To address the question of how to deliver time-sensitive software for cyber-physical systems (CPS) requires a range of modelling and analysis techniques to be developed and integrated. A number of these required techniques are unique to time-sensitive software where timeliness is a correctness property rather than a performance attribute. This paper focuses on how to obtain worst-case estimates of the software’s execution time; in particular, it considers how workload models are derived from assumptions about the system’s run-time behaviour. The specific contribution of this paper is the exploration of the notion that a system can be subject to more than one workload model. Examples illustrate how such multi-models can lead to improved schedulability and hence more efficient CPS. An important property of the approach is that the derived analysis exhibits model-bounded behaviour. This ensures that the maximum load on the system is never higher than that implied by the individual models.

A novel scheme involving experimental analyses and diagnoses is presented for monitoring radio-frequency (RF) and high-speed circuits. A live electrooptic imaging (LEI) camera is used in the scheme and it provides real-time images of the phase evolution of the RF electric field. Besides, it is demonstrated that essential properties of RF wave propagation are easily grasped from visual images; examples of LEI movies and images from which such essential properties can be identified are presented. The subjects of the LEI observations, analyses, and diagnoses are planar RF circuits and a Gbps-class emitter-coupled-logic circuit. In addition, the results of analyses and diagnoses in the space domain are discussed.

Neuroevolution, or evolving neural networks with evolution algorithms such as genetic algorithms, is becoming one of the hottest areas in hybrid systems research. One of the areas that become under research using neuroevolutions is the controllers. In this paper, we shall present two engineering controllers based on neuroevolutions techniques. One of the controllers is used to monitor the temperature and humidity in an industry. This controller is having a linear behavior. The second controller is concerned with scheduling parts in queues in an industry. The scheduling controller is having a nonlinear behavior. The results obtained by the proposed controllers based on neuroevolution are compared with results obtained by traditional methods such as neural networks with backpropagation and ordinary simulation for the controller. The results show that the neuroevolution approaches outperform the results obtained by other methods.

In future real-time systems such as those required for intelligent autonomous vehicle control, we need flexibility in choosing the set of services to support under varying environmental conditions and system states. It is not feasible to make an optimal choice of services at run-time, so we propose a method of ranking the services pre-run-time, based on the ‘utility' of each service. This paper focuses on the problem of calculating a ‘value' for the utility of each service alternative. We show how to derive values systematically and rationally, using Measurement Theory and Decision Analysis. The approach relies on engineering judgement and data input by a domain expert. In the context of autonomous vehicles, we believe that such knowledge would be available, making ‘value-based scheduling' a feasible approach.