Scientific publications

Much of the work to date on dataflow models for signal processing system design has focused on decidable dataflow models that are best suited for one-dimensional signal processing. This chapter reviews more general dataflow modeling techniques that are targeted to applications that include multidimensional signal processing and dynamic dataflow behavior.

Performance models are mostly simple mathematical formulas. The challenge is to model the performance at an appropriate level. In this presentation we introduce several levels of modeling, labeled zeroth order, second order, et cetera. AS illiustration we use the performance of MRI reconstruction.

Dynamic behavior of streaming applications can be effectively modeled by scenario-aware dataflow graphs (SADFs). Many streaming applications must provide timing guarantees (e.g., throughput) to assure their quality-of-service. For instance, a video decoder which is running on a mobile device is expected to deliver a video stream with a specific frame rate.

Kahn and MacQueen have introduced a generic class of determinate asynchronous data-flow applications, called Kahn Process Networks (KPNs) with an elegant mathematical model and semantics in terms of Scott-continuous functions on data streams together with an implementation model of independent asynchronous sequential programs communicating through FIFO buffers with blocking read and non-blocking write operations.

This book discusses various aspects, challenges, and solutions for developing systems-of-systems for situation awareness, using applications in the domain of maritime safety and security. Topics include advanced, multi-objective visualization methods for situation awareness, stochastic outlier selection, rule-based anomaly detection, an ontology-based event model for semantic reasoning, new methods for semi-automatic generation of adapters bridging communication gaps, security policies for systems-of-systems, trust assessment, and methods to deal with the dynamics of systems-of-systems in run-time monitoring, testing, and diagnosis.

Today’s embedded systems have to operate in a wide variety of dynamically changing environmental circumstances. Adaptivity, the ability of a system to autonomously adapt itself, is a means to optimise a system’s behaviour to accommodate changes in its environment. It involves making in-product trade-offs between system qualities at system level.

In the medical domain there is a tension between the requested speed of innovation and the time needed to deliver a certifiable system. To ensure the required safety, usually a long test and integration phase is needed. To shorten this phase and to avoid late bug fixing, the aim is to detect faults (if any) much earlier in the development process.

There is a strong push towards smart buildings that aim to achieve comfort, safety and energy efficiency, through building automation systems (BAS) that incorporate multiple subsystems such as heating and air-conditioning, lighting, access control etc. The design, commissioning and operation of BAS is already challenging when handling an individual subsystem; however when introducing co-operation between systems the complexity increases dramatically.

This paper provides an overview of the architecture for self-organizing, co-operative and robust Building Automation Systems (BAS) proposed by the EC funded FP7 SCUBA1 project. We describe the current situation in monitoring and control systems and outline the typical stakeholders involved in the case of building automation systems.

To provide a good balance between the performance and flexibility of future digital control platforms, we propose an FPGA-based heterogeneous multiprocessor approach, in which the platform is composed of processing elements from a set of parameterizable heterogeneous Application-Specific Instruction-set Processors (ASIPs), connected with an hierarchical interconnect.