The data consist of an application, namely PyFEST, written in Python language, and a file with instructions to install and use the application. It can be used to estimate the frequencies of short-time signals with high accuracy. Along with the application, examples with generated signal (single-ton, multi-tone, noisy, damped etc.) and measured signals are delivered for testing purposes.
The frequencies of the harmonic components are evaluated one-by-one with high accuracy. Because the actions performed do not imply previous expertise, the results are not influenced by human intervention.
The maximum distance at which an electromagnetic (EM) logging while drilling (LWD) tool senses an approaching boundary is considered to be the depth of detection (DOD). Achieving a large DOD while keeping the tool itself compact is what we have always pursued. We proposed a novel transient multicomponent EM LWD method and studied its capability in detecting the formation boundary. Instead of using the transient triaxial measured data directly, a time domain detection mode is defined to sense the boundary. DOD of this time domain EM LWD method can reach tens of meters with a compact transmitter-receiver spacing. Based on the polarity of the signal, directional measurements can also be achieved. In addition, we find that the cross component decays much faster than the coaxial or coplanar components with time in the formation coordinate system. Thus, an algebraic method is proposed to determine the relative dip angle of stratified formation and the inversion process can be avoided. Theoretical simulation results indicate that this determination method obtains the true value at some particular moments. And it is still stable and valid even when considering some random measurement errors. Moreover, linear relationship between the distance to the boundary (DTB) and the time we measure it is established, providing a method to quickly determine the DTB.
Contributors:I Ketut Gede Darma Putra, I Made Suwija Putra, Putu Jhonarendra
The palmprint dataset is captured on left hand. Palmprint dataset is acquired from 15 people with 5 to 8 images of each person. To increase the amount of data in each person, the raw dataset was filtered with Gabor Filter. The characteristics of the Gabor Filter are good applied to palmprint image because the image has many variations of line direction and the thickness. The palmprint dataset has 20 to 32 images each class after applying the Gabor Filter. The author trains the palmprint dataset using the Convolutional Neural Network method.
This a data about the corona virus COVID-19. It contains the actual reported data. Also, it includes the predicted COVID-19 data in the future based on a model developed to predict in the future. The model used will be published in one of the journals later and will be found on my profile with title "Optimistic Prediction Model For the COVID-19 Coronavirus Pandemic based on the Reported Data Analysis".
The daily folder contains the daily data. The predicted folder contains the predicted data for each country. The total cases folder contains the total cases for each country. he section folder contains a latex code for plotting the figures for each country. Also the source file from European Centre for Disease Prevention and Control is included. More updated files available in the website of European Centre for Disease Prevention and Control.
This dataset presents the hiking and climbing opportunities diversity existent at Serra dos Órgãos National Park, Brazil, collected using participatory mapping approaches (Voluntary Geographic Information, Public Participation in Geographic Information Systems and secondary sources). Each trail on the park’s network was classified according to trail management categories.
The dataset includes two files: the excel inventory and the KMZ.
Data in Portuguese.