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- Creator:
- Whittaker, Collin B. and Jorns, Benjamin A.
- Description:
- This work characterized variability in the emitter geometry of a porous electrospray array thruster. It consists of raw topographic maps taken from the thruster, segmented versions of those maps that divide the measurement domain into individual sites within the array, and the geometric parameters describing each of those sites inferred by regressing a geometric model of an emitter against the data. It also includes copies of the computer code used to perform this analysis and plot the results, written in a combination of Python and MATLAB.
- Discipline:
- Engineering
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- Creator:
- El Shair, Zaid A., Abu-raddaha, Abdalmalek , Cofield, Aaron, Alawneh, Hisham, Aladem, Mohamed, Hamzeh, Yazan, and Rawashdeh, Samir A.
- Description:
- The SID dataset was curated to support advanced research in autonomous driving systems, particularly focusing on perception under adverse weather and lighting conditions. This dataset encompasses over 178k high-resolution stereo image pairs organized into 27 sequences, reflecting a rich variety of conditions such as snow, rain, fog, and low light. It covers dynamic changes in driving scenarios and environmental backgrounds, including university campuses, residential streets, and urban settings. The dataset is designed to challenge perception algorithms with scenarios such as partially obscured camera lenses and varying visibility, promoting the development of robust computer vision models. No specialized software or scripts are necessary for accessing the image data, as the files are provided in standard PNG format. However, researchers and developers may require their image processing and computer vision toolkits to utilize the dataset effectively in their work.
- Keyword:
- Autonomous Driving, Adverse Weather, Stereo Vision, Image Dataset, Computer Vision, and Perception Algorithms
- Citation to related publication:
- El-Shair, Z.A., Abu-raddaha, A., Cofield, A., Alawneh, H., Aladem, M., Hamzeh, Y., and Rawashdeh, S.A., 2024. SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions. In 2024 IEEE National Aerospace and Electronics Conference (NAECON). IEEE. In press.
- Discipline:
- Engineering
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- Creator:
- Marks, Thomas A and Gorodetsky, Alex A
- Description:
- WarpX simulations of the 2D axial-azimuthal Hall thruster benchmark, as described in IEPC paper 409 (2024): https://www.thomasmarks.space/files/Marks_T_IEPC_2024_WarpX.pdf Contains one subdirectory: baseline_20us: 20 us of data, saved every 5000 iterations (32 GB) The data is in AMReX plotfile format.
- Keyword:
- hall thruster, plasma, pic, particle in cell, particle, cell, kinetic, electric propulsion, and thruster
- Citation to related publication:
- @inproceedings{marksWarpX2024, title = {Hall thruster simulations in {{Warp-X}}}, booktitle = {38th {{International Electric Propulsion Conference}}}, author = {Marks, Thomas A. and Gorodetsky, Alex A.}, year = {2024}, month = jun, address = {Toulouse, France} }
- Discipline:
- Engineering
-
- Creator:
- El Shair, Zaid A. and Rawashdeh, Samir A.
- Description:
- The MEVDT dataset was created to fill a critical gap in event-based computer vision research by supplying a high-quality, real-world labeled dataset. Intended to facilitate the development of advanced algorithms for object detection and tracking applications, MEVDT includes multi-modal traffic scene data with synchronized grayscale images and high-temporal-resolution event streams. Additionally, it provides annotations for object detection and tracking with class labels, pixel-precise bounding box coordinates, and unique object identifiers. The dataset is organized into directories containing sequences of images and event streams, comprehensive ground truth labels, fixed-duration event samples, and data indexing sets for training and testing. and To access and utilize the dataset, researchers need specific software or scripts compatible with the data formats included, such as PNG for grayscale images, CSV for event stream data, AEDAT for the encoded fixed-duration event samples, and TXT for annotations. Recommended tools include standard image processing libraries for PNG files and CSV or text parsers for event data. A specialized Python script for reading AEDAT files is available at: https://github.com/Zelshair/cstr-event-vision/blob/main/scripts/data_processing/read_aedat.py, which streamlines access to the encoded event sample data.
- Keyword:
- Computer Vision, Event-Based Vision, Object Detection, Object Tracking, and Multi-Modal Vision Dataset
- Citation to related publication:
- El Shair, Z. and Rawashdeh, S., 2024. MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset. Data In Brief (under review)., El Shair, Z. and Rawashdeh, S.A., 2022. High-temporal-resolution object detection and tracking using images and events. Journal of Imaging, 8(8), p.210., El Shair, Z. and Rawashdeh, S., 2023. High-temporal-resolution event-based vehicle detection and tracking. Optical Engineering, 62(3), pp.031209-031209., and El Shair, Z.A., 2024. Advancing Neuromorphic Event-Based Vision Methods for Robotic Perception Tasks (Doctoral dissertation, University of Michigan-Dearborn).
- Discipline:
- Engineering
-
- Creator:
- Ali, Hashim, Subramani, Surya , Sudhir, Shefali , Varahamurthy, Raksha , and Malik, Hafiz
- Description:
- Voice-cloning (VC) systems have seen an exceptional increase in the realism of synthesized speech in recent years. The high quality of synthesized speech and the availability of low-cost VC services have given rise to many potential abuses of this technology such as online smearing campaigns and dissemination of fabricated information etc. A number of detection methodologies have been proposed over the years that can detect voice spoofs with reasonably good accuracy. However, these methodologies are mostly evaluated on clean audio databases, such as Asvspoof 2019. This research aims to evaluate state-of-the-art (SOTA) Audio Spoof Detection approaches in the presence of laundering attacks. In that regard, a new laundering attack database, called ASVspoof Laundering Database, is created. This database is based on the ASVspoof 2019 LA eval database comprising a total of 1388.22 hours of audio recordings. Seven SOTA audio spoof detection approaches are evaluated on this laundered database. The results indicate that SOTA systems perform poorly in the presence of aggressive laundering attacks, especially reverberation and additive noise attacks. This suggests the need for robust audio spoof detection.
- Keyword:
- Audio Forensics, Audio Antispoofing, Audio Deepfakes, ASVSpoof, and Machine Learning
- Discipline:
- Engineering
-
- Creator:
- Fu, Xun, Zhang, Bohao, Weber, Ceri J., Cooper, Kimberly L., Vasudevan, Ram, and Moore, Talia Y.
- Description:
- Tails used as inertial appendages induce body rotations of animals and robots---a phenomenon that is governed largely by the ratio of the body and tail moments of inertia. However, vertebrate tails have more degrees of freedom (e.g., number of joints, rotational axes) than most current theoretical models and robotic tails. To understand how morphology affects inertial appendage function, we developed an optimization-based approach that finds the maximally effective tail trajectory and measures error from a target trajectory. For tails of equal total length and mass, increasing the number of equal-length joints increased the complexity of maximally effective tail motions. When we optimized the relative lengths of tail bones while keeping the total tail length, mass, and number of joints the same, this optimization-based approach found that the lengths match the pattern found in the tail bones of mammals specialized for inertial maneuvering. In both experiments, adding joints enhanced the performance of the inertial appendage, but with diminishing returns, largely due to the total control effort constraint. This optimization-based simulation can compare the maximum performance of diverse inertial appendages that dynamically vary in moment of inertia in 3D space, predict inertial capabilities from skeletal data, and inform the design of robotic inertial appendages.
- Keyword:
- simulation, inertial maneuvering, caudal vertebrae, trajectory optimization, and reconfigurable appendages
- Citation to related publication:
- Xun Fu, Bohao Zhang, Ceri J. Weber, Kimberly L. Cooper, Ram Vasudevan, Talia Y. Moore. (in review) Jointed tails enhance control of three-dimensional body rotation.
- Discipline:
- Engineering and Science
-
- Creator:
- Shah, Bhavarth
- Description:
- The three approaches used three distinct datasets named as follows: Historicalwater_levels.csv, Historical_Precipitation.csv, and Bayesian Statistical dataset.csv. These files are accessible using Microsoft Office or similar software. The machine learning models are developed in Jupyter Notebook (.ipynb) files, named according to the datasets they utilize. However, for the third approach, the models are named Random Forest, LSTM Model Base, and Multivariate LSTM Models. More details are available on the Shah_Bhavarth_Readme.txt. These notebooks can be accessed through Python, Project Jupyter, or Google Colab, and dependencies include libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, Keras, and TensorFlow. The supplementary material also includes Excel files for stage-curve calculations and diversions, named Water_levels_Stage_Curve_Calculations1970-2018.xlsx and Diversions_calculation.xlsx, respectively.
- Keyword:
- Machine learning, Forecasting, Water levels, Mono lake, and Hydrology
- Citation to related publication:
- Shah, Bhavarth. 2024. "Mono Lake Water Levels Forecasting Using Machine Learning." Master’s thesis, University of Michigan, School for Environment and Sustainability. ORCID iD: 0000-0002-2391-8610. https://dx.doi.org/10.7302/22659
- Discipline:
- Science and Engineering
-
- Creator:
- Dwyer, Tobias, Moore, Timothy C., Anderson, Joshua A. , and Glotzer, Sharon C.
- Description:
- This dataset was generated for our work: "Tunable Assembly of Host–Guest Colloidal Crystals". The data set contains data for 5 different binary systems of star particles and convex guests, and one system of only star particles. All simulation were formed at constant pressure. The data set contains GSD files for each of the simulations used in this work along with the corresponding python code used to produce the simulations. We also include the python code and jupyter notebook to produce the free volume calculations used in this work. and How to use this Data: Simulation Data: We include GSD files that can be uploaded into a visualization or analysis software such as Ovito or Freud for independent analysis. Simulation python scripts (workspaces_for_HPMC_simulations.zip): We include the python scripts used in this work for simulating host guest systems at constant pressure. Free Volume Data (Free_volume_calculations_and_analysis.zip): You can run the jupyter notebook included here to reproduce the free volume analysis for this work. We also include the python scripts for the free volume calculation python scripts that get the data for these free volume calculations.
- Citation to related publication:
- Dwyer, T, Moore, TC, Anderson, JA, & Glotzer, SC. Tunable Assembly of Host–Guest Colloidal Crystals. Soft Matter (Provisional Citation)
- Discipline:
- Engineering
-
- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
-
- Creator:
- Agnit Mukhopadhyay, Sanja Panovska, Raven Garvey, Michael Liemohn, Natalia Ganjushkina, Austin Brenner, Ilya Usoskin, Michael Balikhin, and Daniel Welling
- Description:
- In the recent geological past, Earth’s magnetic field reduced to 4% of the modern values and the magnetic poles moved severely apart from the geographic poles causing the Laschamps geomagnetic excursion, which happened about 41 millennia ago. The excursion lasted for about two millennia, with the peak strength reduction and dipole tilting lasting for a shorter period of 300 years. During this period, the geomagnetic field exhibited significant differences from the modern nearly-aligned dipolar field, causing non-dipole variables to mimic a magnetic field akin to the outer planets while displaying a significantly reduced magnetic strength. However, the precise magnetospheric configuration and their electrodynamic coupling with the atmosphere have remained critically understudied. This dataset contains the first space plasma investigation of the exact geomagnetic conditions in the near-Earth space environment during the excursion. The study contains a full 3D reconstruction and analysis of the geospace system including the intrinsic geomagnetic field, magnetospheric system and the upper atmosphere, linked in sequence using feedback channels for distinct temporal epochs. The reconstruction was conducted using the LSMOD.2 model, Block Adaptive Tree Solar wind-Roe-Upwind Scheme (BATS-R-US) Model and the MAGnetosphere-Ionosphere-Thermosphere (MAGNIT) Auroral Precipitation Model, all of which are publicly-available models. The dataset contains the raw data from each of these models, in addition to the images/post-processing results generated using these models. Paleomagnetic data produced by LSMOD.2 can be visualized using a combination of linear plotting and contour plotting tools available commonly in visualization software like Python (e.g. Python/Matplotlib) or MATLAB. Standard tools to read and visualize BATS-R-US and MAGNIT output are already publicly available using IDL and Python (see SpacePy/PyBats - https://spacepy.github.io/pybats.html). For information and details about the post-processed data, visualization and analysis, please contact the authors for details. The anthropological dataset can be visualized using a shape file reader (e.g. Python/GeoPandas) and a linear plotting tool (e.g. Python/Matplotlib).
- Discipline:
- Engineering and Science