En esta tesis se propone entrenar una red neuronal para la detección de coches, utilizando sólo puntos de un LIDAR de 32 canales de la base de datos de NuScenes. El objetivo es conseguir el reconocimiento de coches frontales ...

The main goal of the project is to find correspondences between points in two 3D point clouds using deep learning. A deep learning network is trained to select key points and find correspondences between two point clouds ...

The present Master Thesis describes a new Pose Estimation method based on Convolutional Neural Networks (CNN). This method divides the three-dimensional space in several regions and, given an input image, returns the ...

Bearing faults are the commonest form of malfunction associated with electrical machines. So far, the research has been carried out mainly in the detection of localized faults, but the diagnosis of distributed faults ...

This paper presents the SEPELYNS architecture that permits to in- terconnect multiple spiking neurons focused on hardware implementations. SEPELYNS can connect millions of neur ons with thousands of synapses per neuron ...

This thesis proposes a novel active learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our approach ...

These days, working with deep neural networks goes hand in hand with the use of GPUs. Once a deep neural network has been trained for hours, days, or even weeks on a desktop GPU, it is deployed in the field where it runs ...

A new adaptive nonlinear state predictor (ANSP) is presented for a class of unknown nonlinear systems with input time-delay. A dynamical identification with neu- ral network (NN) is constructed to obtain NN weights and ...

[ANGLÈS] This thesis presents AER-RT network interface, a network interface designed to work together a Multiprocessor System (MPS) and create an efficient and scalable multi-chip SNN network. The objective of AER-RT is ...