An efficient Stone’s BSS (ESBSS) algorithm is proposed based on the joint between original Stone’s BSS (SBSS) and genetic algorithm (GA). Original Stone’s BSS has several advantages compared with independent component analysis (ICA) techniques, where the BSS problem in Stone’s BSS is simplified to generalized eigenvalue decomposition (GEVD), but it’s susceptible to the local minima problem. Therefore, GA is used to overcome this problem and to enhance the separation process. Performance of the proposed algorithm is first tested through a different pdf source, followed by artifact extraction test for EEG mixtures then compared with the original Stone’s BSS (SBSS) and other BSS algorithms. The results demonstrate proposed algorithm efficiency in real time blind extraction of both super-Gaussian and sub-Gaussian signals from their mixtures.
목차
Abstract 1. Introduction 2. Original Stone’s BSS Algorithm 3. Proposed Algorithm: Efficient Stone’s BSS (ESBSS) 4. Results 4.1. Benchmark One: Simulated Data 4.2. Benchmark Two: Real EEG Data 5. Conclusion Acknowledgements References
Ahmed Kareem Abdullah [ College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China, Ministry of Higher Education and Scientific Research, Foundation of Technical Education, AL-Musaib Technical College, Iraq ]
Zhang Chao Zhu [ College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China ]
보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Signal Processing, Image Processing and Pattern Recognition
간기
격월간
pISSN
2005-4254
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7 No.2