López-Pimentel, Juan Carlos

Main Affiliation

Facultad de Ingeniería - CampGDL

Preferred name

Official Name

López Pimentel, Juan Carlos

ORCID

0000-0002-7844-3261

Researcher ID

T-3594-2018

Scopus Author ID

57221468168

Now showing 1 - 10 of 24

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A Digital Identity Blockchain Ecosystem: Linking Government-Certified and Uncertified Tokenized Objects
(MDPI AG, 2025-08-01)
López-Pimentel, Juan Carlos
;
Javier Gonzalez-Sanchez
;
Luis Alberto Morales-Rosales
This paper presents a novel digital identity ecosystem built upon a hierarchical structure of Blockchain tokens, where both government-certified and uncertified tokens can coexist to represent various attributes of an individual’s identity. At the core of this system is the government, which functions as a trusted authority capable of creating entities and issuing a unique, non-replicable digital identity token for each one. Entities are the exclusive owners of their identity tokens and can attach additional tokens—such as those issued by the government, educational institutions, or financial entities—to form a verifiable, token-based digital identity tree. This model accommodates a flexible identity framework that enables decentralized yet accountable identity construction. Our contributions include the design of a digital identity system (supported by smart contracts) that enforces uniqueness through state-issued identity tokens while supporting user-driven identity formation. The model differentiates between user types and certifies tokens according to their source, enabling a scalable and extensible structure. We also analyze the economic, technical, and social feasibility of deploying this system, including a breakdown of transaction costs for key stakeholders such as governments, end-users, and institutions like universities. Considering the benefits of blockchain, implementing a digital identity ecosystem in this technology is economically viable for all involved stakeholders.
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Efficient Deep Learning-Based M-PSK Detection for OFDM V2V Systems Using MobileNetV3
(MDPI AG, 2026-03-11)
Tonix-Gleason, Luis E.
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Del-Puerto-Flores, J. Alberto
;
Peña-Campos, Fernando
;
del Puerto-Flores, Dunstano
;
López-Pimentel, Juan Carlos
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A Transformer-Based Multi-Task Learning Model for Vehicle Traffic Surveillance
(MDPI AG, 2025-11-29)
Fernando Hermosillo-Reynoso
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López-Pimentel, Juan Carlos
;
Erica Ruiz-Ibarra
;
Armando García-Berumen
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Del-Puerto-Flores, J. Alberto
Vehicle traffic surveillance (VTS) systems are based on the automatic analysis of video sequences to detect, classify, and track vehicles in urban environments. The design of new VTS systems requires computationally efficient architectures with high performance in accuracy. Conventional approaches based on multi-stage pipelines have been successfully used during the last decade. However, these systems need to be improved to face the challenges of complex, high-mobility traffic environments. This article proposes an efficient system based on transformer architectures for VTS channels. The proposed analysis system is evaluated in scenarios with high vehicle density and occlusions. The results demonstrate that the proposed scheme reduces the computational complexity required for multi-object detection and tracking and exhibits a Multiple Object Tracking Accuracy (MOTA) of 0.757 and an identity F1 score (IDF1) of 0.832 when compared to conventional multi-stage systems under the same conditions and parameters, along with achieving a high detection precision of 0.934. The results show the viability of implementing the proposed system in practical applications for high-density vehicle VTS channels.
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IoT-Based Smart Gas Meter With LTE Connectivity and Cloud Analytics for Stationary Tanks
(Institute of Electrical and Electronics Engineers (IEEE), 2026)
Valdivia, Leonardo
;
Del-Valle-Soto, Carolina
;
López-Pimentel, Juan Carlos
Millions of homes in developing countries rely on stationary LPG tanks, yet the methods for monitoring fuel levels remain manual, unsafe, and highly inefficient. This paper addresses this issue by presenting the design, development, and implementation of an IoT-based smart gas meter that uses a noninvasive Hall-effect sensor to digitally read existing level gauges. Data is transmitted via LTE, eliminating the need for Wi-Fi and optimizing connectivity. The system is designed for low power consumption, achieving a battery life of more than eight years. Additionally, a cloud architecture is implemented in AWS to process the collected data, allowing real-time analysis, predictive maintenance, and logistics optimization. A field test was also conducted with 15 prototypes, demonstrating accurate gas level monitoring, reliable refill detection, and gas theft prevention.
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Audio’s Impact on Deep Learning Models: A Comparative Study of EEG-Based Concentration Detection in VR Games
(MDPI AG, 2025-10-29)
Gomez Romero Borquez, Jesus Alberto
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Del-Valle-Soto, Carolina
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López-Pimentel, Juan Carlos
;
Del-Puerto-Flores, J. Alberto
;
Francisco R. Castillo-Soria
This 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.
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An Integral Fuzzy Model to Evaluate Slab and Beam Bridges with a Preventive Approach
(MDPI AG, 2025-07-26)
Paola Arriaga-Orejel
;
Luis Alberto Morales-Rosales
;
José Eleazar Arreygue-Rocha
;
Mariano Vargas-Santiago
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López-Pimentel, Juan Carlos
Bridges, owing to their intricacy, represent pivotal yet relatively underexplored assets within the domain of maintenance services in civil engineering. While international evaluation methodologies exist to gauge the overall condition of bridges, they often fall short in establishing interrelationships among individual elements, thereby neglecting insights into the influence exerted by each element’s condition on the bridge’s overall performance. This research introduces an integral fuzzy model evaluation with a preventive approach, designed to assess both the integral condition of a bridge and its constituent elements. Furthermore, the study generates maintenance recommendations, subsequently evaluated by professionals to determine the most suitable course of action based on available resources. To validate the efficacy of the proposed model, a case study involving Bridge 15-016-00.0-0-04.0 PIV, known as “La Cuesta” in Mexico, is presented. The findings indicate that the bridge is in a satisfactory condition and warrants high-priority attention. Bridge analysis is compared with evaluations conducted using the methods of the Secretariat of Infrastructure, Communications, and Transportation (SICT), the American Association of State Highway and Transportation Officials (AASHTO), and the Ministry of Transport and Communications of Peru. The comparative evaluation reveals that our proposed model provides a more detailed representation of deterioration, facilitating more efficient maintenance planning by considering the hierarchical relationships between the bridge’s modules and elements.
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Bridging the Digital Divide in Mexico: A Critical Analysis of Telecommunications Infrastructure and Predictive Models for Policy Innovation
(2024)
Del-Valle-Soto, Carolina
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Ramon A. Briseño
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López-Pimentel, Juan Carlos
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Velázquez, Ramiro
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Valdivia, Leonardo
<jats:p>This work presents an in-depth evaluation of the telecommunications landscape in Mexico from 2015 to 2023. The study’s primary focus is on the disparities in broadband access, telecommunications infrastructure, and digital inclusion across various regions, particularly between urban and rural areas. By employing predictive models and correlation analysis, the paper identifies key factors influencing technology adoption and service bundling in households. A significant contribution of this research lies in its identification of strong correlations between broadband access, GDP growth, and the penetration of multiple telecommunication services such as fixed telephony, broadband internet, and television. The predictive models developed offer crucial insights into the regional inequalities of digital access, revealing patterns that policymakers can use to prioritize infrastructure investments. The findings underscore the essential role of public policy innovation in promoting digital inclusion, particularly in underdeveloped regions, and provide a robust analytical framework for understanding how advanced telecommunications services contribute to socio-economic development. Through this analytical approach, the study demonstrates the critical relationship between telecommunications infrastructure and regional economic performance, offering data-driven recommendations to bridge the digital divide and enhance connectivity in underserved areas. The results offer significant value for future research and policy initiatives aimed at fostering equitable access to Information and communication technologies, promoting economic growth, and ensuring broader societal inclusion in the digital age.</jats:p>
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NFT-Vehicle: A Blockchain-Based Tokenization Architecture to Register Transactions over a Vehicle’s Life Cycle
(2023)
López-Pimentel, Juan Carlos
;
Luis Alberto Morales-Rosales
;
Ignacio Algredo-Badillo
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Del-Valle-Soto, Carolina
<jats:p>The sale of second-hand vehicles is a popular trade worldwide, and vehicle fraud is currently a common issue, mainly because buyers can lack a complete view of the historical transactions related to their new acquisition. This work presents a distributed architecture for stakeholders to register transactions over a vehicle’s life cycle in a blockchain network. The architecture involves a non-fungible token (NFT) linked to a physical motorized vehicle after a tokenization process, which denote as the NFT-Vehicle. The NFT-Vehicle is a hierarchical smart contract designed using an object-oriented paradigm and a modified version of the ERC721 standard. Every stakeholder engages with the NFT-Vehicle through distinct methods embedded within a smart contract. These methods represent internal protocols meticulously formulated and validated based on a finite-state machine (FSM) model. We implemented our design as a proof of concept using a platform based on Ethereum and a smart contract in the Solidity programming language. We carried out two types of proof: (a) validations, following the FSM model to ensure that the smart contract remained in a consistent state, and (b) proofs, to achieve certainty regarding the amount of ETH that could be spent in the life cycle of a vehicle. The results of the tests showed that the total transaction cost for each car throughout its life cycle did not represent an excessive cost considering the advantages that the system could offer to prevent fraud.</jats:p>
Scopus© Citations 7 9 2
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Distributed software architecture for accessing the NFT-Vehicle
(2024)
López-Pimentel, Juan Carlos
;
Luis Alberto Morales-Rosales
;
Del-Valle-Soto, Carolina
;
Del-Puerto-Flores, J. Alberto
12
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Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
(2022)
Alan Torres-Alvarado
;
Luis Alberto Morales-Rosales
;
Ignacio Algredo-Badillo
;
Francisco López-Huerta
;
Mariana Lobato-Baez
<jats:p>The latest generation of communication networks, such as SDVN (Software-defined vehicular network) and VANETs (Vehicular ad-hoc networks), should evaluate their communication channels to adapt their behavior. The quality of the communication in data networks depends on the behavior of the transmission channel selected to send the information. Transmission channels can be affected by diverse problems ranging from physical phenomena (e.g., weather, cosmic rays) to interference or faults inherent to data spectra. In particular, if the channel has a good transmission quality, we might maximize the bandwidth use. Otherwise, although fault-tolerant schemes degrade the transmission speed by solving errors or failures should be included, these schemes spend more energy and are slower due to requesting lost packets (recovery). In this sense, one of the open problems in communications is how to design and implement an efficient and low-power-consumption mechanism capable of sensing the quality of the channel and automatically making the adjustments to select the channel over which transmit. In this work, we present a trade-off analysis based on hardware implementation to identify if a channel has a low or high quality, implementing four machine learning algorithms: Decision Trees, Multi-Layer Perceptron, Logistic Regression, and Support Vector Machines. We obtained the best trade-off with an accuracy of 95.01% and efficiency of 9.83 Mbps/LUT (LookUp Table) with a hardware implementation of a Decision Tree algorithm with a depth of five.</jats:p>
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López-Pimentel, Juan Carlos

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