Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: IResearch Article A Generalized Cauchy Distribution Framework for Problems Requiring Robust Behavior | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID312989 19 pages doi 2010 312989 Research Article A Generalized Cauchy Distribution Framework for Problems Requiring Robust Behavior Rafael E. Carrillo Tuncer C. Aysal EURASIP Member and Kenneth E. Barner Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA Correspondence should be addressed to Rafael E. Carrillo carrillo@ Received 8 February 2010 Revised 27 May 2010 Accepted 7 August 2010 Academic Editor Igor Djurovic Copyright 2010 Rafael E. Carrillo et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Statistical modeling is at the heart of many engineering problems. The importance of statistical modeling emanates not only from the desire to accurately characterize stochastic events but also from the fact that distributions are the central models utilized to derive sample processing theories and methods. The generalized Cauchy distribution GCD family has a closed-form pdf expression across the whole family as well as algebraic tails which makes it suitable for modeling many real-life impulsive processes. This paper develops a GCD theory-based approach that allows challenging problems to be formulated in a robust fashion. Notably the proposed framework subsumes generalized Gaussian distribution GGD family-based developments thereby guaranteeing performance improvements over traditional GCD-based problem formulation techniques. This robust framework can be adapted to a variety of applications in signal processing. As examples we formulate four practical applications under this framework 1 filtering for power line communications 2 estimation in sensor networks with noisy channels 3 reconstruction methods for compressed sensing and 4 fuzzy .
