DPAC - Dependable Platforms for Autonomous systems and Control

The DPAC profile establishes a leading research profile targeting dependable platforms for autonomous systems and control, hosted at Mälardalen University in the Embedded Systems (ES) research environment. This will be accomplished through close collaboration and interaction between ES research groups at MDH and the participating industrial companies. The profile will leverage our solid track record of close cooperation to conduct excellent research, knowledge transfer, and support commercialization with industrial partners. DPAC shall create synergy effects between the partners and a significant increase in coproduction is to be expected.

The ultimate goal of the DPAC profile is to establish a nationally leading and internationally renowned research centre that facilitate close cooperation between academia and industry to achieve a significant increase in research and available knowhow on advanced dependable platforms for embedded systems. Embedded computer systems are nowadays incorporated in many kinds of products including many mission critical applications such as trains, autonomous utility vehicles, aviation, smart grid power management etc. These systems offer advanced functionality and serve an important role for the competitiveness of companies and the future national and global infrastructure. The scientific and technical results of DPAC will support future innovation by providing dependable platforms that can be used to efficiently realize dependable, reliable and safe electronically controlled products.

Four established research groups from MDH will in addition to the staff from companies provide the core competence thrust within DPAC. The research will be organized around three main research areas:

• Predictability and dependability in parallel architectures
• Autonomous systems and control
• Design methodologies

These combined competences give DPAC a unique opportunity to address system-wide research challenges that span several traditional research areas and wide industrial applications as well as forming a robust basis for the research in DPAC.

DPAC brings a wide industrial participation ranging from small-medium enterprises to large multinational corporations. The initial industrial partners are; ABB CRC, ABB Control Technologies, Alten, Arcticus Systems, Bombardier Transportation, BAP, Enea, Ericsson, Hök Instruments, Saab, Volvo Construction Equipment, and Volvo Group Trucks Technology. These companies represent the core of this proposal’s research target and will bring their unique competence and relevant use-cases to facilitate and strengthen the research within DPAC.

DPAC allows a unique opportunity for ES to focus established researchers and new recruits towards the area of dependable systems and platforms. This area is identified as key-area for future growth in both education and research, and where industrial support is already large and anticipated to grow further during the coming decade.

[Show all publications]

Time-Sensitive Networking in Automotive Embedded Systems: State of the Art and Research Opportunities (Sep 2021)
Mohammad Ashjaei, Lucia Lo Bello , Masoud Daneshtalab, Gaetano Patti , Sergio Saponara , Saad Mubeen
Journal of Systems Architecture, 2021 (JSA, 110)

Guest Editorial: Special Issue on Parallel, Distributed, and Network-Based Processing in Next-generation Embedded Systems (Aug 2021)
Saad Mubeen, Lucia Lo Bello , Masoud Daneshtalab, Sergio Saponara
Journal of Systems Architecture, 2021 (JSA, 110)

RoCo-NAS: Robust and Compact Neural Architecture Search (Jul 2021)
Vahid Geraeinejad , Sima Sinaei, Mehdi Modarressi , Masoud Daneshtalab
the international joint conference on neural networks (IJCNN)

A Novel Frame Preemption Model in TSN Networks (Jun 2021)
Mohammad Ashjaei, Mikael Sjödin, Saad Mubeen
Journal of Systems Architecture (JSA)

Simulation and Analysis of In-Orbit Applications under Radiation Effects on COTS Platforms (Mar 2021)
Nandinbaatar Tsog, Saad Mubeen, Moris Behnam, Mikael Sjödin, Fredrik Bruhn
42nd IEEE Aerospace Conference 2021 (IEEEAC2021)

Image Synthesisation and Data Augmentation for Safe Object Detection in Aircraft Auto-Landing System (Feb 2021)
Najda Vidimlic , Alexandra Levin , Mohammad Loni, Masoud Daneshtalab
16th International Conference on Computer Vision Theory and Applications (VISAPP 2021)