Del-Puerto-Flores, J. Alberto
Main Affiliation
Preferred name
Del-Puerto-Flores, J. Alberto
Official Name
Del Puerto Flores, José Alberto
ORCID
0000-0003-4039-1694
Researcher ID
AAR-1386-2020
Scopus Author ID
57206905278
25 results
Now showing 1 - 10 of 25
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Item type:Publication, A Resilient Energy-Efficient Framework for Jamming Mitigation in Cluster-Based Wireless Sensor Networks(MDPI AG, 2025-09-29); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Neural Network Aided M-PSK Detection in 802.11P V2V OFDM Systems Under ICI Conditions(Institute of Electrical and Electronics Engineers (IEEE), 2025); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Verification of a Probabilistic Model and Optimization in Long-Range Networks(MDPI AG, 2025-02-11); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Comparative Study of Brownian Dynamics Based on the Jerk Equation Against a Stochastic Process Under an External Force Field(MDPI AG, 2025-02-28); <jats:p>Brownian motion has been studied since 1827, leading to numerous important advances in many branches of science and to it being studied primarily as a stochastic dynamical system. In this paper, we present a deterministic model for the Brownian motion for a particle in a constant force field based on the Ornstein–Uhlenbeck model. By adding one degree of freedom, the system evolves into three differential equations. This change in the model is based on the Jerk equation with commutation surfaces and is analyzed in three cases: overdamped, critically damped, and underdamped. The dynamics of the proposed model are compared with classical results using a random process with normal distribution, where despite the absence of a stochastic component, the model preserves key Brownian motion characteristics, which are lost in stochastic models, giving a new perspective to the study of particle dynamics under different force fields. This is validated by a linear average square displacement and a Gaussian distribution of particle displacement in all cases. Furthermore, the correlation properties are examined using detrended fluctuation analysis (DFA) for compared cases, which confirms that the model effectively replicates the essential behaviors of Brownian motion that the classic models lose.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Audio’s Impact on Deep Learning Models: A Comparative Study of EEG-Based Concentration Detection in VR Games(MDPI AG, 2025-10-29); ; ; ; Francisco R. Castillo-SoriaThis study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Transformer-Based Multi-Task Learning Model for Vehicle Traffic Surveillance(MDPI AG, 2025-11-29); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance optimization of multiple RIS-assisted multiuser MIMO communication systems(Wiley, 2025-05-16) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks(MDPI AG, 2025-03-20); ; ; José Varela-AldásWireless sensor networks (WSNs) are fundamental for modern IoT applications, yet they remain highly vulnerable to jamming attacks, which significantly degrade communication reliability and energy efficiency. This paper proposes a novel adaptive cluster-based jamming mitigation algorithm designed for heterogeneous WSNs that integrate LoRa and Bluetooth Low Energy (BLE) technologies. The proposed strategy dynamically switches between communication protocols, optimizes energy consumption, and reduces retransmissions under interference conditions by leveraging real-time network topology adjustments and adaptive transmission power control. Through extensive experimental validation, we demonstrate that our mitigation mechanism reduces energy consumption by up to 38% and lowers packet retransmission rates by 47% compared to single-protocol networks under jamming conditions. Additionally, our results indicate that the hybrid LoRa-BLE approach outperforms standalone LoRa and BLE configurations in terms of network resilience, adaptability, and sustained data transmission under attack scenarios. This work advances the state-of-the-art by introducing a multi-protocol interference-resilient communication strategy, paving the way for more robust, energy-efficient, and secure WSN deployments in smart cities, industrial IoT, and critical infrastructure monitoring. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review(2024)11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines(2022); ; <jats:p>Digital communication over power lines is an active field of research and most studies in this field focus on low-voltage (LV) and medium-voltage (MV) power systems. Nevertheless, as power companies are starting to provide communication services and as smart-grid technologies are being incorporated into power networks, high-voltage (HV) power-line communication has become attractive. The main constraint of conventional HV power-line carrier (PLC) systems is their unfeasibility for being migrated to wideband channels, even with a high signal-to-noise ratio (SNR). In this scenario, none of the current linear/non-linear equalizers used in single carrier schemes achieve the complete compensation of the highly dispersive conditions, which limits their operation to 4 kHz channels. In this paper, a new PLC-channel model is introduced for transmission lines incorporating the effects of the coupling equipment. In addition, the use of the single-carrier frequency-division modulation (SC-FDM) is proposed as a solution to operate PLC systems in a wide bandwidth, achieving transmission speeds above those of the conventional PLC system. The results presented in this paper demonstrate the superior performance of the SC-FDM-PLC over conventional PLC systems, obtaining a higher transmission capacity in 10 to 30 times.</jats:p>Scopus© Citations 2 36 1
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