Rafael Magdalena

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Verified

Rafael verified their affiliation via an institutional email.

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• PhD
• Dean of Engineering School at University of Valencia

Let C represent an irreducible algebraic space curve defined by the real polynomials fi(x1, x2, x3) for i = 1, 2. It is a recognized fact that a birational relationship invariably exists between the points on C and those on an associated irreducible plane curve, denoted as Cp. In this work, we leverage this established relationship to delineate the...

In the era of increasing digitalisation, organisations face the critical challenge of detecting anomalies in large volumes of data, which may indicate suspicious activities. To address this challenge, audit engagements are conducted regularly, and internal auditors and purchasing specialists seek innovative methods to streamline these processes. Th...

In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series in video format using a multimodal approach. Through pattern recognition algorithms and segmentation, informative measures of the number of people and image occupancy are extracted at regular intervals, which are then analyz...

In this paper, we summarize two algorithms for computing all the generalized asymptotes of a plane algebraic curve implicitly or parametrically defined. The approach is based on the notion of perfect curve introduced from the concepts and results presented in Blasco and Pérez-Díaz (Comput Aided Geom Des 31(2):81–96, 2014), Blasco and Pérez-Díaz (Co...

This paper delves into the integration of Factories of the Future (FoF) and digital twin technologies within urban contexts, marking a significant leap in Smart Cities development. We present a thorough exploration of the principles and a scientifically grounded framework designed for seamlessly blending advanced manufacturing systems with the urba...

In a context of a continuous digitalisation of processes, organisations must deal with the challenge of detecting anomalies that can reveal suspicious activities upon an increasing volume of data. To pursue this goal, audit engagements are carried out regularly, and internal auditors and purchase specialists are constantly looking for new methods t...

In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series from video format using a multimodal approach. Through pattern recognition algorithms and segmentation, informative measures of the number of people and image occupancy are extracted at regular intervals, which are then anal...

In this paper we present two algorithms for computing the g-asymptotes or generalized asymptotes , of a plane algebraic curve, $$\mathscr {C}$$ C , implicitly or parametrically defined. The asymptotes of a curve $$\mathscr {C}$$ C reflect the status of $$\mathscr {C}$$ C at points with sufficiently large coordinates. It is well known that an asympt...

Over the past few decades, the mathematical community has accumulated a significant amount of pure mathematical data, which has been analyzed through supervised, semi-supervised, and unsupervised machine learning techniques with remarkable results, e.g., artificial neural networks, support vector machines, and principal component analysis. Therefor...

Introduction Neurocognitive impairment is a transdiagnostic feature across several psychiatric and cardiometabolic conditions. The relationship between inflammatory and lipid metabolism biomarkers and memory performance is not fully understood. This study aimed to identify peripheral biomarkers suitable to signal memory decline from a transdiagnost...

The exotic nature of quantum mechanics differentiates machine learning applications in the quantum realm from classical ones. Stream learning is a powerful approach that can be applied to extract knowledge continuously from quantum systems in a wide range of tasks. In this paper, we propose a deep reinforcement learning method that uses streaming d...

In this paper, we first summarize the existing algorithms for computing all the generalized asymptotes of a plane algebraic curve implicitly or parametrically defined. From these previous results, we derive a method that allows to easily compute the whole branch and all the generalized asymptotes of a “special” curve defined in n -dimensional space...

In this paper, we first summarize the existing algorithms for computing all the generalized asymptotes of a plane algebraic curve implicitly or parametrically defined. From these previous results, we derive a method that allows to easily compute the whole branch and all the generalized asymptotes of a special curve defined in n-dimensional space by...

We present symbolic algorithms for computing the g-asymptotes, or generalized asymptotes, of a plane algebraic curve, C, implicitly or parametrically defined. The g-asymptotes generalize the classical concept of asymptotes of a plane algebraic curve. Both notions have been previously studied for analyzing the geometry and topology of a curve at inf...

Objectives To estimate the probability of malignancy of an oral leukoplakia lesion using Deep Learning, in terms of evolution to cancer and high-risk dysplasia. Materials and Methods A total of 261 oral leukoplakia lesions with a mean of 5.5 years follow-up were analysed from standard digital photographs. A deep learning pipeline composed by a U-N...

In this paper we deal with the following problem: given an algebraic plane curve C, implicitly defined, we determine its “asymptotic family”, that is, the set of algebraic curves that have the same asymptotic behavior as C.

Background Systemic, low-grade immune–inflammatory activity, together with social and neurocognitive performance deficits are a transdiagnostic trait of people suffering from type 2 diabetes mellitus (T2DM) and severe mental illnesses (SMIs), such as schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD). We aimed to determi...

Objective: Obesity and metabolic diseases such as metabolic syndrome (MetS) are more prevalent in people with type 2 diabetes mellitus (T2DM), major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). MetS components might be associated with neurocognitive and functional impairments in these individuals. The predictive and di...

Optimal design for model training is a critical topic in machine learning. Active learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional advantage of achieving successful performances with a reduced number of labeled samples. We ana...

The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery using information continuously extracted from a quantum system in a broad range of tasks. The model receives streaming quantum information for learning and decision-making, r...

Background : Neurocognition impairments are critical factors in patients with major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), and also in those with somatic diseases such as type 2 diabetes mellitus (T2DM). Intriguingly, these severe mental illnesses are associated with an increased co-occurrence of diabetes (direct...

Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional advantage of achieving successful performances with a reduced number of labeled samples. We ana...

Background Impairments in neurocognition are critical factors in patients with major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), and also in those with somatic diseases such as type 2 diabetes mellitus (T2DM). Intriguingly, these severe mental illnesses are associated with an increased co-occurrence of diabetes (direct...

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum informa...

Minimal hepatic encephalopathy (MHE) is a neuropsychiatric syndrome produced by central nervous system dysfunction subsequent to liver disease. Hyperammonemia and inflammation act synergistically to alter neurotransmission, leading to the cognitive and motor alterations in MHE, which are reproduced in rat models of chronic hyperammonemia. Patients...

Active learning is a machine learning method aiming at optimal experimental design. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information r...

Actualmente las generaciones más jóvenes conviven plenamente con el mundo virtual. A pesar de las ventajas que puede aportarles el uso de las nuevas tecnologías, especialmente de las redes sociales, no están exentas de peligros que principalmente afectan a la privacidad y seguridad de los internautas, en especial de los usuarios más jóvenes. Este e...

Patients who suffer from chronic renal failure (CRF) tend to suffer from an associated anemia as well. Therefore, it is essential to know the hemoglobin (Hb) levels in these patients. The aim of this paper is to predict the hemoglobin (Hb) value using a database of European hemodialysis patients provided by Fresenius Medical Care (FMC) for improvin...

Extreme Learning Machine (ELM) is an efficient learning algorithm for Single-Hidden Layer Feedforward Networks (SLFNs). Its main advantage is its computational speed due to a random initialization of the parameters of the hidden layer, and the subsequent use of Moore-Penrose's generalized inverse in order to compute the weights of the output layer....

Penicillium fungi are among the main defects that may affect the commercialization of citrus fruits. Economic losses in fruit production may become enormous if an early detection of that kind of fungi is not carried out. That early detection is usually based either on UltraViolet light carried out manually. This work presents a new approach based o...

One major challenge facing modern medicine is to fulfill the promise and potential of the enormous increase in medical data sets of all kinds. Medical Applications of Intelligent Data Analysis: Research Advancements explores the potential of utilizing this medical data through the implementation of developed models in practical applications. This p...

With the recent and enormous increase in the amount of available data sets of all kinds, applying effective and efficient techniques for analyzing and extracting information from that data has become a crucial task. Intelligent Data Analysis for Real-Life Applications: Theory and Practice investigates the application of Intelligent Data Analysis (I...

Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks...

La docència del Tractament Digital de Senyals (TDS) es realitza en diversos mòduls, amb una gran intersecció de continguts. Este treball planteja el desvolupament d’una estratègia que permeta la unificació de continguts de TDS, de manera que es puguen extraure els corresponents a cada assignatura que composen el mapa curricular de TDS. Amés, es pla...

The aim of this work is to study the applicability of reinforcement learning methods to design adaptive treat- ment strategies that optimize, in the long-term, the dosage of erythropoiesis-stimulating agents (ESAs) in the management of anemia in patients undergoing hemodialysis. Adaptive treatment strategies are recently emerging as a new paradigm...

The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant...

This chapter presents the use of Reinforcement Learning (RL) in two real-life problems. The use of RL seems to be adequate for many real problems in which there is a long-term goal to achieve clearly defined by means of rewards, and the way of achieving that goal is by means of some interactive actuations to change the state of the environment that...

Feature Selection (FS) is one of the key stages in classification problems. This paper proposes the use of the area under Receiver Operator Characteristic curves to measure the individual importance of every input as well as a method to discover the variables that yield a statistically significant improvement in the discrimination power of the clas...

This chapter is focused on obtaining an optimal forecast of one month lagged rainfall in Spain. It is assessed by analyzing 22 years of both satellite observations of vegetation activity (e.g. NDVI) and climatic data (precipitation, temperature). The specific influence of non-spatial climatic indices such as NAO and SOI is also addressed. The appro...

The ischemic cardiopathy is the main cause of death in developed countries. New improved drugs and therapies have appeared last years. However, the interventionist strategy and the most powerful drugs may have complications, and hence, it is very important to know the risk of death associated with patients during their stay in the hospital, or in t...

The aim of this study was to analyse the relationship between different small ruminant livestock production systems with different levels of specialization. The analysis is carried out by using the self-organizing map. This tool allows high-dimensional input spaces to be mapped into much lower-dimensional spaces, thus making it much more straightfo...

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, includi...

Stock breeding has been one of the most important sources of food and labour throughout human history. Every advance in this field has always led to important and beneficial impacts on human society. These innovations have mainly taken place in machines or genetics, but data analysis has been somewhat ignored. Most of the published works in data an...

This paper proposes the pedagogic use of MATLA B software in a field where its use is not common, Animal Science (AS). Although it is usual to develop and validate different mathematical models in AS, the use of this software package has not yet become a standard. This paper presents the use of this software for teaching Animal Nutrition, one of th...

In this paper a family of adaptive algorithms robust to impulsive noise and with low computational cost are presented. Unlike other approaches, no cost functions or filtering of the gradient are considered in order to update the filter coefficients. Its initial basis is the basic LMS algorithm and its sign-error variant. The proposed algorithms can...

Este artículo describe los trabajos realizados para permitir a los estudiantes de diversas titulaciones de ingeniería la realización de prácticas a distancia en un laboratorio remoto desde un navegador web. Este hecho posibilita utilizar aparatos y maquinaria a distancia (inicialmente un robot industrial), pudiendo programarlos y visualizar el resu...

Training new electronics engineers presents several major challenges. This paper proposes a new approach for practical lessons in second-level analog electronics, where students get a closer view of real-world practices in electronic engineering. The authors describe and evaluate a more dynamic way of teaching practical lessons in analog electronic...

Complete atrioventricular block (type III AVB) is characterized by an absence of P wave transmission to ventricles. This implies that QRS complexes are generated in an autonomous way and are not coordinated with P waves. This work introduces a new algorithm for the detection of P waves for this type of pathology using noninvasive electrocardiograph...

In this paper, we present two sucessful applications of Reinforcement Learning (RL) in real life. First, the optimization of anemia management in patients undergoing Chronic Renal Failure is presented. The aim is to individualize the treatment (Erythropoietin dosages) in order to stabilize patients within a targeted range of Hemoglobin (Hb). Result...

This work is focused on the usage analysis of a citizen web portal, Infoville XXI (http://www.infoville.es) by means of Self-Organizing Maps (SOM). In this paper, a variant of the classical SOM has been used, the so-called Growing Hierarchical SOM (GHSOM). The GHSOM is able to find an optimal architecture of the SOM in a few iterations. There are a...

The presence of a kind of impulsive noise due to eye blinks is typical during the acquisition of electrooculograms. This paper describes a comparative study of several algorithms used to remove the blink noise in the electroculogram preserving the sharp edges in the signal produced by the so-called saccadic eye movements. Median filters (MF) and se...

This paper presents a fuzzy logic approach to combine cost functions in adaptive systems. The proposed approach is based on the application of a "soft" threshold to switch between two different cost functions. A generic analysis is carried out, being the Huber's function studied as a particular case. A channel equalization problem is used in order...

Fernández, C., Soria, E., Magdalena, R., Martín, J.D. and Mata, C. 2007. Qualitative analysis of feed management practice on goat herds by self organizing maps in Murcia region of Spain. J. Appl. Anim. Res., 32: 41–47.Self organizing maps (SOM) were used to analyze data from ninety four herds. Data were obtained from surveys and management practice...

Artificial neural networks (NN) have been widely used for both prediction and classification tasks in many fields of knowledge; however, few studies are available on dairy science. In this work, we use NN models to predict next week’s goat milk based on the current and previous milk production. A total of 35 Murciano-Granadina dairy goats were sele...

This paper introduces and evaluates BioLab, a tool for teaching biosignal processing. BioLab has been used in the biomedical engineering module that is given in the second semester of the fifth year of the electronic engineering degree at the University of Valencia, Spain. This module and its correspondent curricular pathway are also reviewed. BioL...

This paper describes a comparative study of several algorithms used to remove the blink noise in the electroculogram preserving the sharp edges in the signal produced by the so-called saccadic eye movements. Median filters (MF) and several types of Fir Median Hybrid Filters (FMH) are proposed. 16 real Electrooculogram register with saccadic movemen...

It is common to use classifiers on polisomnographic records in order to determine the different stages during sleep. Most of the times the results yielded by this systems are not coherent with physiological aspects of the sleep. This work uses the Hidden Markov Models as a modeller of the physiological act of sleeping, and uses it as a corrector of...

The proposed method detects fetal R waves on abdominal non-invasive records. An exponentially averaged pattern of the mother PQRST segment is obtained and subtracted. Subsequently the fetal R detector based on a Smoothed Nonlinear Energy Operator (SNEO) is applied to the residual signal. Finally, criteria about amplitude, heart rate and backward se...

In this work, a new methodology for modeling qualitative temporal processes, is proposed. In this development, two stages are considered. First, models are obtained and adapted fitting their free parameters according to the existing time series. Second, Self- Organizing Maps (SOM) are used to establish a clustering of the data using the parameters...

This paper describes the tasks carried out to develop a control tool using the changes detected in gaze, which are captured in the electrooculogram signal. The objective is to use these changes to control a user interface such as Dasher. A software tool for generating visual stimuli and acquiring the eye signal has been developed. These signals wer...

Reinforcement Learning (RL) has as its main objective to maximize the rewards of an objective func- tion. This is achieved by an agent which carries out a series of actions to modify the state of the environment. The reinforcements are the cornerstone of the RL. In this work, a modification of the classic scheme of RL is proposed. Our proposal is b...

This paper presents the application of this new tool of data processing in the study of the problem that arises when a renal transplant is indicated for a paediatric patient. Its aim is the development and validation of a neural network based model which can predict the success of the transplant over the short, medium and long term, using pre-opera...

Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited...

To create an artificial neural network (ANN) to aid in predicting the results of endoscopic treatment for vesico-ureteric reflux (VUR). During 1999-2001 we used endoscopic treatment in 261 ureteric units with VUR of all grades and causes. An ANN based on multilayer perceptron architecture was created using an 11 x 6 x 1 structure, taking the follow...

Correlation coefficient is frequently used to obtain cardiac rhythm by peak estimation and appreciate differences in the signal compared to a pattern. This work focuses on the description of a real-time correlation assessment procedure. Applied to electrocardiogram (ECG) signals, a new correlation value is obtained every new sample and pulse detect...

The objective of the present work has been to develop a collision detection algorithm suitable for real-time applications. It is applicable to box-shaped objects and it is based on the relation between the colliding object positions and the impact point. The most known neural network (multilayer perceptron) trained with the familiar backpropagation...

In this paper, time- and frequency-domain parameters of the heart rate variability (HRV) are investigated in 45 Holter records for a group of diabetic patients with neuropathy (DNG) other group of diabetics without neuropathy (DWNG) and the control group (CG). Obtained results show that: 1) DNG vs. CG present significant differences of parameters i...

In this paper, modifications of spectral coherence for ventricular fibrillation (VF) in the isolated rabbit heart are investigated under coronary perfusion, ventricular dilatation, and the administration of antiarrhythmic drugs (verapamil). Obtained results for the mean magnitude-squared coherence function (MMSC) show that: (i) it decreases during...

The present communication describes a telemedicine approach to a computer-aided healthcare system. The application is a comprehensive tool set, structured around a main navigation bar, which leads to every sub-application. Those sub-applications are window based user-friendly tools, comprising specific health tools for physicians, telemedicine/tele...

A method is described for obtaining the fetal ECG from surface electrodes using a procedure that combines averaging and adaptive techniques. The algorithm proposed consists of an adaptive filter whose reference signal is an impulse synchronized with the maternal QRS and whose desired signal is a fetal abdominal derivation. The filter behaves as an...

This paper describes the design and performance of a secraphone that, when plugged between any conventional telephone set and the public telephone network, protects the speech information travelling through the PSTN. The device has a transparent operating mode that does not alter the signal and a secure mode, accessed upon request of any of the spe...

El aprendizaje efectivo del lenguaje de programación para robots necesita de una constante realización de prácticas. Este trabajo describe la metodología llevada a cabo para conseguir el aprendizaje del lenguaje RAPID. Se ha desarrollado un sistema de aprendizaje para prácticas remotas y presenciales que emplea un robot IRB140 de ABB, un PLC con ta...

Curso y manual de programación inicial basado en el lenguaje C. Aborda tipos de variables y su declaración; los distintos tipos de operadores: aritméticos, de relación e igualdad, lógicos, de manejo de bits, entre otros.