Bridge projects are multidisciplinary research and innovation projects directed by DIREC researchers in collaboration with companies, the public sector and GTS institutes with the aim of increasing the companies’ digitalisation and innovation capacity.
Spin-qubit quantum-dot arrays are one of the most promising candidates for universal quantum computing. However, with the size of the arrays, a bottleneck has emerged: Tuning the many control parameters of an array by hand is time-consuming and very expensive. The nascent spin-qubit industry needs a platform of algorithms that can be fine-tuned to specific sensing hardware, and which allows cold-start tuning of a device.
Building an internet voting system is not easy: The design of new cryptographic protocols is error-prone, and public trust in the elected body is easily threatened. Together with an industrial partner, this project aims to improve the security and quality of the internet voting system and influence regulation on minimum quality requirements for blockchains.
Together with industrial partners, this project will investigate production scenarios where a machine can be operated by untrained personnel by using low-code development for adaptive and re-configurable robot programming of logistic tasks.
There is constant interest for Internet Voting by election commissions around the world. Greenland illustrates this well. Greenland’s election law was changed in 2020, which now permits the use of Internet Voting. Together with the authorities in Greenland, this project will investigate the effects of program verification on public trust in election technologies. The project aims to contribute to making internet elections more credible, which can strengthen developing and post-conflict democracies around the world.
Based on observations of the Earth, a range of Danish public organizations build and maintain important data foundations that are used for decision-making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark. This project aims to support the digital acceleration of the green transition by strengthening the data foundation for environmental data.
Remote and hybrid work will certainly be part of most work practices, but what should these future work practices look like? Should we merely attempt to fix what we already have or can we be bolder and speculate a different kind of workplace future? Together with companies, this project seeks a vision of the future that integrates hybrid work experiences with grace and decency.
IoT devices are blending into the infrastructure of both society and our personal lives. However, many of these devices run in uncontrolled, potentially hostile environments, which makes them vulnerable to security attacks. Moreover, with the increasing number of safety critical IoT devices, such as medical and industrial IoT devices, IoT security is a public safety issue.
Embedded AI will revert the current AI processing flow from collecting data at the edge and processing it at the cloud, to a flow where AI algorithms are migrated from the cloud to a distributed network of AI enabled edge-devices, which will increase responsiveness and functionality, reduced data transfer, and increased resilience, security, and privacy.
How can we leverage the potential benefits of having multiple robots working in parallel to reduce time to completion? If robots are given the task collectively as a swarm, they could potentially coordinate their operation on the fly and adapt based on local conditions to achieve optimal or near-optimal task performance. Together with industrial partners, this project aims to address multi-robot collaboration and design and evaluate technological solutions that enable users to engage and control autonomous multi-robot systems.
Together with clinicians, this project aims to develop explanatory AI that can help medical staff make qualified decisions by taking the role as a mentor who provides feedback and advice for the clinicians. It is important that the explainable AI provides good explanations that are easy to understand and utilize during the medical staff’s workflow.
Together with industry, the project aims to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.
Together with industry, this project aims to develop methods and tools that enable the industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.
Both society and industry have a substantial interest in well-functioning outdoor and indoor mobility infrastructures that are efficient, predictable, environmentally friendly, and safe. For outdoor mobility, reduction of congestion is high on the political agenda as is the reduction of CO2 emissions, as the transportation sector is the second largest in terms of greenhouse gas emissions. For indoor mobility, corridors and elevators represent bottlenecks for mobility in large building complexes.
Today, manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table. Together with FOSS, this project aims to develop and validate a method of automated imaging-based solutions that can replace subjective manual inspection and improve performance, robustness and consistency of the inspection.
Downtime of equipment is costly and a source of safety, security and legal issues. Today, organisations adopt a conservative schedule of preventive maintenance independent of the condition of equipment. This results in unnecessary service costs and occasional interruptions of production due to unexpected failures.
The rapidly growing application of machine learning techniques in cyber-physical systems leads to better solutions and products in terms of adaptability, performance, efficiency, functionality and usability. However, cyber-physical systems are often safety critical, e.g., self-driving cars or medical devices, and the need for verification against potentially fatal accidents is of key importance.
