Research is our passion

We work on extremely innovative projects across different fields, such as manufacturing, agriculture, aerospace engineering, healthcare, telecommunications, and transportation.

We tackle new technological challenges thanks to our talented and skilled team, and with the support of world-class partners.

projects

Discover our research projects

Resilient Trust
Resilient Trust

Resilient Trust

HORIZON-KDT-JU-2022-2-RIA | 2023-2026

ABSTRACT
The Resilient Trust project aims to address the challenges and opportunities presented by the Internet of Things (IoT) and its evolution to IoT5.0, which is characterized by AI-assisted devices. IoT holds the potential to enhance daily life, address societal issues, and improve automation on a global scale. However, it also brings significant security concerns, with the widespread connectivity increasing the risk of network exploitation and the presence of unsecure integrated circuits (ICs) due to complex, global supply chains. Furthermore, the use of AI in IoT introduces new attack vectors. Resilient Trust focuses on mitigating these risks to ensure the sustainable development of IoT5.0. Their solution includes end-to-end protection of ICs, tracking their life cycle from the foundry, and addressing vulnerabilities like IC IP piracy and counterfeiting. Threat modeling, risk analysis, and strong security measures will be employed, with a particular focus on IoT domains requiring enhanced security. Resilient Trust's work will help harness the potential of IoT5.0 while safeguarding against emerging security threats.

OUTCOMES
Resilient Trust will provide secure technologies, incorporating new electronic components, software, and intelligent integration into digital value chains, enhancing Europe's innovation capabilities. It will develop hardware-based device identity and cloud blockchain solutions to combat IC counterfeiting.
Moreover, Resilient Trust will foster research on attack vectors and prevention mechanisms applicable to Industry 5.0, creating expertise and qualified experts in European cybersecurity. By means of this, faster verification cycles and reduced IC risks will benefit industries, like the automotive sector.
Trusted devices will enhance trust in IoT technologies and foster the use of cyber-aware solutions while ensuring the safety of ICs will improve production line safety and supply chains.

PARTNERS
University Gustave Eiffel (FR), Fraunhofer ENAS (DE), IMST (DE) DGS (IT), Akkodis (IT), Permare (IT), Ro Technology (IT), Rulex Innovation Labs (IT), University of Genoa (IT), Unilink (IT), University of L’Aquila (IT), Almende (NL), Intrinsic ID (NL), TU DELFT (NL), Beammwave (SE), University of Lund (SE), Arteris IP (FR), CEA (FR), University of Grenoble (FR), CNRS (FR), MC2 (FR), Seamless Waves (FR), University of Sorbonne (FR), ST Microelectronics (FR), CSEM (CH), Securosys (CH), 3DB Access (CH).

FUNDING BODY
This project has received funding from the KDT Joint Undertaking, European Commission (grant agreement 101112282).

Resilient Trust

HORIZON-KDT-JU-2022-2-RIA | 2023-2026

The Resilient Trust project aims to address the challenges and opportunities presented by the Internet of Things (IoT) and its evolution to IoT5.0, which is characterized by AI-assisted devices.

Resilient Trust

Resilient Trust

HORIZON-KDT-JU-2022-2-RIA | 2023-2026

ABSTRACT
The Resilient Trust project aims to address the challenges and opportunities presented by the Internet of Things (IoT) and its evolution to IoT5.0, which is characterized by AI-assisted devices. IoT holds the potential to enhance daily life, address societal issues, and improve automation on a global scale. However, it also brings significant security concerns, with the widespread connectivity increasing the risk of network exploitation and the presence of unsecure integrated circuits (ICs) due to complex, global supply chains. Furthermore, the use of AI in IoT introduces new attack vectors. Resilient Trust focuses on mitigating these risks to ensure the sustainable development of IoT5.0. Their solution includes end-to-end protection of ICs, tracking their life cycle from the foundry, and addressing vulnerabilities like IC IP piracy and counterfeiting. Threat modeling, risk analysis, and strong security measures will be employed, with a particular focus on IoT domains requiring enhanced security. Resilient Trust's work will help harness the potential of IoT5.0 while safeguarding against emerging security threats.

OUTCOMES
Resilient Trust will provide secure technologies, incorporating new electronic components, software, and intelligent integration into digital value chains, enhancing Europe's innovation capabilities. It will develop hardware-based device identity and cloud blockchain solutions to combat IC counterfeiting.
Moreover, Resilient Trust will foster research on attack vectors and prevention mechanisms applicable to Industry 5.0, creating expertise and qualified experts in European cybersecurity. By means of this, faster verification cycles and reduced IC risks will benefit industries, like the automotive sector.
Trusted devices will enhance trust in IoT technologies and foster the use of cyber-aware solutions while ensuring the safety of ICs will improve production line safety and supply chains.

PARTNERS
University Gustave Eiffel (FR), Fraunhofer ENAS (DE), IMST (DE) DGS (IT), Akkodis (IT), Permare (IT), Ro Technology (IT), Rulex Innovation Labs (IT), University of Genoa (IT), Unilink (IT), University of L’Aquila (IT), Almende (NL), Intrinsic ID (NL), TU DELFT (NL), Beammwave (SE), University of Lund (SE), Arteris IP (FR), CEA (FR), University of Grenoble (FR), CNRS (FR), MC2 (FR), Seamless Waves (FR), University of Sorbonne (FR), ST Microelectronics (FR), CSEM (CH), Securosys (CH), 3DB Access (CH).

FUNDING BODY
This project has received funding from the KDT Joint Undertaking, European Commission (grant agreement 101112282).

smartstop
smartstop

SMART STOP project

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | Duration: 2021-2023

ABSTRACT
The SMART STOP project studies the design and prototyping of a high-tech platform aimed at monitoring and collecting data in the field of smart mobility. The goal is to improve the quality of logistics management processes in a context of urban mobility, thus increasing the efficiency and safety of public services and measuring the value created on the territory. The project involves the validation of the solution developed in the Genoese public transport network.

OUTCOMES
The project aims at creating a platform that includes different data sources (webcams, management data, vehicle data), integrates them consistently, uses them for predictive analysis, and effectively shows them to stakeholders.

PARTNERS
AMT (IT), Aitek (IT), Rulex Innovation Labs (IT), CircleGarage (IT), BF Partners (IT), Maps (IT).

FUNDING BODY
The project was funded by Region Liguria, program: POR FESR 2014-2020, Action 1.2.4.

SMART STOP project

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | Duration: 2021-2023

The SMART STOP project studies the design and prototyping of a high-tech platform aimed at monitoring and collecting data in the field of smart mobility.

smartstop

SMART STOP project

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | Duration: 2021-2023

ABSTRACT
The SMART STOP project studies the design and prototyping of a high-tech platform aimed at monitoring and collecting data in the field of smart mobility. The goal is to improve the quality of logistics management processes in a context of urban mobility, thus increasing the efficiency and safety of public services and measuring the value created on the territory. The project involves the validation of the solution developed in the Genoese public transport network.

OUTCOMES
The project aims at creating a platform that includes different data sources (webcams, management data, vehicle data), integrates them consistently, uses them for predictive analysis, and effectively shows them to stakeholders.

PARTNERS
AMT (IT), Aitek (IT), Rulex Innovation Labs (IT), CircleGarage (IT), BF Partners (IT), Maps (IT).

FUNDING BODY
The project was funded by Region Liguria, program: POR FESR 2014-2020, Action 1.2.4.

Fractal
Fractal

Fractal – Cognitive Fractal and Secure Edge Based On Unique Open-Safe-Reliable-Low Power Hardware Platform Node

H2020- ECSEL-2019-2-RIA | 2020-2023

ABSTRACT
The objective of the FRACTAL project is to introduce a novel approach to reliable edge computing, by creating a Cognitive node under industry standards. This computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes).

Cognitivity is provided by Artificial Intelligence methods, supported by internal and external architectures that allow the node to proactively adapt to changes in the surrounding world. Hence, this node will have the capability of learning in real-time how to improve its performance and dependability despite the uncertainty of the environment. Together with this the project will also consider the enhancement opportunities brought by the continuous emergence of more powerful solutions in the area of Cyber-Physical Systems (CPS), Systems of Systems (SoS) and Internet of Things (IoT). For instance, opportunities coming from advanced microelectronics, high-performance computing, smart system integration, and improved cloud services, traditionally mostly neglected, will prevent the node from failing to meet the stringent requirements for increased autonomy coming from the new application domains.

OUTCOMES
As a result of the integration of these cognitive systems into an edge fractal network, there will be an intrinsic crucial advantage, a combination of safety, adaptability and emergence of new possibilities. Therefore, new industrial functions will flourish through the created space of possibilities of our cognitive systems. This scalable fractal network will transfer all those cognitive advantages to a new Cognitive Edge, a computing paradigm that lies between the physical world and the cloud.

PARTNERS
Ikerlan (ES) - coordinatore, ACP Advanced Circuit Pursuit (CH), Aitek (IT), AVL (AT), Barcelona Supercomputing Center (ES), Beewen (DE), Caf Signalling (ES), Thales (FR), Haltian (FI), Modis Consulting (IT), Offcode (FI), PLC2 (DE), Prointec (ES), Qualigon (DE), Ro Technology (IT), Rulex Innovation Labs (IT) Siemens (AT), Solver Machine Learning (ES), Thales (FR), University of Siegen (DE), Polytechnic University of Valencia (ES), University of L'Aquila (IT), University of Genoa (IT), University of Modena and Reggio Emilia (IT), University of Oulu (FI), Virtual Vehicle Research (AT), Zylk.Net (ES).

FUNDING BODY
This project has received funding from the ECSEL Joint Undertaking, European Commission (grant agreement 877056).

WEBSITE: https://fractal-project.eu/project

Fractal

H2020- ECSEL-2019-2-RIA | 2020-2023

The objective of the FRACTAL project is to introduce a novel approach to reliable edge computing, by creating a Cognitive node under industry standards. This computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes).

Fractal

Fractal – Cognitive Fractal and Secure Edge Based On Unique Open-Safe-Reliable-Low Power Hardware Platform Node

H2020- ECSEL-2019-2-RIA | 2020-2023

ABSTRACT
The objective of the FRACTAL project is to introduce a novel approach to reliable edge computing, by creating a Cognitive node under industry standards. This computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes).

Cognitivity is provided by Artificial Intelligence methods, supported by internal and external architectures that allow the node to proactively adapt to changes in the surrounding world. Hence, this node will have the capability of learning in real-time how to improve its performance and dependability despite the uncertainty of the environment. Together with this the project will also consider the enhancement opportunities brought by the continuous emergence of more powerful solutions in the area of Cyber-Physical Systems (CPS), Systems of Systems (SoS) and Internet of Things (IoT). For instance, opportunities coming from advanced microelectronics, high-performance computing, smart system integration, and improved cloud services, traditionally mostly neglected, will prevent the node from failing to meet the stringent requirements for increased autonomy coming from the new application domains.

OUTCOMES
As a result of the integration of these cognitive systems into an edge fractal network, there will be an intrinsic crucial advantage, a combination of safety, adaptability and emergence of new possibilities. Therefore, new industrial functions will flourish through the created space of possibilities of our cognitive systems. This scalable fractal network will transfer all those cognitive advantages to a new Cognitive Edge, a computing paradigm that lies between the physical world and the cloud.

PARTNERS
Ikerlan (ES) - coordinatore, ACP Advanced Circuit Pursuit (CH), Aitek (IT), AVL (AT), Barcelona Supercomputing Center (ES), Beewen (DE), Caf Signalling (ES), Thales (FR), Haltian (FI), Modis Consulting (IT), Offcode (FI), PLC2 (DE), Prointec (ES), Qualigon (DE), Ro Technology (IT), Rulex Innovation Labs (IT) Siemens (AT), Solver Machine Learning (ES), Thales (FR), University of Siegen (DE), Polytechnic University of Valencia (ES), University of L'Aquila (IT), University of Genoa (IT), University of Modena and Reggio Emilia (IT), University of Oulu (FI), Virtual Vehicle Research (AT), Zylk.Net (ES).

FUNDING BODY
This project has received funding from the ECSEL Joint Undertaking, European Commission (grant agreement 877056).

WEBSITE: https://fractal-project.eu/project

NextPerception
NextPerception

NextPerception – Next generation smart perception sensors and distributed intelligence for proactive human monitoring in health, wellbeing, and automotive systems

H2020- ECSEL-2019-2-RIA | 2020-2023

ABSTRACT
We put our lives increasingly in the hands of smart complex systems making decisions that directly affect our health and wellbeing. This is very evident in healthcare - where systems watch over your health - as well as in traffic - where autonomous driving solutions are gradually taking over control of the car. The accuracy and timeliness of the decisions depend on the systems’ ability to build a good understanding of both you and your environment, which relies on observations and the ability to reason on them.

The goal of this project is to develop next generation smart perception sensors and enhance the distributed intelligence paradigm to build versatile, secure, reliable, and proactive human monitoring solutions for the health, wellbeing, and automotive domains. The project brings together major industrial players and research partners to address top challenges in health, wellbeing, and automotive domains through three use cases: integral vitality monitoring for elderly and exercise, driver monitoring, and providing safety and comfort for vulnerable road users at intersections.

OUTCOMES
This project will bring perception sensing technologies like Radar, LiDAR and Time of Flight cameras to the next level, enhancing their features to allow for more accurate detection of human behaviour and physiological parameters. Besides more accurate automotive solutions ensuring driver vigilance and pedestrian and cyclist safety, this innovation will open up new opportunities in health and wellbeing to monitor elderly people at home or unobtrusively assess health state.

To facilitate building the complex smart sensing systems envisioned and ensure their secure and reliable operation, the new Distributed Intelligence paradigm will be embraced, enhanced and supported by tools. It leverages the advantages of Edge and Cloud computing, building on the distributed computational resources increasingly available in sensors and edge components to distribute also the intelligence.

PARTNERS
VTT Technical Research Centre of Finland (FI) - coordinatore, Acorde Technologies (ES), Aitek (IT), Benete (FI), Robert Bosch (DE), Brno University of Technology (CZ), Camea (CZ), Commeto (BE), Consider IT (DE), CSIC (ES), eLive (FI), Emoj (IT), Evalan (NL), Evotel Informatica (ES), Flir Systems Trading (BE), Hi Iberia (ES), IMA (CZ), IMEC (NL), IMEC (BE), University of Lleida (ES), Stichting Kempenhaeghe (NL), KU Leuven (BE), Macq (BE), Modulight (FI), NXP Semiconductors (DE), NXP Semiconductors (NL), Pozyx Labs (BE), RE:Lab (IT), Rulex Innovation Labs (IT), Smartmicro (DE), Seven Solutions (ES), Smart Roborts (NL), Televic (BE), TNO (NL), TTS (FI), Technical University of Dresden (DE), Technical University of Eindhoven (NL), University of Bologna (IT), University of Bremen (DE), University of Parma (IT), University Suor Orsola Benincasa (IT), University of Torino (IT), University of Vigo (IT).

FUNDING BODY
This project has received funding from the ECSEL Joint Undertaking, European Commission (grant agreement 876487).

WEBSITE: https://www.nextperception.eu

NextPerception

H2020- ECSEL-2019-2-RIA | 2020-2023

The goal of this project is to develop next generation smart perception sensors and enhance the distributed intelligence paradigm to build versatile, secure, reliable, and proactive human monitoring solutions for the health, wellbeing, and automotive domains.

NextPerception

NextPerception – Next generation smart perception sensors and distributed intelligence for proactive human monitoring in health, wellbeing, and automotive systems

H2020- ECSEL-2019-2-RIA | 2020-2023

ABSTRACT
We put our lives increasingly in the hands of smart complex systems making decisions that directly affect our health and wellbeing. This is very evident in healthcare - where systems watch over your health - as well as in traffic - where autonomous driving solutions are gradually taking over control of the car. The accuracy and timeliness of the decisions depend on the systems’ ability to build a good understanding of both you and your environment, which relies on observations and the ability to reason on them.

The goal of this project is to develop next generation smart perception sensors and enhance the distributed intelligence paradigm to build versatile, secure, reliable, and proactive human monitoring solutions for the health, wellbeing, and automotive domains. The project brings together major industrial players and research partners to address top challenges in health, wellbeing, and automotive domains through three use cases: integral vitality monitoring for elderly and exercise, driver monitoring, and providing safety and comfort for vulnerable road users at intersections.

OUTCOMES
This project will bring perception sensing technologies like Radar, LiDAR and Time of Flight cameras to the next level, enhancing their features to allow for more accurate detection of human behaviour and physiological parameters. Besides more accurate automotive solutions ensuring driver vigilance and pedestrian and cyclist safety, this innovation will open up new opportunities in health and wellbeing to monitor elderly people at home or unobtrusively assess health state.

To facilitate building the complex smart sensing systems envisioned and ensure their secure and reliable operation, the new Distributed Intelligence paradigm will be embraced, enhanced and supported by tools. It leverages the advantages of Edge and Cloud computing, building on the distributed computational resources increasingly available in sensors and edge components to distribute also the intelligence.

PARTNERS
VTT Technical Research Centre of Finland (FI) - coordinatore, Acorde Technologies (ES), Aitek (IT), Benete (FI), Robert Bosch (DE), Brno University of Technology (CZ), Camea (CZ), Commeto (BE), Consider IT (DE), CSIC (ES), eLive (FI), Emoj (IT), Evalan (NL), Evotel Informatica (ES), Flir Systems Trading (BE), Hi Iberia (ES), IMA (CZ), IMEC (NL), IMEC (BE), University of Lleida (ES), Stichting Kempenhaeghe (NL), KU Leuven (BE), Macq (BE), Modulight (FI), NXP Semiconductors (DE), NXP Semiconductors (NL), Pozyx Labs (BE), RE:Lab (IT), Rulex Innovation Labs (IT), Smartmicro (DE), Seven Solutions (ES), Smart Roborts (NL), Televic (BE), TNO (NL), TTS (FI), Technical University of Dresden (DE), Technical University of Eindhoven (NL), University of Bologna (IT), University of Bremen (DE), University of Parma (IT), University Suor Orsola Benincasa (IT), University of Torino (IT), University of Vigo (IT).

FUNDING BODY
This project has received funding from the ECSEL Joint Undertaking, European Commission (grant agreement 876487).

WEBSITE: https://www.nextperception.eu

Valu3s
Valu3s

VALU3S – Verification and Validation of Automated Systems Safety and Security

H2020 - ECSEL-2019-2-RIA | 2020-2023

ABSTRACT
In the past years, manufacturers of automated systems and manufacturers of the components used in these systems have been allocating an enormous amount of time and effort in R&D activities, which led to the availability of prototypes demonstrating new capabilities as well as the introduction of such systems to the market within different domains. Manufacturers of these systems need to make sure that the systems function in the intended way and according to specifications which is not a trivial task as system complexity rises dramatically the more integrated and interconnected these systems become with the addition of automated functionality and features to them.

With rising complexity, unknown emerging properties of the system may come to the surface making it necessary to conduct thorough verification and validation (V&V) of these systems. Through the V&V of automated systems, the manufacturers of these systems are able to ensure safe, secure and reliable systems for society to use since failures in highly automated systems can be catastrophic.

The high complexity of automated systems incurs an overhead on the V&V process making it time-consuming and costly. VALU3S aims to design, implement and evaluate state-of-the-art V&V methods and tools in order to reduce the time and cost needed to verify and validate automated systems with respect to safety, cybersecurity and privacy (SCP) requirements. This is a qualification for European manufacturers of automated systems to remain competitive and world leaders in their fields. To this end, a multi-domain framework is designed and evaluated with the aim to create a clear structure around the components and elements needed to conduct V&V process through identification and classification of evaluation methods, tools, environments and concepts that are needed to verify and validate automated systems with respect to SCP requirements.

OUTCOMES
In VALU3S, 13 use cases with specific safety, security and privacy requirements will be studied in detail. Several state-of-the-art V&V methods will be investigated and further enhanced in addition to implementing new methods aiming for reducing the time and cost needed to conduct V&V of automated systems. The V&V methods investigated are then used to design improved process workflows for V&V of automated systems. Several tools will be implemented supporting the improved processes which are evaluated by qualification and quantification of safety, security and privacy as well as other evaluation criteria using demonstrators. VALU3S will also influence the development of safety, security and privacy standards through an active participation in related standardisation groups. VALU3S will provide guidelines to the testing community including engineers and researchers on how the V&V of automated systems could be improved considering the cost, time and effort of conducting the tests.

PARTNERS
VALU3S brings together a consortium with partners from 10 different countries, with a mix of industrial partners (26 partners) from automotive, agriculture, railway, healthcare, aerospace and industrial automation and robotics domains as well as leading research institutes (6 partners) and universities (10 partners) to reach the project goal.
RISE Research Institute of Sweden (SE) - coordinator, Stam (IT), Fondazione Bruno Kessler (IT), Knowledge Centric Solutions (ES), Università degli Studi dell’Aquila (IT), Instituto Superior de Engenharia do Porto (PT), Università degli Studi di Genova (IT), Camea (CZ), Ikerlan (ES), RBG Medical Devices (ES), Universidade de Coimbra (PT), Brno University (CZ), Roboauto (CZ), University of Eskisehir (TR), Royal Institute of Technology (SE), Swedish National Road and Transport Research Institute (SE), University of Castilla – La Mancha, Fraunhofer Institute (DE), Siemens (DE), NXP (DE, FR), Pumacy (DE), United Technologies Research Centre (IE), National University of Ireland Maynooth (IE), İnovasyon Mühendislik (TR), Erarge (TR), Otokar (TR), Techy (TR), Aldakin (ES), Intecs Solutions (IT), Lieber Lieber (AT), Austrian Institute of Technology (AT), Este (IT), Bombardier (SE), QRTECH (SE), CAF Signalling (ES), CardioID (PT), Mondragon University (ES), Infotiv (SE), Berge Consulting (SE).

FUNDING BODY
This project has received funding from the ECSEL Joint Undertaking, European Commission (grant agreement 876852).

Website: https://valu3s.eu

Valu3s

H2020- ECSEL-2019-2-RIA | 2020-2023

VALU3S aims to design, implement and evaluate state-of-the-art V&V methods and tools in order to reduce the time and cost needed to verify and validate automated systems with respect to safety, cybersecurity and privacy (SCP) requirements.

Valu3s

VALU3S – Verification and Validation of Automated Systems Safety and Security

H2020 - ECSEL-2019-2-RIA | 2020-2023

ABSTRACT
In the past years, manufacturers of automated systems and manufacturers of the components used in these systems have been allocating an enormous amount of time and effort in R&D activities, which led to the availability of prototypes demonstrating new capabilities as well as the introduction of such systems to the market within different domains. Manufacturers of these systems need to make sure that the systems function in the intended way and according to specifications which is not a trivial task as system complexity rises dramatically the more integrated and interconnected these systems become with the addition of automated functionality and features to them.

With rising complexity, unknown emerging properties of the system may come to the surface making it necessary to conduct thorough verification and validation (V&V) of these systems. Through the V&V of automated systems, the manufacturers of these systems are able to ensure safe, secure and reliable systems for society to use since failures in highly automated systems can be catastrophic.

The high complexity of automated systems incurs an overhead on the V&V process making it time-consuming and costly. VALU3S aims to design, implement and evaluate state-of-the-art V&V methods and tools in order to reduce the time and cost needed to verify and validate automated systems with respect to safety, cybersecurity and privacy (SCP) requirements. This is a qualification for European manufacturers of automated systems to remain competitive and world leaders in their fields. To this end, a multi-domain framework is designed and evaluated with the aim to create a clear structure around the components and elements needed to conduct V&V process through identification and classification of evaluation methods, tools, environments and concepts that are needed to verify and validate automated systems with respect to SCP requirements.

OUTCOMES
In VALU3S, 13 use cases with specific safety, security and privacy requirements will be studied in detail. Several state-of-the-art V&V methods will be investigated and further enhanced in addition to implementing new methods aiming for reducing the time and cost needed to conduct V&V of automated systems. The V&V methods investigated are then used to design improved process workflows for V&V of automated systems. Several tools will be implemented supporting the improved processes which are evaluated by qualification and quantification of safety, security and privacy as well as other evaluation criteria using demonstrators. VALU3S will also influence the development of safety, security and privacy standards through an active participation in related standardisation groups. VALU3S will provide guidelines to the testing community including engineers and researchers on how the V&V of automated systems could be improved considering the cost, time and effort of conducting the tests.

PARTNERS
VALU3S brings together a consortium with partners from 10 different countries, with a mix of industrial partners (26 partners) from automotive, agriculture, railway, healthcare, aerospace and industrial automation and robotics domains as well as leading research institutes (6 partners) and universities (10 partners) to reach the project goal.
RISE Research Institute of Sweden (SE) - coordinator, Stam (IT), Fondazione Bruno Kessler (IT), Knowledge Centric Solutions (ES), Università degli Studi dell’Aquila (IT), Instituto Superior de Engenharia do Porto (PT), Università degli Studi di Genova (IT), Camea (CZ), Ikerlan (ES), RBG Medical Devices (ES), Universidade de Coimbra (PT), Brno University (CZ), Roboauto (CZ), University of Eskisehir (TR), Royal Institute of Technology (SE), Swedish National Road and Transport Research Institute (SE), University of Castilla – La Mancha, Fraunhofer Institute (DE), Siemens (DE), NXP (DE, FR), Pumacy (DE), United Technologies Research Centre (IE), National University of Ireland Maynooth (IE), İnovasyon Mühendislik (TR), Erarge (TR), Otokar (TR), Techy (TR), Aldakin (ES), Intecs Solutions (IT), Lieber Lieber (AT), Austrian Institute of Technology (AT), Este (IT), Bombardier (SE), QRTECH (SE), CAF Signalling (ES), CardioID (PT), Mondragon University (ES), Infotiv (SE), Berge Consulting (SE).

FUNDING BODY
This project has received funding from the ECSEL Joint Undertaking, European Commission (grant agreement 876852).

Website: https://valu3s.eu

PICK UP
PICK UP

PICK-UP

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | Duration 2018-2020

ABSTRACT
The PICK-UP project aims at implementing innovative methods and tools for energy and environmental management and consumption reduction in heterogeneous urban districts. IoT and Fog Computing sensor networks will be integrated with new models of predictive control, energy data analysis and architectures for the aggregation and integration of distributed power generation sources (Microgrids), and flexible demand (Demand Response).

OUTCOMES
• The issuance of guidelines and tools for the planning and management of sustainable districts (which can then be applied to the concept of smart city
• The development of an energy management system for sustainable and smart districts.
• The creation of a significant smart city demonstration pilot site to be used in local, national and European projects, as well as to attract companies and research institutions at an international level.

PARTNERS
Gruppo Sigla (IT) - coordinator, AlgoWatt (IT), Rulex Innovation Labs (IT), Maps (IT), ABB (IT), Flairbit (IT), Stam (IT).

FUNDING BODY
The project was funded by Region Liguria, program: POR FESR 2014-2020, Action 1.2.4.

WEBSITE: http://www.pickup-energy.it/

Pickup

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | 2018-2020

The PICK-UP project aims at implementing innovative methods and tools for energy and environmental management and consumption reduction in heterogeneous urban districts.

PICK UP

PICK-UP

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | Duration 2018-2020

ABSTRACT
The PICK-UP project aims at implementing innovative methods and tools for energy and environmental management and consumption reduction in heterogeneous urban districts. IoT and Fog Computing sensor networks will be integrated with new models of predictive control, energy data analysis and architectures for the aggregation and integration of distributed power generation sources (Microgrids), and flexible demand (Demand Response).

OUTCOMES
• The issuance of guidelines and tools for the planning and management of sustainable districts (which can then be applied to the concept of smart city
• The development of an energy management system for sustainable and smart districts.
• The creation of a significant smart city demonstration pilot site to be used in local, national and European projects, as well as to attract companies and research institutions at an international level.

PARTNERS
Gruppo Sigla (IT) - coordinator, AlgoWatt (IT), Rulex Innovation Labs (IT), Maps (IT), ABB (IT), Flairbit (IT), Stam (IT).

FUNDING BODY
The project was funded by Region Liguria, program: POR FESR 2014-2020, Action 1.2.4.

WEBSITE: http://www.pickup-energy.it/

Liguria 4P Health
Liguria 4P Health

Liguria 4P Health (Predictive, Personalized, Preventive, Participatory)

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | 2018-2020

ABSTRACT
Development of an innovative solution of personal/mobile healthcare based on the semantic management of clinical data obtained from wearable/environmental created through predictive algorithms in order to create efficient recruiting, care and rehabilitation plans. The system will be delivered in Cloud via App in order to promote the participative interaction between patient and caregiver. A supportive analysis of the Healthcare Services promoters for an appropriate management of the chronicity will be provided.

OUTCOMES
The three total results of the project are:
• R1: realization of a prototype demonstrating the functionalities of the system/product.
• R2: System tests that, if they confirm the positive result concerning the successful achievement of functional goals there will be a Business Plan document attached for the following phase of exploitation. In case of mixed or not totally positive results an analysis of the criticalities will follow the tests.
• R3: study of clinic validation of the system and of PDA/PDTA elaborated during the project in favor of Healthcare Services providers (Enti di Erogazione di Servizi Sanitari).

PARTNERS
Maps (IT) - coordinator, Rulex Innovation Labs (IT), Camelot Biomedical Systems (IT), Nextage (IT), FOS GreenTech (IT), ETT (IT), Netalia (IT).

FUNDING BODY
The project was funded by Region Liguria, program: POR FESR 2014-2020, Action 1.2.4.

Liguria 4P Health

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | 2018-2020

Development of an innovative solution of personal/mobile healthcare based on the semantic management of clinical data obtained from wearable/environmental created through predictive algorithms in order to create efficient recruiting, care and rehabilitation plans.

Liguria 4P Health

Liguria 4P Health (Predictive, Personalized, Preventive, Participatory)

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | 2018-2020

ABSTRACT
Development of an innovative solution of personal/mobile healthcare based on the semantic management of clinical data obtained from wearable/environmental created through predictive algorithms in order to create efficient recruiting, care and rehabilitation plans. The system will be delivered in Cloud via App in order to promote the participative interaction between patient and caregiver. A supportive analysis of the Healthcare Services promoters for an appropriate management of the chronicity will be provided.

OUTCOMES
The three total results of the project are:
• R1: realization of a prototype demonstrating the functionalities of the system/product.
• R2: System tests that, if they confirm the positive result concerning the successful achievement of functional goals there will be a Business Plan document attached for the following phase of exploitation. In case of mixed or not totally positive results an analysis of the criticalities will follow the tests.
• R3: study of clinic validation of the system and of PDA/PDTA elaborated during the project in favor of Healthcare Services providers (Enti di Erogazione di Servizi Sanitari).

PARTNERS
Maps (IT) - coordinator, Rulex Innovation Labs (IT), Camelot Biomedical Systems (IT), Nextage (IT), FOS GreenTech (IT), ETT (IT), Netalia (IT).

FUNDING BODY
The project was funded by Region Liguria, program: POR FESR 2014-2020, Action 1.2.4.

SafeCOP
SafeCOP

SafeCOP – Safe Cooperating Cyber-Physical Systems using Wireless Communication

H2020-ECSEL-2015-1-RIA-two-stage | 2015-2019

ABSTRACT
SafeCOP addresses operating environments with security constraints such as Cooperating CyberPhysical Systems (CO-CPS) characterised by a prevailing use of wireless communication with multiple stakeholders and open and unpredictable operating environments. In this scenario, no stakeholder has overall responsibility for the resulting "system of systems". Even though CO-CPS allow to face and win different challenges of society (present and future), introduce new applications and markets, their certification and development are not adequately addressed by existing practices. The final goal of SafeCOP was therefore to provide an approach to guarantee the safety (understood both as safety and, where necessary, as security) of CO-CPS, thus enabling their development and certification. In order to reach this goal, the project has defined an architecture based on a run-time manager for the detection of abnormal behaviours that, if necessary, can trigger a "degraded but safe" service mode. SafeCOP has also developed methodologies and tools that can be used to certify the correct and safe functioning of a cooperative system. In addition, SafeCOP has extended current wireless technologies to ensure secure cooperation. Finally, SafeCOP has contributed to make new rules and regulations, providing certification authorities and standardisation committees with the scientific solutions needed to create standards that are also effective in addressing issues related to cooperation in a "system of systems".

OUTCOMES
• A methodology to ensure the safety of CO-CPS.
• A reference architecture for run-time management to support CO-CPS engineering and certification.
• An extension of current wireless protocols to secure cooperation.
• New standards and regulations.

PARTNERS
Aitek (IT), ALTE Visetec (FI), ALTEN Sweden (SE), CNR-IEIIT (IT), Danish Technological Institute (DK), DNV GL (NO), Finnish Meteorological Institute (FI), GMV (PT), Polytechnic of Porto - School of Engineering (ISEP) (PT), Intecs (IT), Intelligence Behind Things Solutions (IT), KTH Royal Institute of Technology (SE), Mälardalen University (SE), Maritime Robotics (NO), Odense University Hospital (DK), Polytechnic of Milan (IT), Qamcom Research & Technology AB (SE), Ro Technology (IT), Safety Integrity AB (SE), SICS (SE), SINTEF (NO), Sito (FI), Technical University of Denmark (DK), Technicon (DK), University of L’Aquila (IT).

FUNDING BODY
This project has received funding from the ECSEL Joint Undertaking, European Commission (grant agreement 69252).

Website: http://www.safecop.eu

SafeCop

H2020-ECSEL-2015-1-RIA-two-stage | 2015-2019

SafeCOP addresses operating environments with security constraints such as Cooperating CyberPhysical Systems (CO-CPS) characterised by a prevailing use of wireless communication with multiple stakeholders and open and unpredictable operating environments.

SafeCOP

SafeCOP – Safe Cooperating Cyber-Physical Systems using Wireless Communication

H2020-ECSEL-2015-1-RIA-two-stage | 2015-2019

ABSTRACT
SafeCOP addresses operating environments with security constraints such as Cooperating CyberPhysical Systems (CO-CPS) characterised by a prevailing use of wireless communication with multiple stakeholders and open and unpredictable operating environments. In this scenario, no stakeholder has overall responsibility for the resulting "system of systems". Even though CO-CPS allow to face and win different challenges of society (present and future), introduce new applications and markets, their certification and development are not adequately addressed by existing practices. The final goal of SafeCOP was therefore to provide an approach to guarantee the safety (understood both as safety and, where necessary, as security) of CO-CPS, thus enabling their development and certification. In order to reach this goal, the project has defined an architecture based on a run-time manager for the detection of abnormal behaviours that, if necessary, can trigger a "degraded but safe" service mode. SafeCOP has also developed methodologies and tools that can be used to certify the correct and safe functioning of a cooperative system. In addition, SafeCOP has extended current wireless technologies to ensure secure cooperation. Finally, SafeCOP has contributed to make new rules and regulations, providing certification authorities and standardisation committees with the scientific solutions needed to create standards that are also effective in addressing issues related to cooperation in a "system of systems".

OUTCOMES
• A methodology to ensure the safety of CO-CPS.
• A reference architecture for run-time management to support CO-CPS engineering and certification.
• An extension of current wireless protocols to secure cooperation.
• New standards and regulations.

PARTNERS
Aitek (IT), ALTE Visetec (FI), ALTEN Sweden (SE), CNR-IEIIT (IT), Danish Technological Institute (DK), DNV GL (NO), Finnish Meteorological Institute (FI), GMV (PT), Polytechnic of Porto - School of Engineering (ISEP) (PT), Intecs (IT), Intelligence Behind Things Solutions (IT), KTH Royal Institute of Technology (SE), Mälardalen University (SE), Maritime Robotics (NO), Odense University Hospital (DK), Polytechnic of Milan (IT), Qamcom Research & Technology AB (SE), Ro Technology (IT), Safety Integrity AB (SE), SICS (SE), SINTEF (NO), Sito (FI), Technical University of Denmark (DK), Technicon (DK), University of L’Aquila (IT).

FUNDING BODY
This project has received funding from the ECSEL Joint Undertaking, European Commission (grant agreement 69252).

Website: http://www.safecop.eu

P3C
P3C

P3C – Platform for the Prevention of Chronic Pathologies

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | 2016-2018

ABSTRACT
P3C aimed at creating a virtuous circle involving (1) predictive medicine, (2) medical records (Italian Fascicolo Sanitario Elettronico, FSE), (3) diagnostic appropriateness, and (4) personalized therapy.

The main goal was to benefit from epidemiological inquiries deriving from big laboratory data integrating them with the knowledge of the FSE in order to spot specific patients and, in that case, report them to doctors through the early referral procedure.

The project involved two other companies: Dedalus (before Noemalife), Italian leader in the Healthcare automation, and Nextage who provided its skills concerning both platforms and interfaces for the management of large data quays and genomic data analysis pipeline.

OUTCOMES
For what concerns Rulex Innovation Labs it has been developed an innovative parallelization component that ensures Rulex's scalability beyond one billion data. Other developments were data breakdown logic patterns, a “map-reduce” algorithm of rules allowing to work in a spread environment (several parallel processing servers and/or data in geographically distant sites).

PARTNERS
Dedalus (IT) - coordinator , Nextage (IT), Rulex Innovation Labs (IT).

FUNDING BODY
The project was funded by Region Liguria, program: POR FESR 2014-2020, Action 1.2.4.

P3C

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | 2016-2018

P3C aimed at creating a virtuous circle involving (1) predictive medicine, (2) medical records (Italian Fascicolo Sanitario Elettronico, FSE), (3) diagnostic appropriateness, and (4) personalized therapy.

P3C

P3C – Platform for the Prevention of Chronic Pathologies

POR FESR 2014-2020 Axis 1 “Research and Innovation” – Action 1.2.4 | 2016-2018

ABSTRACT
P3C aimed at creating a virtuous circle involving (1) predictive medicine, (2) medical records (Italian Fascicolo Sanitario Elettronico, FSE), (3) diagnostic appropriateness, and (4) personalized therapy.

The main goal was to benefit from epidemiological inquiries deriving from big laboratory data integrating them with the knowledge of the FSE in order to spot specific patients and, in that case, report them to doctors through the early referral procedure.

The project involved two other companies: Dedalus (before Noemalife), Italian leader in the Healthcare automation, and Nextage who provided its skills concerning both platforms and interfaces for the management of large data quays and genomic data analysis pipeline.

OUTCOMES
For what concerns Rulex Innovation Labs it has been developed an innovative parallelization component that ensures Rulex's scalability beyond one billion data. Other developments were data breakdown logic patterns, a “map-reduce” algorithm of rules allowing to work in a spread environment (several parallel processing servers and/or data in geographically distant sites).

PARTNERS
Dedalus (IT) - coordinator , Nextage (IT), Rulex Innovation Labs (IT).

FUNDING BODY
The project was funded by Region Liguria, program: POR FESR 2014-2020, Action 1.2.4.

publications

Our scientific articles

  • Ferrari Enrico and Muselli Marco, “Efficient constructive techniques for training switching neural networks.” Constructive Neural Networks. Springer, Berlin, Heidelberg, 2009. 25-48.
  • Muselli Marco and Ferrari Enrico, “Coupling Logical Analysis of Data and Shadow Clustering for partially defined positive Boolean function reconstruction.” IEEE Transactions on Knowledge and Data Engineering 23.1 (2011): 37-50.
  • Parodi Stefano et al., “Differential diagnosis of pleural mesothelioma using Logic Learning Machine.” BMC bioinformatics 16.9 (2015): S3.
  • Agosta Giovanni et al., “V2I Cooperation for traffic management with SafeCop.” Digital System Design (DSD), 2016 Euromicro Conference on. IEEE, 2016.
  • Agneessens Alessio et al., “Safe cooperative CPS: A V2I traffic management scenario in the SafeCOP project.” Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), 2016 International Conference on. IEEE, 2016.
  • Parodi Stefano et al., “Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables.” Health informatics journal (2016): 1460458216655188.
  • Parodi Stefano, et al., “Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine.” Journal of gambling studies 33.4 (2017): 1121-1137.
  • Mongelli Maurizio et al., “Performance validation of vehicle platooning via intelligible analytics.” IET Research Journals, 2018: 1–8
  • Fermi Alessandro, et al., “Identification of safety regions in vehicle platooning via machine learning.” 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS). IEEE, 2018.
  • Banerjee Imon et al., “Feature-based Characterisation of Patient-specific 3D Anatomical Models”, Smart Tools and Applications in Graphics – Eurographics Italian Chapter Conference, 2019.
  • Verda Damiano et al., Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods. BMC Bioinformatics 20, 390 (2019).
  • Mongelli Maurizio et al., “Accelerating PRISM validation of vehicle platooning through machine learning,” in 2019 4th International Conference on System Reliability and Safety (ICSRS). IEEE, 2019, pp. 452–456.
  • Mongelli Maurizio et al., “Achieving zero collision probability in vehicle platooning under cyber-attacks via machine learning,” in 2019 4th International Conference on System Reliability and Safety (ICSRS). IEEE, 2019, pp. 41–45.
  • Mongelli Maurizio et al., “Performance Validation of Vehicle Platooning via Intelligible Analytics”, IET Cyber-Physical Systems: Theory & Applications, 19 Oct. 2018.
  • Aiello Maurizio et al., “Unsupervised learning and rule extraction for Domain Name Server tunneling detection” Internet Technology Letters 2019; 2:e85. https://doi.org/10.1002/itl2.85.
  • Barbosa Raul et al., “The VALU3S ECSEL Project: Verification and Validation of Automated Systems Safety and Security,” 2020 23rd Euromicro Conference on Digital System Design (DSD), 2020, pp. 352-359, doi: 10.1109/DSD51259.2020.00064.
  • Lojo Aizea et al., “The ECSEL FRACTAL Project: A Cognitive Fractal and Secure edge based on a unique Open-Safe-Reliable-Low Power Hardware Platform” 2020 23rd Euromicro Conference on Digital System Design (DSD), 2020, pp. 393-400, doi: 10.1109/DSD51259.2020.00069.
  • Gerussi Alessio et al., “Machine learning in primary biliary cholangitis: A novel approach for risk stratification”, Liver Int. 2022; 00: 1– 13. doi:10.1111/liv.15141.

partners

We work with

Università degli Studi di Milano-Bicocca
Università di Torino
Euopean Commission
Filse
National reasearch Council of italy
Polo EASS
Aeneas
ALICE Alliance for Logistics Innovation through Collaboration in Europe
Chips Ju
Polo PLSV
Polo TRANSIT
UniGe