Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad TIN2013-46801-C4-1-

Técnicas de predicción de destinos geográficos futuros en desplazamientos de personas

El desarrollo de este trabajo de investigación se orientará hacia un sector cada vez más amplio de usuarios que dispongan y utilicen frecuentemente dispositivos móviles con capacidades de gestión de base de datos y localización por GPS. Cuanta más capacidad tenga el dispositivo más posibles aplicaciones con conocimiento del contexto futuro podrán implantarse. Aunque las propuestas de esta tesis se pueden aplicar a sistemas de localización diferentes, en la implementación nos centramos en la predicción de recorridos urbanos e inter-urbanos rastreados por receptores GPS. Éstos no permiten el seguimiento en espacios sin visión directa sobre los satélites, por lo que quedan fuera de este trabajo la predicción de destinos en interiores. Nuestra propuesta, al usar como soporte mapas personales creados por el propio usuario al desplazarse diariamente, no necesitará de ningún GIS por lo que será utilizable en lugares donde no exista cartografía detallada, como por ejemplo en mares y océanos. El público objetivo de las predicciones de destino serán personas con todo tipo de hábitos de desplazamientos y que utilicen cualquier medio de transporte en el que puedan utilizarse dispositivos electrónicos (quedan por tanto fuera de nuestra hipótesis los aviones). Capítulo 2: Metodología off-line. Se propondrá una metodología para la recuperación de la información, su preprocesado y los algoritmos de extracción de conocimiento a partir de trazas de movimiento aportadas por diferentes voluntarios. Los objetivos de este capítulo son por una parte estudiar los métodos existentes para extraer los destinos frecuentes y las rutas seguidas, aportar nuevos algoritmos que mejoren los resultados, proponer una metodología con fases bien diferenciadas y estudiar los aspectos prácticos para poder desarrollar una metodología on-line realista. Capítulo 3: Modelo de Markov Oculto sobre "Mapa Soporte" . Basándonos en las rutas que genera un usuario, creamos un mapa personal que nos permite utilizarlo aplicando un Modelo Oculto de Markov (HMM). El mapa personal disminuye en gran medida la cantidad de información utilizada por lo que lo estudiamos para aplicarlo en dispositivos móviles. Capítulo 4: Similitud de recorridos. Las medidas de similitud existentes entre recorridos no se adaptan a nuestras necesidades por lo que propondremos nuevas métricas que permiten comparar tanto recorridos finalizados entre sí como recorridos no finalizados con aquellos que sí lo han hecho, lo que nos permite predecir rutas y destinos. Capítulo 5: Resultados. Revisaremos los resultados obtenidos aplicando la metodología off-line y evaluaremos los modelos de predicción utilizando rutas mediante similitudes y "Mapas soporte" a través de HMM, comparando las dos alternativas. Capítulo 6: Predicción on-line. Una vez comprobados los resultados de los diferentes modelos de predicción, trataremos su implementación en un dispositivo móvil para permitir la predicción en tiempo real. Además se propondrán técnicas para reducir el consumo innecesario de baterías, mejorando la disponibilidad de los servicios de predicción. Capítulo 7: Aplicaciones. Mostraremos las aplicaciones definidas para nuestro sistema de predicción on-line, de modo que se destaque la utilidad del sistema de predicción on-line propuesto. Capítulo 8: Conclusiones y trabajo futuro. En este capítulo se presentan las conclusiones de esta tesis doctoral y las líneas de investigación abiertas sobre las que se trabajaremos en los próximos años. Apéndice A: Sistema GPS Describiremos de forma breve en este apéndice el funcionamiento del Global Positioning System y la estructura de las sentencias NMEA. Apéndice B: Currículum. En este apéndice incluimos las publicaciones y los proyectos en los que hemos participado durante la elaboración de este trabajo. Bibliografía: Tras los dos apéndices, incluimos la bibliografía utilizada en la elaboración del documento

Trip destination prediction based on past GPS log using a Hidden Markov Model

In this paper, a system based on the generation of a Hidden Markov Model from the past GPS log and cur- rent location is presented to predict a user’s destination when beginning a new trip. This approach dras- tically reduces the number of points supplied by the GPS device and it permits a ‘‘support-map” to be generated in which the main characteristics of the trips for each user are taken into account. Hence, in contrast with other similar approaches, total independence from a street-map database is achievedMinisterio de Educación y Ciencia TSI2006–13390-C02–02Junta de Andalucia TIC214

Real-time gun detection in CCTV: An open problem

Object detectors have improved in recent years, obtaining better results and faster inference time. However, small object detection is still a problem that has not yet a definitive solution. The autonomous weapons detection on Closed-circuit television (CCTV) has been studied recently, being extremely useful in the field of security, counter-terrorism, and risk mitigation. This article presents a new dataset obtained from a real CCTV installed in a university and the generation of synthetic images, to which Faster R-CNN was applied using Feature Pyramid Network with ResNet-50 resulting in a weapon detection model able to be used in quasi real-time CCTV (90 ms of inference time with an NVIDIA GeForce GTX-1080Ti card) improving the state of the art on weapon detection in a two stages training. In this work, an exhaustive experimental study of the detector with these datasets was performed, showing the impact of synthetic datasets on the training of weapons detection systems, as well as the main limitations that these systems present nowadays. The generated synthetic dataset and the real CCTV dataset are available to the whole research community.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-

Discrete classification technique applied to TV advertisements liking recognition system based on low‑cost EEG headsets

Background: In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. Methods: By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. Results: The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. Conclusions: This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.Ministerio de Economía y Competitividad HERMES TIN2013-46801-C4-1-rJunta de Andalucia Simon TIC-805

Outdoor exit detection using combined techniques to increase GPS efficiency

The recent boom of GPS (Global Positioning System) as a universal method of location has meant that most people in developed countries have already used this technology sometime in their lives. However, this system suffers from an ever-increasing problem: energy expenditure. GPS receivers have been integrated into increasingly smaller devices such as the latest generation of mobiles, thereby making batterysaving a priority in the use of this technology. This article lays out a series of ideas which, through the use of auxiliary technologies, are able to maximize energy saving. By means of outdoor exit detection, it will be possible to automatically disconnect the GPS while the user stays indoors and later reconnect it on leaving the building.Ministerio de Ciencia e Innovación ARTEMISA TIN2009-14378-C02-0

Start-up of a microalgae-based treatment system within the biorefinery concept: from wastewater to bioproducts

©IWA Publishing [2018]. The definitive peer-reviewed and edited version of this article is published in Water Science & Technology, volume 78, issue 1, p. 114-124, 2018, doi: 10.2166/wst.2018.195 and is available at www.iwapublishing.com.Within the European project INCOVER, an experimental microalgae-based treatment system has been built for wastewater reuse and added-value products generation. This article describes this new experimental plant and the start-up stage, starting from the new design of three semi-closed horizontal photobioreactor (PBR) with low energy requirements for microalgae cultivation (30 m3 total), using agricultural runoff and urban wastewater as feedstock. The inflow nutrients concentration is adjusted to select cyanobacteria, microalgae able to accumulate polyhydroxybutyrates (PHBs), which can be used for bioplastics production. Part of the harvested biomass is used as substrate for anaerobic co-digestion (AcoD) with secondary sludge to obtain biogas. This biogas is then cleaned in an absorption column to reach methane concentration up to 99%. The digestate from the AcoD is further processed in sludge wetlands for stabilization and biofertilizer production. On the other hand, treated water undergoes ultrafiltration and disinfection through a solar-driven process, then it is pumped through absorption materials to recover nutrients, and eventually applied in an agricultural field to grow energy crops by means of a smart irrigation system. This plant presents a sustainable approach for wastewater management, which can be seen as resource recovery process more than a waste treatment.Peer ReviewedPostprint (author's final draft

Prosthetic Memory: Object Memories and Security for Children

Children younger than 3 years old are very special humans, their psychomotor and social development is very fast and parents and relatives would like to know every new detail (when, who, where, what, how and why) in real time. These news are difficult to remember and some kind of diary is needed. Here we propose a “prosthetic memory” based on Digital Object Memories applied to Web of Things using hidden NFC tags in children’s clothes, mobile applications for smartphones and a central server to store the ontologized information

¿Where do we go? OnTheWay: A prediction system for spatial locations

Ponencia presentada en: I International Conference on Ubiquitous Computing. Alcalá de Henares, Madrid, Spain, June 7-9, 2006In ubiquitous computing we need to know the present context in order to interact properly with the nearby smart elements. When we are moving outdoors, mobile devices take a very important role because they provide us with a link between the world outside and ourselves through means of intelligent interfaces. There are a lot of situations in which it would be very useful to know or foresee the future context, i.e. as a geographic environment, in which we could find ourselves in a near future, and at the same time being able to use that information from our devices. Therefore we must preview this location with enough precision and time and be able to use this information from our mobile device. In our “OnTheWay” system, we used GPS technology and databases made of past paths taken by a person, in order to predict the next location, once we had begun a new course, comparing the new one with those ones stored. The results were amazing: from the data collected about paths travelled during a month and five days, we got the actual destination in 98% of cases, when we have only made a 30,35% of the total path. Therefore, including statistic and semantic information will allow us to upgrade our results, due to the sedentary human behaviour, the small number of frequently visited locations and the fact that the paths used to arrive to these locations are usually the sam