Name: Real-Time Knowledge-Based Fuzzy Logic Model for Soft Tissue Deformation (Studies in Computational Intelligence) (en Inglés)
Price: 173.37 USD
Availability: InStock
Author: Joey Sing Yee Tan; Amandeep S. Sidhu
ISBN: 9783030155841

portada Real-Time Knowledge-Based Fuzzy Logic Model for Soft Tissue Deformation (Studies in Computational Intelligence) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Joey Sing Yee Tan; Amandeep S. Sidhu

Año

2019

Idioma

Inglés

N° páginas

Encuadernación

Tapa Dura

ISBN13

9783030155841

N° edición

Categorías

Inteligencia artificial

Real-Time Knowledge-Based Fuzzy Logic Model for Soft Tissue Deformation (Studies in Computational Intelligence) (en Inglés)

Joey Sing Yee Tan; Amandeep S. Sidhu (Autor) · Springer · Tapa Dura

Libro Nuevo

$ 173.37

$ 266.72

Ahorras: $ 93.35

35% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Lunes 24 de Junio y el Lunes 08 de Julio.

Lo recibirás en cualquier lugar de Ecuador entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Real-Time Knowledge-Based Fuzzy Logic Model for Soft Tissue Deformation (Studies in Computational Intelligence) (en Inglés)"

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.