Parallel Implementations of Digital Focus Indices Based on Minimax Search Using Multi-Core Processors

Syed Anwar Hussainy F; Senthil Kumar Thillaigovindan; Ashok J; Sowmia K R; Jayashree K; Priya Vijay; HyungTae Kim; Duk-Yeon Lee; Dongwoon Choi; Jaehyeon Kang; Dong-Wook Lee

연구문헌

영문 논문지

홈 > 연구문헌 > 영문 논문지 > TIIS (한국인터넷정보학회)

TIIS (한국인터넷정보학회)

Current Result Document :

한글제목(Korean Title)	Parallel Implementations of Digital Focus Indices Based on Minimax Search Using Multi-Core Processors
영문제목(English Title)	Parallel Implementations of Digital Focus Indices Based on Minimax Search Using Multi-Core Processors
저자(Author)	Syed Anwar Hussainy F Senthil Kumar Thillaigovindan Ashok J Sowmia K R Jayashree K Priya Vijay HyungTae Kim Duk-Yeon Lee Dongwoon Choi Jaehyeon Kang Dong-Wook Lee
원문수록처(Citation)	VOL 17 NO. 02 PP. 0542 ~ 0558 (2023. 02)
한글내용 (Korean Abstract)
영문내용 (English Abstract)	A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.
키워드(Keyword)	Heart Disease Prediction accuracy integrity Machine learning feature extraction Neural networks Effective Spectrum Sensing Cognitive Radio Networks Improved Support Vector Machine Grasshopper Optimization-based Particle Swarm Algo Digital Focus Index Computer Vision Systems Embedded Systems Mobile Computing Parallel Processing
파일첨부	PDF 다운로드