Defense

Application of CPS technologies deployed in military contexts.
forum

Visible to the public CfP: The Cyber-Physical Systems journal invites you to submit your research

Dear readers,

Please find below a general call for papers to the Cyber Physical Systems journal. I'd be delighted to answer any questions or queries that you may have and look forward reading your research.

With best wishes,

Mr Richard Goodman

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Visible to the public Design and Control of High-performance Provably-safe Autonomy-enabled Dynamic Transportation

Autonomy-enabled transportation networks are rapidly becoming a prominent Cyber-Physical-Systems (CPS) application area with tremendous potential for societal impact, as the autonomous systems technology penetrates into aerial/road vehicles and as the concept of connected vehicles emerge. The potential opportunities are not gone unnoticed. For example, unmanned aerial vehicle (UAV) based delivery networks has already attracted innovative companies like Amazon, Google, and Matternet.

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Visible to the public CPS- Synergy- Collaborative Research- Managing Uncertainty in the Design of Safety-Critical Aviation Systems

The objective of this research is to create tools to manage uncertainty in the design and certification process of safety-critical aviation systems. The research focuses on three innovative ideas to support this objective. First, probabilistic techniques will be introduced to specify system-level requirements and bound the performance of dynamical components. These will reduce the design costs associated with complex aviation systems consisting of tightly integrated components produced by many independent engineering organizations.

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Visible to the public CPS- Breakthrough- Toward Revolutionary Algorithms for Cyber-Physical Systems Architecture Optimization

One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Evolutionary algorithms (EAs), which embody a simplified computational model of the mutation and selection mechanisms of natural evolution, are known to be effective for design optimization. However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture. This project explores techniques, based on the idea of hidden genes, which enable EAs to select a variable number of components, thereby expand