José E. Valdez Rodríguez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
José E. Valdez Rodríguez has a total of 20 co-authors. Here are the top co-authors by number of shared publications:
- Hiram Calvo
Centro de Investigacion en Computacion, Instituto Politecnico Nacional, Mexico City, Mexico
7 shared publications - Edgardo Manuel Felipe Riverón
Instituto Politécnico Nacional
3 shared publications - Marco Antonio Moreno-Armendariz
Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico
3 shared publications - Belem Saldivar
Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Departamento de Control Automático, Av. Instituto Politécnico Nacional 2508, Mexico City 07360, Mexico
1 shared publication - Carlos Aguilar-Ibanez
Instituto Politécnico Nacional
1 shared publication - Carlos Sánchez-Antonio
Cognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, Mexico
1 shared publication
Latest Publications
- 27 October 2026
- Published by Springer Nature
- p. 451-466
Annotation Removal from Thyroid Ultrasound Images Using a U-Net
- 27 October 2026
- Published by Springer Nature
- p. 451-466
Annotation Removal from Thyroid Ultrasound Images Using a U-Net
- Open Access
Classification of Size and Volume Fraction in Low-Absorption Micro- and Nanoparticles via Photoacoustic Sensing Using Continuous Wavelet Transform and Convolutional Neural Networks
Photoacoustic signal analysis in weakly absorbing media remains challenging because of low signal-to-noise ratios. This work proposes a deep learning framework for classifying particle size and concentration in an indirect absorption configuration. We conducted a comparative study using raw temporal signals, Savitzky–Golay filtering, and time–frequency scalograms via Continuous Wavelet Transform (CWT), and evaluated both 1D and 2D convolutional neural network architectures. Experimental validation was performed using poly(methyl methacrylate) (PMMA) microspheres (6 μm and 15 μm) and hydroxyapatite nanoparticles (<200 nm) at volume fractions as low as . While raw signals led to unstable training (accuracy ≈ 47%), CWT-based representations significantly improved performance, achieving near-perfect size discrimination and over 96% accuracy in discrete volume-fraction classification. Grad-CAM analysis confirmed that the model identifies physically meaningful regions of the acoustic waveform, ensuring interpretability. The proposed framework was validated under controlled experimental conditions using discrete particle types and predefined volume-fraction classes, providing a foundation for future extensions toward continuous particle characterization. Ultimately, these findings demonstrate that combining time–frequency representations with deep learning provides a robust, physically consistent approach for particle characterization in turbid media, with significant potential for biomedical diagnostics and material analysis.Open AccessClassification of Size and Volume Fraction in Low-Absorption Micro- and Nanoparticles via Photoacoustic Sensing Using Continuous Wavelet Transform and Convolutional Neural Networks
Photoacoustic signal analysis in weakly absorbing media remains challenging because of low signal-to-noise ratios. This work proposes a deep learning framework for classifying particle size and concentration in an indirect absorption configuration. We conducted a comparative study using raw temporal signals, Savitzky–Golay filtering, and time–frequency scalograms via Continuous Wavelet Transform (CWT), and evaluated both 1D and 2D convolutional neural network architectures. Experimental validation was performed using poly(methyl methacrylate) (PMMA) microspheres (6 μm and 15 μm) and hydroxyapatite nanoparticles (<200 nm) at volume fractions as low as . While raw signals led to unstable training (accuracy ≈ 47%), CWT-based representations significantly improved performance, achieving near-perfect size discrimination and over 96% accuracy in discrete volume-fraction classification. Grad-CAM analysis confirmed that the model identifies physically meaningful regions of the acoustic waveform, ensuring interpretability. The proposed framework was validated under controlled experimental conditions using discrete particle types and predefined volume-fraction classes, providing a foundation for future extensions toward continuous particle characterization. Ultimately, these findings demonstrate that combining time–frequency representations with deep learning provides a robust, physically consistent approach for particle characterization in turbid media, with significant potential for biomedical diagnostics and material analysis. - Open Access
Color Space Comparison of Isolated Cervix Cells for Morphology Classification
Cervical cytology processing involves the morphological analysis of cervical cells to detect abnormalities. In recent years, machine learning and deep learning algorithms have been explored to automate this process. This study investigates the use of color space transformations as a preprocessing technique to reorganize visual information and improve classification performance using isolated cell images. Twelve color space transformations were compared, including RGB, CMYK, HSV, Grayscale, CIELAB, YUV, the individual RGB channels, and combinations of these channels (RG, RB, and GB). Two classification strategies were employed: binary classification (normal vs. abnormal) and five-class classification. The SIPaKMeD dataset was used, with images resized to pixels via zero-padding. Data augmentation included random flipping and ±10° rotations applied with a 50% probability, followed by normalization. A custom CNN architecture was developed, comprising four convolutional layers followed by two fully connected layers and an output layer. The model achieved average precision, recall, and F1-score values of 91.39%, 91.34%, and 91.31% for the five-class case, respectively, and 99.69%, 96.68%, and 96.89% for the binary classification, respectively; these results were compared with a VGG-16 network. Furthermore, CMYK, HSV, and the RG channel combination consistently outperformed other color spaces, highlighting their potential to enhance classification accuracy.Open AccessColor Space Comparison of Isolated Cervix Cells for Morphology Classification
Cervical cytology processing involves the morphological analysis of cervical cells to detect abnormalities. In recent years, machine learning and deep learning algorithms have been explored to automate this process. This study investigates the use of color space transformations as a preprocessing technique to reorganize visual information and improve classification performance using isolated cell images. Twelve color space transformations were compared, including RGB, CMYK, HSV, Grayscale, CIELAB, YUV, the individual RGB channels, and combinations of these channels (RG, RB, and GB). Two classification strategies were employed: binary classification (normal vs. abnormal) and five-class classification. The SIPaKMeD dataset was used, with images resized to pixels via zero-padding. Data augmentation included random flipping and ±10° rotations applied with a 50% probability, followed by normalization. A custom CNN architecture was developed, comprising four convolutional layers followed by two fully connected layers and an output layer. The model achieved average precision, recall, and F1-score values of 91.39%, 91.34%, and 91.31% for the five-class case, respectively, and 99.69%, 96.68%, and 96.89% for the binary classification, respectively; these results were compared with a VGG-16 network. Furthermore, CMYK, HSV, and the RG channel combination consistently outperformed other color spaces, highlighting their potential to enhance classification accuracy. - 1 October 2025
- Published by Springer Nature
- p. 174-186
Representation of Neuro-Symbolic Networks for Sub-algebraic Terms
- 1 October 2025
- Published by Springer Nature
- p. 174-186
Representation of Neuro-Symbolic Networks for Sub-algebraic Terms
- Open Access
A DREM-Based Approach for the Identification of Chaotic Systems
A straightforward methodology for identifying certain classes of chaotic systems based on a novel version of the least-squares method, assuming they are algebraically observable and identifiable with respect to a measurable output, is introduced. This output allows us to express the original system as a chain of integrators, where the last term, which depends on the output and its corresponding time derivatives, lumps the system’s non-linearities. We can factorize this term into a regressor function multiplied by an unknown-parameter vector, suggesting that a high-gain observer can be used to simultaneously and approximately estimate the states of the pure integrator and the evolution of the lumped nonlinear term. This allows us to rewrite the original system as a linear regression equation. This configuration enables the above-mentioned least-squares method to recover the chaotic-system parameters.
Open AccessA DREM-Based Approach for the Identification of Chaotic Systems
A straightforward methodology for identifying certain classes of chaotic systems based on a novel version of the least-squares method, assuming they are algebraically observable and identifiable with respect to a measurable output, is introduced. This output allows us to express the original system as a chain of integrators, where the last term, which depends on the output and its corresponding time derivatives, lumps the system’s non-linearities. We can factorize this term into a regressor function multiplied by an unknown-parameter vector, suggesting that a high-gain observer can be used to simultaneously and approximately estimate the states of the pure integrator and the evolution of the lumped nonlinear term. This allows us to rewrite the original system as a linear regression equation. This configuration enables the above-mentioned least-squares method to recover the chaotic-system parameters.
- Open Access
- 24 July 2025
- Published by MDPI AG in Big Data and Cognitive Computing
- Vol. 9 (8) , 195
Discovering the Emotions of Frustration and Confidence During the Application of Cognitive Tests in Mexican University Students
Emotion detection using computer vision has advanced significantly in recent years, achieving remarkable performance that, in some cases, surpasses that of humans. Convolutional neural networks (CNNs) excel in this task by capturing facial features that allow for effective emotion classification. However, most research focuses on basic emotions, such as happiness, anger, or sadness, neglecting more complex emotions, like frustration. People set expectations or goals to meet; if they do not happen, frustration arises, generating reactions such as annoyance, anger, and disappointment, which can harm confidence and motivation. These aspects make it especially relevant in mental health and educational contexts, where detecting it could help mitigate its adverse effects. In this research, we developed a CNN-based approach to detect frustration through facial expressions. The scarcity of specific datasets for this task led us to create an experimental protocol to generate our dataset. This classification task presents a high degree of difficulty due to the variability in facial expressions among different participants when feeling frustrated. Despite this, our new model achieved an F1-score of , thus obtaining an adequate baseline model.- 24 July 2025
- Published by MDPI AG in Big Data and Cognitive Computing
- Vol. 9 (8) , 195
Open AccessDiscovering the Emotions of Frustration and Confidence During the Application of Cognitive Tests in Mexican University Students
Emotion detection using computer vision has advanced significantly in recent years, achieving remarkable performance that, in some cases, surpasses that of humans. Convolutional neural networks (CNNs) excel in this task by capturing facial features that allow for effective emotion classification. However, most research focuses on basic emotions, such as happiness, anger, or sadness, neglecting more complex emotions, like frustration. People set expectations or goals to meet; if they do not happen, frustration arises, generating reactions such as annoyance, anger, and disappointment, which can harm confidence and motivation. These aspects make it especially relevant in mental health and educational contexts, where detecting it could help mitigate its adverse effects. In this research, we developed a CNN-based approach to detect frustration through facial expressions. The scarcity of specific datasets for this task led us to create an experimental protocol to generate our dataset. This classification task presents a high degree of difficulty due to the variability in facial expressions among different participants when feeling frustrated. Despite this, our new model achieved an F1-score of , thus obtaining an adequate baseline model. - Open Access
- 15 November 2024
- Published by MDPI AG in Mathematics
- Vol. 12 (22) , 3576
TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification
Recent advancements in graph-based text representation, particularly with embedding models and transformers such as BERT, have shown significant potential for enhancing natural language processing (NLP) tasks. However, challenges related to data sparsity and limited interpretability remain, especially when working with small or imbalanced datasets. This paper introduces TTG-Text, a novel framework that strengthens graph-based text representation by integrating typical testors—a symbolic feature selection technique that refines feature importance while reducing dimensionality. Unlike traditional TF-IDF weighting, TTG-Text leverages typical testors to enhance feature relevance within text graphs, resulting in improved model interpretability and performance, particularly for smaller datasets. Our evaluation on a text classification task using a graph convolutional network (GCN) demonstrates that TTG-Text achieves a 95% accuracy rate, surpassing conventional methods and BERT with fewer required training epochs. By combining symbolic algorithms with graph-based models, this hybrid approach offers a more interpretable, efficient, and high-performing solution for complex NLP tasks.
- 15 November 2024
- Published by MDPI AG in Mathematics
- Vol. 12 (22) , 3576
Open AccessTTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification
Recent advancements in graph-based text representation, particularly with embedding models and transformers such as BERT, have shown significant potential for enhancing natural language processing (NLP) tasks. However, challenges related to data sparsity and limited interpretability remain, especially when working with small or imbalanced datasets. This paper introduces TTG-Text, a novel framework that strengthens graph-based text representation by integrating typical testors—a symbolic feature selection technique that refines feature importance while reducing dimensionality. Unlike traditional TF-IDF weighting, TTG-Text leverages typical testors to enhance feature relevance within text graphs, resulting in improved model interpretability and performance, particularly for smaller datasets. Our evaluation on a text classification task using a graph convolutional network (GCN) demonstrates that TTG-Text achieves a 95% accuracy rate, surpassing conventional methods and BERT with fewer required training epochs. By combining symbolic algorithms with graph-based models, this hybrid approach offers a more interpretable, efficient, and high-performing solution for complex NLP tasks.
- 27 December 2023
- Published by Instituto Politecnico Nacional/Centro de Investigacion en Computacion in Computación y Sistemas
- Vol. 27 (4) , 941-953
Comparison of Transfer Style Using a CycleGAN Model with Data Augmentation
Comparison of Transfer Style Using a CycleGAN Model with Data Augmentation
- 27 December 2023
- Published by Instituto Politecnico Nacional/Centro de Investigacion en Computacion in Computación y Sistemas
- Vol. 27 (4) , 941-953
Comparison of Transfer Style Using a CycleGAN Model with Data Augmentation
Comparison of Transfer Style Using a CycleGAN Model with Data Augmentation
- Open Access
Improving Depth Estimation by Embedding Semantic Segmentation: A Hybrid CNN Model
Single image depth estimation works fail to separate foreground elements because they can easily be confounded with the background. To alleviate this problem, we propose the use of a semantic segmentation procedure that adds information to a depth estimator, in this case, a 3D Convolutional Neural Network (CNN)—segmentation is coded as one-hot planes representing categories of objects. We explore 2D and 3D models. Particularly, we propose a hybrid 2D–3D CNN architecture capable of obtaining semantic segmentation and depth estimation at the same time. We tested our procedure on the SYNTHIA-AL dataset and obtained , which is an improvement of 0.14 points (compared with the state of the art of ) by using manual segmentation, and using automatic semantic segmentation, proving that depth estimation is improved when the shape and position of objects in a scene are known.Open AccessImproving Depth Estimation by Embedding Semantic Segmentation: A Hybrid CNN Model
Single image depth estimation works fail to separate foreground elements because they can easily be confounded with the background. To alleviate this problem, we propose the use of a semantic segmentation procedure that adds information to a depth estimator, in this case, a 3D Convolutional Neural Network (CNN)—segmentation is coded as one-hot planes representing categories of objects. We explore 2D and 3D models. Particularly, we propose a hybrid 2D–3D CNN architecture capable of obtaining semantic segmentation and depth estimation at the same time. We tested our procedure on the SYNTHIA-AL dataset and obtained , which is an improvement of 0.14 points (compared with the state of the art of ) by using manual segmentation, and using automatic semantic segmentation, proving that depth estimation is improved when the shape and position of objects in a scene are known. - Open Access
- 12 September 2021
- Published by MDPI AG in Mathematics
- Vol. 9 (18) , 2237
Optic Disc Preprocessing for Reliable Glaucoma Detection in Small Datasets
Glaucoma detection is an important task, as this disease can affect the optic nerve, and this could lead to blindness. This can be prevented with early diagnosis, periodic controls, and treatment so that it can be stopped and prevent visual loss. Usually, the detection of glaucoma is carried out through various examinations such as tonometry, gonioscopy, pachymetry, etc. In this work, we carry out this detection by using images obtained through retinal cameras, in which we can observe the state of the optic nerve. This work addresses an accurate diagnostic methodology based on Convolutional Neural Networks (CNNs) to classify these optical images. Most works require a large number of images to train their CNN architectures, and most of them use the whole image to perform the classification. We will use a small dataset containing 366 examples to train the proposed CNN architecture and we will only focus on the analysis of the optic disc by extracting it from the full image, as this is the element that provides the most information about glaucoma. We experiment with different RGB channels and their combinations from the optic disc, and additionally, we extract depth information. We obtain accuracy values of 0.945, by using the GB and the full RGB combination, and 0.934 for the grayscale transformation. Depth information did not help, as it limited the best accuracy value to 0.934.
- 12 September 2021
- Published by MDPI AG in Mathematics
- Vol. 9 (18) , 2237
Open AccessOptic Disc Preprocessing for Reliable Glaucoma Detection in Small Datasets
Glaucoma detection is an important task, as this disease can affect the optic nerve, and this could lead to blindness. This can be prevented with early diagnosis, periodic controls, and treatment so that it can be stopped and prevent visual loss. Usually, the detection of glaucoma is carried out through various examinations such as tonometry, gonioscopy, pachymetry, etc. In this work, we carry out this detection by using images obtained through retinal cameras, in which we can observe the state of the optic nerve. This work addresses an accurate diagnostic methodology based on Convolutional Neural Networks (CNNs) to classify these optical images. Most works require a large number of images to train their CNN architectures, and most of them use the whole image to perform the classification. We will use a small dataset containing 366 examples to train the proposed CNN architecture and we will only focus on the analysis of the optic disc by extracting it from the full image, as this is the element that provides the most information about glaucoma. We experiment with different RGB channels and their combinations from the optic disc, and additionally, we extract depth information. We obtain accuracy values of 0.945, by using the GB and the full RGB combination, and 0.934 for the grayscale transformation. Depth information did not help, as it limited the best accuracy value to 0.934.