Related papers: Design Contracts For Networked Automation Systems …
This paper proposes a neural architecture search (NAS) method for split computing. Split computing is an emerging machine-learning inference technique that addresses the privacy and latency challenges of deploying deep learning in IoT…
Deep neural networks have demonstrated superior performance on appearance-based gaze estimation tasks. However, due to variations in person, illuminations, and background, performance degrades dramatically when applying the model to a new…
Computer Aided Control System Design (CACSD) allows to analyze complex interconnected systems and design controllers achieving challenging control requirements. We extend CACSD to systems with time delays and illustrate the functionality of…
Due to increasing privacy concerns, neural network (NN) based secure inference (SI) schemes that simultaneously hide the client inputs and server models attract major research interests. While existing works focused on developing secure…
Emergent design failures are ubiquitous in complex systems, and often arise when system elements cluster. Approaches to systematically reduce clustering could improve a design's resilience, but reducing clustering is difficult if it is…
This research focuses on timestamping methods for profiling network traffic in software-based environments. Accurate timestamping is crucial for evaluating network performance, particularly in Time-Sensitive Networking (TSN). We explore and…
Delays and data losses are undesirable from a control system perspective as they tend to adversely affect performance Networked Control Systems (NCSs) are a class of control systems wherein control components exchange information using a…
We present assume-guarantee contracts for continuous-time linear dynamical systems with inputs and outputs. These contracts are used to express specifications on the dynamic behaviour of a system. Contrary to existing approaches, we use…
Designing large-scale control systems to satisfy complex specifications is hard in practice, as most formal methods are limited to systems of modest size. Contract theory has been proposed as a modular alternative to formal methods in…
Deep learning has become in recent years a cornerstone tool fueling key innovations in the industry, such as autonomous driving. To attain good performances, the neural network architecture used for a given application must be chosen with…
In Concurrent engineering, design solutions are not only produced by individuals specialized in a given field. Due to the team nature of the design activity, solutions are negotiated. Our objective is to analyse the argumentation processes…
This paper considers a problem of output agreement in heterogeneous networks with dynamics on the nodes as well as on the edges. The control and disturbance signals entering the nodal dynamics are "unmatched" meaning that some nodes are…
Modern engineering systems include many components of different types and functions. Verifying that these systems satisfy given specifications can be an arduous task, as most formal verification methods are limited to systems of moderate…
Mathematical models involving switches --- in the form of differential equations with discontinuities --- can accomodate real-world non-idealities through perturbations by hysteresis, time-delay, discretization, and noise. These are used to…
There are proposed models of contracts, technological equipment and gas networks and methods of their optimization. The flow in network undergoes restrictions of contracts and equipment to be operated. The values of sources and sinks are…
Concurrent software for engineering computations consists of multiple cooperating modules. The behavior of individual modules is described by means on state diagrams. In the paper, the constraints on state diagrams are proposed, allowing…
With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks. However, it remains unclear if the searched architecture can transfer across different types…
The telecommunications industry is experiencing rapid growth in adopting deep learning for critical tasks such as traffic prediction, signal strength prediction, and quality of service optimisation. However, designing neural network…
We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust…
In this paper we propose and quantitatively evaluate three performance optimization methods that exploit the concept of communication-compute-control co-design by introducing awareness of communication and compute characteristics into the…