Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.
Copyright (C) 2018 Intel Corporation
Welcome to the Intel WiFi RTT (FTM) 40MHz dataset.
The paper and the dataset can be downloaded from:
To cite the dataset and code, or for further details, please use:
Nir Dvorecki, Ofer Bar-Shalom, Leor Banin, and Yuval Amizur, "A Machine Learning Approach for Wi-Fi RTT Ranging," ION Technical Meeting ITM/PTTI 2019
For questions/comments contact:
The zip file contains the following files:
1) This README.txt file.
2) LICENSE.txt file.
3) RTT_data.csv - the dataset of FTM transactions
4) Helper Matlab files:
O mainFtmDatasetExample.m - main function to run in order to execute the Matlab example.
O PlotFTMchannel.m - plots the channels of a single FTM transaction.
O PlotFTMpositions.m - plots user and Access Point (AP) positions.
O ReadFtmMeasFile.m - reads the RTT_data.csv file to numeric Matlab matrix.
O SimpleFTMrangeEstimation.m - execute a simple range estimation on the entire dataset.
O Office1_40MHz_VenueFile.mat - contains a map of the office from which the dataset was gathered.
Running the Matlab example:
In order to run the Matlab simulation, extract the contents of the zip file and call the mainFtmDatasetExample() function from Matlab.
Contents of the dataset:
The RTT_data.csv file contains a header row, followed by 29581 rows of FTM transactions.
The first column of the header row includes an extra "%" in the begining, so that the entire csv file can be easily loaded to Matlab using the command: load('RTT_data.csv')
Indexing the csv columns from 1 (leftmost column) to 467 (rightmost column):
O column 1 - Timestamp of each measurement (sec)
O columns 2 to 4 - Ground truth (GT) position of the client at the time the measurement was taken (meters, in local frame)
O column 5 - Range, as estimated by the devices in real time (meters)
O columns 6 to 8 - Access Point (AP) position (meters, in local frame)
O column 9 - AP index/number, according the convention of the ION ITM 2019 paper
O column 10 - Ground truth range between the AP and client (meters)
O column 11 - Time of Departure (ToD) factor in meters, such that: TrueRange = (ToA_client + ToA_AP)*3e8/2 + ToD_factor (eq. 7 in the ION ITM paper, with "ToA" being tau_0 and the "ToD_factor" lumps up both nu initiator and nu responder)
O columns 12 to 467 - Complex channel estimates. Each channel contains 114 complex numbers denoting the frequency response of the channel at each WiFi tone:
O columns 12 to 125 - Complex channel estimates for first antenna from the client device
O columns 126 to 239 - Complex channel estimates for second antenna from the client device
O columns 240 to 353 - Complex channel estimates for first antenna from the AP device
O columns 354 to 467 - Complex channel estimates for second antenna from the AP device
The tone frequencies are given by: 312.5E3*[-58:-2, 2:58] Hz (e.g. column 12 of the csv contains the channel response at frequency fc-18.125MHz, where fc is the carrier wave frequency).
Note that the 3 tones around the baseband DC (i.e. around the frequency of the carrier wave), as well as the guard tones, are not included.
This is the dataset of mmWave massive MIMO beamspace channels, which is used for the experiment implementation of the paper "Acquiring Measurement Matrices via Deep Basis Pursuit for Sparse Channel Estimation in mmWave Massive MIMO Systems". The source code of the experiment implementation is also open-access on the Github repository DeepBP-AE.
1. The "DeepMIMO_dataset.mat" is the spatial-domain channel dataset that can be reproduced by running "DeepMIMO_Dataset_Generator.m". For more details, refer to the public DeepMIMO dataset "DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications".
2. The "H_beam_sparsity_syn3.mat" is the beamspace-domain channel dataset that can be reproduced by running "deepMIMO_beamspace_channels.m".
3. For more detailed parameter settings, please refer to the paper "Acquiring Measurement Matrices via Deep Basis Pursuit for Sparse Channel Estimation in mmWave Massive MIMO Systems" (currently under review).
PV dataTemperature (Celsius)33.00
Number of cells in series (ns)1
Number of cells in parallel (np)1
I V ccurve
Encoding and decoding tables for 6b8b encoder/decoder for sefl-syncrhonized improved RMII protocol. Proposed encoder/decoder garantee that 2-bit TXD/RXD will change each data transmission cycle, making it possible for RMII interface to work without REF_CLK, TX_EN and CRS_DV lines.
An optimization model with heuristic algorithm is implemented to optimize the virtual resistances of droop control for the grid-connected converters of dispatchable units, such that the power flow can be regulated. The performances of the proposed strategy are evaluated by the case studies of a 12-bus 380 V DC microogrid using matlab and a 32-bus 380 V DC microgrid using a real-time digital simulator.
BCI-Double-ErrP-Dataset is an EEG dataset recorded while participants used a P300-based BCI speller. This speller uses a P300 post-detection based on Error-related potentials (ErrPs) to detect and correct errors (i.e. when the detected symbol does not match the user’s intention). After the P300 detection, an automatic correction is made when an ErrP is detected (this is called a “Primary ErrP”). The correction proposed by the system is also evaluated, eventually eliciting a “Secondary ErrP” if the correction is wrong.
A detailed description of the data is given in “BCI-Double-ErrP-Dataset-instructions.pdf” and a Matlab code example is provided to extract P300 and ErrPs (primary and secondary).
There are 4 folders, one with the datasets of the P300 calibration (session 1), one with the datasets of the ErrP calibration (session 1), one with the datasets of the testing session (session 2), and a folder with the Matlab code to run the example.
This dataset aims at providing a toy demo for the comparison between the doubly orthogonal matching pursuit (DOMP) and its predecessor, the OMP.
- toydemo.m: Main file. It provides a comparison of the algorithms by loading the measurement and performing a behavioral model of the PA.
- demo_meas.mat: example of the input and output measurements of a power amplifier (PA) working under a 15-MHz LTE signal sampled at a sampling rate of 92.16MHz.
- model_gmp_domp_omp.m: generates the model structure and calls the pruning techniques.
- omp_domp.m: executes the OMP and DOMP techniques.
The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR).
The target scene consists of a black card with six cocoa beans of three different fermentation levels (High, correct, and low fermentation), two beans for each class, whose false-color composite is shown in the provided Figure (a), ground-truth map is shown in Fig. (b), and Fig. (c) presents its representative spectral signatures. The spectral image was acquired by the AVT Stingray F-080B camera by acquiring one band each time from 350 - 950 nm. The acquired image has a spatial resolution of 1096x712 pixels and 300 spectral bands of 2 nm width.
The year 2018 was declared as "Turkey Tourism Year" in China. The purpose of this dataset, tourists prefer Turkey to be able to determine. The targeted audience was determined through TripAdvisor. Later, the travel histories of individuals were gathered in four different groups. These are the individuals’ travel histories to Europe (E), World (W) Countries and China (C) City/Province and all (EWC). Then, "One Zero Matrix (OZ)" and "Frequency Matrix (F)" were created for each group. Thus, the number of matrices belonging to four groups increased to eight.
The operational steps of the study are given in Fig. According to this, firstly, the targeted audience was determined through TripAdvisor. Later, the travel histories of individuals were gathered in four different groups. These are the individuals’ travel histories to Europe (E), World (W) Countries and China (C) City/Province and all (EWC). Then, "One Zero Matrix (OZ)" and "Frequency Matrix (F)" were created for each group. Thus, the number of matrices belonging to four groups increased to eight.
For more information, please read the article.
İbrahim Topal, Muhammed Kürşad Uçar, "Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists", IEEE Open Access.