In the present work, a novel methodology for error detection in automatic weather stations has been implemented. Time series acquired from two highly correlated stations with a station under analysis are utilised to obtain a 24-h air temperature forecast that allows to know if a station register erroneous measurements. Four models to obtain a reliable forecast have been analysed, auto-regressive integrated moving average, Long Short-Term Memory (LSTM), LSTM stacked and a convolutional LSTM model with uncertainty error reduction. The analysis carried out exhibits a significant success with the methodology for three stations reaching error values between 0.98 ${}^{\circ }$C and 1.50 ${}^{\circ }$C and correlation coefficients between 0.72 and 0.81. GRAPHICAL ABSTRACT

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.

The detection and clustering of signals sources in a multipath environment through the use of modeling and simulation are mainly investigated in this paper. For this, we use the algorithms Root MUSIC and ESPRIT to estimate the direction of arrival (DoA) of the signals and their replicas through the wireless channel in presence of white noise. For the detection of DoA in 3D, in this paper we present a method to pairing the azimuth and elevation angles, and to separate the location of the signals sources into geographical areas the K-means algorithm is employed. Finally, we present the results of simulation for the method's validation.

This paper presents an overview of the findings in the handover (HO) process performance within three routes in Quito, Ecuador. We used the Net-Monitor Software to gather information from one of the three national mobile operators. Then, we used the R tool to analyze the HO performance. We analyze several performance metrics, such as HO types, HO conditions, and the ping-pong process. Analysis of the results of the outdoor drive tests demonstrate that the radio frequency (RF) parameters, such as Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), power margin, times radio frequency measurements repeats, and HO percentage to nearest BS, are extremely important during different HO types and ping-pong processes because there are statistical differences in these measured RF parameters. The main measurement results demonstrate that RSSI difference between inter HO and intra HO is 20 dB, whereas HOs are performed when the mobile device (MS) gets farther from the base station (BS), approximately 50% of total HOs. Operator achieves a high ping-pong rate of approximately 10% of total HOs.

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.