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6.867-final-project

Adversarial attacks and defenses against federated learning.

a-deep-rl-approach-for-sdn-routing-optimization

A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

a-machine-learning-approach-for-rainfall-estimation-integrating-hetorogeneous-data-sources

This project integrates diverse data sources (weather radar, satellite imagery, ground-based observations) using machine learning for accurate rainfall estimation. It overcomes limitations of individual sources, improving applications in agriculture, hydrology, and disaster management.

a2_doppler_tracking_project

This project is about using the Doppler effect to track satellites

ai-face-recognition

Web Application allows users to recognize and detect position of a human face on uploaded image in form of URL

algorithm-and-data-structures-and-coding-challenges

Coding practices, algorithms and data structures

amazon_rainforest

Label satellite image chips with atmospheric conditions and various classes of land cover / land use.

aot-buckley-bay-wild-fire-2017

MODIS Terra Granule Level 2 - OPTICAL_DEPTH_LAND_AND_OCEAN analysis for Buckley Bay during the Summer 2017 Smoke period. DOY 208 to 217

apt-decoding-for-noaa-satellites

Python SDR is a Software Defined Radio developed in Python with GNU Radio to decode and process weather images from NOAA satellites. It can decode directly from an SDR device, a RAW file or a .wav file, and can perform Doppler correction as well as image syncronization. Finnaly it can return false color images created directly from the infrarred im

atmos

A python package to estimate the atmosphere parameters

atmosphere-satellite-soundings

Introduction exercises

atmospheric-radiation-and-remote-sensing

Energy transfer at infrared, visible, and microwave wavelengths, measurements of atmosphere and surface from satellite. Application of techniques from linear algebra and physics to atmospheric problems. GIS using fiona, cartopy and rasterio

atmospheric-radiation-notebook-data

notebooks and libraries for atsc301

awesome-space

🛰️🚀A list of awesome space-related packages and resources maintained by The Orbital Index

calsat

Some jupyter notebooks and files needed to calculate atmospheric attenuations and signal-related stuff for the Calibration Satellite in the W-band, W-CalSat.

carbon-flux

One of the biggest problems this world is facing now is the emission of carbon dioxide. Since 1958 humans have added 200 gigatons of Carbon dioxide in the atmosphere. In order to measure the CO2 fluxes (in and out of the gas) many organizations has setup flux tower all over United States which measure the amount of CO2 exchanged between atmosphere

channel-fading

In wireless systems, channel fading may either be due to multipath propagation, weather (rain), or shadowing from obstacles.

channelnet

Implementation of the paper "Deep Learning-Based Channel Estimation"

climatemodeling_courseware

Climate Modeling Courseware University Albany, A collection of interactive lecture notes and assignments in Jupyter notebook format.

collision_avoidance_challenge_deep_learning

com-5170-wireless-communication-system_dopplershift

communication-efficient-federated-learning

computation-of-rainfall-attenuation

This is a reproducible script for calculating rainfall attenuation for earth to space communication links using ITU-R P.618-8 Model. It gives flexibility in imputing rain rate data from ITU-R P.837-7 or other sources such as raingauge and satellite measurements.

crfl

CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)

cubesats

Additional materials to "Design of cooperative communication system based on CubeSat satellites" (master thesis) .

datapoisoning_fl

Code for Data Poisoning Attacks Against Federated Learning Systems

deep-denoising-autoencoder-for-seismic-random-noise-attenuation

Deep Denoising Autoencoder for Seismic Random Noise Attenuation

deep-ee-opt

Source code for "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" by Bho Matthiesen, Alessio Zappone, Karl-L. Besser, Eduard A. Jorswieck, and Merouane Debbah, accepted for publication in IEEE Transactions on Signal Processing.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-q-learning-son-perf-improvement

Code for the following publication: F. B. Mismar, J. Choi, and B. L. Evans, "A Framework for Automated Cellular Network Tuning with Reinforcement Learning", IEEE Transactions on Communications, vol. 67, no. 10, Oct. 2019, pp. 7152-7167, DOI 10.1109/TCOMM.2019.2926715.