This study applied modified linear regression in machine learning (ML) to predict the direction of arrival (DoA) in cellular networks using field measurements and radiofrequency parameters. Models were developed from base station data, with preprocessing for pattern identification and formula adjustments to improve the accuracy across angle ranges. Machine learning, tested here as an additional method to traditional techniques, achieved a root mean square error (RMSE) of 3.63 to 17.93, demonstrating enhanced adaptability. While requiring substantial data and computational resources, this approach highlights machine learning’s potential as a valuable tool for DoA estimation in cellular networks.

This paper presents a novel continuous-wave Doppler RADAR system for real-time speed measurement of moving objects, implemented using software-defined radio (SDR). Unlike traditional high-cost solutions typically found in research centers or specialized laboratories, this prototype offers a low-cost, compact, and easily deployable platform that lowers the entry barrier for experimentation and research. Operating within the 70 MHz–6 GHz range, SDR enables highly flexible signal processing; in this implementation, a 5.5 GHz carrier is selected to improve the detection precision by exploiting its reduced bandwidth for more accurate observation of frequency shifts. The carrier is modulated with a 2 kHz signal, and Doppler frequency deviations induced by object motion are processed to calculate velocity. Using a Welch spectral estimator, the system effectively reduces noise and extracts the Doppler frequency with high reliability. The prototype achieves speed measurements up to 196.36 km/h with approximately 2% error in the 0–100 km/h range, confirming its suitability for road traffic monitoring. A key innovation of this work is its single-antenna cross-polarized configuration, which simplifies hardware requirements while maintaining measurement accuracy. Furthermore, the system’s portability and open-access design make it ideal for in-vehicle applications, enabling direct deployment for automotive testing, driver-assistance research, and educational demonstrations. All design files and implementation details are openly shared, eliminating patent restrictions and encouraging adoption in low-resource academic and research environments.

This project presents the design and implementation of an IoT-based system for early detection of Liquefied Petroleum Gas (LPG) leaks in residential environments. Three functional prototypes were developed, integrating gas sensors, microcontrollers, actuators, and wireless modules. The system achieved 89.48% service availability and response times under 2 s from leak detection to data storage and visualization. Operating under a distributed architecture, it enables continuous monitoring, automatic shut-off, and real-time alerts. The novelty lies in the applied integration of electronics, embedded systems, and automation into an affordable, replicable solution, that enhances household safety, promotes preventive behavior, and supports the adoption of affordable, replicable technologies in vulnerable domestic settings.

Controlling the microclimate is vital in fruit fly breeding. This project develops an automated IoT system for monitoring and controlling temperature and relative humidity in chambers, optimizing processes through accessible and flexible technology. It uses a multi-layer system starting with the application layer with the ESP32 for data acquisition and actuator control. The second layer is the network layer, and the perception layer uses VisionFive2 with MQTT and HTTP protocols for communication, as well as NodeRed for flow orchestration and MySQL for data management and storage. In the validation, there are absolute errors of ±0.434 °C and ±0.5 RH, which are values within the acceptable ranges for these applications.

This research article presents a comprehensive study focusing on advancing radar systems for unmanned aerial vehicle (UAV) surveillance in cluttered environments. The proliferation of UAV technology and its diverse applications have raised concerns about airspace security. To tackle this issue, this article introduces a novel simulator designed to evaluate the performance of primary monopulse radar processors. The simulator accurately replicates scenarios involving clutter Weibull distributions, stationary and moving targets, as well as pulse compression situations, thereby enabling precise and controlled evaluations. The study employs the simulator to assess radar processors, including a variant of moving target detection (MTD) and a constant false alarm rate (CFAR) processor. By implementing a rigorous methodology, the article underscores the significance of simulating cluttered conditions in refining the effectiveness of radar processors. The results yield valuable insights, facilitating objective interpretations. The proposed simulator and its implications contribute to enhancing UAV surveillance and airspace security, thereby pushing forward the capabilities of radar systems.

This paper presents the design, implementation, and validation of an IoT-based geolocation system using the Sigfox network to enhance student safety in urban environments. The proposed system integrates a GPS NEO-6M module with a Ufox Devkit, enclosed in a portable housing, to provide low-power and real-time location tracking. A comparative evaluation of three visualization platforms identified Traccar as the most suitable solution, offering superior accuracy, interoperability, and response time. Field tests were conducted in five educational institutions in Ambato, Ecuador, achieving an average geographic accuracy of 4.5 m and operational efficiency ranging from 55% to 78%, depending on network coverage and urban interference. These results demonstrate the feasibility of Sigfox-based geolocation for reliable student monitoring and provide practical insights for deploying scalable, cost-effective safety solutions in educational contexts.