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My curriculum vitae in english, created with the ModernCV template

Aula sobre Gráficos no LaTeX ministrada pelo professor Egmon Pereira a alunos do primeiro período de Eng. da Computação do Cefet-MG Unidade de Timóteo.

Projeto lab CE2

El análisis estadístico de alta dimensión y tamaño de muestra pequeño (HDLSS) se está aplicando cada vez más en una amplia gama de contextos. En tales situaciones, se ve que el popular método de la Máquina de Vectores Soporte (SVM) sufre de ''Acumulación de datos'' en el margen, lo que puede disminuir la capacidad de generalización del modelo. Esto conduce al desarrollo de la Distance Weighted Discrimination para encontrar un hiperplano separador . En el presente trabajo se revisa y reproduce, con detalle en la derivación y solución de la función de pérdida que se resuelve usando SOCP, del método desarrollado en e implementado en el entorno R\cite{R}. Basado en el trabajo e implementación de se aplica y comparan resultados a conjuntos de datos reales y simulados (en medida de lo posible los mismos conjuntos de datos utilizados que en) Palabras clave: SVM, kernel, R (el ambiente de cómputo estadístico) y datos de alta dimensión con tamaño de muestra pequeño (data High Dimension Low Sample Size).

This is a table I used in the paper W. Li, G. Wei, D. Ding, Y. Liu and F. E. Alsaadi, "A New Look at Boundedness of Error Covariance of Kalman Filtering," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 2, pp. 309-314, Feb. 2018. I would like to share some LaTex codes for the table which might be helpful to some readers.

This is the LaTeX template for Climate Informatics 2018 Workshop.

Baseado no Template MDPI/sensors

Juan Ignacio Basilio Flores's CV

With the detectors currently off, LIGO has detected and gathered an abundance of data from the second observing run (O2). Some of which, captures the most recent triggers that are potential candidates for future gravitational waves, are analyzed more thoroughly. My responsibility as a student researcher is to perform independent checks on four of the most recent Compact Binary Coalescence (CBC) triggers. In order to do so, I compare the \(h(t)\) Omega scans of these events to the Gravity Spy classes. Omega scans are a detector characterization tool to help measure the Signal-to-Noise-Ratio (SNR) of transient noises during detections. This helps scientists distinguish the difference between a gravitational wave signal, which looks like a `chirp' versus a glitch in the data. Gravity Spy is a citizen science program that helps LIGO in classifying glitches to improve machine learning for gravitational wave signals. For each event I determine if it looks like one of the known categories of solved or unsolved glitches seen in the Advanced LIGO detectors? My results are then recorded in the O2 event detection checklist. Omega scans are a `burst-type' search pipeline that detect glitches efficiently. The Omega scan is labeled using time measured in seconds on the x-axis, frequency measured in Hz on the y-axis and the signal measured is normalized to demonstrate how `loud' the noise is.