Research groups in Embedded Systems
Artificial Intelligence och Intelligent Systems
Foundational and applied research in Artificial Intelligence and Machine Learning for Intelligent Systems for both industry, medical and business applications. The research focuses on methods and techniques enabling learning, reasoning, experience reuse, and experience sharing. We work with both autonomous AI applications as well as decision support systems.Read more about Artificial Intelligence och Intelligent Systems
Automated Software language and Software engineering
The ASSO research group focuses on automating the engineering of software languages and software by applying advanced computation and data manipulation techniques.Read more about Automated Software language and Software engineering
The research within the Biomedical Engineering group focuses on reliable non-invasive physiological data acquisition and signal processing. The aim is to find solutions to real problems and the projects are performed in close collaboration with the public sector.Read more about Biomedical Engineering
Certifiable Evidences and Justification Engineering
This group performs research on languages, techniques, metrics, and processes for engineering evidence(s) and justifications for the purpose of certification/selfassessment.Read more about Certifiable Evidences and Justification Engineering
Complex Real-Time Embedded Systems
We focus on execution and analysis of real-time systems, with a particular focus on multiprocessor scheduling techniques, synchronization protocols, predictable execution of real-time systems, compositional theory and technology, and similar topics related to predictability of real-time systems.Read more about Complex Real-Time Embedded Systems
Cyber-Physical Systems Analysis
The research group is focused on analyzing cyber-physical systems, as concurrent and distributed systems where embedded computers and networks monitor and control the physical processes.Read more about Cyber-Physical Systems Analysis
Formal Modelling and Analysis of Embedded Systems
Focusing on formal modelling, analysis, and verification techniques for real-time embedded systems. In particular, formal syntax and semantics of componentbased and service oriented models with extra-functional properties such as time or resources.Read more about Formal Modelling and Analysis of Embedded Systems
Heterogeneous systems - hardware software co-design
The group aims to boost exploitation of heterogeneous systems in terms of predictability, eﬀective development and eﬃcient software-hardware integration for next-generation intelligent embedded systems.Read more about Heterogeneous systems - hardware software co-design
Industrial Software Engineering
Focusing on engineering of complex software-intensive embedded systems, covering the entire lifecycle and including technologies, methods and processes. Particular emphasis on component- and model-based software engineering for embedded systems.Read more about Industrial Software Engineering
Learning and Optimisation
The group aims to explore the synergy between machine learning and optimization to achieve collaborative effects in building highly efficient and smart systems.Read more about Learning and Optimisation
Model-Based Engineering of Embedded Systems
Development of methods and tools for model-based engineering of embedded systems. Including: models for architectural and behavioral descriptions of system and requirements for systems, techniques for analyzing and transforming models, and runtime architectures for resource efficient, predictable embedded systems.Read more about Model-Based Engineering of Embedded Systems
Worst-case execution time analysis, as well as design and analysis of languages for real-time and embedded systems. Focusing on static program analysis for embedded systems, specializing in Worst-Case Execution Time analysis.Read more about Programming Languages
Real-Time Systems Design
Focusing on design methods, architectures, and communication for real-time system, with current emphasis on functional safety, cybersecurity, adaptive real-time systems, and software testing.Read more about Real-Time Systems Design
Focusing on bridging the theoretical foundations of dependability and industrial software development practices, with an emphasis on the technology and process aspects of complex dependable systems.Read more about Safety-Critical Engineering
More information about Embedded Systems
Fore more information about Embedded Systems,
please contact Mikael Sjödin, Head of Research.
Ongoing research projects
AIDOaRt är ett tre år långt europeiskt projekt som involverar 32 organisationer, grupperade i kluster från sju olika länder, med fokus på AI-förstärkt automatisering som stöder modellering, kodning, testning, övervakning och kontinuerlig utveckling inom Cyber-Physical Systems (CPS) eller inbyggda system.
Project manager at MDU: Gunnar Widforss
Main financing: ECSEL, VINNOVA
The project's overall aim is to develop an autonomous reconnaissance capability for unmanned aerial systems, by further developing the results that have been achieved in previous joint projects.
Project manager at MDU: Peter Funk
Main financing: Vinnova
Detta projekt kommer att undersöka hur man kan kombinera avancerad akademisk forskning med industriellt relevanta problem och metoder för att tillhandahålla förbättrade och kostnadseffektiva lösningar som är direkt tillämpliga i industrin.
Project manager at MDU: Alessandro Papadopoulos
Main financing: Stiftelsen för Strategisk Forskning (SSF)
The INDTECH project is an industrial graduate school at MDU, focusing on the implementation of Industry 4.0 and applied AI in production systems in collaboration with 12 partner organizations consisting of leading industrial companies, research institutes and technology centers and supporting organizations such as AI Sweden, PiiA, Automation Region and Blue Institute. INDTECH Industrial Technology Graduate School offers advanced training in the field of industrial digitization, a new and emerging field of technology that revolutionizes all aspects of the manufacturing and process industry
Project manager at MDU: Markus Bohlin
Main financing: KK-stiftelsen
The IndTech Industrial Digitalization has its origins in the industry's need to expand its digital infrastructure to exchange data, information, and communication within and between companies. The IndTech Industrial Digitalization aims to develop industrial understanding and insights through industrial cases that explore and demonstrate digitalization in practice.
Project manager at MDU: Jakob Axelsson
Main financing: PiiA, Vinnova
The purpose of this project is to study how system developers can design their products to have capabilities that make them effective in an SoS context, and how SoS designers can compose the available elements, called constituent systems (CS), as efficiently as possible to achieve a particular mission.
Project manager at MDU: Jakob Axelsson
Main financing: KKS / Knowledge Foundation
The project aims to increase the efficiency of analysis and management of risks in critical societal interconnected systems-of-systems. This is achieved by risk analysts, who, with a certain amount of work, become able to identify and reduce significantly more risks than they can manage with today's methods and the same amount of effort.
Project manager at MDU: Jakob Axelsson
Main financing: Myndigheten för samhällsskydd och beredskap (MSB)
The objective of the PRE-fall project is to develop an E-health application and sensor solutions enabling the detection of early deteriorations in physical ability which are related to an increased fall risk, and to decrease the risk through the provision of personalized support. The sensor solutions and user interfaces are developed in user-centered iterative design processes.
Project manager at MDU: Annica Kristoffersson
Main financing: The Knowledge Foundation
Detta projekt kommer att utveckla självövervakande och kontinuerliga inlärningsmetoder för att främja en bredare tillgänglighet av datadrivet prediktivt underhåll i kraftnät.Kontinuerligt (och livslångt) lärande har hög potential att stödja mer grundade och exakta underhållsbeslut genom att hantera förändrade förhållanden i kraftnät, som t.ex. åldrande av elektriska komponenter. Fallstudier med data som samlats in från kraftnät kommer att utföras för att utvärdera effektiviteten i de föreslagna metoderna.
Project manager at MDU: Ning Xiong
Main financing: SSF