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Path reconstruction in diffusion tensor magnetic resonance imaging

Xin Song 1 
1 MOTIVATE - Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé : 139739
Abstract : The complicated underwater environment and the poor underwater vision make super-mini underwater cable robot hardly to be controlled. Traditionally, the manual control method by operators is adopted by this kind of robots. Unfortunately, the robots can hardly work normally in these practical circumstances. Therefore, to overcome these shortcomings and improve the abilities of these underwater cable robots, this paper proposes several improvements, including the system design, the motion controller design, three dimensional obstacle recognition and three dimensional path reconstruction technologies etc. The details are displayed as follow: (1) Super-mini underwater robot system design: several improvement schemes and important design ideas are investigated for the super-mini underwater robot.(2) Super-mini robot motion controller design: The motion controller design of underwater robot in complicated circumstance is investigated. A new adaptive neural network sliding mode controller with balanced parameter controller (ANNSMB) is proposed. Based on the theory of adaptive fuzzy sliding mode controller (AFSMC), an improved algorithm is also proposed and applied to the underwater robot. (3)Research of three dimensional underwater environment reconstructions: The algorithms and the experiments of underwater environment reconstructions are investigated. DT-MRI image processing algorithm and the theory of three dimensional obstacle reconstructions are adopted and improved for the application of the underwater robot. (4) The super-mini underwater robot path planning algorithms are investigated.
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Submitted on : Friday, May 4, 2012 - 10:57:30 AM
Last modification on : Sunday, September 25, 2022 - 3:54:23 AM
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  • HAL Id : tel-00694403, version 1


Xin Song. Path reconstruction in diffusion tensor magnetic resonance imaging. Other. INSA de Lyon, 2011. English. ⟨NNT : 2011ISAL0066⟩. ⟨tel-00694403⟩



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