Review of the behavior of the Google search engine for the generation of data from the artificial intelligence area of Telus International.

• Designed and managed the development of a monitoring and production control system for agricultural fields.• Designed and managed the development of a personal health monitoring system, with immediate action on alerts and a learning system for the prediction of altered health states.• Designed and managed the development of a technological system for the monitoring of different characteristics related to the coverage of insurance policies.• Designed and managed the development of a vehicle monitoring system, whose premise is to detect and resolve risk situations for fleets of different clients. • Designed and managed the development of a safety monitoring system for the mining industry.• Design, develop and implement projects in the telecommunications, medical, manufacturing, mining, insurance and agribusiness industries.• Design, develop and implement computer systems to solve problems in the industrial sector.• Design, develop and implement hardware to solve problems in the industrial sector.• Design and develop process architecture and software.

• Helped design and manage the development of a telecom tower monitoring system.• Design, develop and implement projects in the telecommunications, medical, manufacturing, mining, insurance and agribusiness industries.• Design, develop and implement computer systems to solve problems in the industrial sector.• Design, develop and implement hardware to solve problems in the industrial sector.• Design and develop process architecture and software.

Class focused on the analysis and study of all the technological elements that are used in the hospital environment.

I developed a model for the manufacture of a hypobaric chamber, and I work in the preventive and corrective maintenance of the hospital's medical technology, as well as the administrative aspects of the equipment.

Coordinate teachers, develop curriculum, and teach mathematics and physics classes during Holy Week courses to high school students aspiring to higher education.

Project for ENEIT 2018 (N-Check 2.0): Development of a platform in which, through medical sensors, you can record the data that this device reads and save it on the platform, thus creating a digital clinical history. In addition to providing information about clinics or health centers with specialists who can address the crisis state of a patient in an emergency. The person will be able to access the list and verify the health centers that are available at that moment and the specialists that can attend to him/her. Project for ENEIT 2017 (N-Check): prototype medical telemonitoring device for apnea patients, developed by a team of 5 people. It works with a pulse oximeter connected to a microcontroller with features for the internet of things. The result is sent remotely to a proprietary application where the patient's manager can consult the data. Responsible for the design and programming of the base of the project and the original prototype.

“Characterization of the respiratory pattern in patients with chronic heart failure applying joint symbolic dynamics”: This work proposes a method to identify the respiratory pattern of patients with chronic heart failure (CHF) in order to determine nonperiodic breathing (NPB), periodic breathing (PB) and Cheyne-Stokes respiration (CSR) through nonlinear symbolic analysis of biological signals. The cardiorespiratory profiles of 43 patients were examined; their ECG signals and respiratory pattern were processed, analyzed, and studied for parameters that could be of use, specifically in patient classification. These patients were characterized by means of their cardiorespiratory signals, applying symbolic set dynamics analysis to heart rate and respiratory interval duration. The parameters of greatest statistical significance among all groups were identified through a two-tailed Kruskal-Wallis test (α= 0.05) and by a linear discriminant analysis (LDA) classification method. The best result achieved with this method used 10 characteristics to discriminate patients with an accuracy of 97.67% (Acc). The features to discriminate between groups were related to cardiorespiratory interaction rather than to breathing patterns alone, thus supporting the idea that abnormal breathing patterns derive from physiological abnormalities present in chronic heart failure