9 skills found
NDF-Poli-USP / SpyroWave propagators for seismic domains with application to full waveform inversion.
JuliaPhysics / WaveOpticsPropagation.jlPropagate waves efficiently, optically, physically, differentiably with Julia Lang.
kb173 / Godot Interactive WaterA water shader for Godot with interactive depth that propagates into waves.
sminatos / Tubewave Simulation MatlabMatlab scripts for fast and accurate forward modeling tube waves in vertical seismic profiling using a propagator matrix method
robotsorcerer / LevelsetpyA GPU-accelerated toolbox for hyperbolic PDEs in a weaker (viscosity) sense. It leverages the integral to the solution of the conservation of momentum problem (being equivalent to the derivative of Hamilton-Jacobi equations) in one spatial dimension. We resolve such hyperbolic differential equations using wave-front propagating schemes on a spatial-by-spatial dimension in resolving the classical value in dynamic programming (respectively optimal control and differential games) problems.
Kojey / MSc Whistler Waves DetectorLightning strokes create powerful electromagnetic pulses that result in Very Low Frequency (VLF) waves propagating along the magnetic field lines of the earth. Due to the dipole shape of the geomagnetic field, these waves travel upward from the stroke location out through portions of the plasmasphere and back to the Earth’s surface at the field line foot point in the opposite hemisphere. VLF antenna receivers set up at various high and middle latitude locations can detect whistler waves generated by these lightning strokes. The propagation time delay of these waves is dependent on the plasma density along the propagation path. This enables the use of whistler wave observations for characterising the plasmasphere in terms of particle number and energy density. The dynamics of energetic particle populations in the plasmasphere are an important factor in characterising the risk to spacecraft in orbit around Earth. Annual global lightning flash rates are on the order of 45 flash/s [5]. The resulting high occurrence rate of whistler events makes it impossible to identify and characterise them in a reasonable time. Therefore the automatic detection and characterisation of whistlers are valuable to the study of energetic particle dynamics in the plasmasphere and to develop models for operational use. Lichtenberger [1] developed an automatic detector and analyser based on the Appleton-Hartree dispersion relation and experimental models of particle density distribution. Recent advances in artificial neural network-based image processing methods for example, convolutional networks [6] may be able to provide an alternative method for the automatic identification and characterisation of whistler events in broadband VLF spectra. Model development is based on training a neural-network-based model on a large set of spectrograms with whistler events identified by the nodes of the Automatic Whistler Detection and Analysis Network (AWDAnet [7]). Spectrograms will be presented in the form of images (Figure 1.1) to take advantage of the wide range of image-processing techniques available for this type of object identification.
jbrussell / SEM2D WavepropMatlab code for propagating shear waves through a 2D velocity model
lokyGit / Ionosphere Signals PredictionThis project is about analyzing Ionosphere data and measuring the accuracies of the electromagnetic signal data. The radar statistics were gathered by an arrangement in Goose Bay, Labrador. This system involves a phased array of 16 high-frequency transmitters with an aggregate transferred power on the order of 6.4 kilowatts. Expected waves were handled by exercising an autocorrelation function whose arguments are the time of a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Two attributes per pulse number describe instances in this database. This dataset describes high-frequency antenna returns from high energy particles in the atmosphere, and whether the return shows structure or not. The problem is a binary classification that contains 351 instances and 35 numerical attributes. The majority of the data in this set are continuous data points which range between -1 and 1, with one binomial variable which defines the type of the electromagnetic signals. The objective of the project is to measure the accuracies of ‘good’ instances and ‘bad’ cases by feeding the dataset to the machine learning models mentioned below and report some of the measures to improve the overall performance of the models. Predicting the good and bad signals is very important as these signals propagate through distant places and contribute in providing better communication and help in improving the navigation. We will predict the good and bad signal results using 3 methods - KNN, GLM and decision tree and then use ensemble techniques to improve the accuracy of the model. In the ensemble technique, we will use the stacking method. We observed that generalized linear model has better classification rate among the rest and after implementing stacking technique we were able to improve the overall performance of the stacked models. Introduction Source Information: -- Donor: Vince Sigillito (vgs@aplcen.apl.jhu.edu) -- Date: 1989 -- Source: Space Physics Group, Applied Physics Laboratory, Johns Hopkins University, MD 20723 The first 34 columns are continuous numerical data which represent 17 pulse numbers of received electromagnetic signals. There are two attributes per pulse number, which is the time of the pulse and the pulse number. The 35th column is categorical data "good" or "bad". "good" means those radar showing evidence of some type of structure in the ionosphere. “bad" implies those radar does not indicate their signals pass through the ionosphere. Implementation of the Project First, we install the necessary packages and load the required libraries as mentioned below and then we read the dataset in R. We convert the last column label feature from character to factor. Next, to identify the important features we applied fitted Boruta model with the data and found out that column two i.e, V2 is not important and therefore, we removed V2 from the dataset and Created the significant dataset with important variables only. Then we split the dataset to train dataset and test dataset. Once, we have the training and test datasets we made use of knn() available in Class library for implementing KNN algorithm and glm() to implement logistic regression and rpart () to implement decision tree methods on our dataset. We chose these methods for our prediction and data analysis as we have binomial variable data with a binomial output. Because the above-mentioned algorithms perform better while dealing with categorical data points, we decided to implement the aforesaid classification methods. After completing with our modelling, we decided to improve the resulted accuracies of the models by implementing ensemble technique and we chose stacking for this case because it’s designed to combine model outputs of different types.
Chahak081 / Solution Challenge TEC PredictionThe TEC in the ionosphere is modified by changing solar Extreme UV radiation, geomagnetic storms, and the atmospheric waves that propagate up from the lower atmosphere. Therefore depend on season, geomagnetic conditions, solar cycle and activity which impact climate change.
