Smart hierarchical WiFi localization system for indoors

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014En los últimos años, el número de aplicaciones para smartphones y tablets ha crecido rápidamente. Muchas de estas aplicaciones hacen uso de las capacidades de localización de estos dispositivos. Para poder proporcionar su localización, es necesario identificar la posición del usuario de forma robusta y en tiempo real. Tradicionalmente, esta localización se ha realizado mediante el uso del GPS que proporciona posicionamiento preciso en exteriores. Desafortunadamente, su baja precisión en interiores imposibilita su uso. Para proporcionar localización en interiores se utilizan diferentes tecnologías. Entre ellas, la tecnología WiFi es una de las más usadas debido a sus importantes ventajas tales como la disponibilidad de puntos de acceso WiFi en la mayoría de edificios y que medir la señal WiFi no tiene coste, incluso en redes privadas. Desafortunadamente, también tiene algunas desventajas, ya que en interiores la señal es altamente dependiente de la estructura del edificio por lo que aparecen otros efectos no deseados, como el efecto multicamino o las variaciones de pequeña escala. Además, las redes WiFi están instaladas para maximizar la conectividad sin tener en cuenta su posible uso para localización, por lo que los entornos suelen estar altamente poblados de puntos de acceso, aumentando las interferencias co-canal, que causan variaciones en el nivel de señal recibido. El objetivo de esta tesis es la localización de dispositivos móviles en interiores utilizando como única información el nivel de señal recibido de los puntos de acceso existentes en el entorno. La meta final es desarrollar un sistema de localización WiFi para dispositivos móviles, que pueda ser utilizado en cualquier entorno y por cualquier dispositivo, en tiempo real. Para alcanzar este objetivo, se propone un sistema de localización jerárquico basado en clasificadores borrosos que realizará la localización en entornos descritos topológicamente. Este sistema proporcionará una localización robusta en diferentes escenarios, prestando especial atención a los entornos grandes. Para ello, el sistema diseñado crea una partición jerárquica del entorno usando K-Means. Después, el sistema de localización se entrena utilizando diferentes algoritmos de clasificación supervisada para localizar las nuevas medidas WiFi. Finalmente, se ha diseñado un sistema probabilístico para seguir la posición del dispositivo en movimiento utilizando un filtro Bayesiano. Este sistema se ha probado en un entorno real, con varias plantas, obteniendo un error medio total por debajo de los 3 metros

Fuzzy classifier ensembles for hierarchical WiFi-based semantic indoor localization

The number of applications for smartphones and tablets is growing exponentially in the last years. Many of these applications are supported by the so-called Location Based Services, which are expected to provide reliable real-time localization anytime and anywhere, no matter either outdoors or indoors. Even though outdoors world-wide localization has been successfully developed through the well-known Global Navigation Satellite System technology, its counterpart large-scale deployment indoors is not available yet. In previous work, we have already introduced a novel technology for indoor localization supported by a WiFi fingerprint approach. In this paper, we describe how to enhance such approach through the combination of hierarchical localization and fuzzy classifier ensembles. It has been tested and validated at the University of Edinburgh, yielding promising results.Ministerio de Economía y CompetitividadXunta de Galici

Simple Baseline for Vehicle Pose Estimation: Experimental Validation

Significant progress on human and vehicle pose estimation has been achieved in recent years. The performance of these methods has evolved from poor to remarkable in just a couple of years. This improvement has been obtained from increasingly complex architectures. In this paper, we explore the applicability of simple baseline methods by adding a few deconvolutional layers on a backbone network to estimate heat maps that correspond to the vehicle keypoints. This approach has been proven to be very effective for human pose estimation. The results are analyzed on the PASCAL3DC dataset, achieving state-of-the-art results. In addition, a set of experiments has been conducted to study current shortcomings in vehicle keypoints labelling, which adversely affect performance. A new strategy for de ning vehicle keypoints is presented and validated with our customized dataset with extended keypoints

Urban intersection classification: a comparative analysis

Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.European Commissio

WiFi-based urban localisation using CNNs

IEEE Conference on Intelligent Transportation Systems - ITSC 2019, 27-30/10/2019, Auckland, Nueva Zelanda.The continuous expanding scale of WiFi deployments in metropolitan areas has made possible to find WiFi access points at almost any place in our cities. Although WiFi has been mainly used for indoor localisation, there is a growing number of research in outdoor WiFi-based localisation. This paper presents a WiFi-based localisation system that takes advantage of the huge deployment of WiFi networks in urban areas. The idea is to complement localisation in zones where the GPS coverage is low, such as urban canyons. The proposed method explores the CNNs ability to handle large amounts of data and their high accuracy with reasonable computational costs. The final objective is to develop a system able to handle the large number of access points present in urban areas while preserving high accuracy and real time requirements. The system was tested in a urban environment, improving the accuracy with respect to the state-of-the-art and being able to work in real time

Fail-aware LIDAR-based odometry for autonomous vehicles

Autonomous driving systems are set to become a reality in transport systems and, so, maximum acceptance is being sought among users. Currently, the most advanced architectures require driver intervention when functional system failures or critical sensor operations take place, presenting problems related to driver state, distractions, fatigue, and other factors that prevent safe control. Therefore, this work presents a redundant, accurate, robust, and scalable LiDAR odometry system with fail-aware system features that can allow other systems to perform a safe stop manoeuvre without driver mediation. All odometry systems have drift error, making it difficult to use them for localisation tasks over extended periods. For this reason, the paper presents an accurate LiDAR odometry system with a fail-aware indicator. This indicator estimates a time window in which the system manages the localisation tasks appropriately. The odometry error is minimised by applying a dynamic 6-DoF model and fusing measures based on the Iterative Closest Points (ICP), environment feature extraction, and Singular Value Decomposition (SVD) methods. The obtained results are promising for two reasons: First, in the KITTI odometry data set, the ranking achieved by the proposed method is twelfth, considering only LiDAR-based methods, where its translation and rotation errors are 1.00% and 0.0041 deg/m, respectively. Second, the encouraging results of the fail-aware indicator demonstrate the safety of the proposed LiDAR odometry system. The results depict that, in order to achieve an accurate odometry system, complex models and measurement fusion techniques must be used to improve its behaviour. Furthermore, if an odometry system is to be used for redundant localisation features, it must integrate a fail-aware indicator for use in a safe manner

Human activity recognition applying computational intelligence techniques for fusing information related to WiFi positioning and body posture

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE WCCI 2010, 18/07/2010-23/07/2010, Barcelona, España.This work presents a general framework for people indoor activity recognition. Firstly, a Wireless Fidelity (WiFi) localization system implemented as a Fuzzy Rulebased Classifier (FRBC) is used to obtain an approximate position at the level of discrete zones (office, corridor, meeting room, etc). Secondly, a Fuzzy Finite State Machine (FFSM) is used for human body posture recognition (seated, standing upright or walking). Finally, another FFSM combines bothWiFi localization and posture recognition to obtain a robust, reliable, and easily understandable activity recognition system (working in the desk room, crossing the corridor, having a meeting, etc). Each user carries with a personal digital agenda (PDA) or smart-phone equipped with a WiFi interface for localization task and accelerometers for posture recognition. Our approach does not require adding new hardware to the experimental environment. It relies on the WiFi access points (APs) widely available in most public and private buildings. We include a practical experimentation where good results were achieved.Ministerio de Ciencia e InnovaciónComunidad de Madri

Autocalibración de parámetros extrínsecos de sistemas estéreo para aplicaciones de tráfico

Comunicación presentada en: XXXVII Jornadas de Automática, Madrid, 6 a 8 de septiembre de 2016En este artículo se presenta un método de autocalibración de los parámetros extrínsecos de un sistema estéreo en aplicaciones de tráfico. Dicho método se basa en determinar la geometría de la calzada delante del veh´ıculo. Esta posición relativa varía considerablemente mientras el vehículo circula, por tanto, resulta de gran interés poder estimarla para su aplicación en múltiples aplicaciones basadas en visión por computador, tales como: sistemas avanzados de ayuda a la conducción, vehículos autónomos o robots. Estos continuos cambios en la posición del sistema estéreo se traducen en variaciones en los valores de los parámetros extrínsecos (altura, ángulo de cabeceo y ángulo de alabeo). La validación del método de autocalibración es realizada mediante el empleo de un algoritmo de odometría visual, donde se evalúa la mejora en los resultados que supone conocer en todo momento el valor de los parámetros extrínsecos del sistema estéreo.Este trabajo ha sido parcialmente financiado por el Gobierno de España a través de los proyectos Cycit (TRA2013-48314-C3-1-R y TRA2015-63708-R) y por la Comunidad de Madrid a través del proyecto SEGVAUTO-TRIE S (S2013/MIT-2713)

An Inclusive View of the Disability of Secondary School Students

Achieving the educational inclusion of students with special educational needs (SEN) is one of the significant challenges of the current Spanish educational system. This is a group of students with a high rate of bullying that leads to academic failure, as well as significant psychological and social consequences. Despite the fact that the behaviours and psychological characteristics of their peers seem to influence the degree of inclusion, there is no detail on this subject. Therefore, the aim of this paper is to determine the relationship between emotional intelligence, psychological flexibility, prosocial behaviour and inclusive behaviour. To carry out this study, a sample of 642 students between the ages of 12 and 19 years old participated and answered four questionnaires, one for each variable under study. The relationships established were extracted from different statistical analyses and a hypothesised predictive model. The results obtained revealed that emotional intelligence is positively related to psychological flexibility and prosocial behaviour and that these, in turn, are positively related to the development of inclusive behaviour. Therefore, the importance of considering the variables under study during the teaching–learning processes carried out in the classroom is highlighted