Varela Aldas, José
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Varela Aldas, José
Main Affiliation
Ambato
Email
josevarela@uti.edu.ec
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Scopus Author ID
57192546163

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Enhancing Elderly Care through Low-Cost Wireless Sensor Networks and Artificial Intelligence: A Study on Vital Sign Monitoring and Sleep Improvement

2024 , Carolina Del-Valle-Soto , Ramon A. Briseño , Ramiro Velázquez , Gabriel Guerra-Rosales , Santiago Perez-Ochoa , Isaac H. Preciado-Bazavilvazo , Paolo Visconti , Varela Aldas, José

This research explores the application of wireless sensor networks for the non-invasive monitoring of sleep quality and vital signs in elderly individuals, addressing significant challenges faced by the aging population. The study implemented and evaluated WSNs in home environments, focusing on variables such as breathing frequency, deep sleep, snoring, heart rate, heart rate variability (HRV), oxygen saturation, Rapid Eye Movement (REM sleep), and temperature. The results demonstrated substantial improvements in key metrics: 68% in breathing frequency, 68% in deep sleep, 70% in snoring reduction, 91% in HRV, and 85% in REM sleep. Additionally, temperature control was identified as a critical factor, with higher temperatures negatively impacting sleep quality. By integrating AI with WSN data, this study provided personalized health recommendations, enhancing sleep quality and overall health. This approach also offered significant support to caregivers, reducing their burden. This research highlights the cost-effectiveness and scalability of WSN technology, suggesting its feasibility for widespread adoption. The findings represent a significant advancement in geriatric health monitoring, paving the way for more comprehensive and integrated care solutions.

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Optimal cooperative control of mobile robots based on Pontryagin's Minimum Principle

2022 , Varela Aldas, José

This work presents the cooperative control with optimization for positioning of mobile robots based on Pontryagin's Minimum Principle. The problem to be solved is the minimum energy related to the control actions of the robots. The objective function is subject to the equations of states of the triangular formation of the 3 mobile robots and is restricted to boundary conditions that include the desired form parameters. The mathematical model of the system is based on the kinematic transformation of the position of the robots towards the parameters of the formation. To solve the differential equations obtained, the shooting method is programmed for two initial value problems. The results show the evolution of the robots' positions and the positioning errors that tend to zero, validating this proposal through simulation. In addition, the control actions allow determining the energy index to compare it with a previous work, achieving a lower value. © 2022 IEEE.

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Inverse kinematics of a redundant manipulator robot using constrained optimization

2020 , Varela Aldas, José , Ayala-Chauvin, Manuel Ignacio , Andaluz, V.H. , Santamaría, M.

Redundant manipulative robots are characterized by greater manipulability improving performance but complicating inverse kinematics, on the other hand, optimization techniques allow solving complex problems in robotics applications with greater efficiency. This paper presents the inverse kinematics of a redundant manipulative robot with four degrees of freedom to track a desired trajectory, and considering constraint in manipulability. The optimization problem is proposed using the quadratic position errors of the operative end and the constraint is established by a manipulability index, for this the kinematic model of the robot is determined. The results show the points of singularity of the robot and the performance of the proposal implemented, observing the positional errors and the manipulability for each point of the trajectory. In addition, the optimization is evaluated for two desired manipulability values. Finally, it is concluded that the implemented method optimizes the inverse kinematics to track the desired path while constraining the manipulability. © Springer Nature Switzerland AG 2020.

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Prototype System of Geolocation Educational Public Transport Through Google Maps API

2020 , Salazar F.W. , Naranjo-Ávalos H. , Buele, Jorge , Pintag M.J. , Buenaño É.R. , Reinoso C. , Urrutia-Urrutia P. , Varela Aldas, José

Urban traffic complications in most underdeveloped countries and congestion in all metropolitan areas has become a daily problem with a difficult solution. Disorganized mobility of drivers and pedestrians along with the increase in travel time, non-compliance with schedules, air pollution and intolerable sound levels, have harmful effects on human health. Therefore, this research describes a geolocation system of urban transport through a mobile application developed on the Xamarin platform. Drivers send the latitude and longitude points when starting a route, this data will be sent to the SQL SERVER online database server, using the SmarterASP.NET platform. By developing the geolocation system in ASP.NET, the coordinates are available to users in an interval of 5 s. The developed interface shows a location map, where the route in real time is presented. It also shows the administration of users, drivers, buses, assignment of routes, assignment of buses and registration of static routes. Being a prototype system, the university transport system has been taken as an object of study to corroborate its correct operation with the respective experimental tests. Satisfaction surveys have also been carried out on a group of 300 people, among students and university teachers and their validation is carried out through the Technological Acceptance Model (TAM). To interpret the results, Kendall Tau-b correlation analysis was used, obtaining positive correlation values with a high significance value. © 2020, Springer Nature Switzerland AG.

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Prototype of a Low Cost Turbine for the Generation of Clean Energy in the Ecuadorian Amazon

2020 , Guáitara B. , Buele, Jorge , Salazar F.W. , Varela Aldas, José

Access to electricity supply in remote areas is limited, despite having wealth of other natural resources such as water. This document presents the design and construction of a scale electric generation system, taking advantage of the hydraulic energy produced by a constant flow of water flow. For this, the turbine design is described, which is based on the principles of Francis and Kaplan. In addition, the structural design made in CAD/CAM software and the actual implementation of the system are shown. To determine the generated electric potential, the electromagnetic analysis is performed based on the Maxwell-Faraday equation and the respective calculations. The validation of this proposal is determined by conducting experimental tests with balanced, unbalanced, series and parallel coils and with their implementation in a home. © 2020, Springer Nature Switzerland AG.

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An analysis of IoT technology education based on UIFLOW

2023 , Varela Aldas, José , Junta-Andagana C. , Bran C.

The importance of education in new technologies today drives us to continue exploring available options to introduce students to STEAM competencies. This article presents an analysis of satisfaction in the practical education of IoT technology using a block-based programming language. In this case, the UIFlow development environment, compatible with M5Stack kits, is utilized. A graphical interface for the M5Stack Core2 AWS module's screen is designed to enable data transmission to the Thingspeak platform via the MQTT protocol with a 1-second delay. This analysis was conducted during a practical session with a group of 11 industrial engineering students. The collected data includes session runtimes and a satisfaction assessment using the Post-Study System Usability Questionnaire, specifically adapted for this case. The results indicate an overall satisfaction level of 84.7% within the group. The lowest-scoring aspect pertains to the information provided by UIFlow for problem-solving. © 2023 IEEE.

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Teleoperation of a mobile manipulator with feedback forces for evasion of obstacles [Teleoperación de un manipulador móvil con retroalimentación de fuerzas para evasión de obstáculos]

2019 , Andaluz V.H. , Varela Aldas, José , Chicaiza F.A. , Quevedo W.X. , Ruales, María Belén

This work develops the teleoperation of a mobile manipulator using a kinematic and dynamic control. In addition, the operator receives feedback forces through a haptic device to evade obstacles. The experiments were performed in a local wireless communication network and using the AKASHA robot. The results show an adequate following of the references of control, speed errors are corrected with minimum communication delays. © 2019, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

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Impact of Distributed Energy Resources with Photovoltaic Self-Consumption on an Electrical Distribution Network

2024 , Pedro Torres , Kevin López-Eugenio , Varela Aldas, José

The sustainability of electrical distribution networks is essential for ensuring reliable supply and minimizing environmental impact. This study focuses on analyzing the impact of Distributed Energy Resources with photovoltaic selfconsumption systems on a real electrical distribution network, aiming to identify the benefits and challenges these systems present. The methodology employed includes collecting energy consumption data, selecting candidates for the implementation of photovoltaic systems, and modeling and simulating load flows using CYMDIST software. Data collection was carried out in an electric utility company in Ecuador, identifying users with the highest probability of adopting these systems. Subsequently, the photovoltaic systems were modeled in the electrical network, and the technical impacts were evaluated. The results show that the integration of photovoltaic systems alleviates the load on the distribution network, decreases energy losses, and facilitates a more flexible and adaptive management of energy demand. Finally, it is concluded that photovoltaic systems are expected to be a strong base for the transition towards a more sustainable and resilient electricity system

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Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment

2024 , Faten Khalid Karim , Varela Aldas, José , Mohamad Khairi Ishak , Ayman Aljarbouh , Samih M. Mostafa

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how industries work by connecting devices and sensors and automating regular operations via the Internet of Things (IoTs). IoT devices provide seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD) technique in the IIoT networks. The proposed BMLSSA-CAD technique aims to enhance security in IIoT networks by detecting cyberattacks. In the BMLSSA-CAD technique, the min-max scaler normalizes the input dataset. Additionally, the method utilizes the Chameleon Optimization Algorithm (COA)-based feature selection (FS) approach to identify the optimal feature set. The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. The performance of the BMLSSA-CAD method is examined using UNSWNB51 and UCI SECOM datasets. The experimental validation of the BMLSSA-CAD method highlighted superior accuracy outcomes of 97.84% and 98.93% compared to recent techniques on the IIoT platform

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A Hybrid GAS-ATT-LSTM Architecture for Predicting Non-Stationary Financial Time Series

2025 , Kevin Astudillo , Miguel Flores , Mateo Soliz , Guillermo Ferreira , Varela Aldas, José

This study proposes a hybrid approach to analyze and forecast non-stationary financial time series by combining statistical models with deep neural networks. A model is introduced that integrates three key components: the Generalized Autoregressive Score (GAS) model, which captures volatility dynamics; an attention mechanism (ATT), which identifies the most relevant features within the sequence; and a Long Short-Term Memory (LSTM) neural network, which receives the outputs of the previous modules to generate price forecasts. This architecture is referred to as GAS-ATT-LSTM. Both unidirectional and bidirectional variants were evaluated using real financial data from the Nasdaq Composite Index, Invesco QQQ Trust, ProShares UltraPro QQQ, Bitcoin, and gold and silver futures. The proposed model’s performance was compared against five benchmark architectures: LSTM Bidirectional, GARCH-LSTM Bidirectional, ATT-LSTM, GAS-LSTM, and GAS-LSTM Bidirectional, under sliding windows of 3, 5, and 7 days. The results show that GAS-ATT-LSTM, particularly in its bidirectional form, consistently outperforms the benchmark models across most assets and forecasting horizons. It stands out for its adaptability to varying volatility levels and temporal structures, achieving significant improvements in both accuracy and stability. These findings confirm the effectiveness of the proposed hybrid model as a robust tool for forecasting complex financial time series.

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