國立高雄大學圖資館 |

Language: English

Back

Real-time knowledge-based fuzzy logi...

Sidhu, Amandeep S.

Real-time knowledge-based fuzzy logic model for soft tissue deformation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Real-time knowledge-based fuzzy logic model for soft tissue deformationby Joey Sing Yee Tan, Amandeep S. Sidhu.
Author:	Tan, Joey Sing Yee.
other author:	Sidhu, Amandeep S.
Published:	Cham :Springer International Publishing :2019.
Description:	ix, 88 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Fuzzy logic.
Online resource:	https://doi.org/10.1007/978-3-030-15585-8
ISBN:	9783030155858$q(electronic bk.)

Real-time knowledge-based fuzzy logic model for soft tissue deformation
Tan, Joey Sing Yee.

Real-time knowledge-based fuzzy logic model for soft tissue deformation[electronic resource] /by Joey Sing Yee Tan, Amandeep S. Sidhu. - Cham :Springer International Publishing :2019. - ix, 88 p. :ill., digital ;24 cm. - Data, semantics and cloud computing,v.8322524-6593 ;. - Data, semantics and cloud computing ;v.832..

List of Figures -- List of Tables -- Chapter 1. Introduction -- Chapter 2. Background -- Chapter 3. Methodology -- Chapter 4. Fuzzy Inference System, etc.

This book provides a real-time and knowledge-based fuzzy logic model for soft tissue deformation. The demand for surgical simulation continues to grow, as there is a major bottleneck in surgical simulation designation and every patient is unique. Deformable models, the core of surgical simulation, play a crucial role in surgical simulation designation. Accordingly, this book (1) presents an improved mass spring model to simulate soft tissue deformation for surgery simulation; (2) ensures the accuracy of simulation by redesigning the underlying Mass Spring Model (MSM) for liver deformation, using three different fuzzy knowledge-based approaches to determine the parameters of the MSM; (3) demonstrates how data in Central Processing Unit (CPU) memory can be structured to allow coalescing according to a set of Graphical Processing Unit (GPU)-dependent alignment rules; and (4) implements heterogeneous parallel programming for the distribution of grid threats for Computer Unified Device Architecture (CUDA)-based GPU computing.

ISBN: 9783030155858$q(electronic bk.)

Standard No.: 10.1007/978-3-030-15585-8doiSubjects--Topical Terms:

181981
Fuzzy logic.

LC Class. No.: QA9.64

Dewey Class. No.: 511.313

Real-time knowledge-based fuzzy logic model for soft tissue deformation
LDR:02270nmm a2200337 a 4500 001 554667
003 DE-He213
005 20190406141354.0
006 m d
007 cr nn 008maaau
008 191118s2019 gw s 0 eng d
020 $a 9783030155858$q(electronic bk.)
020 $a 9783030155841$q(paper)
024 7 $a 10.1007/978-3-030-15585-8 $2 doi
035 $a 978-3-030-15585-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA9.64
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 511.313 $2 23
090 $a QA9.64 $b .T161 2019
100 1 $a Tan, Joey Sing Yee. $3 836450
245 1 0 $a Real-time knowledge-based fuzzy logic model for soft tissue deformation $h [electronic resource] / $c by Joey Sing Yee Tan, Amandeep S. Sidhu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 88 p. : $b ill., digital ; $c 24 cm.
490 1 $a Data, semantics and cloud computing, $x 2524-6593 ; $v v.832
505 0 $a List of Figures -- List of Tables -- Chapter 1. Introduction -- Chapter 2. Background -- Chapter 3. Methodology -- Chapter 4. Fuzzy Inference System, etc.
520 $a This book provides a real-time and knowledge-based fuzzy logic model for soft tissue deformation. The demand for surgical simulation continues to grow, as there is a major bottleneck in surgical simulation designation and every patient is unique. Deformable models, the core of surgical simulation, play a crucial role in surgical simulation designation. Accordingly, this book (1) presents an improved mass spring model to simulate soft tissue deformation for surgery simulation; (2) ensures the accuracy of simulation by redesigning the underlying Mass Spring Model (MSM) for liver deformation, using three different fuzzy knowledge-based approaches to determine the parameters of the MSM; (3) demonstrates how data in Central Processing Unit (CPU) memory can be structured to allow coalescing according to a set of Graphical Processing Unit (GPU)-dependent alignment rules; and (4) implements heterogeneous parallel programming for the distribution of grid threats for Computer Unified Device Architecture (CUDA)-based GPU computing.
650 0 $a Fuzzy logic. $3 181981
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Biomedical Engineering and Bioengineering. $3 826326
650 2 4 $a Surgery. $3 274153
650 2 4 $a Regenerative Medicine/Tissue Engineering. $3 675875
700 1 $a Sidhu, Amandeep S. $3 375720
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Data, semantics and cloud computing ; $v v.832. $3 836451
856 4 0 $u https://doi.org/10.1007/978-3-030-15585-8
950 $a Intelligent Technologies and Robotics (Springer-42732)