In this letter, we report an enhanced MFDFA algorithm to compute the singu-larity spectra of the ... more In this letter, we report an enhanced MFDFA algorithm to compute the singu-larity spectra of the so-called E-F valley transition region, which is considered the most complex ionospheric layer because its plasma instability mechanisms are still not well understood. The results found for ∆α confirm the multifractal behaviour which is expected to be related to instabilities in the ionosphere as a whole, allowing, in an integrated scenario, to discuss new thermodynamic and plasma structural properties of the atmospheric layers that make up the Earth's ionosphere. Keywords: ionospheric plasma instabilities, multifractal analysis, equatorial valley region irregularities, inhomogeneous turbulence. With advances in the scientific computing, some nonlinear methods based 1 on fractal formalism and multiplicative cascade processes have found increased 2 application in ionospheric studies to understand the scaling structures and com-3 plexity, in recent times, and also, undergoing continuous improvements and wide 4 usability (Fornari et al., 2016; Neelakshi et al., 2019; Joshi et al., 2020). 5 In the ionospheric studies, the valley region, located in between the top 6 of the E region and the base of the F region with reduction in the electron 7 density, is still a less explored area of research compared to the F region as it 8 can be explored using powerful incoherent and coherent scatter radar and in 9 situ experiments. 10 Our earlier studies have demonstrated the potential of the DFA and MFDFA 11 on ionospheric data. The DFA studies (Fornari et al., 2016; Neelakshi et al., 12 2019) reveal the long-range correlation and non-homogeneous nature of the 13 ionospheric irregularities. We have reported the wide variation in the spec-14 tral indices and their large deviation from the homogeneous turbulence spectral 15 index. Also, we have shown that this wide variation in spectral indices is nei-16 * Neelakshi J.
Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media
In recent decades, the internet access growth has generated a substantial increase in the informa... more In recent decades, the internet access growth has generated a substantial increase in the information circulation in social media. Within the information variety circulating on the internet, extreme social events such as armed conflicts have become areas of great public interest because of their direct influence on society. The study of such data from social media is useful in understanding an event's evolution, in particular how threats over time can generate an endogenous evolution resulting in an extreme event. This chapter uses the technique of sentiment analysis to identify the threat degree of news about armed conflicts distributed in social media. This analysis generates an endogenous threat time series that is used to predict the future threat variation of the analyzed extreme social events. In the prediction of the endogenous time series, the authors apply the deep learning technique in a structure that uses the long short-term memory (LSTM) neural network.
16th International Conference on Information Technology-New Generations (ITNG 2019), 2019
This work presents a parallel implementation of the Two-Dimensional Detrended Fluctuation Analysi... more This work presents a parallel implementation of the Two-Dimensional Detrended Fluctuation Analysis (2D-DFA) algorithms using GPGPU-CUDA through the PyCUDA Wrapper. Both the monofractal and multifractal versions of 2D-DFA present poor performance in their sequential implementations because of high computational complexity, rendering ineffective the use for real-time applications and those classified as Big Data. However, these algorithms present the requirements needed to be parallelized using GPUs. Mainly because the same computation is performed in different sub-matrices of the data. Therefore, the concepts of data parallelism and independence, necessary for computation in GPUs were attending. The parallelization strategy was process simultaneously several sub-matrices, and not parallelizes the internal procedure in each sub-matrix. Matrices with different dimensions and Hurst exponents were generated for the validation of the method implemented. The results showed that both algori...
Resumen El indice geomagnetico Kp se deriva del indice K a partir de las mediciones de trece esta... more Resumen El indice geomagnetico Kp se deriva del indice K a partir de las mediciones de trece estaciones localizadas alrededor de la Tierra entre las latitudes geomagneticas 48◦ y 63◦. Este indice se procesa cada tres horas, es cuasi-logaritmico y estima la actividad geomagnetica. Los valores de Kp estan dentro de un rango de 0 a 9 y conforman un conjunto de 28 valores discretos, se utiliza como uno de los parametros de entrada en muchos modelos ionosfericos y magnetosfericos. El objetivo de este trabajo es utilizar datos historicos del indice Kp para desarrollar una metodologia que permita hacer un pronostico del mismo en un intervalo de tiempo, como minimo, de tres horas. Se prueban cinco diferentes modelos de pronostico de los indices geomagneticos Kp y ap. Se utilizan como datos de entrada a los modelos, una serie temporal de valores del indice Kp desde 1932 hasta el 15/12/2012 a las 21:00 horas UT. La finalidad del modelo es pronosticar los tres valores posteriores al ultimo valor medido del indice Kp (las proximas 9 horas). El modelo AR resulta ser el de menor costo computacional y ofrece buenos resultados. El modelo ARIMA es eficiente para la prediccion del indice Kp en condiciones de perturbacion geomagnetica. Este trabajo ofrece una forma rapida y eficiente de hacer una prediccion del indice Kp, sin necesidad de usar datos de satelites que muchas veces demoran en ser publicados. Aunque se informa que los resultados del pronostico son mejores cuando se utilizan datos de satelites. Segun datos publicados, la correlacion lineal entre los valores pronosticados y los valores reales esta entorno de un 77 %, valor que es mejor que el 68.5% obtenido en este trabajo. Sin embargo, teniendo en cuenta que se trabajo solamente sobre la serie temporal estocastica del Kp, este valor de correlacion puede considerarse satisfactorio.
... Authors: Rosa, R.; Viana, M.; Barbosa, E.; Vijaykumar, N.; Menezes, V.; Zanandrea, A.; Bolzan... more ... Authors: Rosa, R.; Viana, M.; Barbosa, E.; Vijaykumar, N.; Menezes, V.; Zanandrea, A.; Bolzan, M ... shelf is characterized by the presence of a rich suite of quartz sand bedforms having ... in the shallow shelf through bottom interactions Recent analysis of the tidal band obtained from ...
O desenvolvimento de aplicações que lidam com processamento de sinais deve considerar a qualidade... more O desenvolvimento de aplicações que lidam com processamento de sinais deve considerar a qualidade dos dados. Técnicas de aprendizado de máquina e técnicas estatísticas requerem ajustes e normalizações no conjunto de dados antes da análise de um dado fenômeno. Quando um conjunto de dados não é tratado para reduzir inconsistências e ruídos fornecidos por instrumentos ou por condições naturais, a análise acrescenta uma tendência, ou seja, os resultados não podem ser reproduzidos porque o conjunto de dados recebe inconsistências condicionadas pelo ruído. Neste sentido, o trabalho a seguir apresenta um sistema para processamento de sinais e ajuste de dados, utilizando como estudo de caso a aplicação em dados espectrais de supernovas, para configurar uma normalização automática e uniforme em grandes conjuntos de dados. Este trabalho propõe uma estratégia de dupla filtragem utilizando o filtro Savitzky-Golay para otimização da redução de ruído. Este sistema produz um sinal filtrado capaz d...
In this letter, we report an enhanced MFDFA algorithm to compute the singu-larity spectra of the ... more In this letter, we report an enhanced MFDFA algorithm to compute the singu-larity spectra of the so-called E-F valley transition region, which is considered the most complex ionospheric layer because its plasma instability mechanisms are still not well understood. The results found for ∆α confirm the multifractal behaviour which is expected to be related to instabilities in the ionosphere as a whole, allowing, in an integrated scenario, to discuss new thermodynamic and plasma structural properties of the atmospheric layers that make up the Earth's ionosphere. Keywords: ionospheric plasma instabilities, multifractal analysis, equatorial valley region irregularities, inhomogeneous turbulence. With advances in the scientific computing, some nonlinear methods based 1 on fractal formalism and multiplicative cascade processes have found increased 2 application in ionospheric studies to understand the scaling structures and com-3 plexity, in recent times, and also, undergoing continuous improvements and wide 4 usability (Fornari et al., 2016; Neelakshi et al., 2019; Joshi et al., 2020). 5 In the ionospheric studies, the valley region, located in between the top 6 of the E region and the base of the F region with reduction in the electron 7 density, is still a less explored area of research compared to the F region as it 8 can be explored using powerful incoherent and coherent scatter radar and in 9 situ experiments. 10 Our earlier studies have demonstrated the potential of the DFA and MFDFA 11 on ionospheric data. The DFA studies (Fornari et al., 2016; Neelakshi et al., 12 2019) reveal the long-range correlation and non-homogeneous nature of the 13 ionospheric irregularities. We have reported the wide variation in the spec-14 tral indices and their large deviation from the homogeneous turbulence spectral 15 index. Also, we have shown that this wide variation in spectral indices is nei-16 * Neelakshi J.
Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media
In recent decades, the internet access growth has generated a substantial increase in the informa... more In recent decades, the internet access growth has generated a substantial increase in the information circulation in social media. Within the information variety circulating on the internet, extreme social events such as armed conflicts have become areas of great public interest because of their direct influence on society. The study of such data from social media is useful in understanding an event's evolution, in particular how threats over time can generate an endogenous evolution resulting in an extreme event. This chapter uses the technique of sentiment analysis to identify the threat degree of news about armed conflicts distributed in social media. This analysis generates an endogenous threat time series that is used to predict the future threat variation of the analyzed extreme social events. In the prediction of the endogenous time series, the authors apply the deep learning technique in a structure that uses the long short-term memory (LSTM) neural network.
16th International Conference on Information Technology-New Generations (ITNG 2019), 2019
This work presents a parallel implementation of the Two-Dimensional Detrended Fluctuation Analysi... more This work presents a parallel implementation of the Two-Dimensional Detrended Fluctuation Analysis (2D-DFA) algorithms using GPGPU-CUDA through the PyCUDA Wrapper. Both the monofractal and multifractal versions of 2D-DFA present poor performance in their sequential implementations because of high computational complexity, rendering ineffective the use for real-time applications and those classified as Big Data. However, these algorithms present the requirements needed to be parallelized using GPUs. Mainly because the same computation is performed in different sub-matrices of the data. Therefore, the concepts of data parallelism and independence, necessary for computation in GPUs were attending. The parallelization strategy was process simultaneously several sub-matrices, and not parallelizes the internal procedure in each sub-matrix. Matrices with different dimensions and Hurst exponents were generated for the validation of the method implemented. The results showed that both algori...
Resumen El indice geomagnetico Kp se deriva del indice K a partir de las mediciones de trece esta... more Resumen El indice geomagnetico Kp se deriva del indice K a partir de las mediciones de trece estaciones localizadas alrededor de la Tierra entre las latitudes geomagneticas 48◦ y 63◦. Este indice se procesa cada tres horas, es cuasi-logaritmico y estima la actividad geomagnetica. Los valores de Kp estan dentro de un rango de 0 a 9 y conforman un conjunto de 28 valores discretos, se utiliza como uno de los parametros de entrada en muchos modelos ionosfericos y magnetosfericos. El objetivo de este trabajo es utilizar datos historicos del indice Kp para desarrollar una metodologia que permita hacer un pronostico del mismo en un intervalo de tiempo, como minimo, de tres horas. Se prueban cinco diferentes modelos de pronostico de los indices geomagneticos Kp y ap. Se utilizan como datos de entrada a los modelos, una serie temporal de valores del indice Kp desde 1932 hasta el 15/12/2012 a las 21:00 horas UT. La finalidad del modelo es pronosticar los tres valores posteriores al ultimo valor medido del indice Kp (las proximas 9 horas). El modelo AR resulta ser el de menor costo computacional y ofrece buenos resultados. El modelo ARIMA es eficiente para la prediccion del indice Kp en condiciones de perturbacion geomagnetica. Este trabajo ofrece una forma rapida y eficiente de hacer una prediccion del indice Kp, sin necesidad de usar datos de satelites que muchas veces demoran en ser publicados. Aunque se informa que los resultados del pronostico son mejores cuando se utilizan datos de satelites. Segun datos publicados, la correlacion lineal entre los valores pronosticados y los valores reales esta entorno de un 77 %, valor que es mejor que el 68.5% obtenido en este trabajo. Sin embargo, teniendo en cuenta que se trabajo solamente sobre la serie temporal estocastica del Kp, este valor de correlacion puede considerarse satisfactorio.
... Authors: Rosa, R.; Viana, M.; Barbosa, E.; Vijaykumar, N.; Menezes, V.; Zanandrea, A.; Bolzan... more ... Authors: Rosa, R.; Viana, M.; Barbosa, E.; Vijaykumar, N.; Menezes, V.; Zanandrea, A.; Bolzan, M ... shelf is characterized by the presence of a rich suite of quartz sand bedforms having ... in the shallow shelf through bottom interactions Recent analysis of the tidal band obtained from ...
O desenvolvimento de aplicações que lidam com processamento de sinais deve considerar a qualidade... more O desenvolvimento de aplicações que lidam com processamento de sinais deve considerar a qualidade dos dados. Técnicas de aprendizado de máquina e técnicas estatísticas requerem ajustes e normalizações no conjunto de dados antes da análise de um dado fenômeno. Quando um conjunto de dados não é tratado para reduzir inconsistências e ruídos fornecidos por instrumentos ou por condições naturais, a análise acrescenta uma tendência, ou seja, os resultados não podem ser reproduzidos porque o conjunto de dados recebe inconsistências condicionadas pelo ruído. Neste sentido, o trabalho a seguir apresenta um sistema para processamento de sinais e ajuste de dados, utilizando como estudo de caso a aplicação em dados espectrais de supernovas, para configurar uma normalização automática e uniforme em grandes conjuntos de dados. Este trabalho propõe uma estratégia de dupla filtragem utilizando o filtro Savitzky-Golay para otimização da redução de ruído. Este sistema produz um sinal filtrado capaz d...
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