Browsing by Keyword "Sensors"
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Item The AFarCloud ECSEL Project(IEEE, 2019-10) Castillejo, Pedro; Curuklu, Baran; Fresco, Roberto; Johansen, Gorm; Bilbao-Arechabala, Sonia; Martinez-Rodriguez, Belen; Pomante, Luigi; Martinez-Ortega, Jose-Fernan; Santic, Marco; Konofaos, Nikos; Kitsos, Paris; Tecnalia Research & Innovation; BIGDATAFarming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate identification and proper quantification of pathogens affecting plant and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate FARming in the CLOUD) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labour costs. This platform will be integrated with farm management software and will support monitoring and decision-making solutions based on big data and real-time data mining techniques.Item Graph Based Learning for Building Prediction in Smart Cities(2022-04) Garmendia-Orbegozo, Asier; Noye, Sarah; Anton, Miguel Angel; Nunez-Gonzalez, J. David; Tecnalia Research & Innovation; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓNAnticipating pedestrians’ activity is a necessary task for providing a safe and energy efficient environment in an urban area. By locating strategically sensors throughout the city useful information could be obtained. By knowing the average activity of those throughout different days of the week we could identify the typology of the buildings neighboring those sensors. For these type of purposes, clustering methods show great capability forming groups of items that have great similarity intra clusters and dissimilarity inter cluster. Different approaches are made to classify sensors depending on the typology of buildings surrounding them and the mean pedestrians’ counts for different time intervals. By this way, sensors could be classified in different groups according to their activation patterns and the environment in which they are located through clustering processes and using graph convolutional networks. This study reveals that there is a close relationship between the activity pattern of the pedestrians’ and the type of environment sensors that collect pedestrians’ data are located. By this way, institutions could alleviate a great amount of effort needed to ensure safe and energy efficient urban areas, only knowing the typology of buildings of an urban zone.Item A practical approach to cross-agri-domain interoperability and integration(2022) Bilbao-Arechabala, Sonia; Martinez-Rodriguez, Belen; Tecnalia Research & InnovationIn this paper we describe the process for making sensors and IoT devices interoperable with existing agri-solutions, and to federate data and services between two agricultural smart platforms, more precisely the AFarCloud and DEMETER solutions. This approach is in line with EU data-driven strategy and GAIA-X’s federation strategy. Finally, we present the use case where this process has been tested and validated, i.e. Kotipelto farm, a dairy farm located in Ylivieska (Finland).Item Sensor Testing for Smart Mobility Scenarios: From Parking Assistance to Automated Parking(Springer, Cham, 2019) Larrauri, J. Murgoitio; Muñoz, E. D. Martí; Recalde, M. E. Vaca; Hillbrand, B.; Tengg, A.; Pilz, Ch.; Druml, N.Vehicle automation is one of the major challenges of nowadays’ transport system and its goals are to achieve the ideal energy efficiency, the minimum environment impact and the highest safety rate. In this context, IoSense is the project which will deploy new capabilities (Sensors, Components and Systems) through several demonstrators, one of them called “SmaBility” (Smart Mobility scenarios). So, the intelligent perception and decision making for safer and autonomous driving are the main objectives of the SmaBility demonstrator focus on the “Automated parking”. Then this chapter firstly lists the capabilities in the design, modelling and simulation area of each partner (TECNALIA, IFAT and VIF) involved on the title “From Parking Assistance to Automated Parking” within the Smability. In a second stage, several simulations considering a Time-of-Flight (ToF) camera, as the main perception technology, are explained at both levels: Sensor (ToF) and System (Automated parking). In parking assistance scenario (system level), a ToF camera, similar to the previous one analysed at sensor level, is considered as substitute for ultrasonic range sensors. The expected advantages of using such camera include faster answer, better resolution and object recognition capabilities. Combining depth information with a vehicle geometry model and ego-information (position, speed, steering angle), it is possible to estimate distance to collision point and time to collision (TTC) with great accuracy. Finally, summary and conclusions are reported.