Brieva, Jorge

Main Affiliation

Facultad de Ingeniería - CampCM

Preferred name

Brieva, Jorge

Official Name

Brieva Rico, Jorge Eduardo

ORCID

0000-0002-5430-8778

Researcher ID

GMW-951-2022

Scopus Author ID

7005997081

Web Site

https://www.up.edu.mx/investigacion/jorge-eduardo-brieva-rico/

Now showing 1 - 10 of 86

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Non-Contact Respiratory Rate Estimation in Newborns During Quiet Sleep Using Video Magnification Techniques and a 3D Convolutional Neural Network
(IEEE, 2024)
Escobedo Gordillo, Andrés Emiliano
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Rivas-Scott, Orlando Yael
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Brieva, Jorge
;
Moya-Albor, Ernesto
;
Cabon, Sandie
In this paper, we present a new non-contact strategy to estimate the respiratory rate (RR) in a neonatal intensive care unit (NICU) based on the Eulerian motion video magnification technique and a 3D Convolutional Neural Network (3D CNN). The magnification procedure was carried out using the Hermite decomposition. The RR is estimated using a 3D CNN and a region of interest (ROI) detected manually. We have tested the method on 8 infants in NICU during quiet sleep. A contact respiratory signal is acquired synchronously to the videos to compute the RR as reference for training the CNN. To compare the performance of the method, we compute the Mean Absolute Error, the Root Mean Squared Error and metrics from the Bland and Altman analysis to investigate the agreement of the method with respect to the respiratory signal reference. The proposed solution shows an agreement with respect to the reference of 95% and root mean squared error of 2.88. ©The authors ©IEEE.
21
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Edge-Enhanced Knowledge Distillation System for Diabetic Retinopathy Lesions Computer-Aided Diagnosis
(Springer Nature Switzerland, 2025)
Lopez-Figueroa, Alberto
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Jacome-Herrera, Sebastian
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Moya-Albor, Ernesto
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Renza, Diego
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Brieva, Jorge
This work addresses the challenge of deploying computationally intensive Deep Learning (DL) models for Diabetic Retinopathy (DR) lesion detection in clinical settings, particularly on resource-constrained edge devices. DR is a significant global health issue and a leading cause of preventable blindness, making early and accessible detection crucial. We propose a proof-of-concept system utilizing Knowledge Distillation (KD) to create a tiny, efficient DL model for DR lesion detection, specifically designed for embedding into retinal scanners via the NVIDIA Jetson Nano platform. Our novel approach employs a KD framework where a pre-trained Inception-v3 model acts as the ‘teacher,’ fine-tuned on fundus image data. This teacher model distills its knowledge into a compact ‘student’ model based on the MobileNet-v2 architecture, which is trained on a small, synthetically generated dataset optimized through an iterative distillation process using a custom loss function combining Kullback-Leibler divergence and Categorical Cross-Entropy. This method significantly reduces model size and computational requirements while maintaining high diagnostic accuracy, comparable to larger, state-of-the-art models. By enabling real-time, on-device analysis, this embedded AI solution enhances data privacy, ensures consistent performance, and improves the accessibility of advanced DR screening, particularly in remote or underserved healthcare environments. This makes AI-assisted DR detection feasible for widespread clinical adoption directly within scanning devices. ©The authors ©Springer.
24 5
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Data-Driven Innovation for Intelligent Technology : Perspectives and Applications in ICT
(Springer Cham, 2024)
Ponce, Hiram
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Brieva, Jorge
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Lozada Flores, Octavio
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Martinez-Villaseñor, Lourdes
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Moya-Albor, Ernesto
This book focuses on new perspectives and applications of data-driven innovation technologies, applied artificial intelligence, applied machine learning and deep learning, data science, and topics related to transforming data into value. It includes theory and use cases to help readers understand the basics of data-driven innovation and to highlight the applicability of the technologies. It emphasizes how the data lifecycle is applied in current technologies in different business domains and industries, such as advanced materials, healthcare and medicine, resource optimization, control and automation, among others. This book is useful for anyone interested in data-driven innovation for smart technologies, as well as those curious in implementing cutting-edge technologies to solve impactful artificial intelligence, data science, and related information technology and communication problems. ©Springer. ©The authors. ©The editors
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Hybrid Methods to Quantify Ice Front Movement during Freeze-concentration Process
(2018)
Moya-Albor, Ernesto
;
Pardo Benito, José Mauricio
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Gregori, Andres
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Brieva, Jorge
Freeze concentration is an emerging separation method that can be implemented as part of a water purification system, however, its development is still at pilot plant scale. One of the useful parameters for design and control of this process is the growth velocity of the crystal, commonly known as the limit velocity. Above this limit, ice crystals will capture solids and separation will lose quality. Two-hybrid no invasive methods to follow the displacement of the ice front during a freeze concentration (FC) procedure has been tested.The methods included image segmentation, string matching technique and a bio-inspired optical flow algorithm to calculate the ice front velocity. A thermal camera was used in the experiments in order to validate the estimated movement of the ice by the image analysis procedures. Both methods were successful to follow the movement of the ice front, and the estimated displacement was between 4 and 12 pixels in 15 minutes. © 2018 IEEE.
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An Intelligent Human Fall Detection System Using a Vision-Based Strategy
(2019)
Brieva, Jorge
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Ponce, Hiram
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Moya-Albor, Ernesto
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Martinez-Villaseñor, Lourdes
Elderly people is increasing dramatically during the current years, and it is expected that this population reaches 2.1 billion of individuals by 2050. In this regard, new care strategies are required. Assisted living technologies have proposed alternatives to support professional caregivers and families to take care of elderly people, such as in risk of falls. Currently, fall detection systems are able to alleviate the latter problem and reduce the time a person who suffered a fall receives assistance. Thus, this paper proposes a fall detection system based on image processing strategy to extract motion features through an optical flow method. For classification, we use these features as inputs to a convolutional neural network. We applied our approach in a dataset comprises video recordings of one subject performing different types of falls. In experimental results, our approach showed 92% accuracy on the dataset used. © 2019 IEEE.
1 29Scopus© Citations 9
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Non-contact breathing rate monitoring system using a magnification technique and artificial hydrocarbon networks
(2020)
Brieva, Jorge
;
Moya-Albor, Ernesto
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Ponce, Hiram
In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and an Artificial Hydrocarbon Networks (AHN) as classifier. After the magnification procedure, a AHN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. The respiratory rate (RR) is estimated from the classified frames. We have tested the method on 10 healthy subjects in different positions. To compare performance of methods to respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for our strategy is 4.46 ± 3.68% with and agreement with respect of the reference of ˜ 98 %. © 2020 SPIE
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Open Source Implementation for Fall Classification and Fall Detection Systems
(2020)
Ponce, Hiram
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Martinez-Villaseñor, Lourdes
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Nuñez Martínez, José Pablo
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Moya-Albor, Ernesto
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Brieva, Jorge
Distributed social coding has created many benefits for software developers. Open source code and publicly available datasets can leverage the development of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate between each category of falls. In this chapter, we present an open source implementation for fall classification and detection systems using the public UP-Fall Detection dataset. This implementation comprises a set of open codes stored in a GitHub repository for full access and provides a tutorial for using the codes and a concise example for their application. © 2020, Springer Nature Switzerland AG.
Scopus© Citations 2 2 19
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Challenges and Advances in Digital Processing of Fetal Phonocardiography Signal: A Review
(Springer Nature Switzerland, 2025)
Moya-Albor, Ernesto
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Brieva, Jorge
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Gomez-Coronel, Sandra L.
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Renza, Diego
This chapter presents a state-of-the-art review of different investigations focused on Fetal Phonocardiography (fPCG). fPCG signals allow the identification of the fetus’s cardiac alterations during pregnancy through a noninvasive and secure approach. However, fPCG signals present some challenges, for example: very weak signal sources, high levels of noise, source mixing, and significant signal attenuation. This work provides a review of available fPCG datasets and the methods proposed for source separation, extraction, and filtering of fPCG signals, as well as the methods for estimating fetal heart rate (fHR) and detecting fetal Heart Sounds (fHS). Additionally, since it is sometimes necessary to transmit or store fPCG signals, the chapter also discusses signal compression approaches and applications involving fPCG signals. ©The authors ©Springer.
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Towards the Distributed Wound Treatment Optimization Method for Training CNN Models: Analysis on the MNIST Dataset
(IEEE, 2023)
Ponce, Hiram
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Moya-Albor, Ernesto
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Brieva, Jorge
Convolutional neural network (CNN) is a prominent algorithm in Deep Learning methods. CNN architectures have been used successfully to solve various problems in image processing, for example, segmentation, classification, and enhancement task. However, automatic search for suitable architectures and training parameters remain an open area of research, where metaheuristic algorithms have been used to fine-tuning the hyperparameters and learning parameters. This work presents a bio-inspired distributed strategy based on Wound Treatment optimization (WTO) for training the learning parameters of a LenNet CNN model fast and accurate. The proposed method was evaluated over the popular benchmark dataset MNIST for handwritten digit recognition. Experimental results showed an improvement of 36.87% in training time using the distributed WTO method compared to the baseline with a single learning agent, and the accuracy increases 4.69% more using the proposed method in contrast with the baseline. As this is a preliminary study towards the distributed WTO method for training CNN models, we anticipate this approach can be used in robotics, multi-agent systems, federated learning, complex optimization problems, and many others, where an optimization task is required to be solved fast and accurate. © 2023 IEEE.
Scopus© Citations 2 1 14
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Image Encryption and Decryption System through a Hybrid Approach Using the Jigsaw Transform and Langton’s Ant Applied to Retinal Fundus Images
(2021)
Romero Arellano, Andrés Gabriel
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Moya-Albor, Ernesto
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Brieva, Jorge
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Cruz-Aceves, Ivan
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Avina-Cervantes, Juan Gabriel
In this work, a new medical image encryption/decryption algorithm was proposed. It is based on three main parts: the Jigsaw transform, Langton’s ant, and a novel way to add deterministic noise. The Jigsaw transform was used to hide visual information effectively, whereas Langton’s ant and the deterministic noise algorithm give a reliable and secure approach. As a case study, the proposal was applied to high-resolution retinal fundus images, where a zero mean square error was obtained between the original and decrypted image. The method performance has been proven through several testing methods, such as statistical analysis (histograms and correlation distributions), entropy computation, keyspace assessment, robustness to differential attack, and key sensitivity analysis, showing in each one a high security level. In addition, the method was compared against other works showing a competitive performance and highlighting with a large keyspace (>1×101,134,190.38). Besides, the method has demonstrated adequate handling of high-resolution images, obtaining entropy values between 7.999988 and 7.999989, an average Number of Pixel Change Rate (NPCR) of 99.5796%±0.000674, and a mean Uniform Average Change Intensity (UACI) of 33.4469%±0.00229. In addition, when there is a small change in the key, the method does not give additional information to decrypt the image. ©2021 Axioms, MDPI.
1 26Scopus© Citations 13

Brieva, Jorge

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