Centro de investigación en Mecatrónica y Sistemas Interactivos
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Ancestral Knowledge in the Teaching and Learning Process

2025 , Quezada-Sarmiento, Pablo Alejandro , Patricia Marisol Chango-Cañaveral , Jadán Guerrero, Janio

This research explores the importance of ancestral knowledge, such as myths, which are transmitted orally in different families in cultural communities. However, this knowledge combined with popular knowledge can also be applied to education. Therefore, the objective of this work was to design a methodological strategy for ninth grade general education students through a variety of didactic resources in the fields of knowledge of language and national literature and knowledge of language and literature spanish. The work is based on the popular myth of the community “Shakaim/The God of Work”, in which morals and moral values are taught, incorporating popular knowledge, such as reading comprehension

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Real-Time Monitoring Tool for SNN Hardware Architecture

2023 , Zapata M. , Vargas V. , Cagua A. , Alvarez D. , Vallejo B. , Madrenas J.

Spiking Neural Networks (SNN) are characterized by their brain-inspired biological computing paradigm. Large-scale hardware platforms are reported, where computational cost, connectivity, number of neurons and synapses, speed, configurability, and monitoring restriction, are some of the main concerns. Analog approaches are limited by their low flexibility and the amount of time and resources spent on prototype development design and implementation. On the other hand, the digital SNN platform based on System on Chip (SoC) offers the advantage of the Field-programmable Gate Array (FPGA) technology, along with a powerful Advanced RISC Machine (ARM) processor in the same chip, that can be used for peripheral control and high-bandwidth direct memory access. This paper presents a monitoring tool developed in Python that receives spike data from a large-scale SNN architecture called Hardware Emulator of Evolvable Neural System for Spiking Neural Network (HEENS) in order to on-line display in a dynamic raster plot in real-time. It is also possible to create a plain text file (.txt) with the entire spike activity with the aim to be analyzed offline. Overall, the monitoring tool and the HEENS functionalities working together show great potential for an end-user to bring up a neural application and monitor its evolution introducing a low delay, since a FIFO is used to temporarily store the incoming spikes to give the processor time to transmit data to the PC through Ethernet bus, without affecting the neural network execution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Validity and reliability of the executive function scale in Cuban university student

2025 , Diego D. Díaz-Guerra , Marena De La C. Hernández-Lugo , Ramos Galarza, Carlos , Yunier Broche-Pérez

Introduction: Executive functions are higher cognitive skills involved in planning, organization, decision-making, impulse control, and working memory. It is essential to have tools that allow for the accurate and reliable assessment of this construct in university students. This study aims to evaluate the validity and reliability of the Executive Functions Scale for University Students (UEF-1) in the Cuban population. Methods: A cross-sectional study was conducted in which an online survey was administered to 1,092 Cuban university students representing 14 of the country’s 16 provinces. Descriptive analyses, confirmatory factor analyses, and Pearson correlation analyses were used to assess the psychometric properties of the scale. Results: Significant correlations were obtained between the scale factors, and the original seven-factor structure was confirmed. The scale demonstrated good internal consistency and overall reliability (α = 0.91, ω = 0.91). Conclusion: The study provided evidence that the UEF-1 is a reliable and valid tool for assessing executive functions in Cuban university students. This measure provides a comprehensive understanding of the cognitive abilities and functioning of Cuban university students, allowing for the identification of specific areas of executive functioning that may benefit from additional support or intervention.

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Scale reduced to value the sense of coherence: SOC 15 [Escala Reducida Para Valorar el Sentido de Coherencia: SOC 15]

2019 , Ortiz-Granja D. , Acosta-Rodas P. , Lepe-Martínez N. , Valle M.D. , Ramos Galarza, Carlos

Introduction: The sense of coherence is a construct of health that allows the individual to face difficult situations of life. It is configured by three factors: meaning, understanding and management. As a method of assessment of this construct, has been proposed the SOC scale with 29 items in its original version. Objective: The objective of this study is to propose a reduced SOC scale. Methods: We worked with a sample of 445 healthy participants from Quito-Ecuador, 145 men (32.5%) and 300 women (67.4%). Results: It was obtained that the reduced scale of 15 items presents an adequate internal consistency in its three factors: understanding α= .74, management α= .82 and meaning α= .82. In the confirmatory factor analysis, an acceptable adjustment of the reduced model was found (SOC-15) x2= 317.90, DF= 87, p= <. 001, CFI= .92, RMSEA= .07 (.06-.08) and SRMR= .04. Conclusions: The data is discussed in relation to the benefits of counting with a reduced scale for its future application in the clinical and health scientific context. © 2019 Fundacion para la difusion neurologica en Ecuador - FUNDINE. All rights reserved.

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Real-time execution of SNN models with synaptic plasticity for handwritten digit recognition on SIMD hardware

2024 , Bernardo Vallejo-Mancero , Jordi Madrenas , Zapata, Mireya

Recent advancements in neuromorphic computing have led to the development of hardware architectures inspired by Spiking Neural Networks (SNNs) to emulate the efficiency and parallel processing capabilities of the human brain. This work focuses on testing the HEENS architecture, specifically designed for high parallel processing and biological realism in SNN emulation, implemented on a ZYNQ family FPGA. The study applies this architecture to the classification of digits using the well-known MNIST database. The image resolutions were adjusted to match HEENS' processing capacity. Results were compared with existing work, demonstrating HEENS' performance comparable to other solutions. This study highlights the importance of balancing accuracy and efficiency in the execution of applications. HEENS offers a flexible solution for SNN emulation, allowing for the implementation of programmable neural and synaptic models. It encourages the exploration of novel algorithms and network architectures, providing an alternative for real-time processing with efficient energy consumption.

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Combining Image Classification and Unmanned Aerial Vehicles to Estimate the State of Explorer Roses

2024 , David Herrera , Pedro Escudero-Villa , Eduardo Cárdenas , Marcelo Ortiz , Varela Aldas, José

The production of Explorer roses has historically been attractive due to the acceptance of the product around the world. This species of roses presents high sensitivity to physical contact and manipulation, creating a challenge to keep the final product quality after cultivation. In this work, we present a system that combines the capabilities of intelligent computer vision and unmanned aerial vehicles (UAVs) to identify the state of roses ready for cultivation. The system uses a deep learning-based approach to estimate Explorer rose crop yields by identifying open and closed rosebuds in the field using videos captured by UAVs. The methodology employs YOLO version 5, along with DeepSORT algorithms and a Kalman filter, to enhance counting precision. The evaluation of the system gave a mean average precision (mAP) of 94.1% on the test dataset, and the rosebud counting results obtained through this technique exhibited a strong correlation (R2 = 0.998) with manual counting. This high accuracy allows one to minimize the manipulation and times used for the tracking and cultivation process.

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Estimation of Unmodeled Dynamics: Nonlinear MPC and Adaptive Control Law With Momentum Observer Dynamic

2024 , Bryan S. Guevara , Luis F. Recalde , Viviana Moya , Varela Aldas, José , Daniel C. Gandolfo , Juan M. Toibero

This article proposes an enhancement to estimate unmodeled dynamics within the simplified dynamic model of a quadcopter by integrating three key methodologies: Nonlinear Model Predictive Control (NMPC), a Momentum Observer Dynamics (MOD), and an adaptive control law. Termed as Adaptive NMPC with MOD, this integrated approach leverages NMPC, implemented using the CasADi framework, for real-time decision-making, while the momentum observer facilitates system state estimation and uncertainty mitigation. Simultaneously, the adaptive control law adjusts parameters to estimate errors in unmodeled dynamics. Through digital twin and Model in Loop (MiL) simulations, the effectiveness of this framework is demonstrated. Specifically, the study focuses on the simplified quadcopter model, acknowledging often overlooked inherent dynamics resulting from the simplification by not considering the nonlinearities induced by the drone's attitude angles. Addressing these unmodeled dynamics is critical, and the Adaptive NMPC with MOD method emerges as a robust solution, showcasing its potential across various scenarios.

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Virtual Reality as a Learning Mechanism: Experiences in Marketing

2024 , Ramos Galarza, Carlos , Bolaños-Pasquel, Mónica , Cruz-Cárdenas J.

Virtual reality or VR is the simulation of a real or imaginary environment, which can be experienced as an abstraction of the real environment, which in turn allows the user to interact through a technological device within that simulation. Likewise, the user has the ability to modify the surrounding system by means of motion sensors, which will allow him/her to feel, and perceive as he or she is immersed in virtuality. Thanks to VR, sensorial perception is amplified, allowing to enhance our experiences of the real world, so that VR provides us with safe space of learning. Because of it, there has been seen necessary to review the scope of VR in marketing education, addressing that technology advances present excellent tools useful for the training of better professionals, as well as being support into rehabilitation processes, such as in learning difficulties at all marketing educational levels. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Using LSTM Autoencoder and Dynamic Signal Decomposition for Efficient PAPR Reduction in UWA-OFDM

2025 , Mansoor Jan , Syed Agha Hassnain Mohsan , Víctor P. Gil Jiménez , Muhammad Aman , Samih M. Mostafa , Varela Aldas, José

Underwater Acoustic Communication (UWA) is an emerging wireless technology that enables reliable and long range data transmission in challenging underwater environments. To support high data rates and mitigate Inter Symbol interference (ISI) caused by multipath propagation, Orthogonal Frequency Division Multiplexing (OFDM) is widely adopted in UWA systems. High Peak-to-Average Power Ratio (PAPR) continues to be a significant constraint in OFDM based UWA systems, as it causes nonlinear distortion in power-constrained acoustic transmitters and results in spectrum regrowth. This study presents a data driven strategy for PAPR reduction using a Long Short Term Memory-Autoencoder (LSTM-AE) as well as noise reduction from time-domain OFDM signals using the Local Mean Decomposition technique (LMD). The LSTM-AE is designed to learn the basic components of the OFDM waveform and rebuild a low PAPR variant without sacrificing signal integrity. Simulation results indicate that the suggested strategy significantly decreases PAPR while maintaining Bit Error Rate (BER) performance across different Signal-to-Noise Ratio (SNR) levels. Moreover, Complementary Cumulative Distribution Function (CCDF) analysis demonstrates substantial improvement relative to conventional PAPR reduction methods, including Tone Reservation (TR), Partial Transmit Sequence (PTS), Active Constellation Extension (ACE) and Fully Connected Neural Network (FCNN). Compared to the original OFDM signal, the LSTM-AE approach reduced PAPR by 9.4 dB, reaching 4.8 dB. It also maintain better energy efficiency than ACE, FCNN, TR, and PTS at all SNR levels. The suggested LSTM-AE method is a feasible low complexity alternative for improving the efficiency and resilience of UWA OFDM systems

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Intrusion detection system in commands sequences applying one versus rest methodology [Sistema de Detección de Intrusos en secuencia de comandos aplicando la metodología One versus Rest]

2018 , Guevara C.B. , Jadán Guerrero, Janio

The main objective of this work is to develop an intrusion detection algorithm in commands sequences. These sequences are based on user behavior applying in several classification techniques. This algorithm allows obtaining a precision in the identification of fraudulent activities. To develop this algorithm, we have worked with a public database called Unix Commands. In addition, the model applies multiple machine learning techniques such as decision tree C4.5, UCS, and Multilayer Neural Network. In this paper we use two forms for data classification, the first form will be to use the entire dataset with the 7 users, but the difference is that the model applies 5 commands or 16 commands. The model identifies the information of a user and the labeled as normal, otherwise, the user is labeled as an intruder (5 commands - 2 classes, 16 commands - 2 classes). The second form uses the dataset by sequential discrimination (discrimination in form of a decision tree). This methodology is used in the multiclass classification called one versus rest (OVR) (5 commands-OVR, 16 commands-OVR). The algorithm has obtained optimal results in the classification and a low false positive rate. © 2018 AISTI.

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