Scientific publications

Feedback control applications are robust to occasional deadline misses. This opens up the possibility of saving scarce (computation and communication) resources on embedded platforms. Stability and performance requirements of a control loop impose restrictions on acceptable patterns of deadline misses (e.

The booming popularity of data science is also affecting high-tech industries. However, since these usually have different core competencies — building cyber-physical systems rather than e.g. machine learning or data mining algorithms — delving into data science by domain experts such as system engineers or architects might be more cumbersome than expected.

Over the last decades, the complexity of high-tech systems, and the softwaresystems controlling them,has increased considerably. In practice, it is hard to keep knowledge and documentation of these ever-evolving software systems up-to-date with their actual realization; we are dealing with legacy software.

Many high data-rate video-processing applications are subject to a trade-off between throughput and the sizes of buffers in the system (the storage distribution). These applications have strict requirements with respect to throughput as this directly relates to the functional correctness. Furthermore, the size of the storage distribution relates to resource usage which should be minimized in many practical cases.

Complex real-time video processing applications with strict throughput constraints are commonly found in a typical healthcare application. The video processing chain is implemented as Field-Programmable Gate Array (FPGA) accelerators (processing blocks) communicating through a number of First-In First-Out (FIFO) buffers.

High-end manufacturing systems are cyber-physical systems where productivity depends on the close cooperation of mechanical (physical) and scheduling (cyber) aspects. Mechanical and control constraints impose minimal and maximal time differences between events in the product flow. Sequence-dependent constraints are used by a scheduler to optimize system productivity while satisfying operational requirements.

(m, k)-firm real-time tasks must meet the deadline of at least m jobs out of any k consecutive jobs to satisfy the firmness requirement. Scheduling of an (m, k)-firm task requires firmness analysis, whose results are used to provide system-level guarantees on the satisfaction of firmness conditions. We address firmness analysis of an (m, k)-firm task that is intended to be added to a set of asynchronous tasks scheduled under a Static-Priority Preemptive (SPP) policy.

Multi-scale dataflow models have actors acting at multiple granularity levels, e.g., a dataflow model of a video processing application with operations on frame, line, and pixel level. The state of the art timing analysis methods for both static and dynamic dataflow types aggregate the behaviours across all granularity levels into one, often large iteration, which is repeated without exploiting the structure within such an iteration.