This article presents the development of a platform for the validation of controllers applied to photovoltaic systems (PVS) interconnected with the main grid (MG), integrating simulation real-time Hardware in the Loop (HIL) and Internet of Things (IoT). The proposed platform is made up of 5 parts: 1) a control HIL emulator (CHILE) that reproduces the behavior of a photovoltaic array, a power electronic converter for interconnection with the MG, and AC loads, 2) a cloud database implemented in ThingSpeak, 3) a smart sensor (SS) that monitors the behavior of AC loads, 4) a residential PVS with internet connection, and 5) an Android application for remote monitoring. The data generated by the residential system and the SS are stored in the database and from this information the CHILE reproduces its behavior in real time. The CHILE generates the variables related to the behavior of the PVS and transfers this information to the database. The mobile application allows users to view the behavior of the platform remotely. The usefulness of the platform is verified with a controller for the maximum power point tracking and the interconnection of the system with the MG in a 24-hour experiment, during which the behavior of the residential PVS and the AC loads are reproduced in the CHILE. The platform successfully emulates the behavior of the installed PVS with a mean relative error of 0.42 % and the AC load with a mean absolute error of 10 mA. Regarding data transfer in the IoT network, a mean time response of the server of 441.9 ms was observed without data loss during the 24-hours experiment.

Today, maintaining an Internet connection is indispensable; as an example, we can refer to IoT applications that can be found in fields such as environmental monitoring, smart manufacturing, healthcare, smart buildings, smart homes, transportation, energy, and others. The critical elements in IoT applications are both the Wireless Sensor Nodes (WSn) and the Wireless Sensor Networks. It is essential to state that designing an application demands a particular design of a WSn, which represents an important time consumption during the process. In line with this observation, our work describes the development of a modular WSn (MWSn) built with digital processing, wireless communication, and power supply subsystems. Then, we reduce the WSn-implementing process into the design of its modular sensing subsystem. This would allow the development and launching processes of IoT applications across different fields to become faster and easier. Our proposal presents a versatile communication between the sensing modules and the MWSn using one- or two-wired communication protocols, such as I²C. To validate the efficiency and versatility of our proposal, we present two IoT-based remote monitoring applications.

This article presents the development of a low-cost control hardware in the loop platform for the validation and analysis of controllers used for the management of power sharing between the main grid and a DC microgrid. The platform is made up of two parts: a main grid interconnection system emulator (MGISE) and a controller under test (CUT). The MGISE operates on a 260 V DC bus and includes a 1000 W photovoltaic array, a DC variable load and a single H full bridge converter (HFBC). The CUT includes a phase locked loop and a main cascade control structure composed of two PI controllers. Both the MGISE and the CUT were embedded on an NI myRIO-1900 development board and programmed using LabVIEW virtual instrumentation software. These devices communicate with each other using analog signals representing the AC side current, the DC side voltage, and the HFBC control signal. The MGISE operates with an integration time of 6 µs and its performance is validated by comparing it with a simulation in PSIM. The integration time of the MGISE, the development boards used, as well as its programming environment, and the results obtained from the comparison with PSIM simulation, show that the proposed platform is useful for the validation of controllers for power sharing, with a simple implementation process compared to other hardware description methods and with a low-cost platform.

Precise control and monitoring systems are essential for efficient energy consumption in food dehydration. This study develops an applied cyber-physical control system to optimize food dehydration in rotary dryers, integrating fuzzy control algorithms through data acquisition. The system architecture utilizes DHT22 transducers for temperature monitoring, a DHT11 for humidity measurement, an IP65 anemometer for dryer wind speed detection, and a load cell weight tracking system, all connected to an Arduino Mega 2560 R3 microcontroller implementing the integrated fuzzy logic library. Experimental evaluations were performed with different carrot loads (1.5, 2.5, and 3.5 kg), demonstrating optimal performance at the initial load of 3.5 kg with an energy consumption of 11,589 kJ for 9.33 h, achieving a final moisture reduction of 10%. The 1.5 kg sample showed optimal dehydration kinetics at an average dryer hot air velocity of 1.5 m/s, while maximum efficiency (86%) was achieved with the 3.5 kg load, compared to 30% and 17% for the smaller batches. These results validate the integration of cyber-physical systems to optimize the dehydration rate (0.301 kg/h), thereby ensuring product quality in agro-industrial drying applications.

Precise control and monitoring systems are essential for efficient energy consumption in food dehydration. This study develops an applied cyber-physical control system to optimize food dehydration in rotary dryers, integrating fuzzy control algorithms through data acquisition. The system architecture utilizes DHT22 transducers for temperature monitoring, a DHT11 for humidity measurement, an IP65 anemometer for dryer wind speed detection, and a load cell weight tracking system, all connected to an Arduino Mega 2560 R3 microcontroller implementing the integrated fuzzy logic library. Experimental evaluations were performed with different carrot loads (1.5, 2.5, and 3.5 kg), demonstrating optimal performance at the initial load of 3.5 kg with an energy consumption of 11,589 kJ for 9.33 hours, achieving a final moisture reduction of 10%. The 1.5 kg sample showed optimal dehydration kinetics at an average dryer hot air velocity of 1.5 m/s, while maximum efficiency (86%) was achieved with the 3.5 kg load, compared to 30% and 17% for the smaller batches. These results validate the integration of cyber-physical systems to optimize the dehydration rate (0.301 kg/h), thereby ensuring product quality in agro-industrial drying applications.

Coronary atherosclerosis is the most common form of cardiovascular diseases, which represent the leading global cause of mortality in the adult population. The amount of coronary artery calcium (CAC) is a robust predictor of this disease that can be measured using the medical workstations of computed tomography (CT) equipment or specialized tools included in commercial software for DICOM viewers, which is not available for all operating systems. This manuscript presents a web application that semiautomatically quantifies the amount of coronary artery calcium (CAC) on the basis of the coronary calcium score (CS) using the Agatston technique through digital image processing. To verify the correct functioning of this web application, 30 CTCSs were analyzed by a cardiologist and compared to those of commercial software (OsiriX DICOM Viewer).All the scans were correctly classified according to the cardiovascular event risk group, with an average error in the calculation of CS of 1.9% and a Pearson correlation coefficient r = 0.9997, with potential clinical application.