From Research to Application: Projects at Fraunhofer EMFT

Scientists from the Fraunhofer Institutes for Biomedical Engineering (IBMT) in Sulzbach/Saar and the Fraunhofer EMFT in Regensburg are working together with the companies nanoAnalytics GmbH (Münster) and innoMe GmbH (Espelkamp) on a new measurement method for testing the effectiveness of vaccines in the ViroSens project.

Gear oils make a significant contribution to minimising friction and thus ensure the safe operation of production facilities. In the Smart Gear project, researchers at Fraunhofer EMFT are developing solutions to detect a drop in the performance of gear oils with the help of sensor technology and machine learning methods and to predict when the oil needs to be changed.

In the StroBA project, a Fraunhofer EMFT research team at the Oberpfaffenhofen site is taking a closer look at the effects of environmental impact on connector systems in cooperation with Ostwestfalen-Lippe University of Applied Sciences: the aim here is to look at how damage and aging response is influenced by different types of micromotion (rotation and translation) and by design features and line routing.

Data rates up to the TBit/s range – such is the ambitious goal of the internal Fraunhofer project EOS. In order to get within reach of such extremely high data rates, the research team from Fraunhofer HHI, Fraunhofer IIS and Fraunhofer EMFT is seeking to convert several 56 Gbit/s digital-electrical message signals directly and without power-hungry signal processors (DSP) into a multi-stage, optically complex modulation signal.

Edge computing is considered a key for new IoT applications. In order to bring artificial intelligence into future edge products, Fraunhofer EMFT researchers are working together with Fraunhofer IIS and Fraunhofer IPMS as part of the EU ANDANTE project to develop innovative mixed-signal artificial neural network (ANN) accelerator with computation-in-memory (CIM) ability. These are intended to enable the construction of solid hardware and software platforms for AI application development.

How can computing be managed close to the sensor rather than in the cloud in the future? And how can machine learning be implemented on distributed systems in such a setup? Researchers at Fraunhofer EMFT and eleven other Fraunhofer institutes are addressing these highly topical issues in the SecLearn Arrival innovation project. The focus here is on neuromorphic, energy-efficient hardware components and AI algorithms for decentralized learning as well as data protection.

Whether in automobiles – especially in the context of autonomous driving – or future industrial manufacturing: plugs and electrical connection technologies have a key role to play in digital networking. They are the main interface between machines, control units and data processing systems and so they provide the basis for the functionality, simple handling and reliability of automation technology.

In the LEO project (platform technology for the resource-efficient production of conductive tracks on large-area surfaces equipped with electronics), Fraunhofer ISE and Fraunhofer EMFT are bundling their expertise in fine-line metallization and roll-to-roll processing as well as thin-chip integration in foils.

Approximately 10% of the Western population will need to have an angioplasty surgery at some point due to an arrhythmia or for a heart valve replacement. These minimally invasive heart interventions are supported by a variety of intelligent imaging and sensor catheters that act like "eyes and ears" at the point of intervention. Researchers at Fraunhofer EMFT have developed a micropump implemented at the facility further so that it can be integrated into such "smart" cardiac catheters in the future. The pump could be used to transport stem cells into damaged heart tissue so that it regenerates.

Fraunhofer EMFT is developing intelligent plasters for the early detection of pressure ulcers in nursing care. For this purpose, work is being done on an AI-supported prophylaxis that can initiate preventive actions through early detection, thus making life easier for nursing staff and the person treating the patient.