Browsing by Author "Sarria, Milton Orlando"
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Item Incentives for the development of photovoltaic energy in a developing country, case study(Institute of Electrical and Electronics Engineers Inc., 2019-10-17) Moya Chavez, Francisco David; Martínez Ortega, Sara Virginia; Sarria, Milton OrlandoThis paper aims to present the great potential that Colombia has with the use of renewable energies as a developing country. Through the implementation of different policies and laws, it encourages all those who investigate or implement the use of renewable energy. This is motivated as a response to the changes needed towards clean technologies thus contributing to avoid environmental, economic and social problems caused by the excess use of fossil fuels and the misuse of non-renewable natural resources. Through an exhaustive review of the bibliography and the current Colombian regulations, it will be analyzed how the country is prepared to migrate to clean technologies for energy production such as photovoltaic systems. And it will explain the case study of the Universidad Santiago de Cali, where has been implemented solar panels and given the excellent results attained, the university plans to expand the solar panels to all the buildings in the campus.Item Sistema computacional para el diagnóstico de la enfermedad de Parkinson empleando el análisis de señales de voz(Universidad Santiago de Cali, 2019) Moofarry Villaquiran, Jhon Fredy; Sarria, Milton OrlandoParkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. This disorder mainly affects older adults at a rate of about 2%, and about 89% of people diagnosed with PD also have speech disorders. This has led to the development of different research in voice processing for Parkinson's patients, which allows not only a diagnosis of the pathology but also a follow-up of its evolution. In recent years, a large number of studies have focused on the automatic detection of pathologies related to the voice, in order to make objective evaluations of the voice in a non-invasive manner. In cases where the pathology primarily affects the vibratory patterns of vocal folds such as Parkinson's, the analyses typically performed are sustained vowel pronunciations. In this article, it is proposed to use information from slow and rapid variations in voice signals, also known as modulating components, combined with an effective feature reduction approach that will be used as input to the classification system. The proposed approach achieves success rates higher than 88%, surpassing the classical approach based on cepstrales coefficients on the Mel scale (MFCC), this was achieved by extracting characteristics in voice records from a Spanish language database, consequent to this was organized into a vector of characteristics and subsequent estimation was made to the diagnosis of the pathology. The results show that the information extracted from components with slow and fast variations is highly discriminatory to support the assisted diagnosis of PD. This information can also be used as a complement to existing systems.