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Data-Driven Innovation for Intelligent Technology : Perspectives and Applications in ICT

2024 , Ponce, Hiram , Brieva, Jorge , Lozada Flores, Octavio , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Hiram Ponce , Jorge Brieva , Octavio Lozada-Flores , Lourdes Martínez-Villaseñor , Ernesto Moya-Albor

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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Mechatronic Design of a Low-Cost Smart Wheelchair Controlled by Joystick and Voice Commands

2023 , Bobadilla-Rendón, David , Monroy-Rueda de León, Irvine J. , Salazar-Salinas, Gabriel , Stefan-Lepe De Soto, Antonio , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

Elderly people have increased at an accelerated rate in recent years. In Mexico, one of the main problems affecting this population are related to disabilities, specifically limited mobility, i.e., arthritis in older adults. Different technological solutions have been proposed, such as electrical wheelchairs. However, for arthritic people, these wheelchairs are difficult to operate, lacking comfortability, and might be very expensive. In this work, we propose the development of a smart wheelchair for arthritic older adults able to move automatically and controlled by using slight manual movements of the hand and by voice commands. We followed the general methodology of a mechatronic design. A proof-of-concept model of the wheelchair was implemented. For validation, we tested the prototype in different real indoor scenarios. Results conclude that our proposed smart wheelchair complies with the user requirements, it is easy to operate, and the cost is reduced considerably. We anticipate this is a low-cost efficient smart wheelchair prototype that can be further considered for real technological solutions.

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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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Optimal Dataset Size for Fine-Tuning sEMG-Based Hand Gesture Recognition in Rehabilitation Prosthesis

2024 , Andrés Emiliano Escobedo Gordillo , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram , Erick Franco-Gaona , Ivan Cruz-Aceves

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Phase-based motion magnification video for monitoring of vital signals using the Hermite transform

2017 , Brieva, Jorge , Moya-Albor, Ernesto

In this paper we present a new Eulerian phase-based motion magnification technique using the Hermite Transform (HT) decomposition that is inspired in the Human Vision System (HVS). We test our method in one sequence of the breathing of a newborn baby and on a video sequence that shows the heartbeat on the wrist. We detect and magnify the heart pulse applying our technique. Our motion magnification approach is compared to the Laplacian phase based approach by means of quantitative metrics (based on the RMS error and the Fourier transform) to measure the quality of both reconstruction and magnification. In addition a noise robustness analysis is performed for the two methods. © 2017 SPIE.

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A perceptive watermarking approach applied to COVID-19 imaging data

2020 , Gomez-Coronel, Sandra L. , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

This work presents a watermarking algorithm applied to medical images of COVID-19 patients. The principal objective is to protect the information of the patient using an imperceptible watermarking and to preserve its diagnose. Our technique is based on a perceptive approach to insert the watermark by decomposing the medical image using the Hermite transform. We use as watermark two image logos, including text strings to demonstrate that the watermark can contain relevant information of the patient. Some metrics were applied to evaluate the performance of the algorithm. Finally, we present some results about robustness with some attacks applied to watermark images. © 2020 SPIE

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A Vision-based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach

2024 , Cuesta-Solano, Roberto , Moya-Albor, Ernesto , Ponce, Hiram , Brieva, Jorge

The paper presents a fuzzy Q-learning (FQL) and optical flow-based autonomous navigation approach. The FQL method takes decisions in an unknown environment and without mapping, using motion information and through a reinforcement signal into an evolutionary algorithm. The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are “dense” or “thin” which has a relationship with the proximity of objects. The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component. The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python. ©The authors ©INTELLIGENCE SCIENCE AND TECHNOLOGY PRESS INC.

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Non-contact breathing rate monitoring system using a magnification technique and convolutional networks

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

In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and a system based on a Convolutional Neural Network (CNN). After the magnification procedure, a CNN 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. Two strategies are used as input to the CNN. A CNN-ROI proposal where a region of interest is selected manually on the image frame and in the second case, a CNN-Whole-Image proposal where the entire image frame is selected. Finally, the RR is estimated from the classified frames. The CNN-ROI proposal is tested on five subjects in lying face down position and it is compared to a procedure using different image processing steps to tag the frames as inhalation or exhalation. The mean average error in percentage obtained for this proposal is 2.326±1.144%. The CNN-whole-image proposal is tested on eight subjects in lying face down position. The mean average error in percentage obtained for this proposal is 2.115 ± 1.135%. © COPYRIGHT 2020 SPIE. Downloading of the abstract is permitted for personal use ONLY.

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Non-Contact Respiratory Rate Estimation in Newborns During Quiet Sleep Using Video Magnification Techniques and a 3D Convolutional Neural Network

2024 , Andrés Emiliano Escobedo Gordillo , Orlando Yael Rivas-Scott , Brieva, Jorge , Moya-Albor, Ernesto , Sandie Cabon , Fabienne Poree , Patrick Pladys

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Segmentation and motion estimation applied to fetal heart analysis using a multi-texture active appearance model and an optical flow approach

2018 , Escalante Ramírez, Boris , Moya-Albor, Ernesto , Brieva, Jorge , Guzmán Huerta, Mario , Camargo Marín, Lisbeth , Vargas Quintero, Lorena Paola P.

In this work we present a combination of segmentation and motion estimation methods applied to left ventricle evaluation in fetal echocardiographic images which are used for prenatal diagnosis. In our proposed scheme, several features of the ultrasound images are computed and used for both algorithms. A multiresolution framework is employed for the segmentation and motion estimation tasks. The segmentation is achieved using a multi-texture active appearance model based on the Hermite transform. The analysis is performed using the appearance models provided by Hermite coefficients up to third order. The multiresolution approach allows to obtain a robust segmentation to extract the shape of the left ventricle. The obtained results in the segmentation step are used for the motion estimation algorithm. The left ventricle is the structure used for evaluation. The main goal is to determinate the heart movement of fetal heart which can be used for disease detection, characterization and further analysis. Results of the motion estimation process are analyzed and compared with other techniques applied to heart ultrasound data. © SPIE. Downloading of the abstract is permitted for personal use only.