Aarhus University Centre for Digitalisation, Big Data and Data Analytics (DIGIT)
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Aarhus University Centre for Digitalisation, Big Data and Data Analytics
Finlandsgade 22, 8200, Aarhus (Denmark)
The Aarhus University Centre for Digitalisation, Big Data and Data Analytics (DIGIT) works to strengthen Aarhus University's position within the area of digitalization. DIGIT is one of Aarhus University’s strategic research centers working on developing solutions to the World’s Grand Challenges. The main disruptions happening to societies all over the world is driven by new innovations based on novel IT-based solutions. It is important to help the private and public sectors to conduct business in the most optimal way by developing relevant digital solutions.
The center makes use of the unique opportunities that exist in the interaction between classical science, sector-oriented research and engineering activities. Through collaborations on joint research and innovation projects, we focus on developing research-based technologies and solutions to societal challenges, and actively invite industrial partners and the business sector, both locally, regionally, nationally and internationally to assure impact for the research projects.
As DIGIT is an initiative launched by Aarhus University, it operates under the Danish Act on Universities, i.e. government-funded within the public administration under the Minister for Science, Technology and Innovation.
Vision: to carry out excellent cross-disciplinary research to provide a recognized contribution to the worldwide digital transformation of society to the benefit of mankind.
Mission: to strengthen Aarhus University's position in the general area of digitalization, both nationally as well as internationally.
DIGIT’s Research Activities: Big Data Analysis, Block chain, Cyber-Security, Digital Business Development, Internet of Things, Science and Engineering of Machine Intelligence, Smart products with focus on Cyber-Physical Systems. Our research is initially organized across these research themes for each of which we will achieve excellence. We are integrating world class fundamental and applied research activities within these digital disciplines across Business, Mathematics, Computer Science and Engineering. In order to succeed, DIGIT is strongly committed to closely cooperate with relevant industrial and public partners.
DIGIT’s support to SMEs:
It is a regional priority to increase locals SMEs’s digital capacity, more specifically the ones in the manufacturing, healthcare, energy and agricultural areas. DIGIT is engaged in different collaborations with local SMEs, supporting their innovation, research and development activities. Examples to be mentioned here are: access to powerful computers and computer systems, contacts to highly specialized software developers, programmers and senior experts in the field (machine learning, IoT, AI, security, cyber physical systems, and so on), and cross-disciplinary collaboration with other departments working with digitalization challenges, like business development, mathematics and computer science. Finally, DIGIT can introduce and facilitate access to SMEs to a wide range of lab space and facilities at the Department of Engineering, Aarhus University like ICE-lab, Robotics-lab and others. Visit http://eng.au.dk/en/research/laboratory-facilities/ for further information.
Link to national or regional initiatives for digitising industry
Among the national and European policy initiatives to digitize the industry, DIGIT is part of the Manufacturing Academy of Denmark (MADE) (2014-2019). This consists of:
• MADE SPIR (Strategic Platform for Innovation and Research) (2014-2019) which aims to develop Advanced Manufacturing technologies and strengthen the Danish manufacturing ecosystem. MADE SPIR is funded by a mix of public-private funds amounting to DKK 183,5M (24,4 MEuro)
• MADE Digital (2017-2019) which is a research and innovation platform aimed at developing a Danish approach to Industry 4.0, where there is focus on many of Danish SMEs. MADE Digital has a total budget of DKK 196M (25.8 MEuro)
With regard to regional initiatives, the Smart Industry programme (www.industry40.au.dk) is a project supported by the Region of Central Denmark targeting the innovation in the Small and Medium Enterprises (SMEs) in the region with including innovative digitalization technologies to come up with smarter products. The focus of this project are production, agriculture and energy organisations and this project has a budget of 25 MDKK and it is led by Department of Engineering.
Aligned with the Danish national smart specialization strategy (RIS3) of 2014, which has consistently chosen 5 priority areas: 1. Manufacturing & industry; 2. Energy production & distribution; 3. Sustainable innovation; 4. Human health & social work activities; 5. Agriculture, forestry & fishing.
DIGIT has a particular focus on: Manufacturing & industry, Energy production & distribution, and Agriculture, forestry & fishing.
ACoSim: The ACoSim project related to the application of co-simulation to support test and operations was awarded to EMTECH Space P.C. consortium by ESA/ESTEC. EMTECH is the prime contractor and TWT GmbH, Thales Alenia Space Italia (TAS-I) and Aarhus University (Denmark) are sub-contractors. The purpose is to conduct research on co-simulation techniques to be used in the space domain for Verification & Validation (V&V) activities. The project started with a kick-off meeting on 29 January 2018.
BETHE: The first objective of BETHE is to create a number of ground breaking new multiparty computation (MPC) techniques, which will take MPC research from its current state-of-the-art towards a new state where MPC can be applied to the massively complex computational problems that are met in real life computational and economic settings.
The list below includes research projects completed or started by the DIGIT partners before the DIGIT centre was established. They are all related to the digitalisation theme.
MADE SPIR: MADE is applying research, driving innovation and strengthening education to improve the competitiveness of Danish manufacturing. MADE achieves this by working in collaboration with companies, universities and GTS institutes to make Denmark the world’s most competitive manufacturing country. Here Department of Computer Science lead a work package on Lifelong Product Customization, which and also involves Department of Engineering.
MADE Digital: Denmark will digitalize its manufacturing industry to compete globally with an investment of DKK 196m from Innovation Fund Denmark, 49 companies, five universities, three GTS-Institutes and the Confederation of Danish Industry. The project is headed by MADE – Manufacturing Academy of Denmark, with the purpose of strengthening Danish manufacturing with new knowledge and digital technology. Here Department of Computer Science lead a work package on Digital Assistance tools and Department of Engineering lead a work package on Smart Industrial Products.
INTO-CPS: This is a H2020 project entitled “Integrated Tool Chain for Model-based Design of Cyber-Physical Systems” with a budget at EUR 8 million coordinated by Department of Engineering. It aims to develop a new form of interaction between physical objects and their computer control using different software models. The technology will make it easier to develop credible Cyber-Physical Systems (CPSs) which will be important for industry’s ability to innovate in the future.
Smart Industry: This in a project supported by the Region of Central Denmark targeting the innovation in the Small and Medium Enterprises (SMEs) in the region with including innovative digitalization technologies to come up with smarter products. The focus of this project are production, agriculture and energy organisations and this project has a budget of 25 MDKK and it is led by Department of Engineering.
DESTECS: This "Design Support and Tooling for Embedded Control Software" FP7 project focused on developing design methods and tools that bridge the gap between the disciplines involved in designing an embedded system: systems, control, mechanical and software engineering, for example. This project was a precursor to the INTO-CPS project mentioned above and here Department of Engineering lead a work package on tool development.
Future Cropping: This project has a budget of DKK 100 million, where researchers and companies innovate to improve competitiveness in future Danish agricultural crop production using big data. Here Department of Engineering lead the work involving innovative use of open data and Department of Computer Science is also included.
IoF2020 - Internet of Food and Farm 2020: This is a "Large Scale Project” under H2020 with a large network of 73 partners with a total budget in the order of 225 million DKK over four years. The target is to ensure massive use of IoT technologies in EUs agriculture and food industries. Department of Engineering participates in this project.
SAFE: This project deals with safety related to autonomous operations in the agriculture section. Department of Engineering is involved here using machine learning on video streaming.
Off-line and on-line logistics planning of harvesting processes: This project is funded with 6 MDKK by the Danish Innovation Foundation and the participants are AGCO and Department of Engineering. It is targeting digitalisation of harvesting logistics using modelling principles to derive plans for all vehicles involved in such operations.
DABAI: This "DAnish Centre for Big Data Analytics driven Innovation" project is supported by the Danish Innovation Foundation by 17 Million euro. Here Department of Computer Science plays a central role
Data Science on the Desktop: This is a 6MDKK FTP2 project granted 2017, with Department of Computer Science leading the project.
SODA: A 7M€ H2020 project on Scalable Oblivious Data Analytics involving experts on data analytics and CoED. The focus is on facilitating analysis across data from several data controllers with an aim at the health care use cases by always keeping the data in the encrypted domain and carefully controlling the results published from the analysis. Department of Computer Science is contributing existing CoED technology and theoretical knowhow needed to develop new practical CoED techniques for the given domain.
MPCPRO: 15 MDKK ERC Advanced Grant held by Ivan Damgård at CS. The aim is to advance the theoretical foundation of CoED and to increase the technological readiness level of CoED beyond the current state-of-the-art.
FoCC: 7M DKK DFF-FNU Sapere Aude Starting grant. This is a personal grant held by Claudio Orlandi at Department of Computer Science. The aim is to advance the theoretical foundation of CoED.
CSGB: led from Department of Mathematics, is a Villum Foundation funded Centre working on stochastic analysis of advanced bioimaging data. Its specialist areas include stereology, spatial statistics and statistical image analysis.
Next Generation Internet (NGI): The NGI aims to shape the future internet as an interoperable platform ecosystem that embodies the European values: openness, inclusivity, transparency, privacy, cooperation, and protection of data. The NGI will drive this revolution and ensure the progressive adoption of advanced concepts and methodologies spanning the domains of artificial intelligence, Internet of Things, interactive technologies and more, while contributing to making the future internet more human-centric. Aarhus University is a leading actor in the NGI, coordinating one of the pathfinding CSAs and contributing to several other NGI EU projects.
Market and Services
- Agriculture, hunting and forestry
- Electricity, gas and water supply
- Transport, storage and communication
- Public administration and defence
- Manufacture of machinery and equipment
- Manufacture of electrical and optical equipment
- Other Manufacturing
- Other markets
- TRL1 - Basic principles observed and reported
- TRL2 - Technology concept and/or application formulated
- TRL3 - Analytical and experimental critical function and/or characteristic proof of concept
- TRL4 - Component and/or breadboard validation in laboratory environment
- TRL5 - Component and/or breadboard validation in relevant environment
- TRL6 - System/subsystem model or prototype demonstration in a relevant environment
- TRL7 - System prototype demonstration in an operational environment
- Awareness creation
- Ecosystem building, scouting, brokerage, networking
- Visioning and Strategy Development for Businesses
- Collaborative Researchs
- Concept validation and prototyping
- Testing and validation
- Digital Maturity Assessment
- Education and skills development
Machine Learning for Routing in Baggage Handling Systems (Machine Learning)
The research project involves the company BEUMER Group A/S, one of the world leading companies within Baggage Handling Systems (BHSs), and the industrial PhD student at Department of Engineering, Aarhus University, René Arendt Sørensen, working on using Machine Learning technologies for improving routing in BHSs.
Current routing schemes are based on shortest path algorithms and manually adjusted through an expensive and time consuming trial and error process. Previously, the company had to use the real physical system to find better routing schemes, but lately, they acquired an emulator, allowing the developers to find the bottlenecks and errors much faster and without interrupting a running system. In this project, the goal is to further utilize this emulator to train a Deep Neural Network to select the good routing schemes.
The approach is to use a method called Deep Reinforcement Learning, which is a method within machine learning. Instead of learning from data, Reinforcement Learning relies on experiencing an environment and storing its experience in a Deep Neural Network. To achieve this, three abstraction levels are used. First a simple graph network is used, to find out how to design the model, then the emulator containing a digital version of a real system is used to find out how to train the model on a more realistic system, and last, the model must try to control a real system to test the final result.
Contact: Morten Granum, Beumer Group A/S, Morten.Granum@beumergroup.com.More details: http://digit.au.dk/research-projects/machine-learning/
Automatic Scoring and Selection of Embryos for Improving Standard IVF Treatment (ASSIST)
The research project involves the company Vitrolife A/S, specialising in assisted reproductive technology, and the industrial postdoc at the Department of Engineering, Aarhus University, Mikkel Fly Kragh, working on modern machine learning technologies applied on time-lapse microscopy imaging.
In vitro fertilisation (IVF) treatment is a billion dollar industry. The treatment is performed by fertilising a number of eggs by sperm (producing embryos) outside the body where they are cultured for 2-6 days. Finally, one or more embryos are transferred to the mother’s uterus with the aim of establishing a successful pregnancy. The main challenge is to maximise the probability of pregnancy by choosing the most viable embryo(s) for transfer and for potential cryopreservation (freezing). Today, this is done manually, resulting in tedious work and subjective assessments.
The current project investigates modern computer vision and deep learning technology on time-lapse Hoffman modulation contrast (HMC) microscopy imaging. The objectives are to improve clinical workflow and provide objective and possibly new and undiscovered measures of embryo viability directly related to the probability of pregnancy. Although HMC microscopy remains the preferred imaging modality among embryologists, there only exist a few prototype software systems for automated analysis of this type of image data. The project seeks to transfer recent breakthroughs within computer vision and deep learning to a promising but relatively unexplored field.
DIGIT has provided Vitrolife A/S access to powerful computer systems and servers, software and algorithms.
Contact: Henrik Karstoft, Aarhus University, email@example.com.More details: http://digit.au.dk/research-projects/assist/
Towards autonomy in farming operations: Logistics optimization
According to the UN population projections the world population will exceed 9.7 billion by 2050 and 11.2 billion by 2100 . With an ever-increasing population and limited land suitable for cropping, higher utilization of resources and optimized farming is essential in order to meet the increasing food demand. In the last decades farming machinery have been improved and become more efficient, but are now reaching the limits of what is possible. The vision for future farming embody precision farming, where farming operations are to be carried out by fleets of small intelligent autonomous machines rather than large complex machines. Realization of this vision is out of scope for this PhD project, however, using backcasting techniques the technologies needed and intermediate independent solutions have been identified. The first crucial step towards the future vision is creation of a generic framework that can support logistics planning, real-time supervision, and guidance to operators of machinery in farming operations.
In the automotive industry logistics optimization introduced huge savings and it is expected that similar results will apply in the agricultural industry, especially when the fleet size increases. For all farming operations, logistics optimization involves generation of a common plan including individual vehicle routing and possibly inter-vehicle coordination, which is required for instance during unloading in a harvest operation. The overall goal of the logistics optimization is to minimize travel distance and waiting time for the involved resources. Another very important aspect of the optimization is minimization of soil compaction. Soil compaction leads to pour yield output and is a huge challenge with heavy machinery . Through optimization and simulation of farming operations it is possible to visualize the resulting soil compaction and to take well founded decisions on how to minimize it in future operations.
The aim of this project is to support optimization of the full logistic chain in any farming operation, with any type and size of fleet, with main focus on traditional fleet configurations. This optimization support includes off-line simulation and visualization of the farming operation provided by a Planning Tool and a real-time Optimization Guidance System, that enable operators to follow the calculated optimized plan, similar to the GPS in cars. To clarify, this project does not consider autonomy or safety of the involved vehicles, although this is the next step in the backcasting strategy, but relies on operators to control and steer each vehicle according to a given plan.
Contact: Morten Bilde, AGCO, firstname.lastname@example.orgMore details: http://eng.au.dk/forskning/forskningsprojekter/electrical-and-computer-engineering-research-projects/off-line-and-on-line-logistics-planning-of-harvesting-processes/
(part of) Public organization (part of RTO, or university)
Number of employees
- Horizon 2020
- European Regional Development Fund
- National basic research funding
- National specific innovation funding
- Private funding
- Partner resources
Number of customers annually
Type of customers
- SMEs (<250 employees)
- Large companies, multi-nationals
- Research organisations
BEUMER Group A/S
MAN Energy Solutions
- Cyber physical systems (e.g. embedded systems)
- Robotics and autonomous systems
- Internet of Things (e.g. connected devices, sensors and actuators networks)
- Artificial Intelligence and cognitive systems
- Cyber security (including biometrics)
- Data mining, big data, database management
- Augmented and virtual reality, visualization
- Simulation and modelling
- Cloud computing
- ICT management, logistics and business systems