Quispe Bonilla, Max David
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Quispe Bonilla
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Max David
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Ingeniería Eléctrica, Electrónica y de Comunicación
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ISC. Institute of Smart Cities
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Publication Unknown Diseño, desarrollo y evaluación preliminar de un novedoso monitor de signos vitales llevable para vacunos(Universidad Nacional Mayor de San Marcos (Perú), 2019) Quispe Bonilla, Max David; Poma Gutiérrez, Adolfo; Serrano Arriezu, Luis Javier; Led Ramos, Santiago; Quispe Peña, Edgar; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de ComunicaciónEl monitoreo de los diferentes signos vitales en vacunos tiene importancia desde el punto de vista productivo, sanitario y de bienestar animal; sin embargo, existen pocos equipos que tengan el potencial de uso a nivel de campo y que no sean invasivos. Por tal motivo se llevó a cabo el presente trabajo con la finalidad de diseñar, construir y evaluar el uso de un pequeño MOnitor de SIgnos VItales LLevable (MOSIVILLe), que sea capaz de capturar las señales vitales en vacunos bajo condiciones de campo. El diseño y desarrollo del MOSIVILLe se realizó en Lima, Perú, entre enero y octubre de 2017 y la evaluación en campo se realizó en Chota, Perú, utilizando 11 vaquillas entre noviembre de 2017 y febrero de 2018. Con el uso del MOSIVILLe se obtuvieron las señales vitales de vacunos, reconociéndose las ondas P, Q, R, S, T, complejo QRS e intervalo RR del electrocardiograma que condujeron a obtener la frecuencia cardiaca (FC). Adicionalmente, se obtuvo la temperatura de la piel (T°P) y la señal de ventilación con los picos de inhalación, exhalación, tiempo de inspiración y tiempo de espiración que determinan la frecuencia respiratoria (FR). El MOSIVILLe usado en vacunos en condiciones de campo permitió obtener una FC de 70.83±1.47, FR de 25.24±1.64 y una T°P de 31.52±0.40 (promedio ± error estándar), valores que se encuentran en concordancia con la literatura. Se concluye que el MOSIVILLe es una alternativa importante para obtener diversos signos vitales en vacunos bajo condiciones de campo.Publication Open Access Application of artificial intelligence and digital images analysis to automatically determine the percentage of fiber medullation in alpaca fleece samples(Elsevier Science, 2022) Quispe Bonilla, Max David; Serrano Arriezu, Luis Javier; Trigo Vilaseca, Jesús Daniel; Quispe Bonilla, Christian; Poma Gutiérrez, Adolfo; Quispe Peña, Edgar; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaThe aim of this research is to develop and validate two computer programs based on artificial intelligence (AI) and digital image analysis (DIA) in order to determine the incidence of medullation in white alpaca fibers. Two data sets were analyzed: 76 samples of Huacaya alpaca fibers obtained from Huancavelica, Peru, and 200 samples of white alpacas of two genotypes (Huacaya, n =100; Suri, n = 100), obtained from Arequipa, Peru. The preparation of each sample followed the procedure described in IWTO-8-2011. The Pytorch framework was used to generate several training models based on the You Only Look at Once (YOLO) architecture. Circa 4000 pictures of fibers were taken and 661 of them were selected as representative. Using the LabelImg software, the fibers present in each representative picture (approximately 10 fibers/picture) were labeled as one of these two classes: either medullated or non-medullated. Subsequently, the data augmentation technique was applied to expand the data set to 3966 photographs. Thus, 90 of them were used as initial validation data, while the reaming 3876 pictures (containing a total of 23,964 labeled fibers) were used as training data. Matlab was used to develop the DIA-based software. More specifically, algorithms of pre-processing, segmentation, smoothing, skeletonization and Hough transform were implemented to detect medullated and non-medullated fibers. Correlation and linear regression analyses were used to evaluate the models. The medullation percentage results show that there is no statistically significant difference between the AI-based method and the projection microscope method (p-value = 0.668 and 0.672 for the t-student and Wilcoxon tests, respectively). Moreover, the correlation of each of the developed computer methods with the projection microscope method is very strong (r = 0.99 and 0.97). This confirms the software ability to perform the recognition of fibers with and without medullation. Similar results (p-value = 0.357) were obtained when comparing the projection microscope method and DIA-based software method. Finally, using the proposed framework, the average time required to analyze a sample was 19.44 s. As a result, this software allows the implementation of practical, precise, and efficient methodologies to determine the incidence of medullation of alpaca fibers.