The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.

Because battery-powered nodes are required in wireless sensor networks and energy consumption represents an important design consideration, alternate energy sources are needed to provide more effective and optimal function. The main goal of this work is to present an energy harvesting wireless sensor network platform, the Open Wireless Sensor node (WiSe). The design and implementation of the solar powered wireless platform is described including the hardware architecture, firmware, and a POSIX Real-Time Kernel. A sleep and wake up strategy was implemented to prolong the lifetime of the wireless sensor network. This platform was developed as a tool for researchers investigating Wireless sensor network or system integrators.

Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm’s performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal’s isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts.

Despite the great need for computer-related career professionals, some universities that offer specialties to develop professionals with knowledge to cover the mention need, present a decrease in enrollment, which can cause a wide variety of problems. That is why in this paper we set out to investigate what the main factors that directly or indirectly affect student enrollment in computer-related careers are, to establish a literature-based starting point, and to help select the best possible strategy to improve enrollment. To reach this goal, we did a documentary research, categorizing each one of the factors with its respective analysis. In the results, we explain several challenges that educators face, due to the evolution of society needs, two of them are: teaching-learning models and technology; besides, it became evident that the relevance of this academic programs remain available since there is a high demand for these professionals in Mexico, as well as in the world; finally, we showed some intrinsic and extrinsic motivational factors that have an essential relevance at the time that a student chooses the career to study. On the other hand, we reflected that the perception of everything that involves the study of a Computing-related career is a factor to be considered in the selection of an educational program of higher education. This study could be a reference for universities that are facing decreasing enrollment concerns. In conclusion, we analyzed and identified the main factors that have an impact on the demand for a career related to engineering, providing possible lines of action for increasing school enrollment.

Agriculture is essential for food production and raw materials. A key aspect of this sector is harvest, the stage at which the commercial part of the plant is separated. Timely harvesting minimizes post-harvest losses, preserves product quality, and optimizes production processes. Globally, a substantial amount of food is wasted, impacting food security and natural resources. To address this problem, an Adaptive Neuro-Fuzzy Inference System was developed to predict timely harvesting in crops. Stevia, a native plant from Brazil and Paraguay, with an annual production of 100,000 to 200,000 tons and a market of 400 million dollars, is the focus of this study. The system considers soil pH, Brix Degrees, and leaf colorimetry as inputs. The output is binary: 1 indicates timely harvest and 0 indicates no timely harvest. To assess its performance, Leave-One-Out Cross-Validation was used, obtaining an $r^2$ of 0.99965 and an Absolute Residual Error of 0.00064305, demonstrating its accuracy and robustness. In addition, an interactive application that allows farmers to evaluate crop status and optimize decision-making was developed.

Currently, the smart grid concept represents the modern vision of an automated and highly adaptable electrical grid. Supervisory control and data acquisition (SCADA) systems are a fundamental component of a smart grid, enabling communication between field equipment and digital environments. For this purpose, they require industrial frameworks, among which IEC 61850 stands out. IEC 61850 has become a widely adopted standard for substation automation systems (SASs). However, despite its widespread adoption, IEC 61850 faces significant implementation challenges, including the potential complexity of data modeling, which often leads to discrepancies in semantic interpretation and, consequently, different readings among SAS configuration users. A disparity in the semantic interpretation of a process can negatively affect SAS operation, leading to execution errors or interoperability issues. Translating and analyzing SAS configurations can identify and resolve semantic interpretation discrepancies across these systems. The purpose of this research was to determine the degree to which a user interface was perceived as useful to support the translation and analysis of SAS configurations based on the IEC 61850 standard. To this end, a software tool was proposed as the central artifact to address the socio-technical dimension of a custom-built SCADA system at a Latin American state enterprise. The tool serves as the local, intelligent, and real-time operational layer in that system and was rated by users experienced with IEC 61850 as highly usable. The consistently obtained results suggest potential support for those performing the SAS configuration.