Current Projects

Research Projects at the Cyber-Physical Systems Group

USA-funded Projects

CyberHeart

CyberHeart

Funding: USA-NSF

Partners: Carnegie Mellon University, University of Maryland, Stony Brook University, Georgia Tech, Rochester Institute of Technology, University of Pennsylvania, Fraunhofer Center for Experimental Software Engineering, Food and Drug Administration (FDA)

Time Frame: 01. 01. 2015 - 31. 12. 2020

Contact Persons: Radu Grosu, Alena Rodionova, Gerda Belkhofer-Fohrafellner (admin)

Research Team: Ezio Bartocci, Radu Grosu, Anna Lukina, Alena Rodionova, Scott A. Smolka

The NSF-CPS-Frontiers project CyberHeart is part of the NSF’s initiative to advance the state-of-the-art in Cyber-Physical Systems (CPS): engineered systems that are built from, and depend upon, the seamless integration of computation and physical components. The main goal of the project is to develop far more realistic cardiac device models and controllers than the ones that currently exist. The challenge is in the sheer scale of the human’s cardiac CPS: Six billion cells arranged in a very sophisticated network interact with each other in order to synchronise and contract such that they collectively achieve what is commonly called a heart beat, pumping the blood through the entire body. The CyberHeart platform will provide a basis to test and validate medical devices faster and at a far lower cost than existing methods. It will also provide a platform for designing optimal, patient-specific device therapies, thereby lowering the risk to the patient. CyberHeart includes collaborators from nine leading universities and centres: CMU, RIT, UM, GTech, UPenn, FC-ESE, SBU, TUW and FDA.

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EU-funded Projects

AFarCloud

AFarCloud

Funding: EU-ECSEL Joint Undertaking

Partners: 59 organisations

Time Frame: 01. 09. 2018 - 31. 08. 2021

Contact Persons: Radu Grosu, Christian Hirsch

Research Team: Radu Grosu, Christian Hirsch

Farming is facing many economic challenges in terms of productivity and cost-effectiveness, as well as an increasing labour shortage partly due to depopulation of rural areas. Reliable detection, accurate identification and proper quantification of pathogens affecting both plant and animal health, must be kept under control to reduce unnecessary costs, trade disruptions and even human health risks.

AFarCloud addresses the urgent need for a holistic and systematic approach. It will provide a distributed platform for autonomous farming, which will allow the integration and cooperation of Cyber Physical Systems in real-time for increased agriculture efficiency, productivity, animal health, food quality and reduced farm labour costs. This platform will be integrated with farm management software and will support monitoring and decision-making, based on big data and real time data mining techniques.

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iDev40 - Enabling Artificial Intelligence in the European Electronic Component and Systems Industry

iDev40 - Enabling Artificial Intelligence in the European Electronic Component and Systems Industry

Funding: EU-ECSEL Joint Undertaking

Time Frame: 01. 05. 2018 - 30. 04. 2021

Contact Persons: Radu Grosu, Hamidreza Mahyar, Gerda Belkhofer-Fohrafellner

Research Team: Radu Grosu, Hamidreza Mahyar, Ramin Mohammad Hasani

iDev40 focuses on “digitally connecting” value chains to facilitate and strengthen the innovation capacity of large and small European Electronic Components and System actors for sustainably competitive Electronic Components and Systems “Made in Europe”. Digitalization in integrated product development and production as well as along value chains (digital twin) is the key aspect of the iDev40 project. Linking the digital and the real world with new Industry 4.0 technologies, smarter, more flexible development and production processes are realized as well as a real-time communication via the Internet of Things between value chain partners. These new technologies include data centered learning approaches, cyber physical system based platforms connecting production processes from different sites, knowledge/IP sharing and data protection methods, holistic data validation and exploitation from heterogeneous data sources. The socio-technical context is considered through a human centered knowledge base. We aim to utilize Artificial Intelligence (AI) and Deep Learning (DL) techniques to semantically and automatically enrich data with properties which enable secure storage, tailored protection and validation of data. By integrating design and production data the rate of disconnected data sets will be reduced, which will lead to a decrease in data retrieval time and thus enhance the overall data quality. The elaboration of these technology targets is addressed by (1) Data management, sharing and storage; (2) Gathering, curating, pre-processing and updating/validating knowledge; and (3) Semantical annotation and enrichments of data by adopting AI and DL principles.

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Productive4.0

Productive4.0

Funding: EU-ECSEL

Partners: 114 organisations

Time Frame: 01. 05. 2017 - 01. 05. 2020

Contact Persons: Haris Isakovic, Gerda Belkhofer-Fohrafellner (admin), Radu Grosu

Research Team: Haris Isakovic

Electronics and ICT as enabler for digital industry1 and optimized supply chain management covering the entire product lifecycle

The main objective of Productive4.0 is to achieve significant improvement in digitalizing the European industry by means of electronics and ICT. Ultimately, the project aims at suitability for everyday application across all industrial sectors – up to TRL8. It addresses various industrial domains with one single approach, that of digitalisation. What makes the project unique is the holistic system approach of consistently focusing on the three main pillars: digital production, supply chain networks and product lifecycle management.This is part of the new concept of introducing seamless automation and network solutions as well as enhancing the transparency of data, their consistence, flexibility and overall efficiency. Currently, such a complex project can only be realised in ECSEL.The well balanced consortium consists of 45% AENEAS, 30% ARTEMIS-IA and 25% EPOSS partners, thus bringing together all ECSEL communities. Representing over 100 relevant partners from 19 EU-member states and associated countries, it is a European project, indeed.
SemI40 - Power Semiconductor and Electronics Manufacturing 4.0: Machine Learning over Big Data at Infineon

SemI40 - Power Semiconductor and Electronics Manufacturing 4.0: Machine Learning over Big Data at Infineon

Funding: EU-Artemis-ECSEL

Time Frame: 01. 05. 2016 - 30. 04. 2019

Contact Persons: Hamidreza Mahyar, Elahe Ghalebi, Radu Grosu, Gerda Belkhofer-Fohrafellner (admin)

Research Team: Hamidreza Mahyar, Elahe Ghalebi, Anja Zernig, Olivia Bluder, Andre Kästner, Radu Grosu

SemI40 will focus on “smart production” and “cyber-physical production systems” and is one of the largest Industry 4.0 projects in Europe. Industry 4.0 also known as Cyber-Physical Production Systems are systems where sensors and actuators are combined with communication and computation in order to achieve the optimal control of physical production processes. Infineon, one of the world leaders in mixed-signal chip production, has recently embarked in a very innovative and daring project: The complete automation of one of its Villach’s production sites. This automation involves the sensing, processing, and storing of huge amounts of measurement-data related to the processes involved in their chip production. While the semiconductor fabs are currently measured and statistically analyzed after each process, there is no correlation among these measurements so far. Since such a correlation is arguably impossible to derive from first principles in physics and chemistry, we propose in this project to use machine-learning and big-data techniques to automatically learn such correlations. These correlations will be thereafter used to predict anomalies, to diagnose the processes and machines responsible for a failed fab, and to eventually control these processes for optimal fabrication.

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ICT COST Action IC1405 on Reversible Computation - Extending Horizons of Computing

ICT COST Action IC1405 on Reversible Computation - Extending Horizons of Computing

Funding: EU-Horizon 2020, EU-COST

Partners: TU Wien, Universiteit Gent, University of Cyprus, University of Southern Denmark, Datalogisk Institut – Københavns Universitet, University of Turku, CNRS, INRIA, Karlsruhe Institute of Technology, University of Bremen, National Technical University of Athens, Reykjavik University, Trinity College Dublin, University of Bologna, IMT Institute for Advanced Studies Lucca, Eindhoven University of Technology, University of Oslo, University of Lodz, Warsaw University of Technology, Universidade do Minho, Romanian Academy, Ovidius University of Constanta, University of Nis, University of Novi Sad, University of Maribor, "Jožef Stefan" Institute, European Centre for Soft Computing, Halmstad University, Imperial College, University of Copenhagen, University of Turku, Universität Giessen, Waterford Institute of Technology, University of Camerino, University Politehnica Timisoara, Universitat Politecnica de Valencia, Middlesex University, Université Djillali Liabes

Time Frame: 30. 04. 2015 - 29. 04. 2019

Contact Persons: Ezio Bartocci, Radu Grosu, Gerda Belkhofer-Fohrafellner (admin)

Research Team: Ezio Bartocci, Radu Grosu

Reversible computation is an emerging paradigm that extends the standard forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as naturally as it can go forwards. It aims to deliver novel computing devices and software, and to enhance traditional systems by equipping them with reversibility. The potential benefits include the design of revolutionary reversible logic gates and circuits - leading to low-power computing and innovative hardware for green ICT, and new conceptual frameworks, language abstractions and software tools for reliable and recovery oriented distributed systems. Landauer's Principle, a theoretical explanation why a significant proportion of electrical power consumed by current forwards-only computers is lost in the form of heat, and why making computation reversible is necessary and beneficial, has only been shown empirically in 2012. Hence now is the right time to launch a COST Action on reversible computation. The Action will establish the first European (and the world first) network of excellence to coordinate research on reversible computation. Many fundamental challenges cannot be solved currently by partitioned and uncoordinated research, so a collaborative effort of European expertise with an industrial participation, as proposed by this Action, is the most logical and efficient way to proceed.

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AT-funded Projects

CPS/IoT Ecosystem: Preparing Austria for the Next Digital Revolution

CPS/IoT Ecosystem: Preparing Austria for the Next Digital Revolution

Funding: AT-BMWFW: HRSM Grant

Partners: TU Wien, Austrian Institut of Technology (AIT), Institute of Science and Technology (IST)

Time Frame: started 01. 06. 2017

Contact Persons: Radu Grosu, Haris Isakovic

Research Team: Schahram Dustdar, Radu Grosu, Manfred Gruber (AIT), Thomas A. Henzinger (IST), Gerti Kappel, Dejan Nickovic (AIT)

Cyber-physical systems (CPS) are spatially-distributed, time-sensitive, multiscale networked embedded systems, connecting the physical world to the cyber world through sensors and actuators. The Internet of Things (IoT) is the backbone of CPS. It connects the Sensors and Actuators to the nearby Gateways and the Gateways to the Fog and the Cloud. The Fog resembles the human spine, providing fast and adequate response to imminent situations. The Cloud resembles the human brain, providing large storage and analytic capabilities.

In this project we will make Austria a major player in Real-Time (RT) CPS/IoT, by building on its national strengths. In collaboration with renowned Austrian companies such as TTTech or ams AG, we will create an RT CPS/IoT-Ecosystem with more than 5000 sensors and actuators, where we can all experiment with new ideas, and develop this way an Austrian know-how. This effort will be aligned with the strategic Austrian initiatives, Industry 4.0 and Silicon-Austria. The ecosystem will be distributed across Vienna in collaboration with our partners at AIT and IST.

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National Research Network RiSE/SHiNE (PP 12)

National Research Network RiSE/SHiNE (PP 12)

Funding: AT-FWF

Partners: Graz University of Technology (coordinator), Vienna University of Technology, Institute of Science and Technology Austria, Johannes Kepler University Linz, University of Salzburg.

Time Frame: 01. 03. 2015 - 01. 03. 2019

Contact Persons: Ezio Bartocci, Radu Grosu, Gerda Belkhofer-Fohrafellner (admin)

Research Team: Ezio Bartocci, Armin Biere, Roderick Bloem, Krishnendu Chatterjee, Uwe Egly, Radu Grosu, Thomas A. Henzinger, Christoph Kirsch, Laura Kovács, Anna Lukina, Ulrich Schmid, Martina Seidl, Ana Sokolova, Helmut Veith, Georg Weissenbacher, Florian Zuleger

RiSE pursues the long term vision of a hardware/software system design process supported by automatic formal methods based on model checking, decision procedures, and game theory. Simultaneously, the National Research Network has the strategic goal to establish and strengthen Austria as an international hot spot in this research area. In the first three years of the 4-year funding period (Period I), we have made important steps towards both the scientific and the strategic goal. A key lesson from Period I was that non-functional aspects of system quality and correctness are critical, hard to achieve manually, and highly amenable to rigorous reasoning. We view the second period of RiSE 2015–2019 as an opportunity to position Computer Aided Verification closer to other fields of computer science which address non-functional aspects in a rigorous manner. In Period II, nine Project Part Leaders and six (mostly) junior Task Leaders will build upon the foundations established in the first years. The new Tasks that we propose either derive from a cross cutting “collaboration topic” of Period I or are new topics introduced by the recently hired faculty. All Tasks will be jointly investigated by two PIs. While the Research Clusters of Period I reflected the individual expertise of the PIs, we will now organize our Tasks along intersecting Research Lines. Each Research Line of Period II will address a non-functional aspect such as concurrency, probabilistic behavior, reliability, and quantitative measures (timing and resource consumption). This focus reflects a broader understanding of correctness beyond the Boolean notion of functional correctness that was central in Period I. Thus, our thrust will go beyond verification of functional specifications to computer aided design of programs that fulfill both functional and non-functional properties. We have therefore subtitled the second funding Period Systematic Methods in Systems Engineering, or SHiNE.

SHiNE project part PP12: Probabilistic Analysis of Distributed Systems. Modern distributed systems such as cyber-physical systems (CPS) embed sensing, computation, actuation, and communication within various physical substrata, resulting into heterogeneous, open, systems of systems. CPS examples include smart factories, smart transportation, and smart health-care. Openness (entities can join/leave the system), unpredictability (the environment is partially known), and distribution (interactions may propagate in space-time) are serious obstacles in the accurate prediction of the (emergent) behavior of CPS. In general, the exponential explosion of the CPS state space renders exhaustive state-space-exploration techniques, such as classical model checking, intractable. Approximate prediction techniques, such as statistical model checking (SMC) have therefore gained popularity in the past several years. A serious {\em obstacle} in the application of approximate techniques is their poor performance in predicting properties which represent rare~events. In such cases, the number of samples required to attain a high confidence ratio and a low error margin explodes. Two sequential Monte-Carlo techniques, importance splitting (ISpl) and importance sampling (ISam), originally developed in the statistical-physics community, hold the promise to overcome this obstacle. The main idea of the proposed work is therefore to combine Importance Sampling and Importance Splitting within a coherent and unified, control-theoretic framework}, yielding a novel and general class of SMC algorithms.

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IoT4CPS: Trustworthy IoT for CPS

IoT4CPS: Trustworthy IoT for CPS

Funding: AT-FFG

Partners: TU Wien, Austrian Institut of Technology (AIT), Institute of Science and Technology (IST), AVL List GmbH, Donau Uni Krems, Infineon Technologies AG, JKU Linz, Joanneum, NOKIA Österreich, NXP, Salzburg Research, SBA Research, SCCH, Siemens AG Österreich, TTTech Computertechnik AG, TU Graz, X-Net Services GmbH

Time Frame: 01. 12. 2017 - 30. 11. 2020

Contact Persons: Ezio Bartocci (project leader), Radu Grosu, Muhammad Shafique, Gerda Belkhofer-Fohrafellner (admin)

Research Team: Ezio Bartocci (project leader), Radu Grosu, Faiq Khalid, Denise Ratasich, Muhammad Shafique

IoT4CPS will develop guidelines, methods and tools to enable safe and secure IoT-based applications for automated driving and for smart production. The project will address safety and security aspects in a holistic approach both along the specific value chains and the product life cycles. To ensure the outreach of the project activities and results, the relevant stakeholders will be involved throughout the project and results will be disseminated to expert groups and standardization bodies. IoT4CPS will support digitalization along the entire product lifecycle, leading to a time-to-market acceleration for connected and autonomous vehicles. IoT4CPS will provide innovative components, leading to efficiency increases for the deployment of autonomous driving functions and in smart production environments, which will be validated in a vehicle and in a smart production demonstrator.

COCO: Control Equivalence for Cyber-Physical Models

COCO: Control Equivalence for Cyber-Physical Models

Funding: AT-FWF: Lise Meitner Fellowship

Partners: TU Wien (coordinator), IMT Lucca (collaborator)

Time Frame: 01. 08. 2018 - 31. 07. 2020

Contact Persons: Max Tschaikowski (project leader), Radu Grosu (co-applicant), Gerda Belkhofer-Fohrafellner (admin)

Research Team: Max Tschaikowski (project leader), Radu Grosu (co-applicant)

Nonlinear continuous and hybrid dynamical systems are of paramount importance in the modeling of biochemical, cyber-physical and dependable systems. Unfortunately, their precise parametrization is often not possible due to finite-precision measurements or lack of information. Hence, in order to ensure safety in the presence of parameters that are subject to uncertainty, it thus becomes necessary to formally approximate the underlying dynamical system. A possible example may be, for instance, the task of verifying that the autopilot keeps the airplane in stable flight in the presence of realistic turbulence.

Despite the fact that the computation of tight formal approximations received a lot of attention, it remains computationally demanding in the case of nonlinear dynamics. A universal approach for the simplification of difficult problems is that of model reduction where the idea is to formally relate the original problem to a smaller one which can be solved more efficiently. While model reduction techniques have been applied in the context of approximation problems in the past, to the best of our knowledge, no efficient reduction algorithms are available in the case of nonlinear dynamics. The main goal of the project is to close this gap by identifying formal approximation problems as optimal control problems which can be reduced efficiently.