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]

Optimising Vehicular System Architectures with Real-time Requirements: An Industrial Case Study (Oct 2019)
Arman Hasanbegovic , Marcus Ventovaara , Jimmie Wiklander, Saad Mubeen
IEEE 45th Annual Conference of the Industrial Electronics Society (IECON'19)

NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems (Sep 2019)
Mohammad Loni, Ali Zoljodi , Sima Seenan, Masoud Daneshtalab, Mikael Sjödin
The 28th International Conference on Artificial Neural Networks (ICANN 2019)

Multi-objective Optimization of Real-Time Task Scheduling Problem for Distributed Environments (Sep 2019)
Maghsood Salimi , Amin Majd , Mohammad Loni, Tiberiu Seceleanu, Cristina Seceleanu, Marjan Sirjani, Masoud Daneshtalab, Elena Troubitsyna
6th Conference on the Engineering of Computer Based Systems (ECBS 2019)

TOT-Net: An Endeavor Toward Optimizing Ternary Neural Networks (Sep 2019)
Najmeh Nazari , Mohammad Loni, Mostafa E. Salehi , Masoud Daneshtalab, Mikael Sjödin
22nd Euromicro Conference on Digital System Design (DSD 2019)

Optimized Allocation of Fault-tolerant Embedded Software with End-to-end Timing Constraints (May 2019)
Nesredin Mahmud, Cristina Seceleanu, Hamid Reza Faragardi, Guillermo Rodriguez-Navas, Saad Mubeen
Journal of Systems Architecture (JSA)

Towards a Two-layer Framework for Verifying Autonomous Vehicles (May 2019)
Rong Gu, Raluca Marinescu, Cristina Seceleanu, Kristina Lundqvist
11th Annual NASA Formal Methods Symposium (NFM 2019)