Notes

System dysmorphic disorder (BDD) in the orthodontic and orthognathic establishing: A systematic assessment.
Smartphone location recognition aims to identify the location of a smartphone on a user in specific actions such as talking or texting. This task is critical for accurate indoor navigation using pedestrian dead reckoning. Usually, for that task, a supervised network is trained on a set of defined user modes (smartphone locations), available during the training process. In such situations, when the user encounters an unknown mode, the classifier will be forced to identify it as one of the original modes it was trained on. Such classification errors will degrade the navigation solution accuracy. A solution to detect unknown modes is based on a probability threshold of existing modes, yet fails to work with the problem setup. Therefore, to identify unknown modes, two end-to-end ML-based approaches are derived utilizing only the smartphone's accelerometers measurements. Results using six different datasets shows the ability of the proposed approaches to classify unknown smartphone locations with an accuracy of 93.12%. The proposed approaches can be easily applied to any other classification problems containing unknown modes.This paper deals with bistatic track association and deghosting in the classical frequency modulation (FM)-based multi-static primary surveillance radar (MSPSR). The main contribution of this paper is a novel algorithm for bistatic track association and deghosting. The proposed algorithm is based on a hierarchical model which uses the Indian buffet process (IBP) as the prior probability distribution for the association matrix. The inference of the association matrix is then performed using the classical reversible jump Markov chain Monte Carlo (RJMCMC) algorithm with the usage of a custom set of the moves proposed by the sampler. A detailed description of the moves together with the underlying theory and the whole model is provided. Using the simulated data, the algorithm is compared with the two alternative ones and the results show the significantly better performance of the proposed algorithm in such a simulated setup. The simulated data are also used for the analysis of the properties of Markov chains produced by the sampler, such as the convergence or the posterior distribution. At the end of the paper, further research on the proposed method is outlined.Utilising cooling stimulation as a thermal excitation means has demonstrated profound capabilities of detecting sub-surface metal loss using thermography. Biocytin Previously, a prototype mechanism was introduced which accommodates a thermal camera and cooling source and operates in a reciprocating motion scanning the test piece while cold stimulation is in operation. Immediately after that, the camera registers the thermal evolution. However, thermal reflections, non-uniform stimulation and lateral heat diffusions will remain as undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no prior knowledge of the non-defective area in order to effectively distinguish between defective and non-defective areas. In this work, the previously automated acquisition and processing pipeline is re-designed and optimised for two purposes 1-Through the previous work, the mentioned pipeline was used to analyse a specific area of the test piece surface in order to have shown not only the capability of accurately detecting subsurface metal loss as low as 37.5% but also the successful detection of defects that were either unidentifiable or invisible in either the original thermal images or their PCA transformed results.In this work, we investigated two issues (1) How the fusion of lidar and camera data can improve semantic segmentation performance compared with the individual sensor modalities in a supervised learning context; and (2) How fusion can also be leveraged for semi-supervised learning in order to further improve performance and to adapt to new domains without requiring any additional labelled data. A comparative study was carried out by providing an experimental evaluation on networks trained in different setups using various scenarios from sunny days to rainy night scenes. The networks were tested for challenging, and less common, scenarios where cameras or lidars individually would not provide a reliable prediction. Our results suggest that semi-supervised learning and fusion techniques increase the overall performance of the network in challenging scenarios using less data annotations.Recent progress in printable electronics has enabled the fabrication of printed strain sensors for diverse applications. These include the monitoring of civil infrastructure, the gradual aging of which raises concerns about its effective maintenance and safety. Therefore, there is a need for automated sensing systems that provide information on the performance and behavior of engineering structures that are subjected to dynamic and static loads. The application of printed strain sensors in structural health monitoring is of growing interest owing to its large-area and cost-effective fabrication process. Previous studies have proven the suitability of printable strain sensors for dynamic strain measurements on bridges; however, the analysis of the long-term stability of printed sensors during static strain measurements is still lacking. Thus, this study aims to assess the long-term stability of printed strain sensor arrays and their suitability for the static strain analysis of large civil structures. The developed sensors and a dedicated wireless data acquisition system were deployed inside a gravity dam, which was selected as the field test environment. This test environment was chosen owing to the relatively stable temperature inside the dam and the very slow static strain changes associated with periodic water level changes. The results exhibited an average signal drift of 20 μϵ over 127 days. One of the sensor arrays was installed on a small crack in the dam structure; it showed that the sensors can track static strain changes owing to variations in the crack opening, which are related to the water level changes in the dam. Overall, the results of the developed sensors exhibit good strain sensitivity and low signal drift. This indicates the potential suitability of printed sensors for applications in the static strain analysis of engineering structures.
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