Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devices, which helps police authorities carry out identity verification. In this paper, we provide a comprehensive and in-depth engineering study on the different stages of the fingerprint recognition toolchain. The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. The targeted solution will be capable of acquiring 4 fingers at once (except the thumb) in a contactless manner, automatically segmenting the fingertips, pre-processing them (including a specific enhancement), and thus enabling fingerprint comparison against contact-based datasets. For fingertip detection and segmentation, various traditional handcrafted feature-based approaches as well as deep-learning-based ones are investigated. Furthermore, a run-time analysis and first results on the biometric recognition performance are included.

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting.

This work presents a deep learning approach for vehicle detection in satellite video. Vehicle detection is perhaps impossible in single EO satellite images due to the tininess of vehicles (4-10 pixel) and their similarity to the background. Instead, we consider satellite video which overcomes the lack of spatial information by temporal consistency of vehicle movement. A new spatiotemporal model of a compact $3 \times 3$ convolutional, neural network is proposed which neglects pooling layers and uses leaky ReLUs. Then we use a reformulation of the output heatmap including Non-Maximum-Suppression (NMS) for the final segmentation. Empirical results on two new annotated satellite videos reconfirm the applicability of this approach for vehicle detection. They more importantly indicate that pre-training on WAMI data and then fine-tuning on few annotated video frames for a new video is sufficient. In our experiment only five annotated images yield a $F_1$ score of 0.81 on a new video showing more complex traffic patterns than the Las Vegas video. Our best result on Las Vegas is a $F_1$ score of 0.87 which makes the proposed approach a leading method for this benchmark.

Secure, sovereign data sharing is essential for the digital transformation of industrial sectors, yet designing sustainable Business Models (BMs) for multi-stakeholder Data Spaces remains a significant challenge. This study situates BM innovation for Data Spaces in the context of European manufacturing, addressing regulatory, technological, and economic constraints. The methods involve mapping stakeholder needs to platform capabilities, and systematically developing and validating revenue and value-capture models using established frameworks and real-world use cases. Results show that collaborative, modular BMs—built on principles of data sovereignty, interoperability, and trust—enable the creation and distribution of economic and strategic value for operators, data providers, and consumers. Application in predictive maintenance use cases for refineries and wind farms validates these models, demonstrating cost reductions, improved operational efficiency, and compliance with current European data governance standards. The findings suggest that viable BMs for industrial Data Spaces hinge on inclusive stakeholder participation, flexible monetization strategies, and continuous adaptation to regulatory environments. These insights provide practical guidance for stakeholders seeking to realize the full value of data-driven manufacturing ecosystems.

This paper introduces M3fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection. We present the novel federated learning (FL) strategies employed to train M3fed, perform an example experiment with maritime AIS data, and evaluate the results with respect to communication costs and FL model quality by comparing classic centralized M3 and the new federated M3fed.