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Virtual Sensors for Anomaly detection in people with Multiple Sclerosis (Ref. PID2020-112667RB-I00)

The main challenge of this approach is to characterize the state of the patient in a given time, so that the Virtual Sensor is able to detect slight variations in this state. The final goal is to provide therapists with information that allows them to adapt easily the rehabilitation therapies, so that their efficiency is maximized, which will impact on the quality of life of patients.    

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Evolución tecnológica para la automatización multivehicular y evaluación de funciones de conducción altamente automatizadas (AutoEv@l)

  • Lead researchers: Asier Zubizarreta Picó - (01/01/2021 - 31/12/2021)
  • Funding body: Gobierno Vasco/ Eusko Jaurlaritza

This coordinated project has as the main goal to define an appropriate testing environment and methodologies, which includes intelligent management of data, and a set of tools, techniques and controls, to aid in the development of highly automated functions in  vehicles. The project consortium is composed by UPV/EHU (VISENS Group and GDED Group), Tecnalia, MU, AIC, DEIT, Ikerlan, CAM, VICOMTECH. In the consortium, VISENS participates in work package 3: Research and develoment of highly automated sensors and...

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Towards Spiking Neural Networks for ultra-low-power consumption applications (PIBA_2020_1_0008)

  • Lead researchers: Eva Portillo Pérez - (04/11/2020 - 30/11/2023)
  • Funding body: Basque Country Government, Department of Education

A HardWare Implementation of the PWM inspired encoding/decoding algorithm (IHA-PWM). A new supervised training approach of SNN for regression based on both backpropagation and PWM inspired encoding/decoding algorithm. Define the specification for a neuromorphic HardWare implementation of both IHA-PWM and the new supervised training algorithm.

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