Subject
- Biomedical Engineering 9
- Mechanical Engineering 2
- Computer Science 1
- Interdisciplinary Research 1
- Life Sciences 1
- Molecular Pharmacology 1
Creator
- Haiguang Wen 8
- Junxing Shi 8
- Zhongming Liu, 0000-0002-8773-4204 8
- Jiayue Cao 7
- Kun-Han Lu, 0000-0002-0355-8515 7
- Yizhen Zhang 7
- Nina Mahmoudian, 0000-0002-3285-8234 2
- Zihan Wang 2
- Amir Hossein Raffiee 1
- Arezoo M. Ardekani 1
Type
Date
Total is 16 Results
Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data
10.4231/R7N58J9Z
Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He
10/01/2015
A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity
bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning
River Obstacle Segmentation En-route By USV Dataset (ROSEBUD)
10.4231/MMJ2-NH88
Jalil Francisco Chavez Galaviz
,
Jianwen Li
,
Nina Mahmoudian
,
Reeve David Lambert
,
Zihan Wang
06/12/2022
This dataset contains stills from video taken on Sugar Creek and the Wabash River in the US state of Indiana. Images are hand annotated to provide training and testing data for semantic segmentation networks.
autonomous and connected vehicles deep learning Mechanical Engineering River Robotics semantic segmentation
Aerial Fluvial Image Dataset (AFID) for Semantic Segmentation
10.4231/B129-XD47
Li-Fan Wu
,
Nina Mahmoudian
,
Zihan Wang
07/20/2022
816 2K/2.7K per-pixel annotated images with 8 classes: River, Boat, Bridge, Sky, Forest vegetation, Dry sediment, Drone self and Obstacle in river. Fluvial scenes are from Wabash River and Wildcat Creek in Indiana, USA.
Artificial Neural Network (ANN) autonomos vehicles Collision Avoidance deep learning drone Image Dataset Mechanical Engineering navigation RGB image dataset River Robotics semantic segmentation Unmanned Aerial Vehicle Wabash River
A deep learning neural network to extract of P- and S-wave transit times from Vertical Seismic Profile (VSP)
10.4231/TT0F-KH40
Douglas R Schmitt
,
Oumeng Zhang
07/23/2024
This archive contains the training dataset and the Python code to train a deep learning neural net that aims to extract separately P and S wave arrival transit times from synthetic common shot gathers (CSG) in a deviated borehole geometry.
deep learning Machine Learning Machine Learning and Geophysical Signals seismic behavior
Raman spectra-based deep learning – A tool to identify microbial contamination
10.4231/HQGB-K827
Amir Hossein Raffiee
,
Arezoo M. Ardekani
,
Denilson Mendes De Oliveira
,
Mohit S. Verma
,
Murali K. Maruthamuthu
09/03/2020
Raman spectroscopic dataset for the potential microbes contaminating the pharmaceutical industry.
Agricultural and Biological Engineering deep learning microbial contamination Raman
Raw Data and Results from Automatic Murine Cardiac Ultrasound and Photoacoustic Image Segmentations
10.4231/9C8X-H052
Craig J Goergen
,
Hayley Chan
,
Katherine Leyba
,
Olivia Claire Loesch
,
Pierre Sicard
05/25/2023
Accuracy and dice scores from cross-validation reported in the cross-validation results spreadsheet. Radial strain raw data and results reported in strain results spreadsheet. Oxygen saturation values reported in sO2 results spreadsheet.
cardiovascular deep learning Imaging photoacoustic imaging ultrasound
Wheat spike blast image classification using deep convolutional neural networks
10.4231/P0Y7-3428
Carlos Gongora
,
Christian D Cruz
,
Darcy Telenko
,
Jian Jin
,
Mariela Fernandez-Campos
,
Mohammad Jahanshahi
,
Tao Wang
,
Yuting Huang
04/30/2021
The folder includes i) a wheat spike blast image classification CNN model trained to automatically quantify and classify disease severity, ii) the generated datasets that include images of wheat spike blast severity levels under controlled...
Blast Botany and Plant Pathology CNN deep learning disease Magnaporthe oryzae plant disease phenotyping Plant phenotyping Python Wheat wheat blast
Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision
10.4231/R7N58JJ3
Haiguang Wen
,
Jiayue Cao
,
Junxing Shi
,
Kun-Han Lu
,
Yizhen Zhang
,
Zhongming Liu
11/17/2017
This document includes the main source code (Matlab or Python) related to our study.
Biomedical Engineering deep learning FMR1 LIBI Matlab natural vision neural decoding Neural encoding Neuroscience Python Code Source Code
Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 1
10.4231/R7X63K3M
Haiguang Wen
,
Jiayue Cao
,
Junxing Shi
,
Kun-Han Lu
,
Yizhen Zhang
,
Zhongming Liu
09/15/2017
This is a video-fMRI dataset for subject 1 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI),
Biomedical Engineering deep learning fMRI LIBI natural vision neural decoding Neural encoding Neuroscience
Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 2
10.4231/R7NS0S1F
Haiguang Wen
,
Jiayue Cao
,
Junxing Shi
,
Kun-Han Lu
,
Yizhen Zhang
,
Zhongming Liu
09/18/2017
This is a video-fMRI dataset for subject 2 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).
Biomedical Engineering deep learning fMRI LIBI natural vision neural decoding Neural encoding Neuroscience
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