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Grant
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: MG-7.1-2014 | Award Amount: 5.97M | Year: 2015

Transportation sector undergoes a considerable transformation as it enters a new landscape where connectivity is seamless and mobility options and related business models are constantly increasing. Modern transportation systems and services have to mitigate problems emerging from complex mobility environments and intensive use of transport networks including excessive CO2 emissions, high congestion levels and reduced quality of life. Due to the saturation of most urban networks, innovative solutions to the above problems need to be underpinned by collecting, processing and broadcasting an abundance of data from various sensors, systems and service providers. Furthermore, such novel transport systems have to foresee situations in near real time and provide the means for proactive decisions, which in turn will deter problems before they even emerge. Our vision is to provide the required interoperability, adaptability and dynamicity in modern transport systems for a proactive and problem-free transportation system. OPTIMUM will establish a largely scalable, distributed architecture for the management and processing of multisource big-data, enabling continuous monitoring of transportation systems needs and proposing proactive decisions and actions in an (semi-) automatic way. OPTIMUM follows a cognitive approach based on the Observe, Orient, Decide, Act loop of the big data supply chain for continuous situational awareness. OPTIMUMs goals will be achieved by incorporating and advancing state of the art in transport and traffic modeling, travel behavior analysis, sentiment analysis, big data processing, predictive analysis and real-time event-based processing, persuasive technologies and proactive recommenders. The proposed solution will be deployed in real-life pilots in order to realise challenging use cases in the domains of proactive improvement of transport systems quality and efficiency, proactive charging for freight transport and Car2X communication integration.


Grant
Agency: Cordis | Branch: FP7 | Program: CP-FP | Phase: SEC-2011.5.3-4 | Award Amount: 4.22M | Year: 2012

ADVISE aims to design and develop a unification framework for surveillance-footage archive systems, in an effort to deal with the increasingly critical need to provide automated and smart surveillance solutions. This need arises due to the continuous growth of surveillance systems in scale, heterogeneity and utility. There are two major obstacles: the variety on the technical components of the surveillance systems, producing video repositories with different compression formats, indexing systems, data storage formats sources, and the fact that such a system should take into careful consideration the legal, ethical and privacy rules that govern surveillance and the produced content. Towards both, ADVISE has been formed by experts on both technological and legal, ethical, privacy aspects, with valuable experiences in the Security field. To further ensure the applicability of the proposed system, ADVISEs consortium includes some major European security agencies, while will be in collaboration with plenty other through its Advisory Boards. In terms of implementation, the ADVISE system will be split into two major components. The first will be performing the semantically enriched, event based video analysis, which will offer efficient search capabilities into video archives and sophisticated result visualisation. The second will enforce the legal, ethical and privacy constraints that apply to the exchange and processing of the surveillance data. Towards interoperability, the exchanged content and the associated metadata will be transformed into a common format. A Dedicated ADVISE Engine will be develop per peer authority in order to efficiently deal with each peer authoritys technical and Legal/Ethical/Privacy specificities. The components of ADVISE, after negotiating all relevant legal, ethical and privacy constraints, will be able to help the law enforcement authorities fight against crime and terrorism via efficient evidence mining into heterogeneous video archives


A method of resolving conflicts between revisions to a distributed virtual file system is implemented at a computing device that is communicatively connected to a plurality of storage devices. The virtual file system at the computing device has a first revision of the virtual file system. Upon receipt of a request to synchronize the first revision of the virtual file system with the storage devices, the computing device retrieves one or more blocks from the storage devices, which are associated with a second revision of the virtual file system. The computing device then merges a first component of the first revision with a corresponding component of the second revision if a first predefined condition is met or identifies a second component of the first revision as being conflicted with a corresponding component of the second revision if a second predefined set of conditions is met.


Grant
Agency: Cordis | Branch: H2020 | Program: IA | Phase: FCT-05-2014 | Award Amount: 4.91M | Year: 2015

Covert evidence gathering has not seen major changes in decades. Law enforcement Agencies (LEAs) are still using conventional, manpower based techniques to gather forensic evidence. Concealed surveillance devices can provide irrefutable evidences, but current video surveillance systems are usually bulky and complicated, are often used as simple video recorders, and require complex, expensive infrastructure to supply power, bandwidth, storage and illumination. Recent years have seen significant advances in the surveillance industry, but these were rarely targeted to forensic applications. The imaging community is fixated on cameras for mobile phones, where the figures of merit are resolution, image quality, and low profile. A mobile phone with its camera on would consume its battery in under two hours. Industrial surveillance cameras are even more power hungry, while intelligent algorithms such as face detection often require extremely high processing power, such as backend server farms, and are not available in conventional surveillance systems. Here we propose to develop and validate a novel, ultra-low-power, intelligent, miniaturised, low-cost, wireless, autonomous sensor (FORENSOR) for evidence gathering. Its ultra-sensitive camera and built-in intelligence will allow it to operate at remote locations, automatically identify pre-defined criminal events, and alert LEAs in real time while providing and storing the relevant video, location and timing evidence. FORENSOR will be able to operate for up to two months with no additional infrastructure. It will be manageable remotely, preserve the availability and the integrity of the collected evidence, and comply with all legal and ethical standards, in particular those related to privacy and personal data protection. The combination of built-in intelligence with ultra-low power consumption could help LEAs take the next step in fighting severe crimes.


Grant
Agency: Cordis | Branch: FP7 | Program: CP | Phase: FI.ICT-2011.1.8 | Award Amount: 17.36M | Year: 2013

FI-STAR will establish early trials in the Health Care domain building on Future Internet (FI) technology leveraging on the outcomes of FI-PPP Phase 1. It will become self-sufficient after the end of the project and will continue on a sustainable business model by several partners. In order to meet the requirements of a global Health industry FI-STAR will use a fundamentally different, reverse cloud approach that is; it will bring the software to the data, rather than bringing the data to the software. FI-STAR will create a robust framework based of the software to data paradigm. A sustainable value chain following the life cycle of the Generic Enablers (GEs) will enable FI-STAR to grow beyond the lifetime of the project. FI-STAR will build a vertical community in order to create a sustainable ecosystem for all user groups in the global Health care and adjacent markets based on FI-PPP specifications. FI-STAR will deploy and execute 7 early trials across Europe, serving more than 4 million people. Through the trials FI-STAR will validate the FI-PPP core platform concept by using GEs to build its framework and will introduce ultra-light interactive applications for user functionality. It will pro-actively engage with the FI-PPP to propose specifications and standards.FI-STAR will use the latest digital media technology for community building and will proactively prepare for Phase 3 through targeted elicitation of new partners using open calls. Finally, FI-STAR will collaborate with other FI-PPP projects, through the mechanisms in place, by actively interacting with all necessary bodies. FI-STAR is a unique opportunity for implementing Future Internet Private-Public Partnership in the Health Care domain, by offering to the community standardised and certified software including a safe, secure and resilient platform, taking advantage of all Cloud Computing benefits and guaranteeing the protection of sensitive and personal data travelling in Public Clouds.

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