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![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- Creator:
- Zhou, Lingxiao
- Description:
- A work that demonstrates enhancement by nearly two-orders of magnitude of the circular-polarized optical Stark effect in WSe₂ embedded into a Fabry Perot cavity, and use this mechanism to implement a XOR switch.
- Keyword:
- Nonlinear optics and Photonics
- Citation to related publication:
- Lingxiao Zhou, et al. Cavity-Floquet Engineering. Nature Communication (2024)
- Discipline:
- Science and Engineering
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- Creator:
- Towne, Aaron, Saxton-Fox, Theresa, and Parthasarthy, Aadhy
- Description:
- This dataset contains experimental measurements of a zero-pressure-gradient flat-plate boundary layer at five different Reynolds numbers collected using particle image velocimetry. For each Reynolds number, the dataset contains approximately 6000 snapshots of planar velocity fields as well as raw particle image pairs. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘BLexp_README.pdf’ file for more information. We recommend using the ‘BLexp_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.
- Keyword:
- fluid mechanics
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Discipline:
- Engineering and Science
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- Creator:
- Herzog, Joshua M, Verkade, Angela, and Sick, Volker
- Description:
- Data deposited here includes 60 image sets (30 individual participants, and 2 eyes per individual) consisting of raw fluorescence images, diffuse reflection images using ambient lighting, images used for correction, and calibration, and metadata. Images are split into two wavelength bands as described in the methodology. Raw images are stored in Hierarchical Data Format 5 (HDF5) file nodes (one file per eye) and each image node contains a tag for frame rate, exposure duration, and timestamp (stored in ImageData.zip). Summary statistics including demographic data, participant-reported diseases (e.g., diabetes, keratoconus), and pupil size are also stored in a text-based comma-separated table and as an Excel spreadsheet. Finally, 2-channel pseudocolor images and ratiometric grayscale images combining the two fully-processed image bands are stored as portable network graphics (PNG) files (stored in PseudocolorImages.zip).
- Keyword:
- Fluorescence, Imaging, Ocular lens, Corneal disease, Cataract, and Diabetes
- Citation to related publication:
- Herzog, Joshua M., Verkade, Angela, and Sick, Volker. "Corneal shadowgraphy: a simple, low-cost, rapid, and quantitative tool with potential clinical utility." Manuscript in review. 2024. and Herzog, Joshua M., Verkade, Angela, and Sick, Volker. "Quantitative and rapid in vivo imaging of human lenticular fluorescence." Manuscript in review. 2024.
- Discipline:
- Health Sciences and Engineering
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- 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
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- 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
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- 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
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- 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
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- 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
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- 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
![](/data/assets/work-ff055336041c3f7d310ad69109eda4a887b16ec501f35afc0a547c4adb97ee72.png)
- 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