The number of finger vascular pattern datasets available for the research community is scarce, therefore a new finger vascular pattern dataset containing 1440 images is prsented. This dataset is unique in its kind as the images are of high resolution and have a known pixel density. Furthermore this is the first dataset which contains the age, gender and handedness of the participating volunteers as meta data. The images have been captured using a custom designed capturing device. The various aspects of designing this capturing device are addressed in this paper as well. To confirm whether this new dataset is in fact an important contribution some performance figures in terms of EER of several published state-of-the-art algorithms using this new dataset and an existing dataset from the Peking University are presented. Using this new dataset EERs down to 0.4% have been achieved.

Digitalisation in railway networks harnesses digital technologies to optimise operations, leading to enhanced efficiency, and reduced energy consumption. By analysing real-time data, railways can predict maintenance needs, improve passenger experiences, and seamlessly integrate with other transport modes. As societies strive for sustainable transportation solutions, it is imperative to understand and collect digitalisation techniques to enhance efficiency and reduce the ecological footprint of railway networks. This paper serves as a snapshot of the current state of the art addressing the pivotal role of point cloud techniques in advancing railway digitalisation and providing valuable pointers for future research directions. Employing a systematic review approach, our study concentrates exclusively on research centred around railway assets and their digitalisation via point cloud data. We have themed the literature into pre-processing, modelling, and digital twinning. Within this review, we analyse diverse modelling and pre-processing techniques and categorise them for clarity. The digital twin techniques are also collected, though these techniques are scarce in the context of railway infrastructure and point clouds. The paper also presents a compilation of dataset statistics highlighting the scarcity of openly available railway-specific datasets. This scarcity considerably hampers the feasibility of research reproducibility and the comparative analysis of different approaches. Our conclusion reflects on the challenges encountered and proposes a course for future research. Particularly, we conclude that hybrid methodologies that combine machine learning with structure-based techniques hold substantial promise toward creating digital twins, considering the intrinsic characteristics of railway infrastructure.

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.

The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

High-quality finger vein datasets available for the research community are still relatively scarce; therefore, we collected a set of finger vein images of high resolution and a known pixel density. Furthermore, this is the first dataset which contains the age, gender and handedness of the participating data subjects as metadata. This dataset has been collected using a custom-designed biometric capture device. The various aspects of designing this biometric capture device are addressed in this chapter. New insights and continuing work on the design of better capture devices have led to novel ideas which are presented in this chapter. To justify the importance of this dataset, performance figures in terms of EER of several well-established algorithms using this dataset and an existing dataset are compared side by side.