Info hash | c7fda3aebede0dd6fa2b4529814f36da806869e6 |
Last mirror activity | 112d,11:37:14 ago |
Size | 3.60GB (3,597,456,207 bytes) |
Added | 2023-11-28 10:43:52 |
Views | 57 |
Hits | 193 |
ID | 5107 |
Type | multi |
Downloaded | 0 time(s) |
Uploaded by | SManavi |
Folder | Data for Repository |
Num files | 26 files [See full list] |
Mirrors | 0 complete, 1 downloading = 1 mirror(s) total [Log in to see full list] |
Data for Repository (26 files)
raw data/RandomSplit/Spectrum_matched_10-05-2022 15.12.10.905.csv | 147.87MB |
raw data/RandomSplit/Coordinate_matched_10-05-2022 15.12.10.905.csv | 86.63MB |
raw data/3min_second/Spectrum_matched_10-05-2022 15.56.10.800.csv | 14.78MB |
raw data/3min_second/Coordinate_matched_10-05-2022 15.56.10.800.csv | 8.64MB |
raw data/3min_first/Spectrum_matched_10-05-2022 15.50.34.438.csv | 14.81MB |
raw data/3min_first/Coordinate_matched_10-05-2022 15.50.34.438.csv | 8.67MB |
processed data/y_val_Norm_Relative_1606Random_10-05-2022--15-12.csv | 8.93MB |
processed data/y_3min_Norm_10-05-2022_second.csv | 8.95MB |
processed data/y_train_Norm_Relative_1606Random_10-05-2022--15-12.csv | 71.40MB |
processed data/y_test_Norm_Relative_1606Random_10-05-2022--15-12.csv | 8.94MB |
processed data/y_3min_Norm_10-05-2022.csv | 8.96MB |
processed data/x_val_Norm_Relative_1606Random_10-05-2022--15-12.csv | 84.90MB |
processed data/x_train_Norm_Relative_1606Random_10-05-2022--15-12.csv | 679.10MB |
processed data/x_3min_Norm_10-05-2022.csv | 84.97MB |
processed data/x_3min_Norm_10-05-2022_second.csv | 84.90MB |
processed data/x_test_Norm_Relative_1606Random_10-05-2022--15-12.csv | 84.88MB |
MFD data/Transformation_Matrix.mat | 0.28kB |
MFD data/SpatialCoordinate_pred_TestResults_10_05_RandomTest_all22-06-2022.mat | 326.58MB |
MFD data/SpatialCoordinate_pred_TestResults_10_05_3min_second_all22-06-2022.mat | 327.23MB |
MFD data/SpatialCoordinate_pred_TestResults_10_05_3min_first_all22-06-2022.mat | 327.58MB |
MFD data/SpatialCoordinate_GT_TestResults_10_05_RandomTest_all22-06-2022.mat | 401.33MB |
MFD data/SpatialCoordinate_GT_TestResults_10_05_3min_first_all22-06-2022.mat | 404.44MB |
MFD data/SpatialCoordinate_GT_TestResults_10_05_3min_second_all22-06-2022.mat | 402.99MB |
MFD data/RotationMatrix.mat | 0.27kB |
MFD data/ArcNum_5FBGs.mat | 0.20kB |
MFD data/ArcNum_F.mat | 0.22kB |
Type: Dataset
Tags: deep learning, shape sensing, bending birefringence, bending loss, eccentric FBG, fiber sensor, curvature sensing
Bibtex:
Tags: deep learning, shape sensing, bending birefringence, bending loss, eccentric FBG, fiber sensor, curvature sensing
Bibtex:
@article{, title= {Raw and processed data in paper entitled "Deep learning-based approach for high spatial resolution fiber shape sensing"}, journal= {Communications Engineering}, author= {Samaneh Manavi Roodsari}, year= {2023}, url= {}, abstract= {Raw and processed data used in the paper entitled, Deep learning-based approach for high spatial resolution fiber shape sensing, published in the Journal of Communications Engineering. The code for processing the raw data is available in the link provided under the “code availability” section of the paper. Alternative download link: https://zenodo.org/records/13293929}, keywords= {deep learning, shape sensing, bending birefringence, bending loss, eccentric FBG, fiber sensor, curvature sensing}, terms= {}, license= {}, superseded= {} }