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: Research Article Recovery of Myocardial Kinematic Function without the Time History of External Loads | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID310473 9 pages doi 2010 310473 Research Article Recovery of Myocardial Kinematic Function without the Time History of External Loads Heye Zhang Bo Li Alistair A. Young and Peter J. Hunter Bioengineering Institute University of Auckland Auckland 1142 New Zealand Correspondence should be addressed to Heye Zhang Received 30 April 2009 Accepted 24 June 2009 Academic Editor Joao Manuel R. S. Tavares Copyright 2010 Heye Zhang 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. A time-domain filtering algorithm is proposed to recover myocardial kinematic function using output-only measurements without the time history of external loads. The main contribution of this work is that the overall effect of all the external loads on the myocardium is treated as a random variable disturbed by the Gaussian white noise because the external loads of the myocardium are usually unknown in practical exercises. The kernel of our proposed algorithm is an iterative multiframe and sequential filtering procedure consisting of a Kalman filter and a least-squares filter. In our proposed implementation the initial guess of myocardial kinematic function and residual innovation of all the state variables are first computed using a Kalman filter via state space equations only driven by the Gaussian white noise and then the residual innovation is fed into a least-squares filter to estimate the total external loads of the myocardium. In the end the initial guess of myocardial kinematic function is corrected using external loads provided by the least-squares filter. After the introduction of the whole structure of our algorithm we demonstrate the ability of the framework on synthetic data and MR image .