Detecting and counting people using real-time directional algorithms implemented by compute unified device architecture

2017 ◽  
Vol 248 ◽  
pp. 105-111 ◽  
Author(s):  
Yasemin Poyraz Kocak ◽  
Selcuk Sevgen
Keyword(s):  
Real Time ◽  
Compute Unified Device Architecture ◽  
Device Architecture

scholarly journals Performance analysis and GPU parallelisation of ECO object tracking algorithm

2020 ◽  
pp. 109-118
Author(s):  
Ugur Taygan ◽  
Adnan Ozsoy
Keyword(s):  
Performance Analysis ◽  
Object Tracking ◽  
Parallel Programming ◽  
Real Time ◽  
Object Classification ◽  
Tracking Algorithm ◽  
Compute Unified Device Architecture ◽  
Convolution Operators ◽  
Device Architecture ◽  
Programming Tools

The classification and tracking of objects has gained popularity in recent years due to the variety and importance of their application areas. Although object classification does not necessarily have to be real time, object tracking is often intended to be carried out in real time. While the object tracking algorithm mainly focuses on robustness and accuracy, the speed of the algorithm may degrade significantly. Due to their parallelisable nature, the use of GPUs and other parallel programming tools are increasing in the object tracking applications. In this paper, we run experiments on the Efficient Convolution Operators object tracking algorithm, in order to detect its time-consuming parts, which are the bottlenecks of the algorithm, and investigate the possibility of GPU parallelisation of the bottlenecks to improve the speed of the algorithm. Finally, the candidate methods are implemented and parallelised using the Compute Unified Device Architecture.   Keywords: Object tracking, parallel programming.

A New Look-up Table Method of Holographic Algorithms Based on Compute Unified Device Architecture Parallel Computing

2015 ◽  
Vol 35 (2) ◽  
pp. 0209001
Author(s):  
蒋晓瑜 Jiang Xiaoyu ◽  
丛彬 Cong Bin ◽  
裴闯 Pei Chuang ◽  
闫兴鹏 Yan Xingpeng ◽  
赵锴 Zhao Kai
Keyword(s):  
Parallel Computing ◽  
Compute Unified Device Architecture ◽  
Holographic Algorithms ◽  
Table Method ◽  
Device Architecture ◽  
Look Up Table ◽  
New Look

scholarly journals A Three-Dimensional Visualization Framework for Underground Geohazard Recognition on Urban Road-Facing GPR Data

2020 ◽  
Vol 9 (11) ◽  
pp. 668
Author(s):  
Zhenwu Wang ◽  
Benting Wan ◽  
Mengjie Han
Keyword(s):  
Three Dimensional ◽  
Pso Algorithm ◽  
Sampling Point ◽  
Compute Unified Device Architecture ◽  
Point Selection ◽  
Triangulated Irregular Network ◽  
Device Architecture ◽  
Ground Penetrating ◽  
Geometry Method ◽  
Difficult Issue

The identification of underground geohazards is always a difficult issue in the field of underground public safety. This study proposes an interactive visualization framework for underground geohazard recognition on urban roads, which constructs a whole recognition workflow by incorporating data collection, preprocessing, modeling, rendering and analyzing. In this framework, two proposed sampling point selection methods have been adopted to enhance the interpolated accuracy for the Kriging algorithm based on ground penetrating radar (GPR) technology. An improved Kriging algorithm was put forward, which applies a particle swarm optimization (PSO) algorithm to optimize the Kriging parameters and adopts in parallel the Compute Unified Device Architecture (CUDA) to run the PSO algorithm on the GPU side in order to raise the interpolated efficiency. Furthermore, a layer-constrained triangulated irregular network algorithm was proposed to construct the 3D geohazard bodies and the space geometry method was used to compute their volume information. The study also presents an implementation system to demonstrate the application of the framework and its related algorithms. This system makes a significant contribution to the demonstration and understanding of underground geohazard recognition in a three-dimensional environment.

scholarly journals GPU Accelerated Nonlinear Electronic Circuits Solver for Transient Simulation of Systems with Large Number of Components

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1819
Author(s):  
David Černý ◽  
Josef Dobeš
Keyword(s):  
Krylov Subspace ◽  
Direct Method ◽  
Iterative Algorithms ◽  
Transient Simulation ◽  
Compute Unified Device Architecture ◽  
Circuit Simulator ◽  
Simulation Speed ◽  
Device Architecture ◽  
Highly Nonlinear ◽  
Improved Accuracy

GPU cards have been used for scientific calculations for many years. Despite their ever-increasing performance, there are cases where they may still have problems. This article addresses possible performance and memory issues and their solutions that may occur during GPU calculations of iterative algorithms. Specifically, the article focuses on the optimization of transient simulation of extra-large highly nonlinear time-dependent circuits in SPICE-like electronic circuit simulator core enhanced with NVIDIA/CUDA (Compute Unified Device Architecture) interface and iterative Krylov Subspace methods with emphasis on improved accuracy. The article presents procedures for solving problems that may occur during this integration and negatively affect either the simulation speed or the accuracy of the calculation. Finally, a comparison of the implementation of an iterative calculation procedure with the use of GPU cards, calculation by the direct method and calculation on the CPU only is presented.

Research and Application of Carbide Blade Surface Quality Primitive Digital Automatic Detection

2014 ◽  
Vol 971-973 ◽  
pp. 1700-1705
Author(s):  
Xue Jun Tian ◽  
Zhi Peng Dong ◽  
Feng Ye
Keyword(s):  
Hard Alloy ◽  
Rapid Detection ◽  
Production Efficiency ◽  
Surface Defects ◽  
Cutting Tools ◽  
Production Quality ◽  
Detection Methods ◽  
Compute Unified Device Architecture ◽  
Blade Surface ◽  
Device Architecture

Hard alloy has been widely applied as a type of cutter material and cemented carbide cutting tools have become the main tools for processing enterprises in our country. During the blade production process, traditional artificial detection methods for surface defects can't satisfy the demands of production quality and production efficiency any longer. Online automation rapid detection has been realized based on the Compute Unified Device Architecture (CUDA) by utilizing the computing capability of GPU.

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