Ponce, Hiram

Main Affiliation

Facultad de Ingeniería - CampCM

Preferred name

Ponce, Hiram

Official Name

Ponce Espinosa, Hiram Eredín

ORCID

0000-0002-6559-7501

Researcher ID

K-7593-2019

Scopus Author ID

54911890000

Now showing 1 - 10 of 171

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Automatic classification of coronary stenosis using convolutional neural networks and simulated annealing
(CRC Press, 2022)
Gutiérrez, Sebastián
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Cruz-Aceves, Ivan
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Fernandez-Jaramillo, Arturo Alfonso
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Moya-Albor, Ernesto
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Brieva, Jorge
Automatic detection of coronary stenosis plays an essential role in systems that perform computer-aided diagnosis in cardiology. Coronary stenosis is a narrowing of the coronary arteries caused by plaque that reduces the blood flow to the heart. Automatic classification of coronary stenosis images has been re-cently addressed using deep and machine learning techniques. Generally, the machine learning methods form a bank of empirical and automatic features from the angiographic images. In the present work, a novel method for the automatic classification of coronary stenosis X-ray images is presented. The method is based on convolutional neural networks, where the neural architecture search is performed by using the path-based metaheuristics of simulated annealing. To perform the neural architecture search, the maximization of the F1-score metric is used as the fitness function. The automatically generated convolutional neural network was compared with three deep learning methods in terms of the accuracy and F1-score metrics using a testing set of images obtaining 0.88 and 0.89, respectively. In addition, the proposed method was evaluated with different sets of coronary stenosis images obtained via data augmentation. The results involving a number of different instances have shown that the proposed architecture is robust preserving the efficiency with different datasets © 2023 Şaban öztürk. All rights reserved.
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Editorial : International Journal of Distributed Sensor Networks
(2021)
Ponce, Hiram
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Martinez-Villaseñor, Lourdes
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O’Hare, Gregory
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Ayala-Solares, José Roberto
The importance of sensor networks and the integration of signal processing have increased as a consequence of the growth of complex Internet-of-Things (IoT), distributed and wireless network applications, for commercial, medical, context-aware, and industrial domains, among others. The complexity, heterogeneity, and dynamicity of some sensor networks demand new intelligent solutions for data aggregation, integration, and management. Artificial intelligence helps in these sensor network applications to respond to the above-mentioned challenges. In consideration of these issues, this Special Collection offered a platform for researchers to publish recent and original works in different topics, focusing on intelligent systems for sensor networks. Several researchers from different parts of the world submitted their papers. Hence, after a rigorous review process, we accept only seven papers for this collection. An overview of the key contributions of each paper is presented as follows. ©2021 International Journal of Distributed Sensor Networks, SAGE Publications Ltd.
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Heart Rate Estimation using Hermite Transform Video Magnification and Deep Learning
(2018)
Moya-Albor, Ernesto
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Brieva, Jorge
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Ponce, Hiram
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Rivas Scott, Orlando
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Gómez Peña, Cristina Aimée
Monitoring of heart rate can be used in many medical and sports applications. Lack of portability and connection problems make traditional monitoring methods difficult to use outside of clinical environments. The computer vision techniques have been shown that some physiological variables as heart rate can be measured without contact. Video magnification is one of these approach used for the detection of the pulse signal. In this paper we propose a new strategy to magnify motion in a video sequence using the Hermite transform. In addition a deep learning technique is implemented to estimate the beat by beat pulse signal. We trained the system and validated our results using an electronic pulse monitoring device. Our approach is compared with the classical video magnification using a Gaussian pyramid. The results show a better enhancement of spectral information from the colour changes allowing an accurate estimation of the instantaneous beat by beat pulse than the Gaussian approach. ©2018, Institute of Electrical and Electronics Engineers Inc.
1Scopus© Citations 13 14
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Machine Learning Model of Digital Transformation Index for Mexican Households
(2022)
García, Alfredo
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Salazar, Vladimir
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Ponce, Hiram
Digital transformation refers to the change in all aspects of human society by the adoption of digital technologies. Different methodologies and measurements have been proposed to determine the level of digital transformation in regions or countries. In this work, we propose the creation of a digital transformation index for Mexican households using machine learning models for digital transformation measurement analysis and estimation. We include three dimensions in terms of the information and communication technologies infrastructure, availability of services, and usage. We also use a public dataset from the Mexican government to build and train three machine learning models. Experimental results validate that our methodology can deliver a digital transformation measurement using machine learning models consistently with 84% of accuracy and 84% of F1-score. We also prototype a simple web application using the best machine learning model found. We anticipate that measuring the digital transformation in companies, governments, and households allows better decisions in business intelligence and public policy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition
(2015)
Ponce, Hiram
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Martinez-Villaseñor, Lourdes
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Miralles-Pechuán, Luis
In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.
1Scopus© Citations 5 23
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Population-Based Metaheuristic Optimization Using Organized Social Wound Treatment
(2019)
Ponce, Hiram
This paper introduces a new strategy by means of the social wound treatment for improving optimization in population-based metaheuristic methods. This strategy is inspired on the behavior observed in social animals when dealing with injuries at both social and individual levels. An implementation of this strategy is done in the so-called wound treatment optimization (WTO) algorithm. A benchmark on several test functions for optimization was conducted to compare the response between the simple particle swarm optimization (PSO) and the WTO. Also, an application of WTO in decentralized system of robots was developed aiming to learn suitable parameters in the navigation controller of robots. This is the first time that social wound treatment is considered in these optimization methods. © 2019 IEEE.
Scopus© Citations 2 1 11
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RADAMA: Design of an Intelligent Waste Separator with the Combination of Different Sensors
(2021)
Dávila Vega, Raúl Alberto
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Hoyo de la Sema, Mauricio del
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Meza Herz, David
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Jurado Martinon, David
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Menéndez Gomory, María de Lourdes
Nowadays, trash generation is a real problem. It is expected that high-income countries will experience waste generation growth in the future. A forecast shows that by 2050 there will be an exponential increase of close to 3.4 billion tonnes per year. Therefore, it is urgent to reduce the levels of garbage that are produced in the world. A possible solution is to develop recycling systems in all the big cities. In this regard, we propose a mechatronic system to separate solid wastes into four categories. To determine the category to which the inorganic waste belongs, we used a combination of four different sensors: inductive, capacitive, infrared, and ultrasonic sensors. To evaluate the efficiency of this proposal, we carried out tests that showed the individual conduct of every component and the total efficiency of the device, obtaining at least 75 percent in overall efficiency. © 2021 IEEE.
2 16Scopus© Citations 1
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A 3D orthogonal vision-based band-gap prediction using deep learning: A proof of concept
(2022)
Espinosa Loera, Ricardo Abel
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Ponce, Hiram
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ORTIZ-MEDINA, JOSUE
In this work, a vision-based system for the electronic band-gap prediction of organic molecules is proposed using a multichannel 2D convolutional neural network (CNN) and a 3D CNN, applied to the recognition and classification of 2D projected images from 3D molecular structure models. The generated images are input into the CNN for an estimation of the energy gap, associated with the molecular structure. The public data set used in this research was the Organic Materials Database (OMDB-GAP1). A data transformation from the descriptive information contained in the data set to three 2D orthogonal images of molecules was done. The training set is composed of 30,000 images, whereas the testing set was composed of 7500 images, from 12,500 different molecules. The multichannel 2D CNN architecture was optimized via Bayesian optimization. Experimental results showed that the proposed CNN model obtained an acceptable mean absolute error of 0.6780 eV and root mean-squared error of 0.7673 eV, in contrast to two machine learning methods reported in the literature used for band-gap prediction based on conventional density function theory (DFT) methods. These results demonstrate the feasibility of CNN models to materials science routines using orthogonal images projections of molecules. © 2021 Elsevier B.V.
2 16Scopus© Citations 15
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Can crush: An automated waste compacting system for public areas
(2017)
Becker, Juan
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Ponce, Carlos
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Javier Rodriguez
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Vázquez, David
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Ponce, Hiram
Overwhelming amount of waste is generated every year globally. This problem has been shown to be significant interenst in solving the waste managament, e.g. land filling is a common practice. However, environmental issues are presented. In particular, land filling is an expensive method in terms of the volume cost, so compactors are required to accomodate more waste. Different compactors have been developed for both mobile and static usage mode as the automatic compacting receptacles normally used for fast food areas, mall food courts, airports, parks or any public areas. But, they are designed for mixed waste and prevent for recycling. In that sense, this paper aims to present a proof of concept of an automatic compacting recepticle system exclusivey for aluminium cans. Using a mechatronics design, the proposal was implemented in a functional prototype. Preliminary results detected strengths and weaknesses of the proposal, also suggesting that the proposal can be applied for public areas in near future. © 2017 IEEE.
Scopus© Citations 1 1 21
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Electric wheelchair module: Converting a mechanical to an electric wheelchair
(2017)
Galván, Emiliano
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González Mora, José Guillermo
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Hernández Ortega, Guillermo
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Mañón, Santiago
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Ponce, Hiram
There is a growing need for transportation, either inside a house or office, as well as in the streets and other public spaces. This constant need is a disadvantage for anyone with some kind of disability, specially those that suffer motor disabilities. Electric wheelchairs are part of the technological solutions to this demand. However, their cost are very high in constrast to mechanical wheelchairs. In that sense, this paper aims to present a new module device that can be used for converting a mechanical wheelchair to an electrical one. The proposed device was designed for simplicity in installation, high benefit-cost ratio and easiness in control movement. Preliminary results showed the implementation of the proposal in a functional prototype. © 2017 IEEE.
Scopus© Citations 4 2 19

Ponce, Hiram

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