Extensiones para el Ciclo de Mejora Continua en la enseñanza e investigación de Ingeniería Informática

Este trabajo expone cómo añadiendo aspectos relacionados con la vigilancia tecnológica, las técnicas creativas aplicadas a la ingeniería, los modelos de calidad y los fundamentos epistemológicos relacionados con ella y la evaluación formativa, podemos cubrir un ciclo de mejora continua que permita incrementar la calidad de la enseñanza e investigación en Ingeniería Informática.This work shows that, by adding aspects related to technological surveillance, creative techniques applied to engineering, quality models and epistemological foundations associated therein and to formative evaluation, a continuous improvement cycle can be covered which enables the quality of teaching and research in Computer Science to be raised

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-

Performance of Algorithms for Interval Discretization of Biomedical Signals

A methodology to quantify the dependence be tween features using the Ameva discretization algorithm and the advantages of qualitative models is presented in this paper. This approach will be applied over medical data sets. A com parison among Ameva and other related works has been done. The results, as will be depth explained in this paper, show that Ameva-based methodology can be used to determine the depen dence between features in a fast and understandable way from data sets with a high number of attributes and low number of instances. This is a quite important feature in genomic en vironments among others. This methodology has been applied to some well-known medical data sets and the results obtained shown that is a good alternative to other established algorithms in terms of clarity and computational cost.Ministerio de Economía y Competitividad TIN2013-46801-C4-1-RJunta de Andalucía TIC-8052 (Simon

Low Energy Physical Activity Recognition System on Smartphones

An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the 2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.Ministerio de Economía y Competitividad TIN2013-46801-C4-1-rJunta de Andalucía TIC-805

An adaptive methodology to discretize and select features

A lot of significant data describing the behavior or/and actions of systems can be collected in several domains. These data define some aspects, called features, that can be clustered in several classes. A qualitative or quantitative value for each feature is stored from measurements or observations. In this paper, the problem of finding independent features for getting the best accuracy on classification problems is considered. Obtaining these features is the main objective of this work, where an automatic method to select features is proposed. The method extends the functionality of Ameva coefficient to use it in other tasks of machine learning where it has not been defined.Ministerio de Ciencia e Innovación ARTEMISA TIN2009-14378-C02-01Junta de Andalucia Simon TIC-805

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Currently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information requiredMinisterio de Economía y Competitividad HERMES TIN2013-46801-C4-1-rJunta de Andalucía Simon P11-TIC-8052Junta de Andalucía M-Learning P11-TIC-712

A qualitative methodology to reduce features in classification problems

In this paper, a preliminary methodology which quantifies the dependence between features in a data set by using the Ameva discretization algo rithm and the advantages of a qualitative model is developed. Thus, different matrices of inter dependence are built providing a grade of depen dence between two features. This methodology is applied to a well-known data set, obtaining promis ing results for the carried out system.Ministerio de Ciencia e Innovacion TIN2009-14378-C02-01 (ARTEMISA

Hi-Res activity recognition system based on EEG and WoT

Nowadays, the recognition of physical activity (PA) is a well-known problem with many solutions. Sev eral kind of algorithms, using MEMS sensors, al low determine the most likely activity. Indeed, these applications work well when physical activity is performed for long periods of time and steadily. However, indoors, these systems are not entirely suitable and have several problems. In this paper, thanks to the introduction of new context infor mation, such as EEG, and through communication between WoT based elements interface at home, it would be possible to perform a more accurate and low-level recognition. By using uPnP proto col and additional services, information from other smart housing elements with user device itself can be shared, enriching traditional systems based on ac-celerometry.Ministerio de Economía y Competitividad TIN2009-14378-C02-01Junta de Andalucía TIC-805

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rechargeable battery. In this paper, by using discrete techniques based on the Ameva algorithm, an innovative approach for human activity recognition systems on mobile devices is presented. Furthermore, unlike other systems in current use, this proposal enables recognition of high granularity activities by using accelerometer sensors. Hence, the accuracy of activity recognition systems can be increased without sacrificing efficiency. A comparative is carried out between the proposed approach and an approach based on the well-known neural networks.Junta de Andalucia Simon TIC-805

Activity Recognition System Using AMEVA Method

This article aims to develop a minimally intrusive system of care and monitoring. Furthermore, the goal is to get a cheap, comfortable and, especially, efficient system which controls the physical activity car ried out by the user. For this purpose an innovative approach to physical activity recognition is presented, based on the use of discrete variables which employ data from accelerometer sensors. To this end, an innova tive discretization and classification technique to make the recognition process in an efficient way and at low energy cost, is presented in this work based on the χ2 distribution. Entire process is executed on the smartphone, by means of taking the system energy consumption into ac count, thereby increasing the battery lifetime and minimizing the device recharging frequency.Ministerio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon