11 skills found
shredEngineer / MagnetiCalcMagnetiCalc calculates the magnetic field of arbitrary coils.
adekunleoajayi / PowerspecA python package for estimating wavenumber spectral density, flux, and coherence of two-dimensional oceanic fields.
NickSwainston / Pulsar SpectraA simple interface to record pulsar's flux density measurements for a large number of papers and perform fitting of spectral models.
lqueval / BSmagThe BSmag Toolbox is a Matlab toolbox for the numerical integration of the Biot-Savart law. It provides a simple solution to calculate the magnetic flux density generated by an arbitrary current carrying filament in the magnetostatic approximation.
mhardcastle / RadiofluxMeasuring radio flux density with ds9
JortBox / Halo FDCARadio halo Flux Density Calculation Algorithm
ductsoup / PPFDPhotosynthetic Photon Flux Density (PPFD) Measurement (poor man's quantum meter)
Ali-Zolfaghari / Lid Driven Cavity SIMPLEThe lid-driven cavity is a well-known benchmark problem for viscous incompressible fluid flow. We are dealing with a square cavity consisting of three rigid walls with no-slip conditions and a lid moving with a tangential unit velocity. The lower left corner has a reference static pressure of 0. In computational fluid dynamics (CFD), the SIMPLE algorithm is a widely used numerical procedure to solve the Navier–Stokes equations. SIMPLE is an acronym for Semi-Implicit Method for Pressure Linked Equations. The algorithm is iterative. The basic steps in the solution update are as follows: Set the boundary conditions. Compute the gradients of velocity and pressure. Solve the discretized momentum equation to compute the intermediate velocity field. Compute the uncorrected mass fluxes at faces. Solve the pressure correction equation to produce cell values of the pressure correction. Update the pressure field: where urf is the under-relaxation factor for pressure. Update the boundary pressure corrections. Correct the face mass fluxes. Correct the cell velocities by the gradient of the pressure corrections and the vector of central coefficients for the discretized linear system representing the velocity equation and Vol is the cell volume. Update density due to pressure changes.
IraDei / MFD GDDMatlab realization for "Infrared Small Target Detection Based on Flux Density and Direction Diversity in Gradient Vector Field" by D. Liu et al.
Jawad-Dar / Design And Development Of Hybrid Optimization Enabled Deep Q Learning Model For Covid 19 Detection The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. In this paper, the input audio samples are fed into the pre-processing module in which median filtering is done to remove the noise and artifacts from the audio samples. The feature extraction is carried out by considering features, like spectral contrast, Mel frequency cepstral coefficients (MFCC), Empirical Mode Decomposition (EMD) algorithm, spectral flux, Fast Fourier Transform (FFT), spectral roll-off, spectral centroid, Root mean square energy, zero-crossing rate, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude. Moreover, the deep Q network is applied for Covid-19 classification phase wherein the training of deep Q network is done using the proposed optimization algorithm, named Snake Jaya Honey Badger Optimization (SJHBO) algorithm. The proposed SJHBO algorithm is the hybridization of Jaya Honey Badger Optimization (JHBO) along with Snake optimization. Hence, the developed method achieved the better superior performance based on the accuracy, sensitivity and specificity .
antking / Nebulous**Nebulous** is a reversible jump Markov Chain Monte Carlo that takes Cloudy (Ferland et al, 2013) outputs and fits line ratios to find the optimal ionising flux and hydrogen density parameter space the broad line region occupies under a similar assumption of the Locally Optimally Emitting Cloud (LOC) model (Baldwin et al., 1995)
