Abstract: This paper discusses the current trends in medical
image registration techniques and addresses the need to provide a
solid theoretical foundation for research endeavours. Methodological
analysis and synthesis of quality literature was done, providing a
platform for developing a good foundation for research study in
this field which is crucial in understanding the existing levels of
knowledge. Research on medical image registration techniques assists
clinical and medical practitioners in diagnosis of tumours and lesion
in anatomical organs, thereby enhancing fast and accurate curative
treatment of patients. Literature review aims to provide a solid
theoretical foundation for research endeavours in image registration
techniques. Developing a solid foundation for a research study is
possible through a methodological analysis and synthesis of existing
contributions. Out of these considerations, the aim of this paper is
to enhance the scientific community’s understanding of the current
status of research in medical image registration techniques and also
communicate to them, the contribution of this research in the field of
image processing. The gaps identified in current techniques can be
closed by use of artificial neural networks that form learning systems
designed to minimise error function. The paper also suggests several
areas of future research in the image registration.
Abstract: The majority of existing predictors for time series are
model-dependent and therefore require some prior knowledge for the
identification of complex systems, usually involving system
identification, extensive training, or online adaptation in the case of
time-varying systems. Additionally, since a time series is usually
generated by complex processes such as the stock market or other
chaotic systems, identification, modeling or the online updating of
parameters can be problematic. In this paper a model-free predictor
(MFP) for a time series produced by an unknown nonlinear system or
process is derived using tracking theory. An identical derivation of the
MFP using the property of the Newton form of the interpolating
polynomial is also presented. The MFP is able to accurately predict
future values of a time series, is stable, has few tuning parameters and
is desirable for engineering applications due to its simplicity, fast
prediction speed and extremely low computational load. The
performance of the proposed MFP is demonstrated using the
prediction of the Dow Jones Industrial Average stock index.