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Weimin Huang

Researcher at Nanyang Technological University

Publications -  639
Citations -  17757

Weimin Huang is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 59, co-authored 419 publications receiving 15262 citations. Previous affiliations of Weimin Huang include Shenyang Jianzhu University & Ohio State University.

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Proceedings ArticleDOI

GPU-friendly gallbladder modeling for laparoscopic cholecystectomy simulation

TL;DR: A multi-layer mass-spring model which can adapt well to the built-in accelerating algorithms in PhysX-engine of GPU is proposed and can achieve satisfactory results in terms of both visual perception and time performance.

Situation-Aware Patient Monitoring in and around the Bed Using Multimodal Sensing Intelligence

TL;DR: Continuous patient monitoring with situation awareness using multimodality sensors to recognize such dangerous events in and around the bed and provides desirable personalized care at the institutions enabling to prevent potential health and well-being problems in andAround the bed.
Journal ArticleDOI

High‐Efficiency and Long‐life Synergetic Dual‐Oxide/Zeolite Catalyst for Direct Conversion of Syngas into Aromatics

TL;DR: In this article , a synergetic dualoxide/zeolite catalyst was developed to tune the coupling between CO and H2 activation through active H species migration, by adopting ZnZrOx oxide with stable single-layer Zn−O structure for H 2 activation and CeZr Ox oxide with abundant oxygen vacancies as the CO activation component.
Journal ArticleDOI

Preparation of CdS nanoparticles at the monolayer of N-methyl-p-(p-tetradecyloxystyryl)pyridinium iodine

TL;DR: In this paper, CdS nanoparticles were prepared at the monolayer of a cationic functional amphiphile, N -methyl-p -(p -tetradecyloxystyryl)pyridinium iodine, by prior converting of Cd 2+ to CdEDTA 2−.
Journal ArticleDOI

GPU-friendly gallbladder modeling in laparoscopic cholecystectomy surgical training system

TL;DR: A multi-layered mass-spring model which can adapt well to the built-in accelerating algorithms in PhysX-Engine of Graphics Processing Unit (GPU) and achieve satisfactory results in terms of both visual perception and time performance is proposed.