Browsing by Autor "Marcelo Molina Silva"

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Artificial Intelligence Enabled Radio Propagation: Path Loss Improvement and Channel Characterization in Vegetated Environments
(Sociedade Brasileira de Microondas e Optoeletrônica; Sociedade Brasileira de Eletromagnetismo, 2024) Leonardo Henrique Gonsioroski; Amanda Santos; Jairon Viana; Sandra Ferreira; Rogerio Silva; Luiz da Silva Mello; Leni Matos; Marcelo Molina Silva
In this paper, the application of AI and machine learning (ML) to the study of wireless propagation channels is investigated in two parts: first, an artificial neural network model is used to improve path loss prediction, and then, a pattern recognition model using multilayer perceptron (MLP) networks is used to identify and remove impulsive noise in power delay profiles (PDP). These studies were conducted based on field measurements in the 2400 MHz band in a public square with vegetation. The results are analyzed and compared with ordinary least squares (OLS) nonlinear regression results and results from similar studies. The Root Mean Square Error (RMSE) values between the experimental results of mean path loss and those provided by each propagation model are presented. The adjustment performed by OLS nonlinear regression and ANN significantly reduced the RMSE. The best results are those presented by artificial neural networks, with RMSE of 0.39 when using four neurons in the hidden layer of the ANN. The ANN used to identify and remove impulsive noise in power delay profiles (PDP) through pattern recognition proved to be more efficient than the CFAR technique. ANN technique found a larger number of valid multipaths compared to the CFAR technique.
Evaluation of Cooperative Cognitive Radio System for White Spectral Space Detection using the Covariance Detector
(2025) Rafael G. Ramirez Montecinos; Mateo I. Luna Rico; Marcelo Molina Silva; Jussif J. Abularach Arnez; Luiz da Silva Mello; Carlos V. Rodriguez R.; Leonardo Henrique Gonsioroski
Wireless spectrum is increasingly scarce, which motivates the need for robust methods to detect unused bands—especially under challenging conditions like low SNR and fading. This study proposes integrating Spectral Covariance Sensing (SCS) into a cooperative cognitive radio framework, leveraging hard-decision fusion schemes (AND, OR, Majority) to enhance detection stability. Using real Advanced Television Systems Committee(ATSC) signal data, the detection performance was evaluated across various SNR levels. The results show that cooperative sensing significantly improves detection probability under low SNR, with the OR rule achieving the highest detection rate (e.g., ≈90% at –30 dB) and the majority rule providing the best overall trade-off between reliability and false alarms. These findings demonstrate the practical value of cooperative SCS systems in dynamic spectrum environments.
Evaluation of Propagation Models and Machine Learning Through Path Loss Analysis in Different Environments Using Lora Technology at 915 MHz in the Department of La Paz, Bolivia
(2025) Horacio Esprella Quiroga; Marcelo Molina Silva; Hugo Condori Quispe; Leonardo Gonsioroski
The performance of Low-Power, Wide-Area Networks (LPWANs) is critical for long-range communication systems, but signal propagation in complex topographies limits their reliability. Theoretical propagation models often provide inaccurate path loss predictions in such environments, leading to suboptimal network design. This work presents an empirical evaluation of LoRa technology at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 1 5 ~ M H z}$</tex> in La Paz, Bolivia, and compares the accuracy of classical propagation models against data-driven machine learning (ML) techniques. A dataset was constructed from a field measurement campaign conducted in urban (NLOS), suburban (mixed LOS/NLOS), and rural (LOS) scenarios. The Okumura-Hata, 3 GPP UMi, and COST231-WI models were evaluated. Among these, the 3GPP UMi model demonstrated the lowest error, yet yielded a Root Mean Square Error (RMSE) of 11.58 dB against measured data. In contrast, ML models trained on the same data showed higher accuracy. A Random Forest model achieved an RMSE of 3.49 dB, and an Artificial Neural Network model achieved an RMSE of 4.57 dB. The results indicate that ML models provide more accurate path loss predictions than theoretical models in complex terrains, offering an effective tool for planning LoRa-based communication networks.
Optimized Mammography Preprocessing for Tumor Detection with YOLO11-Seg in Young Women from the Bolivian Altiplano
(2025) Leoni Marti Miranda Saravia; Alejandro Rommel Miranda Saravia; Alicia Seminario Vargas; Marcelo Molina Silva; Yancarla Mary Conde Canaviri; Manuel Conde; Leonardo Lamas; Xavier Alexis Murillo Sanchez; M. Martín Sánchez
Early diagnosis of breast cancer in young women presents a critical clinical challenge, particularly in geographic contexts such as the Bolivian Altiplano, where high breast density and limited access to specialized technologies hinder detection. This study evaluates the impact of various image preprocessing techniques on the performance of an automatic detection model based on YOLO11-seg. Using a dataset of mammograms annotated by certified radiologists, transformations such as CLAHE, histogram equalization, Canny filtering, wavelets, and anisotropic diffusion were applied. Standard metrics (mAP, precision, recall) were measured and results were compared in a real clinical setting. Findings show that CLAHE significantly improves the model's ability to detect lesions in dense breasts, achieving a mAP of 71.8%. The results suggest that combining enhancement techniques with AI models can strengthen early detection in high-risk populations, offering a viable and scalable alternative for resource-limited settings.
Preliminary Results of the Indoor Coverage Field Tests of the Advanced ISDB-T System in Brazil
(2023) Amanda B. Santos; Leonardo Henrique Gonsioroski; Rodrigo Ribeiro de Oliveira; Luiz da Silva Mello; Alberto Leonardo Penteado Botelho; Cristiano Akamine; Natália C. Fernandes; Marcelo Molina Silva
This article presents the preliminary results of field tests with the Advanced Integrated Services Digital Broadcasting for Terrestrial Television Broadcasting (ISDB- T) system in indoor environments. Received channel power and error-free system reception threshold records were performed at four different measurement sites. Five different H/V dual-polarized MIMO antennas are used in measurements to evaluate system performance when using antennas from different manufacturers. The results show that the advanced ISDB-T system is robust to indoor penetration losses and when subjected to the use of different antennas.