Browsing by Author "Charrett, Tom"

Now showing 1 - 8 of 8
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    '2D Spatially-Resolved Depth-Section Microfluidic Flow Velocimetry Using Dual Beam OCT'
    (Cranfield University, 2020-04-27 15:33) Hallam, Jonathan; Tatam, Ralph; Rigas, Evangelos; Charrett, Tom
    'Data and associated figures and supplementary materials for '2D Spatially-Resolved Depth-Section Microfluidic Flow Velocimetry Using Dual Beam OCT' published in Micromachines special edition 4th Conference on Microfluidic Handling Systems 2-4th October 2019. Update: 27/04/2020. Added 'requirements.txt' listing exact python packages required and clarified 'readme.txt'.'
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    Data: Automated interlayer wall height compensation for wire based directed energy deposition additive manufacturing
    (Cranfield University, 2024-05-08 16:28) Qin, Jian; Vives, Javier; Rajan, Parthiban; Lasisi, Shakirudeen; Wang, Chong; Charrett, Tom; Ding, Jialuo; Williams, Stewart; Hallam, Jonathan; Tatam, Ralph
    Experimental dataset to support the publication. The data includes all the test and measurement records for the experiment.
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    I2MTC 2024 Glasgow - Towards High Resolution Absolute Angle Sensing using Dual-Wavelength Laser Speckle
    (Cranfield University, 2024-02-27 13:05) Gibson, Sam; Charrett, Tom; Tatam, Ralph
    Data supporting the conference paper 'Towards High Resolution Absolute Angle Sensing using Dual-Wavelength Laser Speckle', presented at the International Instrumentation and Measurement Technology Conference (I2MTC) in Glasgow, in May 2024.
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    Interferometric robot joint angle measurement: data
    (Cranfield University, 2024-01-08 14:55) Naga Narayanan Ayyappan Pillai Rajeshwari, Sivaji; Charrett, Tom; Kissinger, Thomas; Barrington, James; James, Stephen; Tatam, Ralph
    Time series of angle measurement performed on 3 joints of a robotic arm instrumented with an array of optical fibre based angle sensors, exploiting fibre segment interferometry, Data presetned and analysed in: A fibre optic angle sensing tape for applications in robotics and automation
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    Laser speckle velocimetry for robot manufacturing
    (Cranfield University, 2017-07-04 15:10) Charrett, Tom; Bandari, Yashwanth K.; Michel, Florent; Ding, Jialuo; Williams, Stewart; Tatam, Ralph
    Data files associated with SPIE Optical Metrology, Munich 2017 paper: Thomas O. H. Charrett ; Yashwanth K. Bandari ; Florent Michel ; Jialuo Ding ; Stewart W. Williams ; Ralph P. Tatam; Laser speckle velocimetry for robot manufacturing . Proc. SPIE 10329, Optical Measurement Systems for Industrial Inspection X, 103291Z (June 26, 2017); doi:10.1117/12.2269989
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    'Optics and Lasers in Engineering - Absolute angle measurement using dual-wavelength laser speckle: theory and method'
    (Cranfield University, 2023-11-28 11:38) Gibson, Sam; Charrett, Tom; Tatam, Ralph
    Data supporting 'Absolute angle measurement using dual-wavelength laser speckle: theory and method', published in Optics and Lasers in Engineering.
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    Optics Express: "Metre-per-second microfluidic flow velocimetry with dual beam optical coherence tomography" Rigas Hallam Charrett Ford Tatam
    (Cranfield University, 2019-08-06 14:26) Hallam, Jonathan; Tatam, Ralph; Charrett, Tom; Rigas, Evangelos; Ford, Helen
    Images and analysis code associated with Optics Express publication: "Metre-per-second microfluidic flow velocimetry with dual beam optical coherence tomography". The code should be run in a jupyter notebook (or other python implementation).
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    Speckle tracking approaches in speckle correlation sensing
    (Cranfield University, 2017-05-02 13:08) Charrett, Tom; Kotowski, Krzysztof; Tatam, Ralph
    Data and code used to generate the conference paper: "Speckle tracking approaches in speckle correlation sensing" Thomas O. H. Charrett, Krzysztof Kotowski, and Ralph P. Tatam SPIE Optics and Optoelectonics, Prague, 2017. Files: ------ lib_feature_tracking.py - python module/library used to simplify the other scripts feature detectors.py - python script used to test processing times of feature detectors. feature descriptors.py - python script used to test processing times of feature descriptors and matching methods modelled shifts.py - python script used to generate figure 1 - accuracy assesment. experimental shifts.py - python script used to compare feature tracking method with cross correlation using real data (figure 2) experimental rotations.py - python script used to test rotation performance using experimental data. Used to generate figure 3. random positions.npy - 100 x (512,512) independent speckle patterns in numpy binary format. Used for table 1, table 2 and figure 1 linear move direction=0.0 speed=5.0mms-1.npy - 100 x (512,512) speckle patterns recorded using a speckle velocimetry sensor on XY stages travelling at 5mm/s in the y-direction. In numpy binary format.Used for figure 2. z rotation.npy - 721 x (512,512) speckle patterns for angles 0 to 360.0 degrees in 0.5 degree steps. Used for figure 3. Comments: ---------------- OpenCV version: 3.1.0 Numpy python library available at http://www.numpy.org/. Numpy version: 1.10.2 Load numpy binary format using: >>> import numpy as np >>> imgs = np.load( filename )