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Having access to accurate and recent digital twins of infrastructure assets benefits the renovation, maintenance, condition monitoring, and construction planning of infrastructural projects. There are many cases where such a digital twin does not yet exist, such as for legacy structures. In order to create such a digital twin, a mobile laser scanner can be used to capture the geometric representation of the structure. With the aid of semantic segmentation, the scene can be decomposed into different object classes. This decomposition can then be used to retrieve cad models from a cad library to create an accurate digital twin. This study explores three deep-learning-based models for semantic segmentation of point clouds in a practical real-world setting: PointNet++, SuperPoint Graph, and Point Transformer. This study focuses on the use case of catenary arches of the Dutch railway system in collaboration with Strukton Rail, a major contractor for rail projects. A challenging, varied, high-resolution, and annotated dataset for evaluating point cloud segmentation models in railway settings is presented. The dataset contains 14 individually labelled classes and is the first of its kind to be made publicly available. A modified PointNet++ model achieved the best mean class Intersection over Union (IoU) of 71% for the semantic segmentation task on this new, diverse, and challenging dataset.

This paper coupled a unified passive and active microwave observation operator—namely, an enhanced, physically-based, discrete emission-scattering model—with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature (Formula presented.) ((Formula presented.) = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of (Formula presented.) lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of (Formula presented.) reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

Pattern recognition in EMG-based control systems suffer from increase in error rate over time, which could lead to unwanted behavior. This so-called concept drift in myoelectric control systems could be caused by fatigue, sensor replacement and varying skin conditions. To circumvent concept drift, adaptation strategies could be used to retrain a pattern recognition system, which could lead to comparable error rates over multiple days. In this study, we investigated the error rate development over one week and compared three adaptation strategies to reduce the error rate increase. The three adaptation strategies were based on entropy, on backward prediction and a combination of backward prediction and entropy. Ten able-bodied subjects were measured on four measurement days while performing gait-related activities. During the measurement electromyography and kinematics were recorded. The three adaptation strategies were implemented and compared against the baseline error rate and against adaptation using the ground truth labels. It can be concluded that without adaptation the baseline error rate increases significantly from day 1 to 2, but plateaus on day 2, 3 and 7. Of the three tested adaptation strategies, entropy based adaptation showed the smallest increase in error rate over time. It can be concluded that entropy based adaptation is simple to implement and can be considered a feasible adaptation strategy for lower limb pattern recognition.

3D-printing conductive structures have recently been receiving increased attention, especially in the field of 3D-printed sensors. However, the printing processes introduce anisotropic electrical properties due to the infill and bonding conditions. Insights into the electrical conduction that results from the anisotropic electrical properties are currently limited. Therefore, this research focuses on analytically modeling the electrical conduction. The electrical properties are described as an electrical network with bulk and contact properties in and between neighbouring printed track elements or traxels. The model studies both meandering and open-ended traxels through the application of the corresponding boundary conditions. The model equations are solved as an eigenvalue problem, yielding the voltage, current density, and power dissipation density for every position in every traxel. A simplified analytical example and Finite Element Method simulations verify the model, which depict good correspondence. The main errors found are due to the limitations of the model with regards to 2D-conduction in traxels and neglecting the resistance of meandering ends. Three dimensionless numbers are introduced for the verification and analysis: the anisotropy ratio, the aspect ratio, and the number of traxels. Conductive behavior between completely isotropic and completely anisotropic can be modeled, depending on the dimensionless properties. Furthermore, this model can be used to explain the properties of certain 3D-printed sensor structures, like constriction-resistive strain sensors.

Internet of Things (IoT) has been deployed in a vast number of smart applications with the aim to bring ease and comfort into our lives. However, with the expansion of IoT applications, the number of security and privacy breaches has also increased, which brings into question the resilience of existing security and trust mechanisms. Furthermore, the contemporaneous centralized technology is posing significant challenges viz scalability, transparency and efficiency to wide range of IoT applications such as smart logistics, where millions of IoT devices need to be connected simultaneously. Alternatively, IOTA is a distributed ledger technology that offers resilient security and trust mechanisms and a decentralized architecture to overcome IoT impediments. IOTA has already been implemented in many applications and has clearly demonstrated its significance in real-world applications. Like any other technology, IOTA unfortunately also encounters security vulnerabilities. The purpose of this study is to explore and highlight security vulnerabilities of IOTA and simultaneously demonstrate the value of threat modeling in evaluating security vulnerabilities of distributed ledger technology. IOTA vulnerabilities are scrutinized in terms of feasibility and impact and we have also presented prevention techniques where applicable. To identify IOTA vulnerabilities, we have examined existing literature and online blogs. Literature available on this topic is very limited so far. As far as we know IOTA has barely been addressed in the traditional journals, conferences and books. In total we have identified six vulnerabilities. We used Common Vulnerability Scoring System (CVSS v3.0) to further categorize these vulnerabilities on the basis of their feasibility and impact.

Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window sizes than the support vector machine. Classifying the arousal achieved the best performance with a window size of ten seconds, obtaining 56.85% accuracy and a Matthews correlation coefficient (MCC) of 0.1369. Valence recognition had the best performance with a window length of eight seconds at 73.34% accuracy and an MCC value of 0.4669. Spatial information from varying the electrode orders had a small effect on the classification. Overall, valence results had a much more superior performance than arousal results, which were, perhaps, influenced by features related to brain activity asymmetry between the left and right hemispheres.

Cardiac surgery patients infrequently mobilize during their hospital stay. It is unclear for patients why mobilization is important, and exact progress of mobilization activities is not available. The aim of this study was to select and evaluate accelerometers for objective qualification of in-hospital mobilization after cardiac surgery. Six static and dynamic patient activities were defined to measure patient mobilization during the postoperative hospital stay. Device requirements were formulated, and the available devices reviewed. A triaxial accelerometer (AX3, Axivity) was selected for a clinical pilot in a heart surgery ward and placed on both the upper arm and upper leg. An artificial neural network algorithm was applied to classify lying in bed, sitting in a chair, standing, walking, cycling on an exercise bike, and walking the stairs. The primary endpoint was the daily amount of each activity performed between 7 a.m. and 11 p.m. The secondary endpoints were length of intensive care unit stay and surgical ward stay. A subgroup analysis for male and female patients was planned. In total, 29 patients were classified after cardiac surgery with an intensive care unit stay of 1 (1 to 2) night and surgical ward stay of 5 (3 to 6) nights. Patients spent 41 (20 to 62) min less time in bed for each consecutive hospital day, as determined by a mixed-model analysis (p &lt, 0.001). Standing, walking, and walking the stairs increased during the hospital stay. No differences between men (n = 22) and women (n = 7) were observed for all endpoints in this study. The approach presented in this study is applicable for measuring all six activities and for monitoring postoperative recovery of cardiac surgery patients. A next step is to provide feedback to patients and healthcare professionals, to speed up recovery.