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- Creator:
- Moniri, Saman, Xiao, Xianghui, and Shahani, Ashwin J.
- Description:
- The data file is comprised of 22,500 X-ray projections (15 scans of 1500 projections each) recorded during solidification of Al-Ge-Na. The raw data file is in .hdf format and can be reconstructed into .tiff, e.g., by using the TomoPy toolbox in Python.
- Keyword:
- X-ray microtomography, synchrotron, in situ, 4D materials science, irregular eutectic, growth, and solidification
- Citation to related publication:
- Moniri, S., Xiao, X., & Shahani, A. J. (2019). The mechanism of eutectic modification by trace impurities. Scientific Reports, 9(1), 3381. https://doi.org/10.1038/s41598-019-40455-3
- Discipline:
- Engineering
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- Creator:
- Gliske, Stephen V and Stacey, William C
- Description:
- This data is part of a large program to translate detection and interpretation of HFOs into clinical use. A zip file is included which contains hfo detections, metadata, and Matlab scripts. The matlab scripts analyze this input data and produce figures as in the referenced paper (note: the blind source separation method is stochastic, and so the figures may not be exactly the same). A file "README.txt" provides more detail about each individual file within the zip file.
- Keyword:
- hfo, high frequency oscillation, ripple, fast ripple, blind source separation, non-negative matrix factorization, and temporal variability
- Discipline:
- Science, Engineering, and Health Sciences
-
- Creator:
- Carlevaris-Bianco, Nicholas , Ushani, Arash , and Eustice, Ryan
- Description:
- This is a large scale, long-term autonomy dataset for robotics research collected on the University of Michigan’s North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive sensors for odometry collected using a Segway robot. The dataset was collected to facilitate research focusing on longterm autonomous operation in changing environments. The dataset is comprised of 27 sessions spaced approximately biweekly over the course of 15 months. The sessions repeatedly explore the campus, both indoors and outdoors, on varying trajectories, and at different times of the day across all four seasons. This allows the dataset to capture many challenging elements including: moving obstacles (e.g., pedestrians, bicyclists, and cars), changing lighting, varying viewpoint, seasonal and weather changes (e.g., falling leaves and snow), and long-term structural changes caused by construction projects. To further facilitate research, we also provide ground-truth pose for all sessions in a single frame of reference. and A detailed description of the dataset and the methods used to generate it is in the document nclt.pdf. If you use this dataset in your research please cite: Carlevaris-Bianco, N., Ushani, A., Eustice, R. (2021). The University of Michigan North Campus Long-Term Vision and LIDAR Dataset [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/7rnm-6a03
- Keyword:
- Long-term SLAM, place recognition, lidar, computer vision, and field and service robotics
- Citation to related publication:
- Carlevaris-Bianco, Nicholas, et al. “University of Michigan North Campus Long-Term Vision and Lidar Dataset.” The International Journal of Robotics Research, vol. 35, no. 9, Aug. 2016, pp. 1023–1035, doi:10.1177/0278364915614638.
- Discipline:
- Engineering
-
- Creator:
- Sick, Volker , Reuss, David L, and Greene, Mark L
- Description:
- This archive contains data files from spark-ignited homogeneous combustion internal combustion engine experiments. Included are high-resolution two-dimensional two-component velocity fields acquired at two 5 x 6 mm regions, one near the head and one near the piston. Crank angle resolved heat flux measurements were made at a third location in the head. The engine was operated at 40 kPa, 500 and 1300 RPM, motor and fired. Included are in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations.
- Keyword:
- combustion, internal combustion engine, heat Transfer, particle image velocimetry, in-cylinder flow, TCC III engine , optical engine, CFD validation, PIV, boundary layer, and turbulence
- Discipline:
- Engineering
-
- Creator:
- Mathieu, Johanna L, Balzano, Laura, and Ledva, Gregory S
- Description:
- This data set contains the relevant time series for constructing and testing electricity load models within the related paper. The files within are a '.mat' file that contains the data and a 'readme.txt' file detailing the contents of the data.
- Keyword:
- Output feedback, Online learning, Machine learning, Real-time filtering, and Energy disaggregation
- Discipline:
- Engineering
-
- Creator:
- Schiffmann, Philipp, Sick, Volker, and Reuss, David L
- Description:
- This archive contains data files from spark-ignited homogenous combustion internal combustion engine experiments. Included are two-dimensional two-component velocity fields from various measurement planes with maximized field of view, in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations. Fired operation was with stoichiometric propane air, 40kPa MAP, at 1300 RPM.
- Keyword:
- TCC III engine, internal combustion engine, particle image velocimetry, in-cylinder flow, turbulence in engines, CFD validation data, cyclic variability, optical engine, combustion variability, and PIV
- Citation to related publication:
- dx.doi.org/10.1177/1468087417720558
- Discipline:
- Engineering
-
- Creator:
- Schiffmann, Philipp, Sick, Volker, and Reuss, David L
- Description:
- This archive contains data files from spark-ignited homogenous combustion internal combustion engine experiments. Included are two-dimensional two-component velocity fields acquired in a small, high-resolution field of view near the spark plug, and images of hydroxyl radical chemiluminescence recording the early flame-kernel growth. Included are in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations. Included are tables of one-per-cycle parameters for each test with methane or propane at stoichiometric, dilute limit, lean limit, and rich limit, operation conducted at 40kPa and 1300 RPM.
- Keyword:
- OH* imaging, TCC III engine, internal combustion engine, particle image velocimetry, in-cylinder flow, turbulence in engines, CFD validation data, cyclic variability, early flame kernel growth, optical engine, combustion variability, ignition, and PIV
- Citation to related publication:
- dx.doi.org/10.1177/1468087417720558
- Discipline:
- Engineering
-
- Creator:
- Schiffmann, Philipp, Sick, Volker, and Reuss, David L
- Description:
- This archive contains data files from motored internal combustion engine experiments. Included are two-dimensional two-component velocity fields from four measurement planes with maximized field of view. in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations. Motored operating conditions include 40kPa and 90kPa MAP, 800 and 1300 RPM.
- Keyword:
- TCC III engine, internal combustion engine, particle image velocimetry, in-cylinder flow, turbulence in engines, CFD validation data, motored engine, optical engine, cyclic variability , and PIV
- Citation to related publication:
- http://dx.doi.org/10.2516/ogst/2015028
- Discipline:
- Engineering
-
- Creator:
- Vasudevan, Ram, Barto, Charles, Rosaen, Karl, Mehta, Rounak, Matthew, Johnson-Roberson, and Nittur Sridhar, Sharath
- Description:
- A dataset for computer vision training obtained from long running computer simulations
- Keyword:
- autonomous driving, simulation, Computer Vision and Pattern Recognition, deep learning, Computer Science, object detection, and Robotics
- Citation to related publication:
- M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen and R. Vasudevan, "Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?," 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 746-753. Available at https://arxiv.org/abs/1610.01983 and https://doi.org/10.1109/ICRA.2017.7989092
- Discipline:
- Engineering
-
- Creator:
- Malik, Hafiz and Khan, Muhammad Khurran, King Saud University
- Description:
- Details of the microphone used for data collection, acoustic environment in which data was collected, and naming convention used are provided here. 1 - Microphones Used: The microphones used to collect this dataset belong to 7 different trademarks. Table (1) illustrates the number of used Mics of different trademarks and models. Table 1: Trademarks and models of Mics Mic Trademark Mic Model # of Mics Shure SM-58 3 Electro-Voice RE-20 2 Sennheiser MD-421 3 AKG C 451 2 AKG C 3000 B 2 Neumann KM184 2 Coles 4038 2 The t.bone MB88U 6 Total 22 2- Environment Description: A brief description of the 6 environments in which the dataset was collected is presented here: (i) Soundproof room: a small room (nearly 1.5m × 1.5m × 2m), which is closed and completely isolated. With an exception of a small window in the front side of the room which is made of glass, all the walls of the room are made of wood and covered by a layer of sponge from the inner side, and the floor is covered by carpet. (ii) Class room: standard class room (6m × 5m × 3m). (iii) Lab: small lab (4m × 4m × 3m). All the walls are made of glasses and the floor is covered by carpet. The lab contains 9 computers. (iv) Stairs: is in the second floor. The place of recording is 3m × 5m (v) Parking: is the college parking. (vi) Garden: is an open space outside the buildings. 3- Naming Convention: This set of rules were followed as a naming convention to give each file in the dataset a unique name: (i) The file name is 19 characters long, and consists of 5 sections separated by underscores. (ii) The first section is of 3 characters indicates the Microphone trademark. (iii) The second section of 4 characters indicates the microphone model as in table (2). (iv) The third section of 2 characters indicates a specific microphone within a set of microphones of the same trademark and model, since we have more than one microphone of the same trademark and model. (v) The fourth section of 2 characters indicates the environment, where Soundproof room --> 01 Class room --> 02 Lab --> 03 Stairs --> 04 Parking --> 05 Garden --> 06 (vi) The fifth section of 2 characters indicates the language, where Arabic --> 01 English --> 02 Chinese --> 03 Indonesian --> 04 (vii) The sixth section of 2 characters indicates the speaker. Table 2: Microphones Naming Criteria Original Mic Trademark and model --> Naming Convenient Shure SM-58 --> SHU_0058 Electro-Voice RE-20 --> ELE_0020 Sennheiser MD-421 --> SEN_0421 AKG C 451 --> AKG_0451 AKG C 3000 B --> AKG_3000 Neumann KM184 --> NEU_0184 Coles 4038 --> COL_4038 The t.bone MB88U --> TBO_0088 For example: SEN_0421_02_01_02_03 is an English file recorded by speaker number 3 in the soundproof room using microphone number 2 of Sennheiser MD-421
- Keyword:
- audio forensic, multimedia forensics, microphone identification, tamper detection, splicing detection, and codec identification
- Citation to related publication:
- Muhammad Khurram Khan, Mohammed Zakariah, Hafiz Malik & Kim-Kwang Raymond Choo (2018). A novel audio forensic data-set for digital multimedia forensics, Australian Journal of Forensic Sciences, 50:5, 525-542, http://dx.doi.org/10.1080/00450618.2017.1296186
- Discipline:
- Engineering, Government, Politics and Law, and Science