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Many embedded systems have complex timing constraints and, at the same time, have flexibility requirements which prohibit offline planning of the entire system. To support a mixture of time-triggered and event-triggered tasks, some industrial systems deploy a table-driven dispatcher for time-triggered tasks complemented with a preemptive scheduler to allocate the free time slots to event-driven tasks. Contrary to fully preemptive scheduling, limiting the preemptions of tasks to fixed preemptions points may reduce memory requirements and it alleviates the preemption costs in the system. We revisit slotshifting, which at run time mixes time-triggered and event-triggered tasks in a preemptive EDF schedule. In this paper, we extend slotshifting with limited-preemptive execution of event-triggered tasks. We present a synchronization protocol to arbitrate the executions of non-preemptive regions, so that time-triggered tasks keep meeting their timing constraints implicitly. Furthermore, we investigate how to disable preemptions of event-triggered tasks during the execution of the offline-scheduled time-triggered tasks, while keeping the feature of slotshifting to reallocate their slots of execution dynamically at run time.

Mälardalen University has a long history of a successful cooperation and coproduction with the industry and public sector in Sweden. This has eventually led it to become one of the leading higher education institutes in Sweden for excellent coproduction with different societal actors, both internationally and nationally. The university has through its coproduction activities become convinced of its value and of the wide range of opportunities it can bring to all parties involved. In this paper, we share our experience through some good examples both from research and education and discuss what is needed for successful and sustainable coproduction with industry and public sector.

Nelder-Mead method is a powerful mathematical technique for multidimensional unconstrained optimization. The other merit of the Nelder-Mead method is that it does not require any derivative information, which makes it suitable for exploitation in non-smooth regions. This paper proposes a new memetic algorithm incorporating Nelder-Mead method for local search. Nelder-Mead method coupling with GA will lead to utilization of more information derived from mathematical principle in the search process, thereby increasing the efficiency of search. The effectiveness of the proposed algorithm has been demonstrated in a case study of optimization of a fuzzy control system.

Many embedded systems have complex timing constraints and, at the same time, have flexibility requirements which prohibit offline planning of the entire system. To support a mixture of time-triggered and event-triggered tasks, some industrial systems deploy an RTOS with a table-driven dispatcher complemented with a preemptive scheduler to allocate the free time slots to event-driven tasks. Rather than allocating dedicated time-slots to time-triggered tasks, in this work we provide RTOS support to dynamically re-allocate time-slots of time triggered tasks within a pre-computed time range to maximize the available processing capacity for event-triggered tasks. Although the concept - called slotshifting - is not new, we are the first to extend a commercial RTOS with such support. In addition, we extend slot shifting with a run-time mechanism to reclaim resources of time-triggered tasks when their reserved capacities are unused. This mechanism eliminates over-provisioning of capacities to (event-triggered) tasks that have been converted to periodic tasks to resolve interdependencies during off-line synthesis, e.g. allowing for a resource-efficient implementation of a polling task. After implementing our unique RTOS extensions, we investigate the run-time overheads for the corresponding scheduling mechanisms. Measurements show that the increase in terms of absolute run-time overhead is minor compared to an off-the-shelf micro-kernel with a fixed-priority scheduler

Many embedded systems have complex timing constraints and, at the same time, have flexibility requirements which prohibit offline planning of the entire system. To support a mixture of time-triggered and event-triggered tasks, some industrial systems deploy a real-time operating system (RTOS) with a table-driven dispatcher complemented with a preemptive scheduler to allocate free time slots to event-driven tasks. Rather than allocating dedicated time-slots to time-triggered tasks, we propose to dynamically re-allocate time-slots of time-triggered tasks within a pre-computed time range to maximize the available processing capacity for event-triggered tasks. Although the concept - called slotshifting - is not new, we are unaware of a commercial RTOS with such support. After identifying the mechanisms for an RTOS implementation of slotshifting, we discuss the run-time overheads for admitting aperiodic requests into the system1.

In this paper, we present a combined structural and inter-frame skipping method for quality aware processing of MPEG-2 video upon overload situations. The method selects video frames both based on the structural properties, such as frame types, sizes and position in the video stream, as well as on the internal, sub-frame characteristics, i.e., the number of relevant macroblocks within a frame.

In this paper, we propose a novel approach for flexible representation of complex timing constraints for scheduling real-time tasks under different scheduling paradigms. It allows different instantiations of the task attributes, depending on which underlying scheduling policy is used. Scheduling parameters are flexibly derived from original timing constrains, rather than using the same set of task attributes for all schedulers. For each real-time task in the system, temporal feasibility windows are identified, such that if the task executes and completes within its feasibility window, the original timing constraints will be met. Then, scheduler dependent parameters are derived to guarantee the tasks' execution and completion within their feasibility windows.

This is a textbook developed for use in the Master Programme Module E-M.6 "Real-Time Systems" as part of the Postgraduate Distance studies organized by Fraunhofer IESE and the Distance and International Studies Center at the Technical University of Kaiserslauten, Germany.

Many industrial applications with real-time demands are composed of mixed sets of tasks with a variety of requirements. These can be in the form of standard timing constraints, such as period and deadline, or complex, e.g., to express application specific or non temporal constraints, reliability, performance, etc. Arrival patterns determine whether tasks will be treated as periodic, sporadic, or aperiodic. As many algorithms focus on specific sets of task types and constraints only, system design has to focus on those supported by a particular algorithm, at the expense of the rest. In this paper, we present a method to deal with a combination of mixed sets of tasks and constraints: periodic tasks with complex and simple constraints, soft and firm aperiodic, and in particular sporadic tasks. We propose the use of an offline scheduler to manage complex timing and resource constraints of periodic tasks and transform these into a simple EDF model with start-times and deadlines. On top of the offline schedule, sporadic tasks are guaranteed based on their worst-case activation frequencies. Then at run-time, an extension to EDF ensures feasible execution of tasks with complex constraints in the presence of additional tasks or overloads. It allows changes in the offline generated schedule to insert soft and firm aperiodic tasks by shifting the execution of offline scheduled tasks within their feasibility windows. Furthermore, the online algorithm uses the exact knowledge about sporadic arrivals to reduce the pessimism introduced by worst-case assumption, i.e., the resources unused by sporadic tasks are reclaimed to improve response times and acceptance of firm aperiodic tasks. A simulation study underlines the effectiveness of the proposed approach.

In this paper, we will present the collaboration between Malardalen University and the local industry in the context of our two master programs in computer science, Master in Software Engineering and Master in Intelligent Embedded Systems. Within both programs, there is an extensive cooperation with national giants such as ABB, Bombardier, Ericsson, SAAB and Volvo, as well as strategically important, high competence, SMEs. We will describe how we use our industrial partners to assure the quality and industrial relevance of our educational master programs. For example, they provide us sharp industrial equipment, give guest lectures at our courses, organize industrial visits, provide internships and theses for our students, and, whenever possible, give employment to our students. The education method that we use is Problem-Based Learning (PBL), which implies that large parts of the programs are performed in project form. The projects are supplied by our industrial partners, and are solved in collaboration with them. The aim with sharp industrial projects is to prepare students for work in industry directly after the completion of their education, and to make our students are highly competitive on the national and international job market, both in the industry as high-skilled expert and in the academia as researchers or PhD students.