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Embedded Systems

The research specialisation of Embedded Systems focuses on developing the technology that is used to control various products such as cars, robots and machines. This research ranks internationally among the best in the world.

The researchers work close to industry and create technology which makes it possible to increase safety in health care, reduce risks in industry and simplify everyday life through smart solutions in our homes. A large part of this research is conducted in cooperation with industrial partners such as ABB and Volvo.


Four areas of focus

Computer and Data Science (CDS)

Computer and Data Science (CDS) conducts research on novel approaches, algorithms, techniques, and tools in AI (Artificial Intelligence) and machine learning, optimization, heterogeneous data from various sensing devices, software and hardware for robots, brain-like computing, as well as formal methods and static program analysis. Our models and algorithms can make predictions about physical and environmental phenomena and improve the systems’ ability to adapt and refine their behavior in real time, with a high degree of predictability assurance. The research ranges from theoretical contributions to applied ones in close collaboration with industry.

CDS is active in research and education on the following fronts:
1) Artificial intelligence, machine learning and optimization,
2) Formal methods and static analysis of programs, and
3) Robotics and data analysis.

Leader: Cristina Seceleanu External link.

 

Electrical and Computer Engineering (ECE)

The Electrical and Computer Engineering (ECE) research specialization conducts research with a focus on the (embedded) system’s run-time platform including execution of software, communication of data and control, and runtime adaptation mechanisms. Research at ECE addresses systems that are integrated into a physical, computing, or electrical environment, and are of criticality that require high reliability, high degree of safety and security, predictable timing, and performance, and often have limited resources for computation, communication, and energy. Such systems typically combine analog and digital hardware interconnected by wired or wireless communication as well as software for controlling the functionality of the system.

ECE is rooted in the classical academic subjects of Computer Engineering (CE) and Electrical Engineering (EE). Here, MDU’s researchers are particularly active within the following research areas
1) embedded and distributed systems, looking at predictable and efficient run-time platforms, protocols and mechanisms for execution of embedded and distributed systems’ software,
2) data communication, looking at research towards dependable communication through design, measurement and evaluation of theories and algorithms, aiming towards robust protocols for wired and wireless communication in time-critical applications, and
3) automation and control systems, including research towards modelling, analysis, optimization, and design of control systems for industrial applications, with focus on automation, robotics, and distributed systems applications.

Our main application areas include vehicular systems, process automation, and industrial robotics, where several solutions to research challenges are developed in close collaboration with industrial partners.

Leader: Thomas Nolte External link.

 

Medical and health engineering (MHE)

Medical and health engineering is an interdisciplinary field that unites engineering and medical sciences to create cutting-edge technologies with the purpose to diagnose, treat, and prevent diseases, as well as to support human well-being. The field spans from the design and development of new devices, systems and methods to the improvement of existing ones.

The researchers within the Medical and Health Engineering (MHE) research area at MDU have competences in medical engineering, neuroengineering, sensor technology, sensor systems, electrical engineering, computer engineering, computer science, signal processing, artificial intelligence, human-system interaction, user-centered design, as well as physiology, psychology, and health.

A vital part of the research is the collaboration with scientific experts from other engineering disciplines including data communication and robotics, and with stakeholders representing the medical- and health sector, municipalities, and the private sector.

Leader: Maria Lindén External link.

 

Software and Systems Engineering (SSE)

Software and Systems Engineering (SSE) conducts research on theory, methods, processes, algorithms, and tools to support the design, development, testing, and maintenance of industrial software and software-intensive systems, including research on model-based development to simplify development and operation, dependability (e.g., safety and security) to assure that systems can be sufficiently trusted, and software testing, addressing quality attributes needed for dependability and performance.

Leader: Jan Carlson External link.

 

Third-cycle studies - our PhD programmes

Apart from conventional research studies within the subjects of Computer Science and Electronics, two company research schools in Embedded Systems are conducted in collaboration with a number of industrial companies. These company research schools give staff at the companies the opportunity to further educate themselves as researchers and to take doctoral degrees.

  • ITS ESS-H is a company research school in Embedded Sensor Systems for Health, which is funded by the Knowledge Foundation.
  • Array is an company research school in automation, developed in collaboration with several of the world's leading automation companies. It is funded by the Knowledge Foundation.
  • RELIANT is an industrial graduate school for resilient intelligent autonomous systems, funded by the Knowledge Foundation.


Research profile

A research profile is a long-term strategic venture that involves researchers from several areas of skills. MDU's research profiles are conducted in collaboration with the private sector, which means that representatives from the companies involved work side by side with the researchers in the profile. Its purpose is to make use of one another’s skills and thereby achieve a better result.


DPAC

Research profile Dependable platforms for autonomous systems and control

Research profile DPAC

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

Biomedical 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

Data Communication

Data Communication, a part of the Division of Networked and Embedded Systems.

Read more about Data Communication

Dependable Software Engineering

Methods and processes for engineering dependable software systems.

Read more about Dependable Software Engineering

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

Programming Languages

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

Robotics

The robotics research group is mainly conducting research in the area of autonomous collaborating systems.

Read more about Robotics

Safety-Critical Engineering

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

Software Testing Laboratory

Testing of embedded software, empirical studies of software testing, test automation and model-based testing.

Read more about Software Testing Laboratory

Ubiquitous Computing

Computing as environmental process and environment as computing devices.

Read more about Ubiquitous Computing

More information about Embedded Systems

Fore more information about Embedded Systems,
please contact Mikael Sjödin, Head of Research.

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Ongoing research projects

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

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