TEM vision software

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Prototype using Distributed Ruby (https://www.ruby-doc.org/core/classes/DRb.html) for vision-based closed-loop control
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Prototype using Distributed Ruby (https://www.ruby-doc.org/core/classes/DRb.html) for vision-based closed-loop control
Moving the tip using "drag-and-drop" without vision feedback. The circle marks the initial position of the mouse-cursor
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Moving the tip using "drag-and-drop" without vision feedback. The circle marks the initial position of the mouse-cursor
Here vision-based closed-loop control is used to control the position of the tip. The cross-and-circle marks the last known position of the nano-indenter. The cross marks the current nominal position
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Here vision-based closed-loop control is used to control the position of the tip. The cross-and-circle marks the last known position of the nano-indenter. The cross marks the current nominal position

As part of the Nanorobotics project a TEM vision software was developed. The software makes use of a JEOL 3010 (https://www.jeolusa.com/PRODUCTS/ElectronOptics/TransmissionElectronMicroscopesTEM/300kV/JEM3010/tabid/213/Default.aspx) transmission electron microscope with a TVIPS FastScan-F114 camera (https://tvips.com/Prod_F114.php) which is an IIDC/DCAM-compatible firewire camera. The nano-indenter is controlled by a Nanomagnetics SPM controller (https://web.nanomagnetics-inst.com/product_detail.php?product_link=SPM) (the old version of the controller can be accessed with a PCI-DIO24 card (https://www.mccdaq.com/pci-data-acquisition/PCI-DIO24.aspx)).

The software runs under GNU/Linux and it makes use of Damien Douxchamps' libdc1394 (https://damien.douxchamps.net/ieee1394/libdc1394/) to access the camera and Warren Jasper's PCI-DIO24 driver (ftp://lx10.tx.ncsu.edu/pub/Linux/drivers/) to access the PCI-card which interfaces with the SPM controller.

The software was implemented in Ruby (https://www.ruby-lang.org/) using Qt4-QtRuby (https://rubyforge.org/projects/korundum/), HornetsEye, libJIT (https://dotgnu.org/libjit-doc/libjit.html), and a custom Ruby-extension to access the SPM controller via the PCI-DIO24 card. Distributed Ruby (https://www.ruby-doc.org/core/classes/DRb.html) and multiple processes were used to work around the problem that Ruby-1.8 does not offer native threads.

The vision algorithms are configured using a separate program and the configuration is saved in a file using Ruby marshalling (https://www.ruby-doc.org/core/classes/Marshal.html). A plugin-based architecture, which accepts plugins for recognition and tracking, was implemented which allows one to select and configure Normalised Cross-Correlation, Lucas-Kanade tracking, or Connected Component Analysis.

Table of contents
1 Demonstration

1.1 Videos
1.2 Setup procedure

2 Download
3 Future Work

1 See Also
2 External Links

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Demonstration

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Videos

Demonstration of TEM vision software including telemanipulation as well as closed-loop control using machine-vision feedback (also available as DivX3 videos configuration.avi (64 MByte), closed-loop.avi (44 MByte), and interaction.avi (19 MByte))
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Setup procedure

1. The vision algorithms are configured
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1. The vision algorithms are configured
2. The SPM axes are calibrated against the camera image. A fourth step compensates for linear drift occurring during calibration
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2. The SPM axes are calibrated against the camera image. A fourth step compensates for linear drift occurring during calibration
3. Using closed-loop control the nano-indenter is moved along a linear path
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3. Using closed-loop control the nano-indenter is moved along a linear path
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Download

The software can be downloaded here: visiongui-0.2.tar.bz2 (https://vision.eng.shu.ac.uk/jan/visiongui-0.2.tar.bz2)

The software is implemented in Ruby (https://www.ruby-lang.org/). It uses HornetsEye (current development of version 0.32) and Qt4-QtRuby (https://rubyforge.org/projects/korundum/).

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Future Work

Possible future work is

  • port to Ruby-1.9 which has native threads
  • integrate serial-port interface of JEOL TEM
  • access USB-controls for shutter and gain of the TVIPS camera
  • feature-based recognition and tracking (less sensitive to brightness changes)
  • offset- and gain-compensation for camera image
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See Also

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External Links

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