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
Learning Features with Parameter-Free Layers (ICLR 2022)

Learning Features with Parameter-Free Layers (ICLR 2022) Dongyoon Han, YoungJoon Yoo, Beomyoung Kim, Byeongho Heo | Paper NAVER AI Lab, NAVER CLOVA Up

NAVER AI 65 Dec 07, 2022
DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations

DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations This repository contains the data, scripts and baseline co

Alexa 51 Dec 17, 2022
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

yobi byte 29 Oct 09, 2022
Code for NeurIPS 2021 paper: Invariant Causal Imitation Learning for Generalizable Policies

Invariant Causal Imitation Learning for Generalizable Policies Ioana Bica, Daniel Jarrett, Mihaela van der Schaar Neural Information Processing System

Ioana Bica 17 Dec 01, 2022
Solution to the Weather4cast 2021 challenge

This code was used for the entry by the team "antfugue" for the Weather4cast 2021 Challenge. Below, you can find the instructions for generating predi

Jussi Leinonen 13 Jan 03, 2023
MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions

MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions Project Page | Paper If you find our work useful for your research, please con

96 Jan 04, 2023
Implementation for paper: Self-Regulation for Semantic Segmentation

Self-Regulation for Semantic Segmentation This is the PyTorch implementation for paper Self-Regulation for Semantic Segmentation, ICCV 2021. Citing SR

Dong ZHANG 30 Nov 21, 2022
Speedy Implementation of Instance-based Learning (IBL) agents in Python

A Python library to create single or multi Instance-based Learning (IBL) agents that are built based on Instance Based Learning Theory (IBLT) 1 Instal

0 Nov 18, 2021
Source code for our paper "Empathetic Response Generation with State Management"

Source code for our paper "Empathetic Response Generation with State Management" this repository is maintained by both Jun Gao and Yuhan Liu Model Ove

Yuhan Liu 3 Oct 08, 2022
Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

Shaojie Li 34 Mar 31, 2022
A library of scripts that interact with the PythonTurtle module to create games, drawings, and more

TurtleLib TurtleLib is a library of scripts that interact with the PythonTurtle module to create games, drawings, and more! Using the Scripts Copy or

1 Jan 15, 2022
Revisiting, benchmarking, and refining Heterogeneous Graph Neural Networks.

Heterogeneous Graph Benchmark Revisiting, benchmarking, and refining Heterogeneous Graph Neural Networks. Roadmap We organize our repo by task, and on

THUDM 176 Dec 17, 2022
基于YoloX目标检测+DeepSort算法实现多目标追踪Baseline

项目简介: 使用YOLOX+Deepsort实现车辆行人追踪和计数,代码封装成一个Detector类,更容易嵌入到自己的项目中。 代码地址(欢迎star): https://github.com/Sharpiless/yolox-deepsort/ 最终效果: 运行demo: python demo

114 Dec 30, 2022
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation

Paper Khoi Nguyen, Sinisa Todorovic "A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation", accepted to ICCV 2021 Our code is mai

Khoi Nguyen 5 Aug 14, 2022
A PyTorch Implementation of Single Shot MultiBox Detector

SSD: Single Shot MultiBox Object Detector, in PyTorch A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragom

Max deGroot 4.8k Jan 07, 2023
Lane assist for ETS2, built with the ultra-fast-lane-detection model.

Euro-Truck-Simulator-2-Lane-Assist Lane assist for ETS2, built with the ultra-fast-lane-detection model. This project was made possible by the amazing

36 Jan 05, 2023
Rohit Ingole 2 Mar 24, 2022
Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

taganomaly Anomaly detection labeling tool, specifically for multiple time series (one time series per category). Taganomaly is a tool for creating la

Microsoft 272 Dec 17, 2022
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models.

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models

AdvBox 1.3k Dec 25, 2022
Vanilla and Prototypical Networks with Random Weights for image classification on Omniglot and mini-ImageNet. Made with Python3.

vanilla-rw-protonets-project Vanilla Prototypical Networks and PNs with Random Weights for image classification on Omniglot and mini-ImageNet. Made wi

Giovani Candido 8 Aug 31, 2022