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Computer Vision-Based QR Code Detection for Manipulation of a DC Motor in Semi-Controlled Environments
(IEEE, 2023)
Hernández Pérez, M. A.
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Pérez-Daniel, Karina Ruby
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Delgado Reyes, G.
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García Ramírez, P.
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Cane-González, C. J.
The present research work carries out the design and implementation of a computer vision algorithm supported by image pre-processing techniques to improve the QR (Quick Response) code detection rate efficiency in a semi-controlled environment. The proposed algorithm is implemented in an embedded system using a Raspberry Pi model4B+ microcomputer to manipulate a DC motor that emulates the change in direction in a mobile robot. For greater precision and simplicity, the encoded data of the QR code are 1 and 0, which allow turning to the right or left, respectively, on the DC motor. To complement the embedded system, a Raspberry camera, and a control module (current regulator) for the DC motor are used. Finally, efficiency tests are carried out with the proposed algorithm, where the idea is to obtain a lower error factor compared to the tests carried out in classical algorithms that do not have image pre- processing stages. These tests are carried out in a semi-controlled environment, varying the luminosity and distance to detect the QR code. Once the objective is reached, the best light and distance conditions are proposed for detection, allowing the general system to be versatile and fast.
7 2
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Application of parametric activation function A string in the task of multimodal data analysis
(AIP Publishing, 2023)
Verina, Yana V.
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Tolstoukhov, Denis E.
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Pérez-Daniel, Karina Ruby
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Egorov, Dobroslav P.
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Kravchenko, Oleg V.
Data analysis is a dynamically developing field, currently. One of the actual tasks of data analysis is the task of classification. The problem of dividing a specific group of objects into a predetermined number of groups united in various ways is also important. On the other hand, computational performance increases and the volume of observed data increases, therefore, assigning them to certain subgroups becomes more complicated. In this paper, the binary classification problem is solved and a new parametric activation function for the machine learning model under consideration is analyzed. An important difference between the proposed classifier, for example, from standard classifiers based on logistic regression, is the connection with infinitely differentiable splines, the so-called atomic functions. At the same time, it is of interest to study the dependence of the classifier quality on the value of the variable parameter of the activation function. By changing the parameter from the activation function, you can make dependencies on the quality of the presented classifier. The comparison of quality indicators with various parameters of the activation function is considered. As data for model training, cross-validation and testing, an open multimodal data set MELD was used, consisting of a parallel set of videos and their textual interpretation. It is worth noting that MELD is a pre– labeled data corpus. The data were divided into two categories of sentiment analysis: positive and negative. A comparison of the work of a classifier based on parametric AString and logistic regression is given.
13 1
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Similarity Learning for CNN-Based ASL Alphabet Recognition
(2021)
Fierro Radilla, Atoany Nazareth
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Pérez-Daniel, Karina Ruby
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Benítez-García, Gibran
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Najera Garcia, Pedro
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Fuentes Valdez, Ramona
Sign language is an important communication way to convey information among the deaf community, and it is primarily used by people who have hearing or speech impairments. Besides, sign language represents a direct Human-Computer-Interaction (HCI) similar to voice commands therefore, the purpose of this study is to investigate and develop a system for American Sign Language (ASL) alphabet recognition using convolutional neural networks. Our proposal is based on semantic similarity learning using Siamese Convolutional Neural Network to reduce the intra-class variation and inter-class similarity among sign images in a Euclidean space the results of the siamese architecture applied to the ASL alphabet dataset outperform previous works found in the literature. From these results, using t-SNE visualization, we demonstrate that our hypothesis is correct; the ASL recognition improves when increasing the similarity among encoding of the images belonging to the same class and reducing it otherwise. ©2021 The authors and IOS Press. All rights reserved.
Scopus© Citations 1 6 1
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White Blood Cell Detection and Classification in Blood Smear Images Using a One-Stage Object Detector and Similarity Learning
(2022)
Fierro-Radilla, Atoany Nazareth
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Bolaños Cacho, Monica Larre
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Pérez-Daniel, Karina Ruby
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Arredondo Valle, Armando
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López Figueroa, Carlos Alberto
White blood cells are a fundamental part of the immune system which protect human body against infections and diseases. The complete blood count is a routine analysis that provides doctors information about the patients. Monitoring the immune system allows doctor to select preventive treatments against several diseases. The complete blood count relies in a rigorous observation of a blood sample through a microscope; the accuracy of the result depends on the expertise and time of the analyst. In this paper, a novel vision-based method using convolutional neural networks for white blood cell detection and classification is presented. The results show the proposed method is robust against the huge number of easy negatives in object detection, as well, the high inter-class similarity among images can be reduced for a better white blood cell classification. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
10 1
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Analysis and implementation of a car-type mobile robot for semi-planned trajectory tracking using hybrid control
(Institute of Electrical and Electronics Engineers Inc., 2022)
Hernandez-Perez, M. A.
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Pérez-Daniel, Karina Ruby
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Delgado-Reyes, G.
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Garcia Ramirez, P.
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Morales-Layja, L.
The present research work carries out an analysis and implementation of a car-type mobile robot for monitoring planned and semi-planned trajectories through a hybrid control. As a first step, a mathematical model that describes the behavior of a car-type robot based on a differential robot is determined. In addition, numerical simulations are presented in an open-loop and later in a closed-loop using a classic control strategy to follow planned trajectories. Subsequently, in order to verify the simulations, the model, and the designed control strategies, the development and implementation of a SunFounder PiCar-V model car-type mobile robot prototype were carried out. The implementation has been done using the Raspbian operating system and the help of the Raspberry Pi 4 B microcomputer for remote interaction between the prototype and the personal computer. Finally, a camera is implemented as a sensor and a series of electromechanical couplings that help the mobile robot to have greater freedom, vision, and control over trajectory tracking. This adaptation allows obtaining a hybrid control by switching between classic closed-loop control and a visual control supported by the camera incorporated in the robot, thus allowing a greater autonomy of the PiCar-V to follow semi-planned trajectories. © 2022 IEEE.
15 1
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Deep Learning for Multimodal Fall Detection
(2019)
Martinez-Villaseñor, Lourdes
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Pérez-Daniel, Karina Ruby
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Ponce, Hiram
Fall detection systems can help providing quick assistance of the person diminishing the severity of the consequences of a fall. Real-time fall detection is important to decrease fear and time that a person remains laying on the floor after falling. In recent years, multimodal fall detection approaches are developed in order to gain more precision and robustness. In this work, we propose a multimodal fall detection system based on wearable sensors, ambient sensors and vision devices. We used long short-term memory networks (LSTM) and convolutional neural networks (CNN) for our analysis given that they are able to extract features from raw data, and are well suited for real-time detection. To test our proposal, we built a public multimodal dataset for fall detection. After experimentation, our proposed method reached 96.4% in accuracy, and it represented an improvement in precision, recall and F-{1}-score over using single LSTM or CNN networks for fall detection. © 2019 IEEE.
Scopus© Citations 15 15 2
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Watermarking of HDR Images in the Spatial Domain With HVS-Imperceptibility
(2020)
Pérez-Daniel, Karina Ruby
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García-Ugalde, Francisco
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Sánchez, Víctor
This paper presents a watermarking method in the spatial domain with HVS-imperceptibility for High Dynamic Range (HDR) images. The proposed method combines the content readability afforded by invisible watermarking with the visual ownership identification afforded by visible watermarking. The HVS-imperceptibility is guaranteed thanks to a Luma Variation Tolerance (LVT) curve, which is associated with the transfer function (TF) used for HDR encoding and provides the information needed to embed an imperceptible watermark in the spatial domain. The LVT curve is based on the inaccuracies between the non-linear digital representation of the linear luminance acquired by an HDR sensor and the brightness perceived by the Human Visual System (HVS) from the linear luminance displayed on an HDR screen. The embedded watermarks remain imperceptible to the HVS as long as the TF is not altered or the normal calibration and colorimetry conditions of the HDR screen remain unchanged. Extensive qualitative and quantitative evaluations on several HDR images encoded by two widely-used TFs confirm the strong HVS-imperceptibility capabilities of the method, as well as the robustness of the embedded watermarks to tone mapping, lossy compression, and common signal processing operations.
Scopus© Citations 10 40 1
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Rotten Fruit Detection Using a One Stage Object Detector
(2020)
Pérez-Daniel, Karina Ruby
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Fierro Radilla, Atoany Nazareth
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Peñaloza Cobos, José Pablo
Digital images and computer sciences have become two powerful tools in several areas, such as astronomy, medicine, forensics, etc. In the last years, computer sciences are getting involved in agricultural and food science to decide based on estimated or actual parameters named features. Rottenness is the state of decomposing or decaying the quality of the fruit, which not only affects the taste and appearance but also modifies its nutritional composition, causing the presence of mycotoxins dangerous for humans. Nowadays, rottenness detection is carried out using human inspection or using Ultraviolet light to highlight spots of rottenness represented as fluorescence. Recent computer vision approaches address this problem using hyperspectral imaging systems. In this paper, we propose to use a one-stage object detector inspired by RetinaNet to detect whether a fruit is fresh or rotten. One of the main stages of RetinaNet is based on computing a multi-scale convolutional feature pyramid network on top of a backbone. Therefore, in this work, we analyze the performance of RetinaNet using different artificial neural networks as backbone to determine the highest accuracy for fruit and rottenness detection. The experiments were done using a dataset composed of 13599 images divided by 6 classes, 3 fresh fruits, and 3 rotten fruits. The performance evaluation considers the mean average precision in the detection and the inference time of tested backbone models. © 2020, Springer Nature Switzerland AG.
Scopus© Citations 3 8 1
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Siamese Convolutional Neural Network for ASL Alphabet Recognition
(2020)
Fierro Radilla, Atoany Nazareth
;
Pérez-Daniel, Karina Ruby
American sign language is an important communication way to convey information among the deaf community in North America and is primarily used by people who have hearing or speech impairments. The deaf community faces a struggle in schools and other institutions because they usually consist primarily of hearing people. Besides, deaf people often feel misunderstood by people who do not know sign language, for example, family members. In the last two decades, researchers have been proposing automatic sign language recognition systems to facilitate the learning of sign language, and nowadays, computer scientists have focused on using artificial intelligence in order to develop a system capable of reducing the communication gap between hearing and deaf people. In this paper, it is proposed a Siamese convolutional neural network for American sign language alphabet recognition. This siamese architecture allows the computer to reduce the high interclass similarity and high intraclass variations. The results show that the proposed method outperforms the state-of-the-art systems.
Scopus© Citations 1 9 1
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Quadrotor Real-Time Simulation: A Temporary Computational Complexity-Based Approach
(2022)
Delgado-Reyes, Gustavo
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Valdez-Martínez, Jorge Salvador
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Hernández-Pérez, Miguel Ángel
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Pérez-Daniel, Karina Ruby
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García-Ramírez, Pedro Javier
The interaction of digital systems with dynamic systems requires synchrony and the accomplishment of time constrains, so the simulation of physical processes needs an implementation by means of real-time systems (RTS). However, as it can be expected, every simulation and/or implementation might demand too many computational resources, surpassing the capacity of the processor used by computational systems. This is the reason for the need to perform a temporary computational complexity analysis based on the study of the behavior of the execution times of the implemented simulation. In this regard, the Real-Time Operating Systems (RTOS) feature time managing tools, which allow their precise measurement and the establishment of scheduling criteria in process execution. Therefore, this research proposes accomplishing a temporary computational complexity analysis of the real-time simulation by an embedded system (ES) of an unmanned aerial vehicle (UAV) propelled by four rotors. Derived from this analysis, formal definitions are elaborated and proposed, which establish a close relationship between the temporary computational complexity and typical real-time temporary constraints. To the best of the author’s knowledge, the definitions presented in this article have not been reported in the literature. Furthermore, to perform the temporary computational complexity analysis of the UAV, the mathematical modeling based on the Euler–Lagrange approach is presented in detail. Finally, simulations were performed using a real-time system implemented on the Embedded Computer System (ECS) Raspberry Pi 2 Model B+, in order to validate the suggested definitions.
Scopus© Citations 5 8 2

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