Moya-Albor, Ernesto
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Moya-Albor, Ernesto
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Moya Albor, Ernesto
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emoya
Moya-Albor, E.
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55523223700
Researcher ID
J-1891-2019

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Now showing 1 - 10 of 64
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Lack of Delta-Sarcoglycan (Sgcd) Results in Retinal Degeneration

2019 , Pérez Ortiz, Andric Christopher , Peralta-Ildefonso, Martha Janneth , Lira, Esmeralda , Ramírez-Sánchez, Israel , Brieva, Jorge , Moya-Albor, Ernesto , Clapp, Carmen , Luna-Angulo, Alexandra , Rendon, Álvaro , Adan-Castro, Elva , Ramírez-Hernández, Gabriela , Díaz-Lezama, Nundehui , Coral-Vázquez, Ramón , Estrada Mena, Francisco Javier

Age-related macular degeneration (AMD) is the leading cause of central vision loss and severe blindness among the elderly population. Recently, we reported on the association of the SGCD gene (encoding for δ-sarcoglycan) polymorphisms with AMD. However, the functional consequence of Sgcd alterations in retinal degeneration is not known. Herein, we characterized changes in the retina of the Sgcd knocked-out mouse (KO, Sgcd−/−). At baseline, we analyzed the retina structure of three-month-old wild-type (WT, Sgcd+/+) and Sgcd−/− mice by hematoxylin and eosin (H&E) staining, assessed the Sgcd-protein complex (α-, β-, γ-, and ε-sarcoglycan, and sarcospan) by immunofluorescence (IF) and Western blot (WB), and performed electroretinography. Compared to the WT, Sgcd−/− mice are five times more likely to have retinal ruptures. Additionally, all the retinal layers are significantly thinner, more so in the inner plexiform layer (IPL). In addition, the number of nuclei in the KO versus the WT is ever so slightly increased. WT mice express Sgcd-protein partners in specific retinal layers, and as expected, KO mice have decreased or no protein expression, with a significant increase in the α subunit. At three months of age, there were no significant differences in the scotopic electroretinographic responses, regarding both a- and b-waves. According to our data, Sgcd−/− has a phenotype that is compatible with retinal degeneration. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

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Image analysis applied to a freeze-concentration water purification system

2017 , Pardo Benito, José Mauricio , Moya-Albor, Ernesto , Ortega Ibarra, Germán , Brieva, Jorge

Freeze concentration is a promising water purification technology, specially due to the low levels of energy consumption when compared with other processes such as evaporation. Nevertheless, its development is still at pilot plant scale and more knowledge of the process is needed. One of the important parameters needed for design and control of the process is the growth velocity of the crystal, commonly known as the limit velocity. Above this velocity ice crystals will entrap solids and separation will not be successful. In this work, we describe a non-invasive technique to measure this limit velocity based on image analysis. Photographs were taken every 30 minutes in an experimental rig that allowed to freeze the sample unidirectionally. Back lighting was used in order to observe variations in luminosity at the samples surface. Image processing algorithms were used to segment the image, to identify the freezing front and to model its movement. Under the experimental conditions of this work, it was determined that the limiting freezing front velocity was 1.17 mm/h, which is a valuable information for the design and control of the process. © 2018 IMEKO-International Measurement Federation Secretariat. All rights reserved.

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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 , Moya-Albor, Ernesto , Brieva, Jorge , Cruz-Aceves, Ivan , Avina-Cervantes, Juan Gabriel , Hernandez-Gonzalez, Martha Alicia , Lopez-Montero, Luis Miguel

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.

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A non-contact heart rate estimation method using video magnification and neural networks

2020 , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Heart rate (HR) monitoring is a significant task in many medical, sports and aged care in assisted living applications, among other disciplines. In the literature, several works have reported effectiveness in addressing the measurement of HR using contact sensors such as adhesive or dry electro-conductive electrodes. However, there are several issues associated with contact sensors like portability problems, skin irritation, discomfort and body movement constraints. In this regard, this paper presents a non-contact HR estimation method using vision-based methods and neural networks. This work uses a bio-inspired Eulerian motion magnification approach to highlight the blood irrigation process of the cardiac pulse, which is later inputted to a feed-forward neural network trained to estimate the HR. For experimental analysis, we compare two magnification procedures, based on Gaussian and Hermite decomposition, over video recordings collected from the wrists of five subjects. Results show that the Hermite-based magnification method is robust under noise analysis (4.24 bpm of root mean squared-error in the worst case scenario). Furthermore, our results demonstrate that the Hermite-based method is competitive in the state-of-the-art (1.86 bpm in average of root mean squared-error) and can be implemented using a single camera for contactless HR estimation. ©2020 IEEE Instrumentation and Measurement Magazine, Institute of Electrical and Electronics Engineers Inc.

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RADAMA: Design of an Intelligent Waste Separator with the Combination of Different Sensors

2021 , Dávila Vega, Raúl Alberto , Hoyo de la Sema, Mauricio del , Meza Herz, David , Jurado Martinon, David , Menéndez Gomory, María de Lourdes , Pérez Martínez, Alan Gerardo , Moya-Albor, Ernesto , Ponce, Hiram , Brieva, Jorge

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.

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A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT

2023 , Gomez-Coronel, Sandra L. , Moya-Albor, Ernesto , Brieva, Jorge , Romero Arellano, Andrés

Currently, algorithms to embed watermarks into digital images are increasing exponentially, for example in image copyright protection. However, when a watermarking algorithm is applied, the preservation of the image’s quality is of utmost importance, for example in medical images, where improper embedding of the watermark could change the patient’s diagnosis. On the other hand, in digital images distributed over the Internet, the owner of the images must also be protected. In this work, an imperceptible, robust, secure, and hybrid watermarking algorithm is presented for copyright protection. It is based on the Hermite Transform (HT) and the Discrete Cosine Transform (DCT) as a spatial–frequency representation of a grayscale image. Besides, it uses a block-based strategy and a perfectibility analysis of the best embedding regions inspired by the Human Vision System (HVS), giving the imperceptibility of the watermark, and a Singular-Value Decomposition (SVD) approach improved robustness against attacks. In addition, the proposed method can embed two watermarks, a digital binary image (LOGO) and information about the owner and the technical data of the original image in text format (MetaData). To secure both watermarks, the proposed method uses the Jigsaw Transform (JST) and the Elementary Cellular Automaton (ECA) to encrypt the image LOGO and a random sequence generator and the XOR operation to encrypt the image MetaData. On the other hand, the proposed method was tested using a public dataset of 49 grayscale images to assess the effectiveness of the watermark embedding and extraction procedures. Furthermore, the proposed watermarking algorithm was evaluated under several processing and geometric algorithms to demonstrate its robustness to the majority, intentional or unintentional, attacks, and a comparison was made with several state-of-the-art techniques. The proposed method obtained average values of PSNR = 40.2051 dB, NCC = 0.9987, SSIM = 0.9999, and MSSIM = 0.9994 for the watermarked image. In the case of the extraction of the LOGO, the proposal gave MSE = 0, PSNR ≫ 60 dB, NCC = 1, SSIM = 1, and MSSIM = 1, whereas, for the image MetaData extracted, it gave BER = 0% and (Formula presented.). Finally, the proposed encryption method presented a large key space ((Formula presented.)) for the LOGO image. © 2023 by the authors. Multidisciplinary Digital Publishing Institute (MDPI).

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Heart Rate Estimation using Hermite Transform Video Magnification and Deep Learning

2018 , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram , Rivas Scott, Orlando , 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.

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A novel artificial organic control system for mobile robot navigation in assisted living using vision- and neural-based strategies

2018 , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

Robots in assisted living (RAL) are an alternative to support families and professional caregivers with a wide range of possibilities to take care of elderly people. Navigation of mobile robots is a challenging problem due to the uncertainty and dynamics of environments found in the context of places for elderly. To accomplish this goal, the navigation system tries to replicate such a complicated process inspired on the perception and judgment of human beings. In this work, we propose a novel nature-inspired control system for mobile RAL navigation using an artificial organic controller enhanced with vision-based strategies such as Hermite optical flow (OF) and convolutional neural networks (CNNs). Particularly, the Hermite OF is employed for obstacle motion detection while CNNs are occupied for obstacle distance estimation. We train the CNN using OF visual features guided by ultrasonic sensor-based measures in a 3D scenario. Our application is oriented to avoid mobile and fixed obstacles using a monocular camera in a simulated environment. For the experiments, we use the robot simulator V-REP, which is an integrated development environment into a distributed control architecture. Security and smoothness metrics as well as quantitative evaluation are computed and analyzed. Results showed that the proposed method works successfully in simulation conditions.

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Segmentation and optical flow estimation in cardiac CT sequences based on a spatiotemporal PDM with a correction scheme and the Hermite transform

2016 , Barba-J., Leiner , Moya-Albor, Ernesto , Escalante-Ramírez, Boris , Brieva, Jorge , Vallejo Venegas, Enrique

Purpose: The left ventricle and the myocardium are two of the most important parts of the heart used for cardiac evaluation. In this work a novel framework that combines two methods to isolate and display functional characteristics of the heart using sequences of cardiac computed tomography (CT) is proposed. A shape extraction method, which includes a new segmentation correction scheme, is performed jointly with a motion estimation approach.Methods: For the segmentation task we built a Spatiotemporal Point Distribution Model (STPDM) that encodes spatial and temporal variability of the heart structures. Intensity and gradient information guide the STPDM. We present a novel method to correct segmentation errors obtained with the STPDM. It consists of a deformable scheme that combines three types of image features: local histograms, gradients and binary patterns. A bio-inspired image representation model based on the Hermite transform is used for motion estimation. The segmentation allows isolating the structure of interest while the motion estimation can be used to characterize the movement of the complete heart muscle.Results: The work is evaluated with several sequences of cardiac CT. The left ventricle was used for evaluation. Several metrics were used to validate the proposed framework. The efficiency of our method is also demonstrated by comparing with other techniques.Conclusion: The implemented tool can enable physicians to better identify mechanical problems. The new correction scheme substantially improves the segmentation performance. Reported results demonstrate that this work is a promising technique for heart mechanical assessment. © 2016 Elsevier Ltd.

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A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks

2020 , Brieva, Jorge , Ponce, Hiram , Moya-Albor, Ernesto

The monitoring of respiratory rate is a relevant factor in medical applications and day-to-day activities. Contact sensors have been used mostly as a direct solution and they have shown their effectiveness, but with some disadvantages for example in vulnerable skins such as burns patients. For this reason, contactless monitoring systems are gaining increasing attention for respiratory detection. In this paper, we present a new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN). The system tracks chest movements of the subject using two strategies: using a manually selected ROI and without the selection of a ROI in the image frame. The system is based on the classifications of the frames as an inhalation or exhalation using CNN. Our proposal has been tested on 10 healthy subjects in different positions. To compare performance of methods to detect 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 the automatic strategy is 3.28± 3.33% with and agreement with respect of the reference of 98%. © 2020 by the authors.

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