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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.


Six areas of focus

The research is conducted in the following areas:

  • Real-time systems
  • Software development
  • Dependable systems
  • Verification and validation
  • Sensor Systems and Health
  • Robotics and Avionics

Furthermore research is conducted in wireless communication and artificial intelligence.


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 EASY is a company research school in Computer Science and Software Technology in which eight companies participate. This is funded by the Knowledge Foundation.
  • 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.


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

ESS-H+

Research profile Embedded Sensor Systems for Health Plus

Research profile ESS-H+

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 Research leader Mikael Sjödin.

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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

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 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)

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