Person:
Solano Quinde, Lizandro Damián

Birth Date

1975-10-09

ORCID

0000-0001-7427-4889

Scopus Author ID

24776925400

Afiliación

Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones(DEET), Cuenca, Ecuador
Universidad de Cuenca, Departamento de Ingeniería Civil, Cuenca, Ecuador
Universidad de Cuenca, Facultad de Ingeniería, Cuenca, Ecuador
Universidad de Cuenca, Departamento de Ciencias de la Computación, Cuenca, Ecuador

País

Ecuador

Organizational Units

Organizational Unit

Facultad de Ingeniería

La Facultad de Ingeniería, a inicios de los años 60, mediante resolución del Honorable Consejo Universitario, se formalizó la Facultad de Ingeniería de la Universidad de Cuenca, conformada por las escuelas de Ingeniería Civil y Topografía. Esta nueva estructura permitió una mayor especialización y fortalecimiento en áreas clave para el desarrollo regional. Cuenta con programas académicos reconocidos internacionalmente, que promueven y lideran actividades de investigación. Aplica un modelo educativo centrado en el estudiante y con procesos de mejora continua. Establece como prioridad una educación integra, la formación humanística es parte del programa de estudios que complementa a la sólida preparación científico-técnica. Las actividades culturales pertenecen a un programa permanente y activo al interior de nuestras dependencias, a la par de proyectos que desde el alumnado y bajo la supervisión de docentes cumplen con servicios de apoyo a nivel local y regional; promoviendo así una vinculación estrecha con la comunidad.

Job Title

Profesor (T)

Last Name

Solano Quinde

First Name

Lizandro Damián

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Now showing 1 - 10 of 17

Multi-GPU implementation of the horizontal diffusion method of the weather research and forecast model
(ASSOCIATION FOR COMPUTING MACHINERY INC, 2016-03-12) Solano Quinde, Lizandro Damián; Gualan Saavedra, Ronald Marcelo; Zúñiga Prieto, Miguel Ángel
The Weather Research and Forecasting (WRF), a next generation mesoscale numerical weather prediction system, has a considerable amount of work regarding GPU acceleration. However, the amount of works exploiting multi-GPU sys- tems is limited. This work constitutes an effort on using GPU computing over the WRF model and is focused on a computationally intensive portion of the WRF: the Horizontal Diffusion method. Particularly, this work presents the enhancements that enable a single-GPU based implementation to exploit the parallelism of multi-GPU systems. The performance of the multi-GPU and single-GPU based implementations are compared on a computational domain of 433x308 horizontal grid points with 35 vertical levels, and the resulting speedup of the kernel is 3.5x relative to one GPU. The experiments were carried out on a multi-core computer with two NVIDIA Tesla K40m GPUs.
Grid platform for medical federated queries supporting semantic and visual annotations
(SPIE, 2015-11-17) Gualan Saavedra, Ronald Marcelo; Guillermo Anguisaca, Juan Carlos; La Cruz Puente Alexandra; Pérez Rocano, Wilson Rodrigo; Solano Quinde, Lizandro Damián
Grid computing has been successfully applied on teleradiology, leading to the creation of important platforms such as MEDICUS, VirtualPACS and mantisGRID, among others. These platforms are studied on the basis of their available documentation in order to compare and discuss differences and similarities, advantages and disadvantages between them. Then, a grid platform architecture is proposed, based on the best features of the surveyed platforms with an additional emphasis on general federated queries involving CBIR (Content-Based Image Retrieval) and Semantic Annotations.
Mobile teleradiology system suitable for m-health services supporting content and semantic based image retrieval on a grid infrastructure
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-16) La Cruz Puente Alexandra; Espinoza Mejía, Jorge Mauricio; Medina Molina, Ruben; Pérez Rocano, Wilson Rodrigo; Saquicela Galarza, Víctor Hugo; Solano Quinde, Lizandro Damián; Vega, F; Vidal, M.-E
Teleradiology systems tackle the problem of transferring radiological images between medical image workstations for facilitating different medical activities, e.g., diagnosis, treatment and follow up a patient, medical training, or consulting second opinion. Nowadays, m-Health (aka mobile health) is becoming popular because of high quality of mobile displays, although remains a work in progress. In this paper a mobile teleradiology system is reported, which main contribution is the development of a platform: (1) supported by a Grid infrastructure, (2) using biomedical ontologies for adding semantic annotations on medical images, and (3) supporting semantic and content-based image retrieval. Images are located physically in different repositories like; hospitals and diagnostic imaging centers. All these features make the system ubiquitous, portable, and suitable for m-Health services.
Characterizing artifacts in RR stress test time series
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-16) Astudillo Salinas, Darwin Fabián; Medina Molina, Ruben; Palacio Baus, Kenneth Samuel; Solano Quinde, Lizandro Damián; Wong De Balzan, Sara
Electrocardiographic stress test records have a lot of artifacts. In this paper we explore a simple method to characterize the amount of artifacts present in unprocessed RR stress test time series. Four time series classes were defined: Very good lead, Good lead, Low quality lead and Useless lead. 65 ECG, 8 lead, records of stress test series were analyzed. Firstly, RR-time series were annotated by two experts. The automatic methodology is based on dividing the RR-time series in non-overlapping windows. Each window is marked as noisy whenever it exceeds an established standard deviation threshold (SDT). Series are classified according to the percentage of windows that exceeds a given value, based upon the first manual annotation. Different SDT were explored. Results show that SDT close to 20% (as a percentage of the mean) provides the best results. The coincidence between annotators classification is 70.77% whereas, the coincidence between the second annotator and the automatic method providing the best matches is larger than 63%. Leads classified as Very good leads and Good leads could be combined to improve automatic heartbeat labeling.
A systematic mapping study of specification languages in cloud services development
(Springer, 2019) Bermeo Conto, Jorge Luis; Zúñiga Prieto, Miguel Ángel; Solano Quinde, Lizandro Damián
Specification languages offer abstractions and notations that facilitate the systematic and analytical reasoning about important aspects in a specific domain problematic. In a software engineering process domain, the usage of specification languages improve the quality and delivery time of the artefacts generated during the execution of the process activities. Cloud applications, or cloud services, are service-oriented applications whose consumption is constantly growing; however, their development require support for new roles and activities. In this work we are interested in knowing how specification languages are being used by researchers and practitioners to support the development of cloud services. This work presents a systematic mapping that provides guidance to determine the current state and to characterize the specification languages that support the service life cycle activities in a cloud services development domain.
RR stress test time series classification using neural networks
(Institute of Electrical and Electronics Engineers Inc., 2018) Jaramillo Ayavaca, Wilson Xavier; Astudillo Salinas, Darwin Fabián; Solano Quinde, Lizandro Damián; Palacio Baus, Kenneth Samuel; Wong de balzan , Sara Null; Solano Quinde, Lizandro Damián
The RR time series, obtained from the R waves of the ECG, are a representation of the heart rate. This work presents the use of an artificial neural network (ANN) to classify RR time series from an ECG stress test. Four classes of RR time series were defined very good, good, low quality and useless. We use a preprocessing stage to split input data vectors into N W data windows for which we compute the standard deviation of the RR interval (SD RR ) to generate the input features vector of a multilayer perceptron network architecture. We introduce a saturation value S in order to limit SD RR values. 520 RR time series from 65 records of ECG stress test were analyzed. Experiments were performed to explore the influence of parameters S and N W . 40 subjects records are used in training and the remaining for testing. The classification results show a matching correlation ratio above 71%, which is higher than the …
Coordinating learning analytics policymaking and implementation at scale
(2020) Pesántez Cabrera, Paola Gabriela; Solano Quinde, Lizandro Damián; Sigüenza Guzmán, Lorena Catalina; Zúñiga Prieto, Miguel Ángel
Many Latin‐American institutions recognise the potential of learning analytics (LA). However, the number of actual LA implementations at scale remains limited, notwithstanding considerable effort made to formulate guidelines and frameworks to support the LA policy development. Guidance on how to coordinate the interaction between the LA policymaking and implementation is mostly missing, leaving a difficult challenge up to practitioners. In this study we propose a coordination model to support future LA initiatives at scale. We explore the problem by comparing two cases in Belgium and Ecuador. Following up we use the LA implementation timeline as a driver for planning the interaction between the policymaking and implementation. We continue by testing an application of the model with LA experts predominantly from Latin‐American institutions, asking them to map low‐level items of the SHEILA policy framework to four implementation phases. The results of this mapping support that LA policy building can be spread over time, that it can coincide with LA implementation at scale, and that both efforts can be coordinated. It is hoped that this study will provide additional guidance for future Latin‐American and other LA initiatives.
WebMedSA: A web-based framework for segmenting and annotating medical images using biomedical ontologies
(SPIE, 2015-11-17) Vega, F; Espinoza Mejía, Jorge Mauricio; La Cruz Puente Alexandra; Pérez Rocano, Wilson Rodrigo; Saquicela Galarza, Víctor Hugo; Solano Quinde, Lizandro Damián; Tello Guerrero, Marco Andrés
Advances in medical imaging have fostered medical diagnosis based on digital images. Consequently, the number of studies by medical images diagnosis increases, thus, collaborative work and tele-radiology systems are required to effectively scale up to this diagnosis trend. We tackle the problem of the collaborative access of medical images, and present WebMedSA, a framework to manage large datasets of medical images. WebMedSA relies on a PACS and supports the ontological annotation, as well as segmentation and visualization of the images based on their semantic description. Ontological annotations can be performed directly on the volumetric image or at different image planes (e.g., axial, coronal, or sagittal); furthermore, annotations can be complemented after applying a segmentation technique. WebMedSA is based on three main steps: (1) RDF-ization process for extracting, anonymizing, and serializing metadata comprised in DICOM medical images into RDF/XML; (2) Integration of different biomedical ontologies (using L-MOM library), making this approach ontology independent; and (3) segmentation and visualization of annotated data which is further used to generate new annotations according to expert knowledge, and validation. Initial user evaluations suggest that WebMedSA facilitates the exchange of knowledge between radiologists, and provides the basis for collaborative work among them.
Temporal analysis of 911 emergency calls through time series modeling
(Springer, 2020) Robles Granda, Pablo Dario; Tello Guerrero, Marco Andres; Solano Quinde, Lizandro Damián; Zúñiga Prieto, Miguel Ángel
We present two techniques for modeling time series of emergency events using data from 911 emergency calls in the city of Cuenca-Ecuador. We study state-of-the-art methods for time series analysis and assess the benefits and drawbacks of each one of them. In this paper, we develop an emergency model using a large dataset corresponding to the period January 1st 2015 through December 31st 2016 and test a Gaussian Process and an ARIMA model for temporal prediction purposes. We assess the performance of our approaches experimentally, comparing the standard residual error (SRE) and the execution time of both models. In addition, we include climate and holidays data as explanatory variables of the regressions aiming to improve the prediction. The results show that ARIMA model is the most suitable one for forecasting emergency events even without the support of additional variables.
Modeling 911 emergency events in Cuenca-Ecuador using geo-spatial data
(CITT 2018, 2019) Robles Granda, Pablo Dario; Tello Guerrero, Marco Andres; Zúñiga Prieto, Miguel Ángel; Solano Quinde, Lizandro Damián
We present several techniques for modeling emergency events using data from 911 emergency calls in the city of Cuenca-Ecuador. We apply three types of models. First, we use a probabilistic description of events using Gaussian kernels based on both, regular segmentation and mixture models, to represent the spatial distribution of occurrences. Second, we verify the qualitative relation of the clusters obtained with our kernel model with respect to the geo-political organization of the city. Finally, we develop an emergency model using a large dataset corresponding to the period January 1st 2015 through December 31st 2016 and test various data mining algorithms for prediction purposes. We verify the usefulness of our approach experimentally.