Hector Alonso Guerrero Osuna

Urban LoRaWAN deployments are engineering systems whose connectivity, energy use, reliability, and operating cost depend on informed gateway-placement and configuration decisions. Although LoRa is widely adopted for smart-city and industrial Internet of Things (IoT) applications, the methods used to construct gateway coverage analyses remain fragmented across propagation modeling, field measurement, simulation, optimization, and emerging data-driven approaches. This technical survey critically synthesizes the representative literature on methodologies for LoRa gateway coverage analysis in urban environments. Its contributions are threefold: it organizes the evidence around performance evaluation, configuration and optimization, localization, applications, communication technologies, security, and comparison with alternative LPWANs; it maps those methods onto a system-level workflow linking inputs, inference, decisions, and validation; and it identifies reporting and validation conditions needed for transferable engineering evidence. The cited literature prominently represents empirical measurements and simulation models, whereas integrated intelligent-system workflows that combine urban-context data, real-time measurements, predictive models, and decision support remain underdeveloped. Inconsistent environmental assumptions, incomplete reporting of validation settings, and nonuniform performance metrics limit transferability between cities. This survey therefore formulates a system-level design framework and research agenda centered on digital representations of the radio environment, hybrid physics- and data-driven models, adaptive configuration, and measurable validation of reliability, energy efficiency, and cost. These findings will support researchers and engineering practitioners in designing trustworthy, scalable, and adaptive IoT connectivity systems for urban environments.

This paper addresses experimental PID tuning for DC motor actuators when an accurate plant model is unavailable or impractical to obtain. Controller tuning is formulated as a constrained closed-loop optimization process in which each PID gain set is deployed on the physical system, produces an experimental dataset, and is evaluated through a performance index. The objective function combines tracking error, control effort, and control-signal variation, while penalty terms identify and penalize actuator saturation, excessive overshoot, settling-time violations, steady-state error, and divergent responses. Candidate controllers are generated using a constrained Gaussian Cross-Entropy Method and evaluated directly on the physical actuator. The proposed host-embedded architecture separates two computational time scales: PID execution, encoder processing, and data acquisition are performed in real time on the embedded platform, whereas population sampling, candidate ranking, and distribution updates are executed on the host computer between experiments. Within the reported experimental campaign, the sampling distribution progressively concentrates toward gain regions associated with lower closed-loop cost under the prescribed admissibility criteria. The resulting framework provides a structured and traceable procedure for physical controller deployment, data acquisition, constrained performance evaluation, and episodic PID retuning without explicit plant identification. The reported results demonstrate the feasibility of the architecture on the considered platform, without implying comparative superiority, run-to-run statistical convergence, or analytical closed-loop stability.

Phase-angle AC control is a low-cost technique for regulating power in resistive loads, but its performance depends on accurate trigger timing. This study quantitatively compares an ESP32-based phase-angle controller implemented in MicroPython and in native C using ESP-IDF. Firing delay was measured over 1000 consecutive cycles at firing angles from 10° to 150° under a 60 Hz supply, and the timing error was converted into equivalent angular deviation. The native C implementation reduced the mean timing error from 218.2–234.7 μs in MicroPython to −10.3–6.1 μs after calibration, corresponding to an average improvement of approximately 225 μs or 4.86° across the tested angles. In the current dataset, the measured standard deviation remained angle-dependent and numerically similar in both environments, ranging from 2.5 to 10.1 μs. Oscilloscope measurements confirmed the expected phase-angle operation and the practical timing displacement between firmware strategies. The results show that the principal advantage of the native implementation is improved absolute synchronization accuracy, whereas the residual short-term jitter remains dominated by the shared detection and triggering chain.

Microcontroller programming is a key element in engineering education, enabling students to acquire practical skills in embedded systems. Despite its importance, limited access to physical laboratories at many institutions restricts hands-on learning experiences. Remote laboratories have been introduced as a solution; however, most rely solely on external visual feedback, which constrains observability and limits analysis of internal program behavior. This paper introduces a remote laboratory for microcontroller education utilizing an STM32 platform within a Cloud–IoT framework. The proposed system allows real-time monitoring of internal firmware variables through compilation artifacts, without requiring direct interaction with traditional debugging tools. A pilot study involving 40 students in a microcontrollers course, who engaged with the system remotely using standard development workflows, was conducted. Usability was measured via the System Usability Scale (SUS), yielding an average score of 75.19 and reflecting high satisfaction. The platform enables real-time observation of internal system states during program execution, facilitating program analysis while maintaining an experience similar to traditional on-site laboratories. These findings indicate that the system offers a robust and scalable alternative for embedded systems education, with improved observability over existing remote laboratory solutions.

In precision agriculture, identifying pests such as the whitefly (Bemisia tabaci) is a significant challenge, as precise knowledge of these insects is essential for developing effective Integrated Pest Management (IPM) strategies. Automated daily monitoring within IPM programs optimizes the diagnostic registration stage by reducing logistical expenses and manual errors, enabling early pest treatment interventions and providing quantitative data for informed decision-making. In this study, an image bank was processed using a Quaternionic Gabor Filter (QGF) algorithmto highlight textural features through hypercomplex correlation. The highlighted objects were then processed by a YOLOv8 pretrained model to identify Bemisia tabaci. Experimental results demonstrate that this combination achieves a precision of 0.868 and an mAP@0.5 of 0.950, while a PSNR of 34.10 dB ensures the structural integrity of the enhanced images. Although the total execution time averages 2.3 s per image due to preprocessing complexity, the GPU inference time of 10.3 ms confirms the potential for high-speed detection. This approach significantly enhanced the morphological features of Bemisia tabaci, increasing the robustness of the detection model and narrowing down processing conditions for yellow trap samples to strengthen precision in the semi-arid regions of Zacatecas, Mexico.

Accurate fruit ripeness assessment is essential for optimizing harvest timing and ensuring postharvest quality, particularly in climacteric fruits such as peaches, which exhibit rapid and heterogeneous ripening. This study proposes a deep learning-based approach for multistage peach ripeness classification across five maturity levels (green, green-blushed, blushed, yellow-blushed, and fully yellow). Four datasets were constructed using controlled image acquisition, segmentation, data augmentation, and perceptual hashing to mitigate data leakage. The performance of AlexNet, EfficientNet-B0, and three YOLO (You Only Look Once) architectures (YOLOv8, YOLOv11, and YOLOv12) was evaluated using standard metrics, including accuracy, precision, recall, F1 score, mAP, and inference speed. Results show that YOLO-based models significantly outperform classical networks, achieving accuracies between 95.25% and 98.3% and mAP@0.5 above 98.25%, while also reducing inference time to 8.1–12.7 ms compared with 722.23 ms for AlexNet and 171.87 ms for EfficientNet-B0. In a practical sorting experiment with 214 peaches, YOLOv12 achieved 92.06% accuracy, demonstrating robust real-world performance. Misclassifications were primarily observed between adjacent ripeness stages. These findings indicate that YOLO-based models provide an effective and scalable solution for real-time fruit sorting, while the use of perceptual hashing enhances dataset reliability and model generalization for deployment in agricultural quality control systems.

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies.

La enseñanza y el mantenimiento tradicional de la maquinaria industrial están basados en manuales, lo que dificulta el desarrollo de habilidades prácticas y la comprensión de componentes internos. Esto tiene como consecuencia el aumento de incidentes operativos, desgaste físico de la maquinaria y cortes por mal manejo. El objetivo principal de este proyecto consiste en diseñar un gemelo digital de una rebanadora de carne con realidad aumentada para facilitar el aprendizaje. Primero, se diseñó un modelo tridimensional de la rebanadora de carne con Blender® basado en la máquina física; luego, se importó a Unity®, un motor de realidad aumentada multiplataforma y compatible con Vuforia®, una plataforma de RA especializada que permite reconocer imágenes, objetos y espacios reales para superponer contenido digital. Se creó un objetivo de imagen para la geolocalización del modelo 3D en el entorno físico. El resultado fue una aplicación que permite a los usuarios manipular y visualizar la rebanadora, así como sus componentes y nombres, en realidad aumentada, simulando el proceso de despiece de forma segura e interactiva. Se espera que esta herramienta facilite el proceso de aprendizaje de los ingenieros especializados en maquinaria industrial, en comparación con los métodos tradicionales, reduciendo la brecha entre la teoría y la práctica, a la vez que minimiza los riesgos operativos. Este enfoque demuestra el potencial que existe entre los gemelos digitales y la realidad aumentada, proporcionando un recurso didáctico moderno para la formación técnica segura de los estudiantes.

Iron oxide particles (magnetite Fe₃O₄, hematite α-Fe₂O₃, and maghemite γ-Fe₂O₃) are prevalent constituents of atmospheric particulate matter (PM) and have gained increasing attention due to potential health implications. This scoping review provides a broad mapping of published in vivo and in vitro studies addressing the biological and toxicological effects of iron oxide particles across particle size fractions (PM10, PM2.5, PM1.0, and nanoscale) and exposure routes, including inhalation, intranasal instillation, and intravenous administration. As a scoping review, no formal risk-of-bias appraisal was conducted; however, studies were selected through predefined eligibility criteria and a structured screening workflow. Iron oxide exposure is consistently associated with oxidative stress and inflammatory responses, while mitochondrial dysfunction, genotoxicity, and neurological effects are frequently reported depending on particle characteristics and exposure context. Among studies with explicit crystalline phase identification, magnetite is most frequently associated with higher biological reactivity, whereas hematite and maghemite display more variable and context-dependent responses. Limited human evidence aligns with experimental findings, identifying magnetite-rich nanoparticles in neural and cardiovascular tissues alongside markers of oxidative and mitochondrial damage. Overall, this scoping review highlights dominant research trends, mechanistic pathways, and key knowledge gaps regarding iron oxide-containing PM, emphasizing the need for integrative approaches linking atmospheric particle characterization with toxicological research.