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Tags: NanoBio Node

Tools (1-20 of 26)

  1. Traction Force Microscopy

    17 Apr 2014 | Tools | Contributor(s): Bara Saadah, AbderRahman N Sobh, Alireza Tofangchi

    This is a tool for particle motion tracking with respect to physical forces.

    https://nanohub.org/resources/parttrack

  2. nanoDDSCAT+

    13 Aug 2014 | Tools | Contributor(s): AbderRahman N Sobh, Sarah White, Jeremy Smith, Nahil Sobh, Prashant K Jain

    Combines the Discrete Dipole Scattering (DDSCAT) tool with the DDAConvert tool for a single workflow for custom shapes.

    https://nanohub.org/resources/ddaplus

  3. Nanoindentation

    28 Oct 2013 | Tools | Contributor(s): Slim Kibech, AbderRahman N Sobh, Pratik Naik, Nahil Sobh, Iwona Jasiuk

    Nanoindentation for Mechanical Characterization of Soft Materials

    https://nanohub.org/resources/nanoindentation

  4. nanoDDSCAT

    23 Apr 2013 | Tools | Contributor(s): Prashant K Jain, Nahil Sobh, Jeremy Smith, AbderRahman N Sobh, Sarah White, Jacob Faucheaux, John Feser

    Calculate scattering and absorption of light by targets with arbitrary geometries and complex refractive index.

    https://nanohub.org/resources/dda

  5. cadnano to PDB File Converter

    13 Nov 2013 | Tools | Contributor(s): Jejoong Yoo, AbderRahman N Sobh, Chen-Yu Li, Aleksei Aksimentiev

    Accepts CadNano files in the form of *.json and outputs a file in *.pdb which can be read by programs such as VMD.

    https://nanohub.org/resources/cadnanocvrt

  6. Molecular Dynamics Showcase

    06 Jul 2013 | Tools | Contributor(s): Michael McLennan, Chen-Yu Li, john stone, Aleksei Aksimentiev

    View interesting features of a molecular dynamics trajectory file

    https://nanohub.org/resources/mdshowcase

  7. [Illinois]: Error Gradient Estimations Due to Parallel Perturbation of Weights

    07 Jul 2013 | Tools | Contributor(s): AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using simultaneous perturbation of weights.

    https://nanohub.org/resources/pertgradll

  8. [Illinois]: Perturbative Reinforcement Learning to Develop Distributed Representations

    10 Jul 2013 | Tools | Contributor(s): AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains three-layered networks of sigmoidal units to associate patterns.

    https://nanohub.org/resources/pertdistrep

  9. [Illinois]: Perturbative Reinforcement Learning Using Directed Drift

    10 Jul 2013 | Tools | Contributor(s): AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using a real-valued adaptation of the directed drift algorithm.

    https://nanohub.org/resources/pertdd

  10. [Illinois]: Temporal Difference, Iterative Dynamic Programming, and Least Mean Squares

    11 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson

    This tool updates state values using the Temporal Difference Algorithm.

    https://nanohub.org/resources/tempdiff

  11. [Illinois]: Direction Selectivity

    13 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool implements a simple direction selective network.

    https://nanohub.org/resources/dirselectivity

  12. [Illinois]: Predict Correct Set Up

    15 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool sets up a predictor-corrector model of target tracking

    https://nanohub.org/resources/predcorsetup

  13. [Illiniois]: SynchUp

    16 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool computes synchronous updates of autoassociative networks.

    https://nanohub.org/resources/synchup

  14. [Illinois]: AsynchUp

    16 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, NanoBio Node, Jessica S Johnson

    This tool computes asynchronous updates of autoassociative networks.

    https://nanohub.org/resources/asynchup

  15. [Illinois] KohonenSOM

    09 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, Jessica S Johnson, NanoBio Node

    This tool implements the Kohonen self-organizing map (SOM) algorithm

    https://nanohub.org/resources/kohonensom

  16. [Illinois]: BUTDprobInference

    17 Jul 2013 | Tools | Contributor(s): Bara Saadah, Jessica S Johnson, NanoBio Node

    This tool stimulates bottom-up/top-down processing in the visual system using probabilistic inference.

    https://nanohub.org/resources/butdprobinf

  17. [Illinois]: BUTDjointDistribution

    16 Jul 2013 | Tools | Contributor(s): Bara Saadah, Jessica S Johnson, NanoBio Node

    this tool simulates bottom-up/top-down processing in the visual system using the joint distribution

    https://nanohub.org/resources/butdjdist

  18. [Illinois]: Error Gradient Estimations Due to Perturbation of One Weight at a Time

    29 Jun 2013 | Tools | Contributor(s): AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using perturbation of one weight at a time.

    https://nanohub.org/resources/pertgrad1by1

  19. [Illinois]: Avoidance Learn Simulation with 'Call' Neuron

    25 Jun 2013 | Tools | Contributor(s): AbderRahman N Sobh, NanoBio Node, Jessica S Johnson

    This script simulates avoidance learning as a reinforcement learning with two upper motoneurons (sumo and fumo) and one "call" neuron.

    https://nanohub.org/resources/avoidlearncall

  20. [Illinois]: Avoidance Learn Simulation

    20 Jun 2013 | Tools | Contributor(s): AbderRahman N Sobh, NanoBio Node, Jessica S Johnson

    This script simulates avoidance conditioning as reinforcement learning with two upper motoneurons (SUMO and FUMO).

    https://nanohub.org/resources/avoidlearn

nanoHUB.org, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.