438 skills found · Page 5 of 15
egyptdj / Graph Neural MappingPyTorch implementation of the paper Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis
jorainer / Metabolomics2018Workshop illustrating mass spectrometry data analysis in R and use of the updated xcms functionality for the preprocessing of LC-MS data.
gagneurlab / Dependencies DNALMCode repository for the manuscript: Nucleotide dependency analysis of DNA language models reveals genomic functional elements
slightlynybbled / WeibullWeibull analysis, test design, and some Weibayes functionality for Python3.5+
evanpeikon / Functional Enrichment AnalysisA DIY guide to gene ontology, pathway, and gene set enrichment analysis
jiwoongbio / FMAPFunctional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies
sktime / XpandasUniversal 1d/2d data containers with Transformers functionality for data analysis.
AkiNikolaidis / PyBASCBootstrapped Analysis of Stable Clusters- A semi-automated fMRI individual and group level functional parcellation technique
mrahman4 / Aws AIFlutter package to wrap Amazon artificial intelligence (AI) services, which provide flutter community developers with the ability to add intelligence to their applications through an API call to pre-trained services rather than developing and training their own models. Amazon AI services are : * Amazon Rekognition : built on technology used by Amazon Prime Photos to analyze billions of images daily, is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images, as well as search and compare faces between images. * Amazon Polly (still not implemented): a service that turns text into lifelike speech. Polly lets you create applications that speak in over two dozen languages with a wide variety of natural sounding male and female voices to enable you to build entirely new categories of speech-enabled products. * Amazon Lex (still not implemented) : uses the same technology as Amazon Alexa to provide advanced deep learning functionalities of automatic speech recognition (ASR) and natural language understanding (NLU) to enable you to build applications with conversational interfaces, commonly called chatbots.
ottaviadipasquale / React FmriREACT: Receptor-Enriched Analysis of functional Connectivity by Targets
mxliu / ACTION SoftwareOpen-Source Python Software for Functional MRI Analysis
fdaPDE / FdaPDE RThe R wrapper to the fdaPDE library for physics-informed spatial and functional data analysis.
OWI-Lab / Py Fatiguepy-fatigue bundles the main functionality for performing cyclic stress (fatigue) analysis and cycle-counting.
abhilash12iec002 / Penetration Depth Evaluation Of L And S Band SAR SignalsWe study the functional relationship between the dielectric constant of soil-water mixture and penetration depth of microwave signals into the ground at different frequency (L&S) band and incidence angles. Penetration depth of microwave signals into the ground depends on the incidence angle and wavelength of radar pulses and also on the soil properties such as moisture content and textural composition. It has been observed that the longer wavelengths have higher penetration in the soil but the penetration capability decreases with increasing dielectric behaviour of the soil. Moisture content in the soil can significantly increase its dielectric constant. Various empirical models have been proposed that evaluate the dielectric behaviour of soil-water mixture as a function of moisture content and texture of the soil. In this analysis we have used two such empirical models, the Dobson model and the Hallikainen model, to calculate the penetration depth at L- and C-band in soil and compared their results. We found that both of these models give different penetration depth and show different sensitivity towards the soil composition. Hallikainen model is more sensitive to soil composition as compared to Dobson model. Finally, we explore the penetration depth at different incidence angle for the proposed L- and S-band sensor of upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission by using Hallikainen empirical model. We found that the soil penetration depth of SAR signals into the ground decreases with the increase in soil moisture content, incident angle and frequency. References [1] A. Singh, G. K. Meena, S. Kumar and K. Gaurav, "Evaluation of the Penetration Depth of L- and S-Band (NISAR mission) Microwave SAR Signals into Ground," 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), New Delhi, India, 2019, pp. 1-1. doi: 10.23919/URSIAP-RASC.2019.8738217 keywords: {Synthetic aperture radar;Dielectrics;Moisture;Soil moisture;Sensors;Remote sensing}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8738217&isnumber=8738126 [2] Singh, A., Meena, G. K., Kumar, S., and Gaurav, K.: ANALYSIS OF THE EFFECT OF INCIDENCE ANGLE AND MOISTURE CONTENT ON THE PENETRATION DEPTH OF L- AND S-BAND SAR SIGNALS INTO THE GROUND SURFACE, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 197-202, https://doi.org/10.5194/isprs-annals-IV-5-197-2018, 2018. [3] ABHILASH SINGH (2019). Penetration depth evaluation at L-and S-band SAR signals (https://www.mathworks.com/matlabcentral/fileexchange/73040-penetration-depth-evaluation-at-l-and-s-band-sar-signals), MATLAB Central File Exchange. Retrieved October 19, 2019.
metageni / SUPER FOCUSA tool for agile functional analysis of shotgun metagenomic data
cran / Fda:exclamation: This is a read-only mirror of the CRAN R package repository. fda — Functional Data Analysis. Homepage: http://www.functionaldata.org
montilab / CaDrACandidate Drivers Analysis: Multi-Omic Search for Candidate Drivers of Functional Signatures
kmi / IserveiServe is what we refer to as service warehouse which unifies service publication, analysis, and discovery through the use of lightweight semantics as well as advanced discovery and analytic capabilities. iServe provides the typical features of service registries and additional functionality that exploits service descriptions, service annotations and further data gathered and derived from the semantic analysis of these descriptions, data crawled from the Web, periodic monitoring and user activities.
KamitaniLab / BrainDecoderToolbox2Matlab library for brain decoding analysis (BrainDecoderToolbox2 data format, machine learning analysis, functional MRI)
LucaAmbrogioni / Functional GP Analysis In PythonA python toolbox for GP analysis written in a functional programming style
