The downloadable files contain all data and associated scripts that generate results as seen in the article. The major component description and detailed setup and run instructions are also provided in the README file.

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Dataset of rosbags collected during autonomous drone flight inside a warehouse of stockpiles. PCD files created using reconstruction method proposed by article.

Data still being move to IEEE-dataport. 

Instructions: 

Bag files contais multiple topics. Proposed method uses mainly Velodyne lidar pointcloud information and DJI imu

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209 Views

CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed.

Instructions: 

Download and extract the ZIP file containing all files. There is python code available (under 'scripts') to easily load the data set. Other programming languages should also handle .jpg, .hdf and .csv files for easy access. For easy access with python, a pickle dump file has been added. This has no extra information compared to the .csv file.

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A Traffic Light Controller PETRI_NET (Finite State Machine) Implementation.

 

An implementation of FSM approach can be followed in systems whose tasks constitute a well-structured list so all states can be easily enumerated. A Traffic light controller represents a relatively complex control function

Instructions: 

This file would need to be unzipped for access

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Dataset of GPS, inertial and WiFi data collected during road vehicle trips in the district of Porto, Portugal. It contains 40 trip datasets collected with a smartphone fixed on the windshield or dashboard, inside the road vehicle. The dataset was collected and used in order to develop a proof-of-concept for "MagLand: Magnetic Landmarks for Road Vehicle Localization", an approach that leverages magnetic anomalies created by existing road infrastructure as landmarks, in order to support current vehicle localization system (e.g. GNSS, dead reckoning).

Instructions: 

Dataset is organized in folders by date.Inside each folder, it is separated in folders by collection app or equipment.Inside collection app/equipemnt folders, it is separated by sensor.For each sensor there is a time series per trip.For details about the trips, including vehicles, smartphones, apps, and dates for data collection please read "README.txt".

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This dataset features cooking activities with recipes and gestures labeled. The data has been collected using two smartphones (right arm and left hip), two smartwatches (both wrists) and one motion capture system with 29 markers. There were 4 subjects who prepared 3 recipes (sandwich, fruit salad, cereal) 5 times each. The subjects followed a script for each recipe but acted as naturally as possible

Instructions: 

You can use our tutorials to get started https://abc-research.github.io/cook2020/tutorial/

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782 Views

Authors’ multimedia video of VO2 patch with resistive heater electrically isolated by a thin SiO2 layer and increase in temperature (by Joule heating) inducing phase transition, observed by a change in color.

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The file 'GPS_P2.zip' is the dataset collected from the GNSS sensor of "Xinda" autonomous vehicle in the Connected Autonomous Vehicles Test Fields (the CAVs Test Fields) Weishui Campus,Chang'an University.

The file 'fault.zip' is the simulated faults in the healthy data in '.mat' format, where X_abrupt, X_noise and X_drift represent abrupt faults, noise and drift in the long run are added into the healthy data, respectively.

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202 Views

The development of electronic nose (e-nose) for a rapid, simple, and low-cost meat assessment system becomes the concern of researchers in recent years. Hence, we provide time-series datasets that were recorded from e-nose for beef quality monitoring experiment. This dataset is originated from 12 type of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat.

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220 Views

 

This data is related to the article “On the Spectral Quality of Time-Resolved CMOS SPAD-Based Raman Spectroscopy with High Fluorescence Backgrounds” that have been submitted to the IEEE Sensors Journal.

 

 

Instructions: 

This data is related to the article “On the Spectral Quality of Time-Resolved CMOS SPAD-Based Raman Spectroscopy with High Fluorescence Backgrounds” that have been submitted to the IEEE Sensors Journal. The folder named “Fluorescence_to_Raman_ratio_(post-it_notes)” contains the data that was collected in the measurements where the effects of fluorescence-to-Raman ratio on the spectral quality were studied. Please, see the measurement procedures and results from the article under sections III. B and IV. A, respectively. The folder named “Recording_time_and_excitation_intensity_(oils)” contains the data that was collected in the measurement where the effects of the recording time and excitation intensity on the spectral quality was studied. Please, see the measurement procedures and results from the article under sections III. C and IV. B, respectively.

 

The measurement data is stored to the text files named as “Data.txt”. The datafiles have 8 columns and 256 rows. The columns represent the 8 time bins of the sensor and the rows in the datafiles represent the 256 spectral columns in the line sensor. The numbers in the cells of the datafiles represent the photon counts at a specific time bin and spectral column, i.e. at a specific wavenumber. The text files named as “Wavenumber_axis.txt” under the two main data folders contains the wavenumber values for each of the spectral columns in the sensor for the different measurements. The folders named as “DCR_corresction_data.txt” contains the dark count correction data for the different measurements.

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