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:
- Artificial intelligence, machine learning and optimization,
- Formal methods and static analysis of programs, and
- 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
- embedded and distributed systems, looking at predictable and efficient run-time platforms, protocols and mechanisms for execution of embedded and distributed systems’ software,
- 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
- 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.
- Array External link. 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 External link. is an industrial graduate school for resilient intelligent autonomous systems, funded by the Knowledge Foundation.
Research profiles
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 SystemsAutomated 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 engineeringBiomedical 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 EngineeringCertifiable 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 EngineeringComplex 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 SystemsCyber-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 AnalysisData Communication
Data Communication, a part of the Division of Networked and Embedded Systems.
Read more about Data CommunicationDependable Software Engineering
Methods and processes for engineering dependable software systems.
Read more about Dependable Software EngineeringFormal Modelling and Analysis of Embedded Systems
Focusing on formal modelling, analysis, and verification techniques for real-time embedded systems.
Read more about Formal Modelling and Analysis of Embedded SystemsHeterogeneous 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-designIndustrial 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 EngineeringLearning 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 OptimisationModel-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 SystemsNeuroengineering
The neuroengineering team focuses on advancing Brain-Machine Interface (BMI) technology. We are specifically interested in closed-loop neurofeedback systems in its interaction with the brain, thus including both systems engineering and neuroscience.
Read more about NeuroengineeringProgramming 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 LanguagesReal-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 DesignRobotics
The robotics research group is mainly conducting research in the area of autonomous collaborating systems.
Read more about RoboticsSafety-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 EngineeringSoftware Testing Laboratory
Testing of embedded software, empirical studies of software testing, test automation and model-based testing.
Read more about Software Testing LaboratoryUbiquitous Computing
Computing as environmental process and environment as computing devices.
Read more about Ubiquitous ComputingMore information about Embedded Systems
Fore more information about Embedded Systems,
please contact Jan Carlson, Head of Research.
Ongoing research projects
The objective of the project is to get a better understanding of measuring techniques to monitor physiological parameters regarding lung function, by integrating sensors solutions that include analyze and see the possibility to use this sensor solutions as biofeedback to be able to provide comprehensive insights to patients regarding their health status and improve adherence to self-management strategies outside clinical settings.
Project manager at MDU: Azadeh Ghajarjazy
Main financing: Center för välfärdsförändring
The purpose of the project is to, in close collaboration with the department of rehabilitation medicine at Danderyd Hospital, implement ElectroEncephaloGraphy (EEG) mediated NeuroFeedback (NF) rehabilitation for stroke patients to study 1) the mechanisms of neuroplasticity that enable improved motor recovery and 2) the impact of severity of motor dysfunction on a) neuroplasticity, and b) on the hand grasping motor outcome of the NF rehabilitation.
Project manager at MDU: Elaine Åstrand
CPMXai will develop a digital twin for cognitive predictive maintenance through automatic data labelling, AI/ML and Explainable AI (XAI) to reduce unwanted situations and enhance maintenance in manufacturing and production processes.
Project manager at MDU: Shahina Begum
Main financing: Vinnova, Product2030
This initiative will conduct key research needed to enable ubiquitous access to cloud services in real-time, making this technology available also to operations in industrial systems.
Project manager at MDU: Thomas Nolte
Data-driven development methods show great promise in producing accurate models for perception functions such as object detection and semantic segmentation, however most of them lack a holistic view for being implemented in dependable systems. This project proposal aims at producing Machine Learning (ML) models of robust nature to meet and stay ahead of emerging certification requirements.
Project manager at MDU: Masoud Daneshtalab
Main financing: Stiftelsen för strategisk forskning
The primary purpose of the project is to develop an AI-based trusted smart system (TSS) for real-time cardiovascular health monitoring, which will enable accurate, non-invasive measurement and prediction of physiological parameters such as blood pressure and arterial stiffness, based on photoplethysmography (PPG) signals.
Project manager at MDU: Maria Lindén
Main financing: Mälardalens universitet
In this joint project, we aim at decreasing the power consumption and computation load of the current image processing platform by employing the concept of computation reuse.
Project manager at MDU: Masoud Daneshtalab
Main financing: STINT - The Swedish Foundation for International Cooperation in Research and Higher Education
GreenDL aims to provide novel theoretical foundations and practical algorithms to automatically design scalable energy-efficient DL models with low energy footprint and facilitate fast deployment of complicated DL models for various Edge devices satisfying given hardware constraints This project will remarkably investigate performance analysis and modeling, optimization, and learning algorithms followed by extensive experiments.
Project manager at MDU: Masoud Daneshtalab
Main financing: Vetenskapsrådet
HIVEMIND is a project aiming to advance responsible and human-centric software engineering methods, tools and best practices leveraging AI and data technologies to accelerate the whole software development lifecycle.
Project manager at MDU: Mobyen Uddin Ahmed
Main financing: European Union’s Horizon 2020
The project goal is to overcome the main challenges of synthesis and verification of safe and secure AD controllers, which exist in the current automotive industry in Sweden.
Project manager at MDU: Rong Gu
Main financing: KK-stiftelsen
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
ITS-EASY is an industrial research school in Embedded Software and Systems, affiliated with the School of Innovation, Design and Engineering (IDT) at Mälardalen University (MDU), as an integrated part of the MDU strategic research area Embedded Systems (ES).
Project manager at MDU: Kristina Lundqvist
Main financing: The Knowledge Foundation
MONA LISA utnyttjar data som genereras vid utveckling och produktion av komplexa CPS. Det gör det genom att analysera och visualisera beteendet hos modeller, hårdvara och mjukvara. MONA LISA säkerställer att nya system utformas med sådana möjligheter i åtanke och kommer att integrera data som genereras av nästa generations designverktyg
Project manager at MDU: Wasif Afzal
Main financing: Vinnova
The project focuses on modeling and analyzing event-based asynchronous autonomous systems for safety assurance, performance evaluation, and optimization
Project manager at MDU: Marjan Sirjani
Main financing: The 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 project aims to develop an ICT-enabled, data-driven, decision-support system that implements a practical economic replacement time model based on high-quality, real cost data and environmental parameters.
Project manager at MDU: Wasif Afzal
Main financing: Vinnova
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.
Project manager at MDU: Annica Kristoffersson
Main financing: The Knowledge Foundation
The overall objective of SEINE is to develop novel techniques, industrial tools and validators to support automatic self-configuration of predictable industrial communication networks.
Project manager at MDU: Saad Mubeen
Main financing: KK-Stiftelsen
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
Our project centers around urban food production and consumption and local chain, foodtech, alternative future food and local varieties of Scandinavian region that could all together build a resilient Nordic food system.
Project manager at MDU: Baran Cürüklü
Main financing: Europeiska unionen
Software Center is a collaboration between five academic partners and thirteen companies, dedicated to accelerating industrial digitalization and to support a continuous exchange of knowledge between the companies.
Project manager at MDU: Jan Carlson
The long-term goal of this project is to identify key factors that hinder the combination of model- based development and continuous integration, and to develop methods, techniques and tools to help alleviate them.
Project manager at MDU: Jan Carlson
The goal of the TRUSTY project is to provide adaptation in the level of transparency to enhance the trustworthiness of AI-powered decisions in the context of remote digital towers (RDTs).
Project manager at MDU: Mobyen Uddin Ahmed
Main financing: EU, Sesear3 joint undertaking
The primary focus area is gAI-based ‘forecasting’. In complex operational scenarios, it will monitor, detect, and forecast industrial equipment and machine conditions considering different data modalities, e.g. image, text, and tabular data demonstrated in the use cases.
Project manager at MDU: Shahina Begum
Main financing: Vinnova
Worst-Case Execution Time (WCET) analysis tries to find an upper bound for the time needed to execute a program. Such WCET bounds are very important when designing and verifying real-time systems. Current industrial practice is to estimate these bounds from measurements, something often complicated and error-prone.
Project manager at MDU: Björn Lisper