{"id":340,"date":"2018-06-04T13:05:42","date_gmt":"2018-06-04T13:05:42","guid":{"rendered":"https:\/\/megamart2-ecsel.eu\/?page_id=340"},"modified":"2019-10-28T23:30:54","modified_gmt":"2019-10-28T23:30:54","slug":"use-cases","status":"publish","type":"page","link":"https:\/\/megamart2-ecsel.eu\/use-cases\/","title":{"rendered":"Use Cases"},"content":{"rendered":"
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The case study selected by Thales is a Flight Management System (FMS). It is an industrial avionics application whose purpose in modern avionics is to provide the crew with centralized control for the aircraft navigation sensors, computer based flight planning, fuel management, radio navigation management, and geographical situation information. Taking charge of a wide variety of in-flight tasks, the FMS allows to reduce the workload and thus the size of the crew. This avionic function is responsible for the flight management services allowing inflight guidance of the aircraft through optimized trajectories computations.<\/p>\n
The application is representative of a commercial Flight Management System and has been redesign and rewritten in a multi-threaded way so that the application can be executed in a distributed way. Even if it is a simplified version of a Flight Management System, the application is still representative of avionics application in term of performance and memory requirements.<\/p>\n
The figure above gives a functional overview of the Flight Management System. This application is responsible of the Inflight guidance of aircrafts based on the use of flight plans. Merging sensors information allows the application to evaluate the plane\u2019s localization, which together with the pre-set flight plans allows computing some trajectories that will be translated into guidance actions to guide the plane.<\/p>\n
In the current approach, Thal\u00e8s applies Model-based systems engineering for a component based design and UML for detailed implementation; use of in-house code generators for up to 40% of the source code, verification and testing. The main challenges are linking development models and verification and validation activities, test case generation of performance test cases, architecture analysis, model-based tools collaboration.<\/p>\n
With MegaM@Rt, Thal\u00e8s intends to reduce the effort for verification and validation while preserving quality and performance. MegaM@Rt will contribute to overall improvement of the development process.<\/p>\n
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During the last years IKERLAN has been working on several projects for the development of different platforms for monitoring, control and supervision of remote IoT devices. These platforms have been used in different industrial and non-industrial environments, i.e. smart warehouses, smart buildings, smart manufacturing, consumer goods, etc.<\/p>\n
Trying to avoid repeating similar work in each project, IKERLAN is going to develop a generic system that can be accessible and useful for a plethora of industrial companies (different domains and applications) with different functional and extra-functional
requirements. This could allow to have a system that could be easily adapted to many industrial domains, having a set of core technologies reused in every project.<\/p>\n
Due to the complexity of developing a generic IoT device management ecosystem with these characteristics, it is vitally important to have a platform lifecycle process (embedded devices and the cloud) that guarantees product quality, security and reliability. In this way, IKERLAN is going to improve their IoT device ecosystem lifecycle process: focusing on the relation between the design time and runtime, also taking into account the verification and validation processes in a continuous way, reducing costs without losing quality.<\/p>\n
As described above, the IKERLAN-KONNEKT platform consists of several devices within the entire value chain for monitoring, controlling and supervising remote IoT devices. Based mainly on embedded devices at edge or fog and the cloud supervision system, IKERLAN poses the following challenges for the MegaM@Rt2 project:<\/p>\n
With MegaM@Rt, IKERLAN aims to improve the different phases of the life cycle of the projects it carries out for companies in various sectors. The final aim is to improve both the efficiency of project development and the quality of the projects.<\/p>\n
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UnicamVELOCITY is a CAMEA\u2019s solution for section speed enforcement (also called section, point-to-point or average speed control or\u00a0 check). When compared to spot speed enforcement systems (radars, inductive loops, LIDARs etc.) which only measure the actual speed in one place, the main extra feature is that the average speed of the vehicle is measured in the entire specified section. The\u00a0 system has been certified for speed enforcement.<\/p>\n
The principle of the averaging speed measurement is simple, there are two camera equipped sites with the know, well measured distance between them. As they can be tens of meters or couple kilometre far, the precise time synchronization could be an issue. Using global navigation satellite system seems as the best option. Video-based detection is continuously performed (using detail cameras) and LP for each passing vehicle is being detected and subsequently read using specialized OCR algorithm. The detected LPs are then matched and when match is found, the average speed is calculated based on the known distance travelled and time elapsed (between the two detections). Then, it is usually compared with the limit set and a violation report could be generated.<\/p>\n
The UnicamVELOCITY system is composed as a combination of a local processing on site (LP detection and OCR) with all the\u00a0 infrastructure around (video cameras, IR flashes, PC, networking, etc.), and background processing running on server side.<\/p>\n
CAMEA uses multiple levels of processing in its system. Part of the computation is done locally and the rest is done on the server. Currently, on the sites Unicam systems use Ethernet cameras (manufactured by CAMEA) wired to computationally powerful PCs capable of running powerful video detection algorithms. The detector also supports additional logic e.g. for controlling IR flashes on demand\/trigger. The detection results are then sent to the server computer and processed in the meaning of the matching corresponding detection and calculating average speed. When speed limit is violated, detailed evidence images can be requested from the site (which saves network bandwidth).<\/p>\n