http://hdl.handle.net/10366/4387 2026-05-02T21:09:30Z http://hdl.handle.net/10366/171092 [EN]In recent years, maintenance work on public transport routes has drastically decreased in many countries due to difficult economic situations. The various studies that have been conducted by groups of drivers and groups related to road safety concluded that accidents are increasing due to the poor conditions of road surfaces, even affecting the condition of vehicles through costly breakdowns. Currently, the processes of detecting any type of damage to a road are carried out manually or are based on the use of a road vehicle, which incurs a high labor cost. To solve this problem, many research centers are investigating image processing techniques to identify poor-condition road areas using deep learning algorithms. The main objective of this work is to design of a distributed platform that allows the detection of damage to transport routes using drones and to provide the results of the most important classifiers. A case study is presented using a multi-agent system based on PANGEA that coordinates the different parts of the architecture using techniques based on ubiquitous computing. The results obtained by means of the customization of the You Only Look Once (YOLO) v4 classifier are promising, reaching an accuracy of more than 95%. The images used have been published in a dataset for use by the scientific community. 2020-01-01T00:00:00Z http://hdl.handle.net/10366/169915 [EN]This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe. 2024-01-01T00:00:00Z http://hdl.handle.net/10366/169914 [EN]This research presents software for empirically analyzing Java Virtual Machine (JVM) parameter configurations to enhance web application performance. Using tools like JMeter and cAdvisor in a controlled hardware environment, it collects and analyzes performance metrics. Tailored JVM settings for high request loads improved CPU efficiency by 20% and reduced memory usage by 15% compared to standard configurations. For I/O intensive operations with large files, optimized JVM configurations decreased response times by 30% and CPU usage by 25%. These findings highlight the impact of tailored JVM settings on application responsiveness and resource management, providing valuable guidance for developers and engineers. 2024-01-01T00:00:00Z http://hdl.handle.net/10366/169913 [EN]In recent years, the utilization of augmented reality (AR) and virtual reality (VR) has emerged as a transformative approach in education, revolutionizing traditional teaching methods. This study seeks to explore the efficacy of AR and VR as pedagogical resources for enhancing the teaching of the solar system. The research process involved the development of an application comprising two modules, AR and VR, which were evaluated to assess their impact on the teaching process. Furthermore, a comparative study was conducted to evaluate the immersiveness, interactivity, and ease of use offered by these technologies. The findings demonstrate that both AR and VR demonstrate promise in supporting the teaching process, with the VR module garnering particularly positive evaluations. However, it is crucial to acknowledge existing barriers in underprivileged communities, where public schools face limited investments in technology infrastructure. These limitations hinder the widespread implementation of such immersive experiences and their potential to foster new knowledge acquisition. 2023-01-01T00:00:00Z http://hdl.handle.net/10366/169912 [EN]There is growing concern among users of computer systems about how their data is handled. In this sense, IT (Information Technology) professionals are not unaware of this problem and are looking for solutions to meet the requirements and concerns of their users. During the last few years, various techniques and technologies have emerged that allow us to answer to the problem posed by users. Technologies such as edge computing and techniques such as one-shot learning and data augmentation enable progress in this regard. Thus, in this article, we propose the creation of a system that makes use of these techniques and technologies to solve the problem of face recognition and form a low-cost security system. The results obtained show that the combination of these techniques is effective in most of the face detection algorithms and allows an effective solution to the problem raised. 2022-01-01T00:00:00Z http://hdl.handle.net/10366/169911 [EN]Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision. 2022-12-23T00:00:00Z http://hdl.handle.net/10366/169381 [EN] This paper presents some efficient suboptimal detectors, based on statistical descriptors, which take advantage of the high-resolution characteristics of the high-resolution radars (HRR). Which are one of the first stages of the sensor-based localization and tracking technologies. The detection performance has been studied under noise and sea clutter conditions, with non-coherent data from both real and synthetic extended targets. We have also made an adaptation of the classical moving window detection technique for the high-resolution radars, making use of it as a reference technique to evaluate the results obtained with the detection techniques that we present. The experimental results were obtained with the ARIES radar, a maritime surface surveillance LFM-CW HRR operating in X-band. 2012-08-24T00:00:00Z http://hdl.handle.net/10366/169379 [EN] With oil reserves running out, silicon solar cells offer an alternative source of energy. How do they work and how can we exploit their full potential? Indirectly, the Sun is the source of most of the energy we use on Earth: not only of fossil fuels and biomass, but also wind and tidal energy, to mention just a few. Increasingly, there is interest in capturing the energy from the Sun more directly, using photovoltaic cells. A relatively old, medium-sized star made of hot plasma, the Sun radiates energy as electromagnetic radiation over a wide spectrum. At a distance of 150 million kilometres, our planet receives an irradiance of around 1366 W/m2 (1 W= 1 J·s) from the Sun, but not all of this actually reaches us because Earth’s atmosphere reflects and absorbs about 30 % of this energy. Nonetheless, every square metre of Earth’s surface receives an average of nearly 1000 Joules per second from the Sun. To put this into perspective, the total energy consumed globally in 2010 was around 5 x 1020 J. If we assume that our planet is a perfect sphere with a radius of 6370 km, Earth receives 1.8 x 1017 J/s, of which about 1.3 x 1017 J/s reaches Earth’s surface. Thus in one hour, the Sun provides Earth with all the energy we need for a whole year. It isn’t quite that simple, however. Due to meteorological factors, the Sun’s declination and Earth’s rotation, the irradiance is actually closer to 230 W/m2. If we repeat the last calculation using that figure, the time needed to power Earth with energy from the Sun for a year is about five and a half hours – still an impressively short time. Solar radiation is therefore a promising energy reservoir, but how can we collect it and use it? 2012-05-22T00:00:00Z http://hdl.handle.net/10366/169377 In this paper we report on an experimental study of the spectral response of different compounds in the THz range. We set up a THz Time Domain Spectroscopy System (TDS) that uses a Ti:Sapphire femtosecond laser and a pair of LTD GaAs photoconductive antennas for THz generation and detection. The THz TDS set up was validated through the matching of the main lines of the water vapor spectra in the 0.1-3THz range against the HITRAN database. Finally, the THz spectra of two commercial chemical compounds (paracetamol and ibuprofen) were obtained; the large difference found between the two spectra will easily allow to distinguish between both substances. Further work will be necessary to understand the fingerprints of those substances. 2012-09-14T00:00:00Z http://hdl.handle.net/10366/169332 [EN]This paper treats a comparative study of neural networks and fuzzy neural networks used to model a complex biotechnological process: an activated sludge process taken from a real wastewater treatment plant. The neural networks used in this work are a multiplayer perceptron network and two recurrent neural networks: the Elman one and a neural network that represents the state space model. And other two fuzzy neural networks: the ANFIS network that calculates a Takagy-Sugeno type fuzzy logic system and a neurofuzzy system called FasArt, which is based on the Adaptive Resonance Theory (ART) but it also introduces fonnalisms from the fuzzy set theory. A comparative study of the five networks is carried out using real data collected fonn the plant in order to identify the dynamic behaviour ofthe sludge process in the wastewater plant. 2003-01-01T00:00:00Z http://hdl.handle.net/10366/169331 [EN]A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time-varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow-up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law. 2019-01-01T00:00:00Z http://hdl.handle.net/10366/169330 [EN]This paper presents a procedure for the closed-loop stability analysis of a certain variant of the strategy called Fuzzy Model-Based Predictive Control (FMBPC), with a model of the Takagi-Sugeno type, applied to the wastewater treatment process known as the Activated Sludge Process (ASP), with the aim of simultaneously controlling the substrate concentration in the effluent (one of the main variables that should be limited according to environmental legislations) and the biomass concentration in the reactor. This case study was chosen both for its environmental relevance and for special process characteristics that are of great interest in the field of nonlinear control, such as strong nonlinearity, multivariable nature, and its complex dynamics, a consequence of its biological nature. The stability analysis, both of fuzzy systems (FS) and the very diverse existing strategies of nonlinear predictive control (NLMPC), is in general a mathematically laborious task and difficult to generalize, especially for processes with complex dynamics. To try to minimize these difficulties, in this article, the focus was placed on the mathematical simplification of the problem, both with regard to the mathematical model of the process and the stability analysis procedures. Regarding the mathematical model, a state-space model of discrete linear time-varying (DLTV), equivalent to the starting fuzzy model (previously identified), was chosen as the base model. Furthermore, in a later step, the DLTV model was approximated to a local model of type discrete linear time-invariant (DLTI). As regards the stability analysis itself, a computational method was developed that greatly simplified this difficult task (in a local environment of an operating point), compared to other existing methods in the literature. The use of the proposed method provides useful conclusions for the closed-loop stability analysis of the considered FMBPC strategy, applied to an ASP process; at the same time, the possibility that the method may be useful in a more general way, for similar fuzzy and predictive strategies, and for other complex processes, was observed. 2021-01-01T00:00:00Z http://hdl.handle.net/10366/169329 [ES]En este trabajo se aborda la integración de dos métodos o estrategias de Control Predictivo basado en Modelos, a saber: Control Predictivo basado en Modelos Borrosos (FMBPC) y Control Predictivo en Lazo Cerrado (CLP MPC). La primera de estas estrategias utiliza principios de Control Predictivo Funcional (PFC) y está enmarcada, al mismo tiempo, en el ámbito del Control Inteligente (IC). La integración tiene como principal objetivo proporcionar a la estrategia de control no lineal FMBPC un procedimiento de optimización que permita el manejo automático de restricciones en la variable de control. La solución propuesta consiste en hacer uso de una estructura complementaria de tipo CLP MPC para determinar mediante optimización, en cada instante de muestreo, los valores óptimos de un cierto término aditivo, a sumar a la ley de control FMBPC, de tal modo que se satisfagan las restricciones. El modelo de predicciones y la ley de control base necesarios para realizar los cálculos en la estructura CLP MPC son proporcionados por la estrategia FMBPC. La estrategia mixta FMBPC/CLP propuesta ha sido validada, en simulación, aplicándola al control de fangos activados en plantas de tratamiento de aguas residuales (EDAR), poniendo el foco en la imposición de restricciones a la acción de control. Los resultados obtenidos son satisfactorios, observando un buen rendimiento del algoritmo de control diseñado, al tiempo que se garantiza tanto la satisfacción de las restricciones, que era el principal objetivo, como la estabilidad del sistema en lazo cerrado. 2021-01-01T00:00:00Z http://hdl.handle.net/10366/169328 [EN]The control of wastewater treatment plants (WWTPs) is an ecologically, economically, and socially important objective. In the case of plants using activated sludge (ASP) processes, their control presents a significant challenge due to the complexity of the dynamics of these processes (a consequence of their biological nature). To objectively evaluate control strategies, standardized benchmark simulation models (BSMs) are used. This article tests the feasibility and evaluates the performance, in a simulation environment, of a specific fuzzy model-based predictive control strategy, called FMBPC/CLP, applied to the BSM1 reference model. In each iteration, this strategy first uses an FMBPC-type algorithm, which determines the basic control action (based on a fuzzy model and applying functional predictive control) that guarantees the local stability of the closed-loop system. Then, a second predictive control algorithm, called closed-loop predictive control (CLP-MPC), calculates a compensating term that is added to the basic control law and ensures compliance with constraints in the control action. In the simulation experiments carried out, the plant structure described in the BSM1 benchmark (reactor divided into five tanks, followed by a settling tank) was maintained, but the default control configuration was modified. The alternative control configuration designed for the BSM1 test bench includes two control loops: one to regulate the oxygen concentration in compartment 5 of the reactor (maintaining the PI algorithm of the default control configuration) and another loop to regulate the nitrate concentration (nitrate and nitrite) in tank 2 and, simultaneously, the ammonia concentration in tank 5, using the alternative FMBPC/CLP strategy. This control hybrid configuration was tested and evaluated considering values of the influent (dry, rainy, and stormy weather), and performance measurement criteria, both standardized in the BSM1 platform. The base model of the plant to be controlled, necessary for the FMBPC strategy, is obtained by prior fuzzy identification, from open-loop input and output data. The identification is achieved with the help of a software tool that uses mathematical clustering methods (based on the Gustafson–Kessel algorithm) that allow for the extraction of fuzzy models of the Takagi–Sugeno type from the numerical input–output data of a given plant. The FMBPC strategy is potentially appropriate for the control of complex, changing or unknown systems and this article demonstrates that this strategy is viable, with satisfactory performance, and that it can even be competitive when compared with more traditional control strategies. 2025-12-26T00:00:00Z http://hdl.handle.net/10366/169215 [EN]Advances in bioinformatics have contributed towards a significant increase in available information. Information analysis requires the use of distributed computing systems to best engage the process of data analysis. This study proposes a multiagent system that incorporates grid technology to facilitate distributed data analysis by dynamically incorporating the roles associated to each specific case study. The system was applied to genetic sequencing data to extract relevant information about insertions, deletions or polymorphisms. 2012-01-01T00:00:00Z http://hdl.handle.net/10366/169214 [EN]E-healthcare has acquired great importance in recent years and requires the development of innovative solutions. This paper presents a telemonitoring system aimed at enhancing remote healthcare for dependent people at their homes. The system deploys a service-oriented architecture over a heterogeneous Wireless Sensor Networks infrastructure to create smart environments. Such architecture can be executed over multiple wireless devices independently of their microcontroller or the programming language they use. Furthermore, the system allows the interconnection of several networks from different wireless technologies, such as ZigBee or Bluetooth. This approach provides the system better flexibility to change its functionalities and components after deployment than other analyzed proposals. The system description, its architecture, and preliminary results of the system prototype implemented in a real environment are presented. 2011-01-01T00:00:00Z