CAPE is a European-funded project targeting to reshape edge-cloud computing by defining edge micro data centers as a new unit of computing. Fully committing to open source, CAPE develops a fully Composable Infrastructure (CI) for high-performance edge server hardware platforms grounded in open, forward-looking standards. Together with an open-source software stack covering the Edge-Cloud Continuum, this holistic approach boosts power and energy efficiency while reducing resource overprovisioning. Completely based on open standards, CAPE strengthens the digital sovereignty Europe needs in a challenging future. This work gives an overview of the current architectural blueprint of the project, focusing on integrating game-changing technologies like Compute Express Link (CXL) for compute and memory disaggregation, pushing open source cluster management, and AI-assisted deployment software stacks using Infrastructure from Code (IfC). The proposed approaches and benefits for future Edge-Cloud data centers are demonstrated within three use cases, ranging from Smart Grid and Edge-AI to Satellite Data Processing.

This paper addresses the need for an indoor-focused, easy-to-replicate road-sign dataset that captures unprocessed raw image data. Existing datasets typically focus on processed RGB images, which limits their usefulness for research on embedded, end-to-end machine learning. To fill this gap, a Raspberry Pi Camera Module v1.3 was mounted on autonomous mini robots, which ran continuously in various indoor settings. Over a period of two months, approximately 70,000 10-bit, 5-megapixel images were stored as TIFF files. Exposure and ISO were intentionally varied to introduce motion blur, noise, overexposure, and underexposure. The resulting labeled dataset comprises around 47,000 bounding boxes for 87 sign categories, including danger, regulatory, directional, demo-specific, and unknown signs. This work provides a compact, low-cost framework that enables researchers and educators to explore algorithms on raw images in a reproducible indoor setting through both long-term data collection and in-classroom demonstrations.

This paper explores a method for generating plausible cable routings in free space by combining curve-energy formulations from structural dynamics, knot theory, and robot path planning. The approach minimizes a composite energy functional—accounting for smoothness and collision avoidance—while encouraging a predefined cable length through an arc-length energy term. Preliminary results demonstrate the method’s ability to produce smooth, collision-free cable curves in simple example tasks, offering a physics-inspired foundation for early-stage design of electrical wiring in free space.

Although digital twins are increasingly being used to represent physical assets in industrial automation, most of them remain passive, merely building a digital shadow of the asset. Their potential as active, autonomous components in cognitive control architectures remains largely unexplored. This paper presents a novel approach to realize executable digital twins within cognitive operators by embedding the Sense–Plan–Act paradigm into standardized submodels of the Asset Administration Shell. Specifically, the submodel Time Series Data is used to capture dynamic system state for the sensing phase, while the Asset Interfaces Description represents executable interactions for the planning phase. By enabling each assets’ cognitive operator to interpret these submodels, distributed systems can reason and act through their digital representation, while maintaining semantic interoperability and compliance with standards. The concept is validated in a decentralized task allocation scenario using modular autonomous robots. Initial results confirm the technical feasibility and reusability of the approach and highlight the potential of semantically enriched digital twins in future industrial systems.

Klarhorst C, Quirin D, Hesse M, Rückert U. A Digital Twin Implementation for the AMiRo. In: 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2024: 1-4.

One necessary part of Industry 4.0 is the availability and accessibility of data processing pipelines. This paper shows the ongoing development of ML4ProFlow, a framework that brings together the following parts: First, it provides the management of execution environments. Second, it specifies processing modules that focus on reusability and cross-platform usage. Third, it comes with a benchmarking automation to help developers implementing and analyzing modules and their combination. Those three integral parts of the framework are presented and the usability is shown.

Today's high complexity and required expertise in various disciplines for data-based evaluations of shop-floor assets is challenging. This paper describes the ongoing development towards an Industry 4.0 ecosystem enabling Smart Services and shop-floor assets to network autonomously. Three partial solutions are combined for this purpose: Industry 4.0 digital twins, automated data streams and a Smart Service toolbox. A prototypical implementation proves the general practicability. Furthermore, future work is outlined to achieve full autonomy.

Embedded streaming applications are facing increasingly demanding performance requirements in terms of throughput. A common mechanism for providing high compute power with a low energy budget is to use a very large number of low-power cores, often in the form of a Massively Parallel System on Chip (MPSoC). The challenge with programming such massively parallel systems is deciding how to optimally map the computation to individual cores for maximizing throughput. In this work we present an automatic parallelizing compiler for the StreamIt programming language that efficiently and effectively maps computation to individual cores. The compiler must be both effective, meaning that it does a good job of optimizing for throughput; but also efficient, in that the time taken to find such a mapping must scale well as the number of cores and size of the Stream program increases. We improve on previous work that used Integer Linear Programming (ILP) to map StreamIT programs to multicore systems by formulating the mapping problem in a different way using mostly real rather than integer variables. Using so called Mixed Integer Linear Programming (MILP) dramatically reduces the cost compared to standard ILP. This alternative formulation creates what we call an optimistic solution that we then need to adjust slightly to obtain a final feasible solution. We show that this new approach is always close, if not better in terms of effectiveness, while being dramatically better in terms of scalability and efficiency.