Autonomous Trustworthy Monitoring and Diagnosis of CubeSat Health (AtMonSat)

Report (2022)

This document is the final report concluding the execution of the AtMonSat project co-funded by the European Space Agency (ESA) under the Open Space Innovation Platform (OSIP) and the University of ... [more ▼]

This document is the final report concluding the execution of the AtMonSat project co-funded by the European Space Agency (ESA) under the Open Space Innovation Platform (OSIP) and the University of Luxembourg. AtMonSat concerns on-board fault detection using artificial neural networks for CubeSat systems and related spacecraft where computing resources are limited. In particular, the concrete problem scenario of malfunctioning of CubeSat board elements is considered. The AtMonSat final report provides the problem statement, discusses the performed experiments designed to generate proper sets of data, and presents the details of the proposed solution. The report shows the devised framework to be both effective and suitable for implementation on a CubeSat. [less ▲]

An efficient approach towards the source-target control of Boolean networks

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020), 17(6), 1932-1945

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target ... [more ▼]

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimised if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach. [less ▲]

GPU-accelerated steady-state computation of large probabilistic Boolean networks

in Formal Aspects of Computing (2019), 31(1), 27-46

Reviving the two-state Markov chain approach

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), 15(5), 1525-1537

Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such ... [more ▼]

Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall. Secondly, we conduct an extensive experimental comparison of the extended two-state Markov chain approach and another approach based on the Skart method. We analyse the results with machine learning techniques and we show that statistically the two-state Markov chain approach has a better performance. Finally, we demonstrate the potential of the extended two-state Markov chain approach on a case study of a large PBN model of apoptosis in hepatocytes. [less ▲]

ASSA-PBN: A Toolbox for Probabilistic Boolean Networks

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), 15(4), 1203-1216

A new decomposition method for attractor detection in large synchronous Boolean networks

in Proceedings of the 3rd International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (2017)

ASSA-PBN 2.0: A software tool for probabilistic Boolean networks.

in Proceedings of 14th International Conference on Computational Methods in Systems Biology (2016)

Improving BDD-based attractor detection for synchronous Boolean networks.

in Science China Information Sciences (2016), 59(8), 0801011-08010116

Chemometric analysis of attenuated total reflectance infrared spectra of Proteus mirabilis strains with defined structures of LPS.

in Innate Immunity (2016), 22(5), 325-335

Activity tracking: A new attack on location privacy

in Proceedings of the 3rd IEEE Conference on Communications and Network Security (CNS'15) (2015)