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
A deep learning framework for historical document image analysis

DIVA-DAF Description A deep learning framework for historical document image analysis. How to run Install dependencies # clone project git clone https

9 Aug 04, 2022
Code of our paper "Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning"

CCOP Code of our paper Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning Requirement Install OpenSelfSup Install Detectron2

Chenhongyi Yang 21 Dec 13, 2022
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items

A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items This repository co

Taimur Hassan 3 Mar 16, 2022
The full training script for Enformer (Tensorflow Sonnet) on TPU clusters

Enformer TPU training script (wip) The full training script for Enformer (Tensorflow Sonnet) on TPU clusters, in an effort to migrate the model to pyt

Phil Wang 10 Oct 19, 2022
Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking Part-Aware Measurement for Robust Multi-View Multi-Human 3D P

19 Oct 27, 2022
Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Introduction This repository contains my unofficial reimplementation of the standard ECAPA-TDNN, which is the speaker recognition in VoxCeleb2 dataset

Tao Ruijie 277 Dec 31, 2022
code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

PreSumm This code is for EMNLP 2019 paper Text Summarization with Pretrained Encoders Updates Jan 22 2020: Now you can Summarize Raw Text Input!. Swit

Yang Liu 1.2k Dec 28, 2022
TFOD-MASKRCNN - Tensorflow MaskRCNN With Python

Tensorflow- MaskRCNN Steps git clone https://github.com/amalaj7/TFOD-MASKRCNN.gi

Amal Ajay 2 Jan 18, 2022
[CVPR'21] Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation

Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation Weixiang Yang, Qi Li, Wenxi Liu, Yuanlong Yu, Y

118 Dec 26, 2022
Source code for From Stars to Subgraphs

GNNAsKernel Official code for From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness Visualizations GNN-AK(+) GNN-AK(+) with Subgra

44 Dec 19, 2022
zeus is a Python implementation of the Ensemble Slice Sampling method.

zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Bl

Minas Karamanis 197 Dec 04, 2022
classification task on dataset-CIFAR10,by using Tensorflow/keras

CIFAR10-Tensorflow classification task on dataset-CIFAR10,by using Tensorflow/keras 在这一个库中,我使用Tensorflow与keras框架搭建了几个卷积神经网络模型,针对CIFAR10数据集进行了训练与测试。分别使

3 Oct 17, 2021
On Evaluation Metrics for Graph Generative Models

On Evaluation Metrics for Graph Generative Models Authors: Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham Taylor This is the offic

13 Jan 07, 2023
Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch

Semantic Segmentation Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch Features Applicable to followin

sithu3 530 Jan 05, 2023
Flaxformer: transformer architectures in JAX/Flax

Flaxformer is a transformer library for primarily NLP and multimodal research at Google.

Google 116 Jan 05, 2023
NLMpy - A Python package to create neutral landscape models

NLMpy is a Python package for the creation of neutral landscape models that are widely used by landscape ecologists to model ecological patterns

Manaaki Whenua – Landcare Research 1 Oct 08, 2022
Cache Requests in Deta Bases and Echo them with Deta Micros

Deta Echo Cache Leverage the awesome Deta Micros and Deta Base to cache requests and echo them as needed. Stop worrying about slow public APIs or agre

Gingerbreadfork 8 Dec 07, 2021
Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification

S-multi-SNE Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification A repository containing the code to reproduce the findings

Theodoulos Rodosthenous 3 Apr 15, 2022
Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

PLBART Code pre-release of our work, Unified Pre-training for Program Understanding and Generation accepted at NAACL 2021. Note. A detailed documentat

Wasi Ahmad 138 Dec 30, 2022
Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

Sarus published models Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are

Sarus Technologies 39 Aug 19, 2022