Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first symptoms of drowsiness. The method used for the extraction of the eye region is Mediapipe, chosen for its high accuracy and robustness. Three neural networks were analyzed based on InceptionV3, VGG16 and ResNet50V2, which implement deep learning. The database used is NITYMED, which contains videos of drivers with different levels of drowsiness. The three networks were evaluated in terms of accuracy, precision and recall in detecting drowsiness in the eye region. The results of the study show that all three convolutional neural networks have high accuracy in detecting drowsiness in the eye region. In particular, the Resnet50V2 network achieved the highest accuracy, with a rate of 99.71% on average. For better visualization of the data, the Grad-CAM technique is used, with which we obtain a better understanding of the performance of the algorithms in the classification process.

Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address the challenge of drowsiness detection is to anticipate drowsiness by alerting drivers in a timely and effective manner. Thus, this paper presents a Convolutional Neural Network (CNN)-based approach for drowsiness detection by analyzing the eye region and Mouth Aspect Ratio (MAR) for yawning detection. As part of this approach, endpoint delineation is optimized for extraction of the region of interest (ROI) around the eyes. An NVIDIA Jetson Nano-based device and near-infrared (NIR) camera are used for real-time applications. A Driver Drowsiness Artificial Intelligence (DD-AI) architecture is proposed for the eye state detection procedure. In a performance analysis, the results of the proposed approach were compared with architectures based on InceptionV3, VGG16, and ResNet50V2. Night-Time Yawning–Microsleep–Eyeblink–Driver Distraction (NITYMED) was used for training, validation, and testing of the architectures. The proposed DD-AI network achieved an accuracy of 99.88% with the NITYMED test data, proving superior to the other networks. In the hardware implementation, tests were conducted in a real environment, resulting in 96.55% and 14 fps on average for the DD-AI network, thereby confirming its superior performance.

Precision air conditioning (PAC) systems are prone to various types of failures, leading to inefficiencies, increased energy consumption, and possible reductions in equipment performance. This study proposes an automatic real-time fault detection and diagnosis system. It classifies events as either faulty or normal by analyzing key status signals such as pressure, temperature, current, and voltage. This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. The work was carried out on a Rittal PAC, model SK3328.500 (cooling unit), installing capacitive pressure sensors, Hall effect current sensors, electromagnetic induction voltage sensors, infrared temperature sensors, and thermocouple-type sensors. For the implementation of the system, a dataset of PAC status signals was obtained, initially consisting of 31,057 samples after a preprocessing step using the Random Under-Sampler (RUS) module. A database with 20,000 samples was obtained, which includes normal and failed operating events generated in the PAC. The selection of the models is based on accuracy criteria, evaluated by testing in both offline (database) and real-time conditions. The Support Vector Machine (SVM) model achieved 93%, Decision Tree (DT) 93%, Gradient Boosting (GB) 91%, K-Nearest Neighbors (KNN) 83%, and Naive Bayes (NB) 77%, while the Random Forest (RF) model stood out, having an accuracy of 96% in deferred tests and 95.28% in real-time. Finally, a validation test was performed with the best-selected model in real time, simulating a real environment for the PAC system, achieving an accuracy rate of 93.49%.

Hydroponics in recirculation systems faces significant challenges in regulating critical parameters, such as pH and electrical conductivity (EC), especially in adverse environmental conditions, such as high altitudes. This paper presents the design and validation of a fuzzy controller integrated with IoT for NFT-type hydroponic systems, implemented on low-cost hardware and tested in the city of Cusco (3339 m.a.s.l.). Unlike previous studies that are limited to simulations or laboratory tests, the proposal was validated under real growing conditions, demonstrating its practical viability. The system incorporates a fuzzy controller based on simple rules, an IoT module with ESP32 for remote monitoring via Blynk, and an accessible and replicable architecture. The results demonstrate stable performance in pH and EC regulation, with adequate response times, minimal overshoot, and reduced errors, achieving levels comparable to those of higher-cost commercial solutions. The main contribution of this study is the demonstration that an intelligent, economical, and replicable system can be applied in agricultural environments with limited resources, offering a viable alternative for improving productivity in high-altitude hydroponic systems.

Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first symptoms of drowsiness. The method used for the extraction of the eye region is Mediapipe, chosen for its high accuracy and robustness. Three neural networks were analyzed based on: InceptionV3, VGG16 and ResNet50V2 that implement deep learning. The database used is NITYMED, which contains videos of drivers with different levels of drowsiness. The three networks were evaluated in terms of accuracy, precision and recall in detecting drowsiness in the eye region. The results of the study show that all three convolutional neural networks have high accuracy in detecting drowsiness in the eye region. In particular, the Resnet50V2 network achieved the highest accuracy, with a rate of 99.71% on average. For better visualization of the data, the Grad-CAM technique is used, with which we get a better understanding of the performance of the algorithms in the classification process.

Measuring solar irradiance is key to assessing the conversion efficiency of photovoltaic (PV) modules. Also, PV modules can be used to estimate irradiance through their electrical response to solar radiation using closed-form models (CFMs). This paper presents a prototype design for irradiance estimation based on evaluating three CFMs by implementing a maximum power point tracking (MPPT) system and a surface temperature measurement system. The system employs an incremental conductance (IC)-based control algorithm, which is optimized to eliminate oscillations at the maximum power point (MPP) and ensure efficient MPP tracking. Experimental validation of the implemented circuits is carried out using Arduino Nano, calibrated sensors, and low-cost electronic devices. Tests in real conditions were performed for four days under different irradiance scenarios, using two monocrystalline PV modules: one with 10 years of use and one new one. The accuracy of the CFMs was evaluated using the mean absolute percentage error (MAPE) and root mean squared error indicators, comparing their estimates with measurements from a Davis Instruments pyranometer. The most accurate CFM obtained a MAPE of 4.38% with the 10-year module and 3.26% with the new module. The results show that the proposed methodology provides estimates with an error of less than 5%, which validates its applicability under various climatic conditions, even with old PV modules.