video graphic card

2011 ◽  
Keyword(s):  
Graphic Card

scholarly journals Development of Scaled Real Time Augmented Reality Sandbox

2019 ◽  
Vol 8 (3S) ◽  
pp. 164-169
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Real World ◽  
Graphic Card ◽  
3D Camera ◽  
Basic Functions ◽  
Rain Fall

This paper presents a scaled version of Augmented Reality Sandbox (ARSAND). Original ARSAND consist of short throw projector, Kinect 3D camera, dedicated computer with high end graphic card and sandbox. ARSAND has been gaining recognition and popularity for its rapid and accurate capabilities in designing landscapes and simulating geographical real-world terrain. The proposed scaled version of the ARSAND consists of ultra-short throw projector with different configuration, Kinect 3D camera, dedicated computer with high end graphic card, and calibrated sandbox with less amount of sand. The proposed scaled version of ARSAND is able to perform basic functions of the original ARSAND such as simulating real-world terrain and rain fall.

scholarly journals Toward Long-term Laptop Performances

2021 ◽  
Author(s):  
Khaled Moustafa
Keyword(s):  
Graphic Card ◽  
New Generation ◽  
Over Time

To improve laptops’ performances over time and to keep them continuously upgradable, I suggest a new laptop design that includes external specific slots for CPU, RAM, Storage and Graphic card that should be connected to their internal counterparts to enhance their whenever needed. With each a new generation of these components, laptops can therefore be upgraded constantly to be up to date in terms of performance and versatility.

Cuda Based Fuzzy C-Means Acceleration for the Segmentation of Images with Fungus Grown in Foam Matrices

2012 ◽  
Vol 17 (4) ◽  
pp. 191-200 ◽  
Author(s):  
Zdzisława Rowińska ◽  
Jarosław Gocławski
Keyword(s):  
Polyurethane Foam ◽  
Gpu Computing ◽  
Graphic Card ◽  
Fuzzy C Means ◽  
Programming Tools ◽  
Multiple Cores ◽  
Cuda Programming ◽  
The Matrix ◽  
Segmentation Of Images ◽  
Matrix Base

Abstract In the paper authors verify the advantages of GPU computing applied to fuzzy c-means segmentation. Three different algorithms implementing FCM method have been compared by their execution times. All tests refer to the images of polyurethane foam matrices filled with fungus (mould). They are aimed at separating mould regions from the matrix base. The authors proposed a method using CUDA programming tools, which significantly speedsup FCM computations with multiple cores built in a graphic card.

scholarly journals Graphic-card cluster for astrophysics (GraCCA) – Performance tests

New Astronomy ◽  
2008 ◽  
Vol 13 (6) ◽  
pp. 418-435 ◽  
Author(s):  
Hsi-Yu Schive ◽  
Chia-Hung Chien ◽  
Shing-Kwong Wong ◽  
Yu-Chih Tsai ◽  
Tzihong Chiueh
Keyword(s):  
Performance Tests ◽  
Graphic Card

Exploiting Graphic Card Processor Technology to Accelerate Data Mining Queries in SAP NetWeaver BIA

2008 ◽  
Author(s):  
Christoph Weyerhaeuser ◽  
Tobias Mindnich ◽  
Franz Faerber ◽  
Wolfgang Lehner
Keyword(s):  
Data Mining ◽  
Graphic Card

scholarly journals Definisi paten serta inventor pada produk ASUS

2018 ◽  
Author(s):  
galih wicaksono
Keyword(s):  
Optical Storage ◽  
Graphic Card

ASUA didirikan oleh 4 insyinyur komputer, yaitu Wayne Hseih, Ted Hsu, MT Liao dan TH Tung. Keempatnya memiliki visi dan misi yang sama untuk memajukan industry TI di Taiwan. ASUS sudah memiliki beberapa produk yang mereka buat, diantarnya motherboard, graphic card, nitebook, netbook, produk networking server dan workstation, monitor, produk multimedia, optical storage, peripheral dan aksesoris computer Dan Anti-Dust Cooling. Dengan menggunakan teknologi yang dipatenkan oleh ASUS yaitu Anti-Dust Cooling akan membuat komputer tidak cepat panas walaupun di pakai sampai 48 jam nonstop performa mesin akan tetap lancer walaupun dipakai lama.Kata Kunci : hak paten dan inventor produk asus

scholarly journals Definis paten serta inventor pada produk asus

2018 ◽  
Author(s):  
galih wicaksono
Keyword(s):  
Optical Storage ◽  
Graphic Card

ASUS didirikan oleh 4 insyinyur komputer, yaitu Wayne Hseih, Ted Hsu, MT Liao dan TH Tung. Keempatnya memiliki visi dan misi yang sama untuk memajukan industry TI di Taiwan. ASUS sudah memiliki beberapa produk yang mereka buat, diantarnya motherboard, graphic card, nitebook, netbook, produk networking server dan workstation, monitor, produk multimedia, optical storage, peripheral dan aksesoris computer Dan Anti-Dust Cooling. Dengan menggunakan teknologi yang dipatenkan oleh ASUS yaitu Anti-Dust Cooling akan membuat komputer tidak cepat panas walaupun di pakai sampai 48 jam nonstop performa mesin akan tetap lancer walaupun dipakai lama.Kata Kunci : hak paten dan inventor produk asus

scholarly journals Accelerating Smith-Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU-GPU Collaborative System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Liu ◽  
Yang Hong ◽  
Chun-Yuan Lin ◽  
Che-Lun Hung
Keyword(s):  
Database Search ◽  
Experimental Results ◽  
Alignment Method ◽  
Biological Sequence ◽  
Graphic Processing Units ◽  
Protein Database ◽  
Graphic Card ◽  
Collaborative System ◽  
Frequency Distance ◽  
Graphic Processing

The Smith-Waterman (SW) algorithm has been widely utilized for searching biological sequence databases in bioinformatics. Recently, several works have adopted the graphic card with Graphic Processing Units (GPUs) and their associated CUDA model to enhance the performance of SW computations. However, these works mainly focused on the protein database search by using the intertask parallelization technique, and only using the GPU capability to do the SW computations one by one. Hence, in this paper, we will propose an efficient SW alignment method, called CUDA-SWfr, for the protein database search by using the intratask parallelization technique based on a CPU-GPU collaborative system. Before doing the SW computations on GPU, a procedure is applied on CPU by using the frequency distance filtration scheme (FDFS) to eliminate the unnecessary alignments. The experimental results indicate that CUDA-SWfr runs 9.6 times and 96 times faster than the CPU-based SW method without and with FDFS, respectively.

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