As for the experiment results in the manuscript, we would like to provide the corresponding Transmitted, Measured and Processed data zipped in the folder “Transmitted_Measured_Processed_DATA”, for readers who are interest in the SC MIMO transceiver and want to reproduce the experiment results shown in the manuscript. All the parameters including sampling rate, modulation frequency, etc., are the same as those in Experiment part.

Instructions: 

The detalied file description is ziped in the Attachment.

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60 Views

The provided dataset computes the exact analytical bit error rate (BER) of the NOMA system in the SISO broadcast channels with the assumption of i.i.d Rayleigh fading channels. The reader has to decide on the following input: 1) Number of users. 2) Modulation orders. 3) Power assignment. 4) Pathloss. 5) Transmit signal-to-noise ratio (SNR). The output is stored in a matrix where different rows are for different users while different columns are for different transmit SNRs.

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Another raw ADS-B signal dataset with labels, the dataset is captured using a BladeRF2 SDR receiver @ 1090MHz with a sample rate of 10MHz

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This dataset is being used to evaluate PerfSim accuracy and speed against a real deployment in a Kubernetes cluster based on sfc-stress workloads.

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This dataset was used in our work "See-through a Vehicle: Augmenting Road Safety Information using Visual Perception and Camera Communication in Vehicles" published in the IEEE Transactions on Vehicular Technology (TVT). In this work, we present the design, implementation and evaluation of non-line-of-sight (NLOS) perception to achieve a virtual see-through functionality for road vehicles.

Instructions: 

Non-Line of Sight Perception Vehicular Camera Communication

This project is an end-end python-3 application with a continuous loop captures and analyses 100 frames captured in a second to derive appropriate safety warnings.

Contact

Dr. Ashwin Ashok, Assistant Professor, Computer Science, Georgia State University

Collaborators

Project contents

This project contains 3 modules that should be run in parallel and interact with each other using 3 CSV files.

Modules

  1. non-line-of-sight-perception
  2. intelligent-vehicular-perception_ivp
  3. warning-transmission

CSV Files

  1. packet.csv
  2. perceived_info.csv
  3. receiver_action.csv

Usage :

Folling commands must be run in parallel. For more information on libraries needed for execution, see detailed sections below.

# Terminal 1
python3 non-line-of-sight-perception/VLC_project_flow.py zed

# Terminal 2
python3 intelligent-vehicular-perception_ivp/src-code/ivp_impl.py

# Terminal 3
python3 warning-transmission/send_bits_to_transmitter.py

1. non-line-of-sight-perception : Object Detection and Scene Perception Module

This folder, For the YOLO-V3 training and inference: This project is a fork of public repository keras-yolo3. Refer the readme of that repository here. Relevant folders from this repository have been placed in training and configuration folders in this repository.

Installation of python libraries

Use the package manager pip to install foobar.

pip install opencv-python
pip install tensorflow-gpu
pip install Keras
pip install Pillow

Hardware requirements

  1. This code was tested on Jetson Xavier, but any GPU enabled machine should be sufficient.
  2. Zed Camera: Uses Zed camera to capture the images. (Requires GPU to operate at 100 fps).
  3. (Optional) the code can be modified as per the comments in file to use zed, 0 for the camera, or ' the video path' for mp4 or svo files)

Output

perceived_info.csv

2. intelligent-vehicular-perception_ivp : Safety Message/Warning Mapping Module

This module is responsible for making intelligent recommendation to the driver as well as generating Safety Warnings for the following vehicles in the road. The module ouuputs with a fusion of Received Safety Warning through VLC channel and the vehicle's own Scene Perception Data.

Python Library Dependencies

  • json
  • operator
  • csv
  • enum
  • fileinput
  • re

Input

Output

The output is two-fold.

  • packet.csv : Intelligent Recommendation to the Driver.
  • receiver_action.csv : Generated Packet bits. Each Packet bits are logged into the 'packet.csv' file. This CSV files works as a queue. Every new packet logged here eventually gets transmitted by the VLC transmission module.

3. warning-transmission: Communication Module

Detailed transmitter notes including hardware requirement is present in transmitter_notes.txt

Python Library Dependencies

  • serial

Input

  • packet.csv : Intelligent Recommendation to the Driver.

Output

LED flashes high/low in correspondence to the packets in input file.

Dataset used for training the model

The dataset has been generated using Microsoft VoTT.

This is a "Brakelight" labelled dataset that can be used for training Brakelight detection models. The dataset contains brakelights labelled on images from

*Reference : Cui, Z., Yang, S. W., & Tsai, H. M. (2015, September). A vision-based hierarchical framework for autonomous front-vehicle taillights detection and signal recognition. In Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on (pp. 931-937). IEEE.

Labeled Dataset contains 1720 trained images as well a csv file that lists 4102 bounding boxes in the format : image | xmin | ymin | xmax | ymax | label

This can be further converted into the format as required by the training module using convert_dataset_for_training.py - (Replace annotations.txt with the Microsoft VoTT generated CSV) .

 

Acknowledgements

This work has been partially supported the US National Science Foundation (NSF) grants 1755925, 1929171 and 1901133.

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79 Views

This dataset contains 15 years of data about IT-vacancies from 2006 to 2020 downloaded from hh.ru using their public API. This site contains about 3 million vacancy descriptions posted by mainly Russian companies.

This dataset can be used for analyzing trends in IT or for creating new educational programs.

Instructions: 

Just extract the files. Some vacancy descriptions are long, so make sure that your CSV library can work with them.

The list of columns are:
id
description
key_skills schedule_id
schedule_name
accept_handicapped
accept_kids
experience_id
experience_name
specializations
contacts
billing_type_id
billing_type_name
allow_messages
premium
driver_license_types
accept_incomplete_resumes
employer_id
employer_name
employer_vacancies_url
employer_trusted
employer_alternate_url
employer_industries
response_letter_required
type_id
type_name
has_test
response_url
test_required
salary_from
salary_to
salary_gross
salary_currency
archived
name
insider_interview
area_id
area_name
area_url
created_at
published_at
address_city
address_street
address_building
address_description
address_lat
address_lng
alternate_url
apply_alternate_url
code,department_id department_name
employment_id
employment_name
prof_classes_found
terms_found

The columns prof_classes_found and terms_found are computed by us.

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51 Views

Three well-known Border Gateway Anomalies (BGP) anomalies:
WannaCrypt, Moscow blackout, and Slammer, occurred in May 2017, May 2005, and January 2003, respectively.
The Route Views BGP update messages are publicly available from the University of Oregon Route Views Project and contain:
WannaCrypt, Moscow blackout, and Slammer: http://www.routeviews.org/routeviews/.

Instructions: 

Raw data from the "route collector route-views2" are organized in folders labeled by the year and month of the collection date.
Complete datasets for WannaCrypt, Moscow blackout, and Slammer are available from the Route Views route collector route-views2 site:
University of Oregon Route Views Project: http://www.routeviews.org/routeviews/
Collectors: http://www.routeviews.org/routeviews/index.php/collectors/
Route Views Collector Map: http://www.routeviews.org/routeviews/index.php/map/
University of Oregon Route Views Archive Project: http://archive.routeviews.org/
MRT format RIBs and UPDATEs (quagga bgpd, from route-views2.oregon-ix.net): http://archive.routeviews.org/bgpdata/
Tools: http://www.routeviews.org/routeviews/index.php/tools/
The date of last modification and the size of the datasets are also included.

BGP update messages are originally collected in multi-threaded routing toolkit (MRT) format.
"Zebra-dump-parser" written in Perl is used to extract to ASCII the BGP updated messages.
The 37 BGP features were extracted using a C# tool to generate uploaded datasets (csv files).
Labels have been added based on the periods when data were collected.

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Appendix data for paper

Instructions: 

Appendix data for paper

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As an alternative to classical cryptography, Physical Layer Security (PhySec) provides primitives to achieve fundamental security goals like confidentiality, authentication or key derivation. Through its origins in the field of information theory, these primitives are rigorously analysed and their information theoretic security is proven. Nevertheless, the practical realizations of the different approaches do take certain assumptions about the physical world as granted.

Instructions: 

The data is provided as zipped NumPy arrays with custom headers. To load an file the NumPy package is required.

The respective loadz primitive allows for a straight forward loading of the datasets.

To load a file “file.npz” the following code is sufficient:

import numpy as np

measurement = np.load(’file.npz ’, allow pickle =False)

header , data = measurement [’header ’], measurement [’data ’]

The dataset comes with a supplementary script example_script.py illustrating the basic usage of the dataset.

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149 Views

This survey covers more than 150 published papers related to sub-6 GHz wideband LNAs from IEEE publications such as ISSCC, JSSC, TMTT, RFIC, MWCL, TCAS and NEWCAS published in the last 20 years. The considered LNAs are classified according to the technology node and its topology. The presented database is a useful tool for investigating technology trends and comparing the performance of common LNA design styles. 

Instructions: 

- The database is organized by technology and topology. 

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109 Views

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