Mariusz Pelc

Embedding Dynamic Behaviour into a Self-configuring Software System

This paper describes a methodology for embedding dynamic behaviour into software components. The implications and system architecture requirements to support this adaptivity are discussed. This work is part of a European Commission funded and industry supported project to produce a reconfigurable middleware for use in automotive systems. Such systems must be trustable against illegal internal behaviour and activity with external origins, additional devices for example. Policy-based computing is used here as an example of embedded logic. A key contribution of this work is the way in which static and dynamic aspects of the system are interfaced, such that the behaviour can be changed very flexibly (even during run-time), without modification, recompilation or redeployment of the embedded application code. An implementation of these concepts is presented, focussing on achieving trust in the use of dynamic behaviour.

A Run-Time Configurable Software Architecture for Self-Managing Systems

This paper describes a highly flexible component architecture, primarily designed for automotive control systems, that supports distributed dynamically- configurable context-aware behaviour. The architecture enforces a separation of design-time and run-time concerns, enabling almost all decisions concerning runtime composition and adaptation to be deferred beyond deployment. Dynamic context management contributes to flexibility. The architecture is extensible, and can embed potentially many different self-management decision technologies simultaneously. The mechanism that implements the run-time configuration has been designed to be very robust, automatically and silently handling problems arising from the evaluation of self- management logic and ensuring that in the worst case the dynamic aspects of the system collapse down to static behavior in totally predictable ways.

Implementation of an inexpensive EEG headset for the pattern recognition purpose

There are many types of bio-signals with various control application prospects. In this work possible control application domain of electroencephalographic signal obtained from an easily available, inexpensive EEG headset - Emotiv EPOC was presented. This work also involved application of an embedded system platform. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. Potential implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. In this work traditional, statistical methods were neither used nor described.

Context-Aware Adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, hav- ing a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are stat- ically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met.

Flexible and Robust Run-Time Configuration for Self-Managing Systems

This paper describes a methodology for deploying flexible dynamic configuration into embedded systems whilst preserving the reliability advantages of static systems. The methodology is based on the concept of decision points (DP) which are strategically placed to achieve fine-grained distribution of self-management logic to meet application-specific requirements. DP logic can be changed easily, and independently of the host component, enabling self-management behavior to be deferred beyond the point of system deployment. A transparent Dynamic Wrapper mechanism (DW) automatically detects and handles problems arising from the evaluation of self-management logic within each DP and ensures that the dynamic aspects of the system collapse down to statically defined default behavior to ensure safety and correctness despite failures. Dynamic context management contributes to flexibility, and removes the need for design-time binding of context providers and consumers, thus facilitating run-time composition and incremental component upgrade.

Context-aware reconfiguration of autonomic managers in real-time control applications

We consider autonomic applications to systems for which continuous perfect monitoring of state is not possible. We use Exact-State Observers (ESO) to provide enhanced information about the system state. To achieve optimal configuration of the autonomic controller itself, over a wide range of environmental operating conditions, and across a wide range of unique application domains, we implement a new architecture for dynamic supervision and control systems in which a policy-based autonomic engine automatically selects both its monitoring and actuator components to suit ambient operating conditions. By using a suite of ESOs tuned for different tradeoffs between real-time responsiveness and extent of system disturbance tolerated, and a policy mechanism to contextually select the most appropriate observer at any given time, we achieve self-configuring and self-optimising behaviours whilst keeping the complexity, resource-requirements and adaptation latency low.

A Middleware Approach to Dynamically Configurable Automotive Embedded Systems

This paper presents an advanced dynamically configurable middleware for automotive embedded systems. The layered architecture of the middleware, and the way in which core and optional services provide transparency and flexible platform independent support for portability, is described. The design of the middleware is positioned with respect to the way it overcomes the specific technical, environmental, performance and safety challenges of the automotive domain. The use of policies to achieve flexible run-time configuration is explained with reference to the core policy technology which has been extended and adapted specifically for this project. The component model is described, focussing on how the configuration logic is distributed throughout the middleware and application components, by inserting ‘decision points’ wherever deferred logic or run-time context-sensitive configuration is required. Included in this discussion are the way in which context information is automatically provided to policies to inform context-aware behaviour; the dynamic wrapper mechanism which isolates policies, provides transparency to software developers and silently handles run-time errors arising during dynamic configuration operations.

Practical Implementation of a Middleware and Software Component Architecture Supporting Reconfigurability of Real-Time Embedded Systems

In the current drive towards dynamic self-managing systems, a particular challenge is the development of coherent architectures of context-aware middleware and components. The embedded class of systems brings the additional challenges of resource limitations and difficulty to upgrade deployed code.This paper describes a complete implementation of middleware and component architecture that facilitates flexible run-time configuration via embedding of dynamically replaceable decision logic into software components An automotive air conditioning system application is described illustrating the approach, using a variety of context sources.

Computer Cluster as a Tool in the Continuous Exact State Reconstruction

In this paper the application of computer cluster in the task of computation of an exact integral state observer is presented. Selected problems connected with the implementation of parallel computing in the real time control systems are discussed. The paper focuses most of all on the potential advantages of such approach in the context of improvement of the control algorithm performance

Application of kernel density estimators for analysis of EEG signals

Nowadays analysis of EEG signals is a very popular area of biomedical engineering and science for both civil and military markets. In this paper a novel analysis of EEG signal with the implementation of kernel density estimators in order to construct densitograms of the examined EEG signals was presented. This approach allows obtaining the statistically filtered signals, which enables to conduct the analysis in easier and quicker way. It is also important to mention that analysed signals were obtained from an inexpensive, easily available on the open market headset --- Emotiv EPOC. This paper also contains illustration of signal processing and justification of the chosen approach by spectral analysis.