Subject
Creator
- Ahmed Khaled Soliman 1
- Andres Torres 1
- Chang Heon Lee 1
- Charles Fieseler 1
- Chven Mitchell 1
- Guilherme A. Ribeiro 1
- Itamar Roth 1
- Jan Allebach 1
- Jiayin Liu, 0000-0001-5630-6824 1
- Laura J Pyrak-Nolte, 0000-0001-6826-5214 1
Type
- Dataset 4
Date
- 2022 4
subject: Machine Learning date: 2022
Total is 4 Results
Multi-Species Prediction of Physiological Traits with Hyper-Spectral Modeling
10.4231/FPHP-0153
Meng-yang Lin, Mitchell R Tuinstra, 0000-0002-5322-6519
02/11/2022
High-throughput hyperspectral imaging in corn and sorghum can be used in multi-species models to predict water and nitrogen status of plants within and across these crop species.
Abiotic stress Agronomy Corn Ecophysiology High-throughput Phenotyping Machine Learning nitrogen content partial least square regression relative water content Remote Sensing Sorghum
Gait phase estimation using wearable sensors and PCA based linear regression model
10.4231/Q0HY-AT09
Ahmed Khaled Soliman, Andres Torres, Chang Heon Lee, Guilherme A. Ribeiro, Li-fan Wu, Mo Rastgaar, 0000-0003-4638-3345
05/16/2022
Estimating gait realtime through 2 wearable sensors and a PCA based linear regression model.
Acceleration Biomedical Engineering Machine Learning Mechanical Engineering PCA Robotics Stepwise Multiple linear Regression Wearable Device
A Machine Learning Approach to Design of Aperiodic, Clustered-Dot Halftone Screens via Direct Binary Search
10.4231/AMGQ-0T59
Itamar Roth, Jan Allebach, Jiayin Liu, 0000-0001-5630-6824, Orel Bat Mor, Oren Haik, Shani Gat, Tal Frank, Yitzhak Yitzhaky
06/01/2022
This dataset contains two parts: one has halftone patches that were used to predicts the quality level and scale using machine learning methods. The second part contains full versions of halftone images so viewers can zoom in to see the details.
direct binary search Electrical and Computer Engineering Halftone screen Machine Learning
Data for Characterization of Acoustic Emissions from Analogue Rocks using Sparse Regression-DMDc
10.4231/4K64-4818
Charles Fieseler, Chven Mitchell, Laura J Pyrak-Nolte, 0000-0001-6826-5214, Nathan Kutz
06/14/2022
Acoustic waveforms collected during the monitoring of moisture loss in synthetic rock samples composed of mortar, and mortar with either distributed clay or localized clay, under ambient laboratory conditions.
acoustic signals clay cracking Fractures Machine Learning Physics
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