a JSR chamber. The turbulent structures inside the JSR and the nozzles are captured using Large Eddy Simulations. We conducted numerically a parametric study to evaluate the effects of thermodynamic conditions and geometrical parameters on the mixing characteristics. More specifically, the diameter of the spherical chamber is modified together with the diameter of the nozzles through which fresh gases are fed. The characterization of the gas flow inside a typical spherical JSR layout and results derived by the normalized standard deviation of a tracer mass fraction show that a reduction of the JSR diameter at high pressures improves the homogeneity. Further, we propose a new optimized configuration consisting of six nozzles pointing to the center of the reactor which provides a more uniform composition compared to the standard JSR design.

The paper introduces 3D least squares matching as a technique to analyze multi-temporal micro-tomography data in civil engineering material testing. Time series of tomography voxel data sets are recorded during an in-situ tension test of a strain-hardening cement-based composite probe at consecutive load steps. 3D least squares matching is a technique to track cuboids in consecutive voxel data sets minimizing the sum of the squares of voxel value differences after a 12-parameter 3D affine transformation. For a regular grid of locations in each voxel data set of the deformed states, a subvoxel-precise 3D displacement vector field is computed. Discontinuities in these displacement vector fields indicate the occurrence of cracks in the probes during the load tests. These cracks are detected and quantitatively described by the computation of principal strains of tetrahedrons in a tetrahedral mesh, that is generated between the matching points. The subvoxel-accuracy potential of the technique allows the detection of very small cracks with a width much smaller than the actual voxel size.

Airborne LiDAR bathymetry is an efficient technique for surveying the bottom of shallow waters. In addition, the measurement data contain valuable information about the local turbidity conditions in the water body. The extraction of this information requires appropriate evaluation methods examining the decay of the recorded waveform signal. Existing approaches are based on several assumptions concerning the influence of the ALB system on the waveform signal, the extraction of the volume backscatter, and the directional independence of turbidity. The paper presents a novel approach that overcomes the existing limitations using two alternative turbidity estimation methods as well as different variants of further processed full-waveform data. For validation purposes, the approach was applied to a data set of a shallow inland water. The results of the quantitative evaluation show, which method and which data basis is best suited for the derivation of area wide water turbidity information.

This paper will describe a novel approach for the calibration of an underwater laser triangulation system. Underwater triangulation systems, consisting of a line laser and a camera can be used to determine the geometry of submerged objects or the topography of a water body bottom. Placing camera and laser line projector inside a waterproof housing leads to refraction effects at the air-glass-water interfaces, both of the laser light-sheet and image rays. This implies a deformed laser plane in the water and a curved line on the object surface. The proposed approach strictly models the geometry between camera, laser and housing. First experiments show, that the calibration method can be applied for water depth measurements with accuracies of 0.2–0.3 mm at depths in the order of 100 mm.

A critical part of Automated Material Handling Systems (AMHS) is the task allocation and dispatching strategy employed. In order to better understand and investigate this component, we here present an extensive experimental evaluation of three different approaches with randomly generated, as well as custom designed, environment configurations. While previous studies typically focused on use cases based on highly constrained navigation capabilities (e.g., overhead hoist transport systems), our evaluation is built around highly mobile, free-ranging vehicles, i.e., Autonomous Mobile Robots (AMR) that are gaining popularity in a broad range of applications. Consequently, our experiments are conducted using a microscopic agent-based simulation, instead of the more common discrete-event simulation model. Dispatching methods often are built around the assumption of the asynchronous evaluation of an event-based model, i.e., vehicles trigger a cascade of individual dispatching decisions, e.g., when reaching intersections. We find that this does not translate very well to a fleet of highly mobile systems that can change direction at any time. With this in mind, we present formulations of well known dispatching approaches that are better suited for a synchronous evaluation of the dispatching decisions. The formulations are based on the Stable Marriage Problem (SMP) and the Linear Sum Assignment Problem (LSAP). We use matching and assignment algorithms to compute the actual dispatching decisions. The selected algorithms are evaluated in a multi-agent simulation environment. To integrate a centralised fleet management, a digital twin concept is proposed and implemented. By this approach, the fleet management is independent of the implementation of the specific agents, allowing to quickly adapt to other simulation-based or real application scenarios. For the experimental evaluation, two new performance measures related to the efficiency of a material handling system are proposed, Travel Efficiency and Throughput Effort. The experimental evaluation indicates that reassignment mechanisms in the dispatching method can help to increase the overall efficiency of the fleet. We did not find significant differences in absolute performance in terms of throughput rate. Additionally, the difference in performance between SMP- and LSAP-based dispatching with reassignment seems negligible. We conclude with a discussion, where we consider potential confounding factors and relate the findings to previously reported results found in the literature.

In this work, the influence of H2 addition on the auto-ignition and combustion properties of CH4 is investigated experimentally and numerically. Experimental ignition delay times (IDT) are compared with simulations and laminar burning velocities (LBVs), and extinction limits/extinction strain rates (ESRs) are compared with data from the literature. A wide variety of literature data are collected and reviewed, and experimental data points are extracted for IDT, LBV and ESR. The results are used for the validation of existing reaction mechanisms. The reaction mechanisms and models used are able to reproduce the influence of H2 addition to CH4 (e.g., shortening IDTs, increasing ESRs and increasing LBVs). IDTs are investigated in a range from 6 to 15 bar and temperatures from 929 to 1165 K with H2 addition from 10 to 100 mol%. We show that LBV and ESR are predicted in a wide range by the numerical simulations. Moreover, the numerical simulations using detailed Aramco Mech 3.0 (581 species) are compared with the derived reduced reaction mechanism UCB Chen (49 species). The results show that the reduced chemistry obtained by considering only the IDT is also valid for LBV and ESR.

The paper describes and compares the workflows and results of generating digital elevation models (DEMs) of underwater areas from airborne laser scanning and aerial stereo images. Based on a combined laser scanning/image data set of an artificial lake, both methods are described and pros/cons are highlighted. The authors focus on the final results, especially on accuracy, completeness and spatial resolution of the underwater DEM’s. Further, practical aspects of processing and complexity of both methods are highlighted too.

Thanks to the rapid technological progress in the field of mobile devices, smartphones are increasingly becoming valuable for science. They can serve as photogrammetric measurement devices with built-in cameras, micro-electro-mechanical systems for orientation and position assessment, as well as powerful processing units allowing field-based data acquisition and processing. This paper outlines a comprehensive investigation focusing on the accuracy and stability of smartphone camera rotation parameters determined by built-in smartphone sensors. For that purpose, the rotation parameters were measured under a range of different conditions. Four test scenarios were defined considering indoor- and outdoor measurements using three different devices being in static and dynamic modes. Furthermore, the influence of magnetic perturbations was investigated. The rotation parameters were determined from the measurements applying different sensor fusion approaches. Reference values for accuracy assessment were provided by a superior precision inertial measurement unit that measured the rotation parameters simultaneously to the smartphone in each experiment. The analysis of the smartphone-based rotation parameters, separated in the Euler angles azimuth, pitch and roll, shows average accuracies below 2° for pitch and roll. In comparison, azimuth shows significantly lower accuracies of more than 30° especially when the smartphone is in motion and when it is exposed to magnetic perturbations. In this regard, advanced multi-sensor fusion approaches were examined that handle such interferences to considerably improve the accuracy of azimuth measurements. In conclusion, a summary of accuracies and stabilities to be expected from smartphone sensors is given referring to ambient conditions and investigated sensor fusion strategies.

Pixel-wise classification of remote sensing imagery is highly interesting for tasks like land cover classification or change detection. The acquisition of large training data sets for these tasks is challenging, but necessary to obtain good results with deep learning algorithms such as convolutional neural networks (CNN). In this paper we present a method for the automatic generation of a large amount of training data by combining satellite imagery with reference data from an available geospatial database. Due to this combination of different data sources the resulting training data contain a certain amount of incorrect labels. We evaluate the influence of this so called label noise regarding the time difference between acquisition of the two data sources, the amount of training data and the class structure. We combine Sentinel-2 images with reference data from a geospatial database provided by the German Land Survey Office of Lower Saxony (LGLN). With different training sets we train a fully convolutional neural network (FCN) and classify four land cover classes (Building, Agriculture, Forest, Water). Our results show that the errors in the training samples do not have a large influence on the resulting classifiers. This is probably due to the fact that the noise is randomly distributed and thus, neighbours of incorrect samples are predominantly correct. As expected, a larger amount of training data improves the results, especially for the less well represented classes. Other influences are different illuminations conditions and seasonal effects during data acquisition. To better adapt the classifier to these different conditions they should also be included in the training data.

Helical structures can be found in nature at various length scales ranging from the molecular level to the macroscale. Due to their ability to store mechanical energy and to optimize the accessible surface area, helical shapes contribute particularly to motion-driven processes and structural reinforcement. Due to these special features, helical fibers have become highly attractive for biotechnological and tissue engineering applications. However, there are only a few methods available for the production of biocompatible helical microfibers. Given that, we present here a simple technique for the fabrication of helical chitosan microfibers with embedded magnetic nanoparticles. Composite fibers were prepared by wet-spinning and coagulation in an ethanol bath. Thereby, no toxic components were introduced into the wet-spun chitosan fibers. After drying, the helical fibers had a diameter of approximately 130 µm. Scanning electron microscopy analysis of wet-spun helices revealed that the magnetic nanoparticles agglomerated into clusters inside the fiber matrix. The helical constructs exhibited a diameter of approximately 500 µm with one to two windings per millimeter. Due to their ferromagnetic properties they are easily attracted to a permanent magnet. The results from the tensile testing show that the helical chitosan microfibers exhibited an average Young’s modulus of 14 MPa. By taking advantage of the magnetic properties of the feedstock solution, the production of the helical fibers could be automated. The fabrication of the helical fibers was achieved by utilizing the magnetic properties of the feedstock solution and winding the emerging fiber around a rotating magnetic collector needle upon coagulation. In summary, our helical chitosan microfibers are very attractive for future use in magnetic tissue engineering or for the development of biocompatible actuator systems.

A network consisting of six cameras was set up on both sides of Lange Glacier on King George Island, Antarctica, for a period of two years to monitor changes in the glacier’s motion behaviour. The cameras were observing spatio-temporal glacier surface velocity vector fields as well as the position of the glacier front. Velocity field information was obtained from image sequences by applying subpixel accuracy photogrammetric image sequence analysis techniques. Georeferencing in a superordinate coordinate system was performed via integrated photogrammetric-geodetic network adjustment. As a result, velocity fields were determined with maximum glacier surface velocities in the order of 1.5 meter per day. The results of terrestrial camera image sequence processing can be used as validation and calibration reference for satellite image based glacier velocity dynamics calculations. Moreover, the very high temporal resolution of the image sequences taken at 20 minute time intervals can also be used to analyse highly dynamic processes.

We present calving flux, calving rate, frontal retreat and volume change rate results in Lange Glacier from 2015 to 2017, including also velocity calculations in 2015, 2017 and 2018. The peak velocities at glacier front were between 1.0 and 1.5 [md-1] near the center line with a maximum frontal retreat of 100 [m]. The glacier presents almost no volume change rate during the period analyzed and the average ice discharge is 0.026 [km3 yr-1].

This publication concentrates on the photogrammetric crack width measurement of crack patterns of concrete probes under impact loading in high-speed stereo image sequences. The presented algorithm works for non-planar specimens with deformations that only appear tangential to the surface and the method is based on triangle mesh analysis. Experiments were conducted with cylindrical specimens with an impact load affecting parallel to the main axis of the cylinder.

For the correct usage and analysis within a BIM environment, image-based point clouds that were created with Structure from Motion (SfM) tools have to be transformed into the building coordinate system via a seven parameter Helmert Transformation. Usually control points are used for the estimation of the transformation parameters. In this paper we present a novel, highly automated approach to calculate these transformation parameters without the use of control points. The process relies on the relationship between wall respectively plane information of the BIM and three-dimensional line data that is extracted from the image data. In a first step, 3D lines are extracted from the oriented input images using the tool Line3D++. These lines are defined by the 3D coordinates of the start and end points. Afterwards the lines are matched to the planes originating from the BIM model representing the walls, floors and ceilings. Besides finding a suitable functional and stochastic model for the observation equations and the adjustment calculation, the most critical aspect is finding a correct match for the lines and the planes. We therefore developed a RANSAC-inspired matching algorithm to get a correct assignment between elements of the two data sources. Synthetic test data sets have been created for evaluating the methodology.

Airborne LiDAR bathymetry allows an efficient and area-wide acquisition of water bottom points in shallow water areas. However, the measurement method is severely limited by water turbidity, impending a reliable detection of water bottom points at higher turbidity or in deeper water bodies. This leads to an incomplete acquisition of the water bottom topography. In this contribution, advanced processing methods are presented, which increase the penetration depth compared to the original processed data and enable a reliable extraction and detection of bottom points in deeper water bodies. The methodology is based on the analysis of correlated neighborhood information assuming a steady water bottom. The results confirm a significantly higher penetration depth with a high reliability of the additionally extracted water bottom points along with a larger coverage of the water bottom topography.

Cadastral mapping of favela’s agglomerated buildings in informal settlements at Level of Detail 1 (LoD1) usually requires specific surveys and extensive manual data processing. Therefore, there is a demand for including the favelas in the city map production on the basis of Lidar surveys, as well as the detection of their vertical growth. However, the currently developed algorithms for automatically extracting buildings from airborne Lidar data have mainly been tested only for regular building reconstruction. This study aims to develop a Lidar data processing pipeline enabling to compute metrics related to intraurban informal settlements. To do so, we present a procedure to generate favela’s buildings delineation, height, floors’ number and built area and apply them to six case studies in favela typo-morphologies. We conducted an exploratory analysis in order to obtain the adequate parameters of the processing pipeline and its evaluation, using open source, free license and self-developed software. The results are compared to reference data from the manual stereo plotting, achieving a quality index in the building reconstruction about 70%. We also calculated the growth density, measured by gross Floor Area Ratio index inside settlement, revealing values from 29% to 74% considering different time periods.

The automatic extraction of windows from photogrammetric data has achieved increasing attention in recent times. An unsupervised windows extraction approach from photogrammetric point clouds with thermal attributes is proposed in this study. First, point cloud segmentation is conducted by a popular workflow: Multiscale supervoxel generation is applied to the image-based 3D point cloud, followed by region growing and energy optimization using spatial positions and thermal attributes of the raw points. Afterwards, an object-based feature (window index) is extracted using the average thermal attribute and the size of the object. Next, thresholding is applied to extract initial window regions. Finally, several criterions are applied to further refine the extraction results. For practical validation, the approach is evaluated on an art nouveau building row façade located at Dresden, Germany.

Airborne Lidar Bathymetry is a laser scanning technique to measure waterbody bottom topography in shallow waterbodies with limited turbidity. The topic has recently gained relevance due to the advent of new sensor technologies allowing for much higher spatial resolution in bathymetry data capture and due to guidelines demanding regular monitoring of waterbodies. In our contribution, we focus on three important aspects of lidar bathymetry: In the first part, systematic effects of wave patterns will be analysed in order to derive waterbody coordinate correction terms. In the second part, we will apply waveform-stacking techniques to enhance the detectability of water bottom points in lidar bathymetry full waveform signals. In the third part, a dedicated full wave-form analysis procedure is shown, which allows for deriving turbidity information from the decay of the signal intensity in the waterbody.

E-learning is increasingly used to support student learning in higher education, facilitating administration of online formative assessments. Although providing diagnostic, actionable feedback is generally more effective, in current practice, feedback is often given in the form of a simple proportion of correctly solved items. This study shows the validation process of constructing detailed diagnostic information on a set of skills, abilities, and cognitive processes (so-called attributes) from students’ item response data with diagnostic classification models. Attribute measurement in the domain of statistics education is validated based on both expert judgment and empirical student data from a think-aloud study and large-scale assessment administration. The constructed assessments provide a valid and reliable measurement of the attributes. Inferences that can be drawn from the results of these formative assessments are discussed and it is demonstrated how this information can be communicated to students via learning dashboards to allow them to make more effective learning choices.

Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split hybrid mode, whereas powertrain configurations with multimode transmissions enable switching between those modes during vehicle operation. Hence, depending on the current operation state of the vehicle, the most appropriate mode in terms efficiency can be selected. This, however, requires an operating strategy, which controls the mode selection as well as the torque distribution between the combustion engine and electric motor with the aim of optimal battery depletion and minimal fuel consumption. A well-known approach is the equivalent consumption minimization strategy (ECMS). It can be applied by using optimizations based on a prediction of the future driving behavior. Since the outcome of the ECMS depends on the quality of this prediction, it is crucial to know how accurate the predictions must be in order to obtain acceptable results. In this contribution, various prediction methods and real-time capable ECMS implementations are analyzed and compared in terms of the achievable fuel economy. The basis for the analysis is a holistic model of a state-of-the-art PHEV powertrain configuration, comprising the multimode transmission, corresponding powertrain components, and representative real-world driving data.

Single-crystal sapphire is utilized as a high-performance engineering material, especially in extreme and harsh environments. However, due to its extreme hardness and brittleness, the machinability of sapphire is still a challenge. By means of nanoindentation and plunge-cut experiments, the anisotropic brittle-ductile transition of the prismatic M-plane and rhombohedral R-plane is examined by analyzing crack morphologies and the critical depth-of-cut (CDC). The experimental results of the nanoindentation tests are correlated to the plunge-cut experiment. Both the prism plane and the rhombohedral crystal plane exhibit a two-fold symmetry of ductility with various crack patterns along the machined grooves. The direction-dependent plasticity of the hexagonal sapphire crystal is mainly connected to a twinning process accompanied by slip dislocation. Keywords: Sapphire, Anisotropy, Brittle-ductile transition, Orthogonal cutting

Grassland ecology experiments in remote locations requiring quantitative analysis of the biomass in defined plots are becoming increasingly widespread, but are still limited by manual sampling methodologies. To provide a cost-effective automated solution for biomass determination, several photogrammetric techniques are examined to generate 3D point cloud representations of plots as a basis, to estimate aboveground biomass on grassland plots, which is a key ecosystem variable used in many experiments. Methods investigated include Structure from Motion (SfM) techniques for camera pose estimation with posterior dense matching as well as the usage of a Time of Flight (TOF) 3D camera, a laser light sheet triangulation system and a coded light projection system. In this context, plants of small scales (herbage) and medium scales are observed. In the first pilot study presented here, the best results are obtained by applying dense matching after SfM, ideal for integration into distributed experiment networks.

The paper presents an approach for the generation of digital elevation models (DEMs) of underwater areas from aerial images. Standard software-products do not provide the possibility to measure correctly through refractive interfaces, such as water. Existing solutions for that problem are based on oriented images and known water levels with the DEM points determined by forward intersection based on reconstructed image ray paths (ray tracing). In this article we present an integrated procedure for image orientation as well as DEM mass point determination from aerial imagery containing both land and underwater areas. The proof of concept was done by capturing UAV imagery of shallow water areas of a high-alpine lake in the Swiss alps. In the paper the processed dataset will be presented. Furthermore, the extraction and matching of image-points observed through water are discussed. The accuracy potential as well as practical limitations of processing multimedia-data are analysed.

Rivers with small- and medium-scaled catchments have been increasingly affected by extreme events, i.e. flash floods, in the last years. New methods to describe and predict these events are developed in the interdisciplinary research project EXTRUSO. Flash flood events happen on small temporal and spatial scales, stressing the necessity of high-resolution input data for hydrological and hydrodynamic modelling. Among others, the benefit of high-resolution digital terrain models (DTMs) will be evaluated in the project. This article introduces a boat-based approach for the acquisition of geometrical and morphological data of small rivers and their banks. An unmanned water vehicle (UWV) is used as a multi-sensor platform to collect 3D-point clouds of the riverbanks, as well as bathymetric measurements of water depth and river morphology. The UWV is equipped with a mobile Lidar, a panorama camera, an echo sounder and a positioning unit. Whole (sub-) catchments of small rivers can be digitalized and provided for hydrological modelling when UWV-based and UAV (unmanned aerial vehicle) based point clouds are fused.

Airborne LiDAR bathymetry (ALB) requires a refraction correction on the basis of Snell’s law at the air-water interface and a speedof- light correction to be applied on the raw laser data in order to achieve a geometric accurate representation of the water bottom. Strictly speaking, this requires exact knowledge about the local water surface inclination. If this information is not available, certain simplifications have to be introduced in correction methods. Common correction methods assume either a horizontal or a locally tilted planar water surface as well as an infinitesimally small thin laser ray, thus neglecting effects caused by the finite laser pulse diameter penetrating a curved surface. In our simulation approach, the refraction of finite diameter laser pulses passing the air/water interface is modeled differentially in a strict manner. The simulation tool is able to predict wave induced coordinate errors which have to be expected due to the neglections made in common refraction correction methods. Moreover, wave pattern dependent correction terms were be derived from systematic portions of the errors revealed by the simulations. The goal of this paper is to experimentally validate the coordinate errors predicted by the simulation tool. For that purpose, airborne laser bathymetry data of a 12 by 50 meter open air wave pool were processed, and the results were compared to reference data of the empty pool acquired by terrestrial laser scanning. The comparison showed that the effects predicted in the numerical simulation are confirmed by the experimental validation.

An automatic building façade thermal texture mapping approach, using uncooled thermal camera data, is proposed in this paper. First, a shutter-less radiometric thermal camera calibration method is implemented to remove the large offset deviations caused by changing ambient environment. Then, a 3D façade model is generated from a RGB image sequence using structure-from-motion (SfM) techniques. Subsequently, for each triangle in the 3D model, the optimal texture is selected by taking into consideration local image scale, object incident angle, image viewing angle as well as occlusions. Afterwards, the selected textures can be further corrected using thermal radiant characteristics. Finally, the Gauss filter outperforms the voted texture strategy at the seams smoothing and thus for instance helping to reduce the false alarm rate in façade thermal leakages detection. Our approach is evaluated on a building row façade located at Dresden, Germany.

An automatic spatio-temporal flow velocity measurement approach, using an uncooled thermal camera, is proposed in this paper. The basic principle of the method is to track visible thermal features at the water surface in thermal camera image sequences. Radiometric and geometric calibrations are firstly implemented to remove vignetting effects in thermal imagery and to get the interior orientation parameters of the camera. An object-based unsupervised classification approach is then applied to detect the interest regions for data referencing and thermal feature tracking. Subsequently, GCPs are extracted to orient the river image sequences and local hot points are identified as tracking features. Afterwards, accurate dense tracking outputs are obtained using pyramidal Lucas-Kanade method. To validate the accuracy potential of the method, measurements obtained from thermal feature tracking are compared with reference measurements taken by a propeller gauge. Results show a great potential of automatic flow velocity measurement in small rivers using imagery from a thermal camera.

This paper deals with the determination of crack widths of concrete beams during load tests from monocular image sequences. The procedure starts in a reference image of the probe with suitable surface texture under zero load, where a large number of points is defined by an interest operator. Then a triangulated irregular network is established to connect the points. Image sequences are recorded during load tests with the load increasing continuously or stepwise, or at intermittently changing load. The vertices of the triangles are tracked through the consecutive images of the sequence with sub-pixel accuracy by least squares matching. All triangles are then analyzed for changes by principal strain calculation. For each triangle showing significant strain, a crack width is computed by a thorough geometric analysis of the relative movement of the vertices.

Collisions between space debris and satellites in Earth’s orbits are not only catastrophic to the satellite, but also create thousands of new fragments, exacerbating the space debris problem. One challenge in understanding the space debris environment is the lack of data on fragmentation and breakup caused by hypervelocity impacts. In this paper, we present an experimental measurement technique capable of recording 3D position and velocity data of fragments produced by hypervelocity impact experiments in the lab. The experimental setup uses stereo high-speed cameras to record debris fragments generated by a hypervelocity impact. Fragments are identified and tracked by searching along trajectory lines and outliers are filtered in 4D space (3D + time) with RANSAC. The method is demonstrated on a hypervelocity impact experiment at 3.2 km/s and fragment velocities and positions are measured. The results demonstrate that the method is very robust in its ability to identify and track fragments from the low resolution and noisy images typical of high-speed recording.

A correlation between patellar kinematics and anterior knee pain is widely accepted. However, there is no consensus on how they are connected or what profile of patellar kinematics would minimize anterior knee pain. Nevertheless, answering this question by merging existing studies is further complicated by the variety of ways to describe patellar kinematics. Therefore, this study describes the most frequently used conventions for defining patellar kinematics, focusing on the rotations. The similarities and differences between the Cardan sequences and angles calculated by projecting axes are analyzed. Additionally, a tool is provided to enable the conversion of kinematic data between definitions in different studies. The choice of convention has a considerable impact on the absolute values and the clinical characteristics of the patello-femoral angles. In fact, the angles that result from using different mathematical conventions to describe a given patello-femoral rotation from our analyses differ up to a Root Mean Squared Error of 111.49° for patellar flexion, 55.72° for patellar spin and 35.39° for patellar tilt. To compare clinical kinematic patello-femoral results, every dataset must follow the same convention. Furthermore, researchers should be aware of the used convention’s implications to ensure reproducibility when interpreting and comparing such data.

This study evaluated the individual and combined effects of inhibitory compounds formed during pretreatment of lignocellulosic biomass on the growth of Bacillus subtilis. Ten inhibitory compounds commonly present in lignocellulosic hydrolysates were evaluated, which included sugar degradation products (furfural and 5-hydroxymethylfurfural), acetic acid, and seven phenolic compounds derived from lignin (benzoic acid, vanillin, vanillic acid, ferulic acid, p-coumaric acid, 4-hydroxybenzoic acid, and syringaldehyde). For the individual inhibitors, syringaldehyde showed the most toxic effect, completely inhibiting the strain growth at 0.1 g/L. In the sequence, assays using mixtures of the inhibitory compounds at a concentration of 12.5% of their IC50 value were performed to evaluate the combined effect of the inhibitors on the strain growth. These experiments were planned according to a Plackett–Burman experimental design. Statistical analysis of the results revealed that in a mixture, benzoic acid and furfural were the most potent inhibitors affecting the growth of B. subtilis. These results contribute to a better understanding of the individual and combined effects of inhibitory compounds present in biomass hydrolysates on the microbial performance of B. subtilis. Such knowledge is important to advance the development of sustainable biomanufacturing processes using this strain cultivated in complex media produced from lignocellulosic biomass, supporting the development of efficient bio-based processes using B. subtilis.

Although simulation models are heavily used in biomechanical research and testing of TKA implants, pre-clinical tools for a holistic estimation of implant performance under dynamic loading conditions are rare. The objective of this study was the development of an efficient pre-clinical test method for analyzing knee contact mechanics and kinematics based on a dynamic FE model and to evaluate the effects of the proximo-distal patella position and the patellar tendon stiffness on the patellar kinematics. A finite element-based workflow for knee prostheses designs was developed based on standardized in vivo load data, which included the tibial forces and moments. In a new research approach, the tibial forces are used as input for the model, whereas the tibial moments were used to validate the results. For the standardized sit down, stand up, and knee bend load cycles, the calculated tibial moments show only small deviations from the reference values—especially for high flexion angles. For the knee bend cycle, the maximum absolute value of patellar flexion decreases for higher patellar tendon stiffness and more distally placed patellar components. Therefore, patella-related clinical problems caused by patella baja may also arise if the patellar tendon is too weak for high tibiofemoral flexion angles.