Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Overview

Spectrum Surveying:

The Python code in this repository implements the simulations and plots the figures described in the paper “Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs” by Raju Shrestha, Daniel Romero, and Sundeep Prabhakar Chepuri.

Requirements:

Python 3.6 or later. Use the package manager pip to install the following packages:

tensorflow
scipy
cvxpy
cvxopt
matplotlib
pandas
joblib
sklearn
opencv-python

Guidelines

First, download all the files and folders from this repository. Then, the simulations can be executed by running the file run_experiment.pyfrom the command prompt. One needs to provide the experiment number (e.g. 2001) as an argument while executing the file run_experiment.pyto select the simulation you want to run. The experiments reproducing different figures in the paper are organized in the methods located in the file Experiments/spectrum_surveying_experiments.py. The comments before each method indicate which figure(s) on the paper it generates. For example, to run the experiment no 2001 in the Experiments/spectrum_surveying_experiments.py, in the command prompt, execute the command $ python run_experiment.py 2001.

For more information about the simulation environment, please check here.

Citation

If our code is helpful in your research or work, please cite our paper: “Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs.”

Contact

Please feel free to contact us by email if you have any issues in running the code.

Owner
Universitetet i Agder
Universitetet i Agder
Python Jupyter kernel using Poetry for reproducible notebooks

Poetry Kernel Use per-directory Poetry environments to run Jupyter kernels. No need to install a Jupyter kernel per Python virtual environment! The id

Pathbird 204 Jan 04, 2023
Train robotic agents to learn pick and place with deep learning for vision-based manipulation in PyBullet.

Ravens is a collection of simulated tasks in PyBullet for learning vision-based robotic manipulation, with emphasis on pick and place. It features a Gym-like API with 10 tabletop rearrangement tasks,

Google Research 367 Jan 09, 2023
A PaddlePaddle version image model zoo.

Paddle-Image-Models English | 简体中文 A PaddlePaddle version image model zoo. Install Package Install by pip: $ pip install ppim Install by wheel package

AgentMaker 131 Dec 07, 2022
Weakly supervised medical named entity classification

Trove Trove is a research framework for building weakly supervised (bio)medical named entity recognition (NER) and other entity attribute classifiers

60 Nov 18, 2022
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling For Official repo of NU-Wave: A Diffusion Probabilistic Model for Neural Audio Up

Rishikesh (ऋषिकेश) 38 Oct 11, 2022
An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions

Agar.io_Q-Learning_AI An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available act

1 Jun 09, 2022
Simulation-based performance analysis of server-less Blockchain-enabled Federated Learning

Blockchain-enabled Server-less Federated Learning Repository containing the files used to reproduce the results of the publication "Blockchain-enabled

Francesc Wilhelmi 9 Sep 27, 2022
This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

SBEVNet: End-to-End Deep Stereo Layout Estimation This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by D

Divam Gupta 19 Dec 17, 2022
Auto HMM: Automatic Discrete and Continous HMM including Model selection

Auto HMM: Automatic Discrete and Continous HMM including Model selection

Chess_champion 29 Dec 07, 2022
The official implementation of ICCV paper "Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds".

Box-Aware Tracker (BAT) Pytorch-Lightning implementation of the Box-Aware Tracker. Box-Aware Feature Enhancement for Single Object Tracking on Point C

Kangel Zenn 5 Mar 26, 2022
Minimal PyTorch implementation of YOLOv3

A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation.

Erik Linder-Norén 6.9k Dec 29, 2022
BEGAN in PyTorch

BEGAN in PyTorch This project is still in progress. If you are looking for the working code, use BEGAN-tensorflow. Requirements Python 2.7 Pillow tqdm

Taehoon Kim 260 Dec 07, 2022
🕵 Artificial Intelligence for social control of public administration

Non-tech crash course into Operação Serenata de Amor Tech crash course into Operação Serenata de Amor Contributing with code and tech skills Supportin

Open Knowledge Brasil - Rede pelo Conhecimento Livre 4.4k Dec 31, 2022
Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

Weakly Supervised Segmentation with TensorFlow This repo contains a TensorFlow implementation of weakly supervised instance segmentation as described

Phil Ferriere 220 Dec 13, 2022
MonoScene: Monocular 3D Semantic Scene Completion

MonoScene: Monocular 3D Semantic Scene Completion MonoScene: Monocular 3D Semantic Scene Completion] [arXiv + supp] | [Project page] Anh-Quan Cao, Rao

298 Jan 08, 2023
PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

Federated Learning with Non-IID Data This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vik

Youngjoon Lee 48 Dec 29, 2022
Run containerized, rootless applications with podman

Why? restrict scope of file system access run any application without root privileges creates usable "Desktop applications" to integrate into your nor

119 Dec 27, 2022
torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations

💎A high level pipeline for face landmarks detection, supports training, evaluating, exporting, inference and 100+ data augmentations, compatible with torchvision and albumentations, can easily instal

DefTruth 142 Dec 25, 2022
This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.

Official Pytorch Implementation for GLFC [CVPR-2022] Federated Class-Incremental Learning This is the official implementation code of our paper "Feder

Race Wang 57 Dec 27, 2022
An easy-to-use app to visualise attentions of various VQA models.

Ask Me Anything: A tool for visualising Visual Question Answering (AMA) An easy-to-use app to visualise attentions of various VQA models. Please click

Apoorve 37 Nov 13, 2022