Pinned Repositories
2.680-Marine-Vehicle-Autonomy
This course covers basic topics in autonomous marine vehicles, focusing mainly on software and algorithms for autonomous decision making (autonomy) by underwater vehicles operating in the ocean environments, autonomously adapting to the environment for improved sensing performance. It will introduce students to underwater acoustic communication environment, as well as the various options for undersea navigation, both crucial to the operation of collaborative undersea networks for environmental sensing. Sensors for acoustic, biological and chemical sensing by underwater vehicles and their integration with the autonomy system for environmentally adaptive undersea mapping and observation will be covered. The subject will have a significant lab component, involving the use of the MOOS-IvP autonomy software infrastructure for developing integrated sensing, modeling and control solutions for a variety of ocean observation problems, using simulation environments and a field testbed with small autonomous surface craft and underwater vehicles operated on the Charles River.
2020a_IMT_SSH_mapping_NATL60
A student challenge on both mapping of satellite altimeter sea surface height data and its dynamical update through web portals for scientific teams interested in the data challenge. Organisation: IMT-Atlantique, MEOM@IGE, Ocean-Next and CLS.
2020a_SSH_mapping_NATL60
A challenge on the mapping of satellite altimeter sea surface height data organised by MEOM@IGE, Ocean-Next and CLS.
2021_MSSP
This repository contains the established models discussed in the paper: "A Physics-constrained Deep Learning Based Approach for Acoustic Inverse Scattering Problems"
2L_QG_EOF_DMD
Deterministic and stochastic (projector operator stochastic parameterization) 2-layer QG model
3D_Parabolic_Equation_CUDA
Acoustic Parabolic Equation model with CUDA acceleration.
ML-Applied-for-Physics
paraPropPython
split-step parabolic equation solver for in-ice radio propagation
Rafelski_LandOceanModel
Code for land uptake of CO2 and ocean uptake of CO2. Original author: Lauren Rafelski
transfer-learning-soil-moisture-prediction
Improved Daily SMAP Satellite Soil Moisture Prediction over China using deep learning model with transfer learning
cxzhangqi's Repositories
cxzhangqi/2021_MSSP
This repository contains the established models discussed in the paper: "A Physics-constrained Deep Learning Based Approach for Acoustic Inverse Scattering Problems"
cxzhangqi/Analyzing-the-Effects-of-Ocean-Pollution-in-Future
The ocean plays a significant role in the ecosystem of the planet because it produces more than half of the world’s oxygen and absorbs 50 times more carbon dioxide than our atmosphere (“Why should we”, 2017). Ocean also contributes to a huge diversity in the ecology of marine life. However, the massive amount of carbon dioxide and debris entering the ocean is altering the quality of the ocean. The Pacific Ocean dataset from 2010 to 2017 will be used to analyze the quality of ocean water. The reason for choosing the Pacific Ocean is because it contains the most debris on the ocean. The collection of debris is so large in the ocean that it is renowned as the Great Pacific Garbage Patch (“Garbage Patches”, 2013). The main objective of this project is to know the effects of the carbon dioxide and debris on the quality of the ocean within that period. Since the dataset span is only for 7 years, this project hypothesizes that atmospheric carbon dioxide, salinity, and temperature will not change the quality of the ocean. However, if there is a change, it is important to analyze the condition of the ocean in the future, and review if it is sustainable in our succeeding generation. To conduct this project, the researcher will review different literature articles, and identify critical research questions. The researcher will create the database using SQL and find the answers to those critical questions using queries and displayed in the statistical visualization using Tableau. The result from the data analysis showed that there has been an increase in the carbon dioxide, sea surface temperature in the Pacific Ocean between those 7 years. The salinity of the ocean has changed and there are massive plastics present in the Pacific Ocean.
cxzhangqi/Atmospheric-normal-modes
Two Chebyshev Spectral Methods for Solving Normal Modes in Atmospheric Acoustics.
cxzhangqi/BHT-ARIMA
Code for paper: Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting (AAAI-20)
cxzhangqi/Burgers_DDP_and_TL
cxzhangqi/cluster_ssh
Second generals project, identifying patterns in sea level variability in AVISO altimetry and CESM1-Large Ensemble
cxzhangqi/ColumnModelOptimizationProject
A project to optimize oceanic "column models" for surface boundary layer turbulence
cxzhangqi/energy-optimization-wireless-sensor-network
Optimization of energy consumption and end to end delay in a wireless sensor network using duty-cycle MAC protocols
cxzhangqi/Evaporation-Duct-Machine-Learning
Evaluate the performance of machine learning models to predict evaporation ducts
cxzhangqi/fortran_geostrophic_velocites
cxzhangqi/heat-transfer
Solving the Heat Transfer differential equation using the FDTD (Finite-Difference Time-Domain)
cxzhangqi/I-Simpa
An Open Source software for 3D sound propagation modelling
cxzhangqi/leach
leach is a routing protocol in wireless sensor network which is used to extend the lifetime of wireless sensor network by reducing the energy consumption.
cxzhangqi/Legendre-Galerkin
Three Legendre-Galerkin Spectral Methods for Solving Normal Modes of Underwater Acoustic Propagation
cxzhangqi/Linear-Stability-Calculators
Code will solve the linear stability problems for a variety of physicsl models: quasi-geostrophy, shallow water and quasi-hydrostatic. The code is in either Julia or Python.
cxzhangqi/machine_learning_for_climate
A set of pre-processing codes, A deep convolutional neural net and post-processing codes for classifying turbulent climate patterns
cxzhangqi/MastersProject
The python code generated during the course of my Master's thesis. The intended usage is for modelling the effect of environmental parameters on acoustic localisation methods.
cxzhangqi/Metrics_NATL60
A set of functions to ease the intercomparison exercise between reconstruction/prediction methods applied to NATL60 dataset
cxzhangqi/MicroInverse
MicroInverse is a Python package for inversion of a transport operator from tracer data
cxzhangqi/MLD-Project
Repository with routines to manipulate the ARMOR 3D dataset, which contains mixed layer depth values.
cxzhangqi/NATL60
NATL60 is a set of libraries to deal with NATL60 maps and both nadir/swot NATL60-based datasets
cxzhangqi/Predicting_Energy_Consumption_With_Convolutional_Neural_Networks
This repository contains code that implements a pipeline (employing raster.io, gdal, and SentinelHub API) in order to create a Convolutional Neural Network with Transfer Learning (created using TensorFlow) designed to predict the localized energy consumption based off satellite imagery.
cxzhangqi/RCESN_spatio_temporal
Spatio-temporal forecasting of Lorenz96 with RC-ESN, RNN-LSTM and ANN
cxzhangqi/roms
Regional Ocean Modeling System (with ice)
cxzhangqi/specfem2d
SPECFEM2D simulates forward and adjoint seismic wave propagation in two-dimensional acoustic, (an)elastic, poroelastic or coupled acoustic-(an)elastic-poroelastic media, with Convolution PML absorbing conditions.
cxzhangqi/TianChi_AIEarth
TianChi AIEarth Contest Solution
cxzhangqi/Time-Series-Analysis-Tutorial
时间序列分析教程
cxzhangqi/UWCNN-SD
A Two-Stage Underwater Enhancement Network Based on Structure Decomposition and Characteristics of Underwater Imaging, IEEE Journal of Oceanic Engineering, 2021
cxzhangqi/woss-ns3
WOSS is a multi-threaded C++ framework that permits the integration of any existing underwater channel simulator that expects environmental data as input and provides as output a channel realization. Currently, WOSS integrates the Bellhop ray-tracing program. Thanks to its automation the user only has to specify the location in the world and the time where the simulation should take place. This is done by setting the simulated date and the wanted latitude and longitude of every node involved. The simulator automatically handles the rest (see technical description). WOSS can be integrated in any network simulator or simulation tool that supports C++.
cxzhangqi/xroms
Create xarray dataset and xgcm grid based on Regional Ocean Modeling System (ROMS) output