Learning to count with deep object features

Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.Comment: This paper has been accepted at Deep Vision Workshop at CVPR 201

Project of monitoring the wind tunnel of the ETSEIAT’s Aerospace Engineering laboratory (Software)

Monitoritzar i automatitzar el funcionament del túnel de vent del laboratori d'Enginyeria Aeroespacial. Es vol substituir el funcionament manual actual per un d'automatitzat, de forma que es puguin programar assajos i adquirir les dades de forma automàticaThe ETSEIAT’s aerospace engineering laboratory’s wind tunnel used to vary its airflow speed thanks to a potentiometer and the air properties were manually measured. Hence, monitoring the tunnel’s behaviour was the goal. This aim was to be achieved by making use of an Arduino, the proper sensors and genuine software in order to have better control of the velocity and to gather data automatically. Taking this into consideration, a Matlab code which interacts with Arduino was created. It collects the temperature, the atmospheric pressure and the differential pressure at the nozzle, and saves and plots in real time all the data. Moreover, it calculates and stores the air speed as well as comparing it to the desired velocity so as to obtain the error, which is aimed to be minimized, and is later on processed by a PID controller. After mounting the whole system with the appropriate hardware, the sensors were tested and some of them re-calibrated to reduce, as much as possible, all the uncertainties and get a simultaneously efficient, accurate and robust system. Once all the difficulties have been solved it is possible to say that the project has finally been carried out with a greatly satisfactory result and it will be a useful tool for the university

El rápido crecimiento de algunas entidades no lucrativas. El ejemplo de la Fundació Pere Tarrés

.La Fundació Pere Tarrés es una organización no lucrativa que ha vivido un crecimiento rápido durante el período 1985-95. La experiencia en la gestión de esta entidad favorece un conjunto de reflexiones del autor sobre el management de este tipo de situaciones en una ONL. Las distintas etapas de evolución, con los puntos de inflexión que facilitan la siguiente, permiten el establecimiento de criterios sobre la toma de decisiones. Otros aspectos sobre los que se profundiza específicamente son la financiación de una organización en crecimiento y las pautas de participación del equipo humano. El éxito en el trabajo coordinado de voluntarios y profesionales posibilitará el avance hacia los fines por los que trabaja toda ONL