Ayala-Chauvin, Manuel Ignacio

Preferred name

Main Affiliation

Centro de Investigación de Ciencias Humanas y de la Educación

Ambato

mayala@uti.edu.ec

ORCID

0000-0002-3911-377X

Scopus Author ID

57215428914

Now showing 1 - 10 of 43

Some of the metrics are blocked by your
consent settings
Road Drainage Infrastructure Diagnostics and Deficiency Indexing in ENSO-Vulnerable Andean Corridors: A STEM–PjBL Field Assessment
(MDPI AG, 2026-05-15)
Benavides-Muñoz, Holger Manuel
;
Ayala-Chauvin, Manuel Ignacio
;
Benavides-Ortega, Leirys María
Road drainage infrastructure in ENSO-vulnerable Andean regions faces compounding threats from climatic variability, geometric inadequacy, and systemic maintenance neglect. This study presents a STEM-integrated Project-Based Learning (PjBL) diagnostic framework applied to 42 road segments along corridors connecting Loja, Ecuador, selected through a purposive-stratified spatial-coverage protocol. Using ArcGIS Survey123, standardised field data were collected on structure presence, geometry, failure modes, and condition across four structure types: crown gutters, road gutters, hydraulic chutes, and culverts. The Composite Drainage Deficiency Index (DDI, 0–100) was derived from five equally weighted binary indicators and validated through Monte Carlo Dirichlet weight-perturbation analysis and jackknife leave-one-out resampling, confirming rank-order invariance to admissible alternative weightings. The results reveal severe systemic deficiencies, including crown gutters absent at 88.1% (95% CI: 75.0–94.8) and road gutters at 81.0% (95% CI: 66.7–90.0) of sites. Every segment exhibited at least one drainage failure (100%; 95% CI: 91.6–100). The DDI identified 73.8% of segments in the High or Critical band (DDI ≥ 60; mean = 60.2 ± 20.4). Hierarchical clustering isolated one geometric outlier whose exclusion altered the aggregate metrics by <1.2%. These findings establish a georeferenced baseline for maintenance prioritisation and validate the methodological reproducibility of academically integrated field protocols for infrastructure diagnostics. © 2026 by the authors.
Some of the metrics are blocked by your
consent settings
U-Net Models for Breast Cancer Detection: Improving Diagnostic Accuracy and Specificity
(2024)
Dayanara Yánez-Arcos
;
Ayala-Chauvin, Manuel Ignacio
;
Elena Blanco-Romero
Breast cancer remains a critical global health issue, necessitating continuous research and innovative approaches for diagnosis, treatment, and prevention. This study evaluates the effectiveness of U -Net models in enhancing diagnostic precision and efficiency using real hospital samples. We aim to improve key diagnostic metrics such as accuracy, sensitivity, and specificity through the application of U-Net models. Our image classification model, tailored for 256 × 256 × 3 input images, excels in detecting and categorizing tumor cells. The architecture begins with initial convolutional layers featuring 64 filters, progresses to layers with 128 filters, and includes a Dropout layer to prevent overfitting. The deep network for object detection utilizes both region proposal and regression/classification approaches, achieving 92.27% confidence and 100% accuracy. Additionally, our deep learning algorithms accurately segment nuclei in histopathological images, employing a clustering strategy that delivers 88.81% confidence and 100% accuracy. Visual results demonstrate precise tumor cell localization and prediction confidence. Performance metrics from ten experimental runs indicate average confidence levels between 74.19% and 92.31%, with 90.0% accuracy and specificity in benign analysis. The model's ability to classify non-carcinomas versus carcinomas achieved an AUC of 0.78, illustrating its effective differentiation between classes.
10
Some of the metrics are blocked by your
consent settings
IoT Monitoring to Control a Bicycle Parking Lot
(2022)
Ruales, María Belén
;
Lara-Alvarez P.
;
Riba C.
;
Ayala-Chauvin, Manuel Ignacio
In recent years, the development of new technologies has improved the management of resources and services at the urban level. In this sense, several cities worldwide have developed intelligent infrastructures such as Smart Cities in which, through data collection and management, they aim to achieve social, environmental and economic improvements. Innovative bike racks are a promising solution to traffic-related problems in major cities around the world; however, there is a lack of low-cost solutions for controlling and monitoring bike racks and thus boosting the mobility of cyclists. This paper presents a system to monitor and control a bicycle parking lot. In order to achieve this goal, software and hardware specifications were defined and characterised by the control system. The conceptual design and detail of the prototype and the materialisation proceeded, where technology with ESP8266 microcontrollers and Raspberry Pi+Ethernet/WiFi microprocessors was used in the MQTT communication protocol to implement its architecture. The system implements in the bicycle parking lot of the Universidad Tecnológica Indoamérica. The series of data collected allowed for determining the frequency of use. With this, a database creates where the information on the frequency of use of bicycles is stored. Finally, through a mobile application, the availability of parking spaces can be consulted, and bikes in the parking lot can monitor. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
43
Some of the metrics are blocked by your
consent settings
Predictive Maintenance in Industrial Robotics Using Big Data: Techniques, Challenges, and Opportunities
(2024)
Ayala-Chauvin, Manuel Ignacio
;
Avilés-Castillo, Fátima
;
Dayanara Yánez-Arcos
;
Buele, Jorge
In industrial robotics, predictive maintenance is important to improve efficiency and reduce costs, addressing early detection and diagnosis of failures. The use of Big Data allows us to identify patterns and trends that at first glance are complex. This review examines research on the application of big data in predictive maintenance of industrial robots, which use advanced techniques such as cloud-based architectures, filtering algorithms, and machine learning. The review methodology included an analysis of the big data techniques used, the challenges identified, and the opportunities presented. The results show significant improvements in the accuracy of predictions and fault diagnoses. Key anomaly drivers were identified that improved production performance and enabled accurate fault identification and reduced downtime in industrial robots. Despite the benefits, challenges remain in data security and communications latency, underscoring the need to develop innovative algorithms and techniques to balance computing load and minimize delays. The continuous evolution of these techniques promises to improve the failure management capacity in industrial robotics, thus optimizing the operability and efficiency of robotic systems.
14
Some of the metrics are blocked by your
consent settings
Automation of an Electro-Hydraulic Test Bench Using a Weitek CMT3092 HMI- PLC
(2022)
Altamirano-Haro D.
;
Sánchez-Díaz P.E.
;
Buele, Jorge
;
Ayala-Chauvin, Manuel Ignacio
The industrial environment demands mean that future professionals must acquire more and more technical skills. However, this represents a high investment that many higher education institutions cannot afford. Therefore, the laboratory equipment is updated, and this study begins with the automation of an electro-hydraulic test bench that was manual. For this, a PLC - Weintek was selected, whose programming was carried out in ladder language using CODESYS as a development platform, using an open Modbus programming code through the SFD block language. A human-machine interface (HMI) on a touch screen in the system allows user input (with various hierarchies) and operations control. The operator can also store data for later analysis. Finally, the validation of this proposal is carried out with the respective experimental tests, obtaining a significant reduction in the execution time of the three proposed tasks and improvement of learning conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Scopus© Citations 1 33
Some of the metrics are blocked by your
consent settings
Sustainable Development in Higher Education Curricula for Software Engineering Chairs
(2023)
León Toro, Jenny Marcela
;
Buele, Jorge
;
Camino-Morejón V.M.
;
Ayala-Chauvin, Manuel Ignacio
Nowadays, society demands that high quality teaching practices must be part of the curriculum in higher education institutions. The interdisciplinarity view of the contents taught has made the technical aspects of engineering merge with social, cultural, and economic nuances. In this sense, the new generations of students show their interest in learning and carrying out activities that contribute to sustainability, for this reason, the inclusion of ecological themes in the subjects of computer science and software career is required. A bibliographical analysis was carried out that allowed recognition of main concepts and methodologies applied to the subject. As a result of them, an adjustment of chairs is presented allowing integrating conventional teaching with the new trends of green technology. Reforms were implemented from introductory courses to theoretical knowledge of green software, to the development of web applications with the same approach. In the same way, it involves the management of computer projects, modeling, monitoring, and optimization of resources, and green evaluation. According to the socio-educational model, the articulation between technology and sustainability will allow managing software projects that provide real solutions to problems in context. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
41
Some of the metrics are blocked by your
consent settings
Radar system for the reconstruction of 3D Objects: A preliminary study
(2020)
Barberán, J.
;
Moreta, D.
;
Ayala-Chauvin, Manuel Ignacio
;
Obregón, J.
;
Domínguez, R.
Objects recognition and their reconstruction is a process that involves a significant economic investment. This manuscript presents the basis for the design of a system that detects objects, extracts its main characteristics and digitally reconstructs them in three dimensions considering a reduced economic investment. The popular technological tool Kinect on its version 2.0 and MATLAB software have been linked to develop an efficient algorithm. Next, the process to obtain this prototype is briefly described, as well as the results from the corresponding experimental tests. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.
Scopus© Citations 1 24
Some of the metrics are blocked by your
consent settings
System for Monitoring and Controlling Industrial Lighting with Amazon Alexa
(2021)
Ayala-Chauvin, Manuel Ignacio
;
Saá F.
;
Villarroel-Córdova F.
;
de la Fuente-Morato A.
Intelligent devices, used along with sensors, are becoming more commonplace in industrial contexts. One such device, Amazon Echo (which runs Amazon Alexa), can be used to interact with other industrial systems via voice commands. Taking advantage of this, a skill to control the illumination system of a company has been developed, while also being able to measure power consumption in real time. Besides Echo, the system employs easily obtainable electronic components such as NodeMCU4 and Sonoff Pow, while running open-source software like IDE Arduino and Amazon Developer. Besides the voice commands, the system can be controlled via a cell phone touch app and a manual system. Tests show the skill successfully controls the illumination system and provides accurate power consumption data in real time. This skill can also be replicated in other industrial contexts. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Scopus© Citations 1 16
Some of the metrics are blocked by your
consent settings
Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador
(2023)
Ayala-Chauvin, Manuel Ignacio
;
Avilés-Castillo F.
;
Buele, Jorge
Latin America has shown increased big data adoption since 2012; Ecuador is now entering this transformative field. Science and engineering in Ecuador have benefited most from data analysis, with untapped potential in health and services sectors. Big data is shaping sectors in Ecuador, including disaster prediction, agriculture, smart city development, and electoral data analysis. Despite public sector inefficiencies, residential ICT adoption provides opportunities for Ecuador’s smart city advancements. Despite some data underutilization, big data’s transformative potential is evident in Ecuador’s healthcare and education advancements. Highlights: Data analysis is increasingly critical in aiding decision-making within public and private institutions. This paper scrutinizes the status quo of big data and data analysis and its applications within Ecuador, focusing on its societal, educational, and industrial impact. A detailed literature review was conducted from academic databases such as SpringerLink, Scopus, IEEE Xplore, Web of Science, and ACM, incorporating research from inception until May 2023. The search process adhered to the PRISMA statement, employing specific inclusion and exclusion criteria. The analysis revealed that data implementation in Ecuador, while recent, has found noteworthy applications in six principal areas, classified using ISCED: education, science, engineering, health, social, and services. In the scientific and engineering sectors, big data has notably contributed to disaster mitigation and optimizing resource allocation in smart cities. Its application in the social sector has fortified cybersecurity and election data integrity, while in services, it has enhanced residential ICT adoption and urban planning. Health sector applications are emerging, particularly in disease prediction and patient monitoring. Educational applications predominantly involve student performance analysis and curricular evaluation. This review emphasizes that while big data’s potential is being gradually realized in Ecuador, further research, data security measures, and institutional interoperability are required to fully leverage its benefits. © 2023 by the authors.
27
Some of the metrics are blocked by your
consent settings
Segmentation of Energy Consumption Using K-Means: Applications in Tariffing, Outlier Detection, and Demand Prediction in Non-Smart Metering Systems
(2025)
Darío Muyulema-Masaquiza
;
Ayala-Chauvin, Manuel Ignacio
The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) and Random Forest (RF) models were utilized to forecast demand, with the latter yielding an R2 of 0.67. The findings delineated eight distinct clusters, facilitating the formulation of more representative rates, the identification of outliers through the interquartile range (IQR) method, and the enhancement of consumption estimation. It is concluded that this unsupervised segmentation approach constitutes a robust and cost-effective tool for energy planning in network environments devoid of smart infrastructure.
10

Ayala-Chauvin, Manuel Ignacio

Filters

Settings

Sort By

Results per page