Desarrollo de paneles de control para redes IoT basados en NodeRed

El presente proyecto se enmarca dentro del Aula IoT (Internet of Things) financiada por la Cátedra Telefónica de la UPV en la Escuela de Ingenieros de Telecomunicación. El objetivo del proyecto es el acceso a los datos generados por los sensores en una red IoT (datos reales o sintéticos) y generar los paneles de control (o dashboards) adecuados para su monitorización y control. Para dicho objetivo se utilizará el software NodeRed. El resultado del proyecto se integrará con los proyectos encargados de generar los datos que emulen una SmartCity.The present project is part of the "Aula IoT" (Internet of Things) funded by the "Cátedra Telefónica" of the UPV in the School of Telecommunications Engineers. The objective of the project is to access the data generated by the sensors in an IoT network (real or synthetic data) and generate the control panels (or dashboards) suitable for monitoring and control. For this purpose, the NodeRed software will be used. The result of the project will be integrated with the projects responsible for generating the data that emulates a SmartCity.García Jiménez, S. (2018). Desarrollo de paneles de control para redes IoT basados en NodeRed. http://hdl.handle.net/10251/114979TFG

IMPLEMENTACIÓN DE UN PROTOTIPO DE UN SISTEMA PARA LA INTEGRIDAD DEL VÍDEO EN TIEMPO REAL UTILIZANDO BLOCKCHAIN

[ES] El objetivo del TFM es el desarrollo de un sistema prototipo basado en sistemas de vídeo, que se encargue de capturar secuencias de vídeo en tiempo real y generar la protección necesaria para garantizar su integridad utilizando la tecnología Blockchain.[EN] The objective of the TFM is the development of a prototype system based on video systems, which is responsible for capturing video sequences in real time and generating the necessary protection to guarantee their integrity using Blockchain technology.García Jiménez, S. (2020). IMPLEMENTACIÓN DE UN PROTOTIPO DE UN SISTEMA PARA LA INTEGRIDAD DEL VÍDEO EN TIEMPO REAL UTILIZANDO BLOCKCHAIN. http://hdl.handle.net/10251/152449TFG

Analysis of the relationship between coastal development and the alteration of beach shorelines: a retrospective view based on spatial indicators

The construction of port infrastructures in urbanized coastal territories causes a great impact in the short and long term. The analysis of this impact is a very complex field due to the high number of variables involved. The criteria for analyzing these phenomena are usually based on the characteristics of the infrastructure to be built, but in the case of highly urbanized areas, there are other variables that are not normally taken into account. With the aim of giving an alternative approach to traditional analysis methods and relating the maximum number of possible variables, our study is given a multiparametric and retrospective approach based on GIS indicators. For this, the coastal area of the southeast of Spain is analyzed with the cartographic information that we have from the last 50 years. The changes suffered in the coastline caused by the construction of ports are analyzed in two dimensions and the statistical correlation of the different variables studied and the impact suffered on the coast are studied, such as the formation of tombolos and hemitombolos or salients, as well as coastal regression, even generation of sludge due to imbalances in sedimentary dynamics. The results can be used to enrich and improve the traditional analysis models for singular cases such as those studied

Gait-based Gender Classification Considering Resampling and Feature Selection

Two intrinsic data characteristics that arise in many domains are the class imbalance and the high dimensionality, which pose new challenges that should be addressed. When using gait for gender classification, benchmarking public databases and renowned gait representations lead to these two problems, but they have not been jointly studied in depth. This paper is a preliminary study that pursues to investigate the benefits of using several techniques to tackle the aforementioned problems either singly or in combination, and also to evaluate the order of application that leads to the best classification performance. Experimental results show the importance of jointly managing both problems for gait-based gender classification. In particular, it seems that the best strategy consists of applying resampling followed by feature selection

Classification of high dimensional and imbalanced hyperspectral imagery data

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of using together these two techniques, and also to evaluate the application order that leads to the best classification performance. Experimental results demonstrate the significance of combining these preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order of application corresponds to first a resampling algorithm and then PCA, this is a question that still needs a much more thorough investigationPartially supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596–C04–04/ESP and TIN2009–14205–C04–04, and by Fundació Caixa Castelló–Bancaixa under grant P1–1B2009–0

One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices

In the dissimilarity representation paradigm, several prototype selection methods have been used to cope with the topic of how to select a small representation set for generating a low-dimensional dissimilarity space. In addition, these methods have also been used to reduce the size of the dissimilarity matrix. However, these approaches assume a relatively balanced class distribution, which is grossly violated in many real-life problems. Often, the ratios of prior probabilities between classes are extremely skewed. In this paper, we study the use of renowned prototype selection methods adapted to the case of learning from an imbalanced dissimilarity matrix. More specifically, we propose the use of these methods to under-sample the majority class in the dissimilarity space. The experimental results demonstrate that the one-sided selection strategy performs better than the classical prototype selection methods applied over all classes

A literature review on the application of evolutionary computing to credit scoring

The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through a Postdoctoral Fellowship [223351], the Spanish Ministry of Economy under grant TIN2013-46522-P and the Generalitat Valenciana under grant PROMETEOII/2014/062