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Digital filters, together with signal processing, are being employed in the new technologies
and information systems, and implemented in different areas and applications. Digital
filters and signal processing are used with no costs and they can be adapted to different
cases with great flexibility and reliability.
This book presents advanced developments in digital filters and signal processing methods
covering different case studies. They present the main essence of the subject, with the
principal approaches to the most recent mathematical models that are being employed
worldwide.
An approach employing digital filters and signal processing methods based on wavelet
transforms is presented in order to be applied in the maintenance management of wind
turbines. It is completed with other techniques as the fast Fourier transform. It leads to a
reduction of operating costs, availability, reliability, lifetime and maintenance costs.
The wavelet transforms are also employed as a spectral analysis of exons in
deoxyribonucleic acid (DNA) signals. These regions are diffused in a noise created by a
mixture of exon-intron nucleotides. A better identification of exons results in fairly complete
translation of RNA from DNA. Researchers have proposed several techniques based on
computational and statistical signal processing concepts but an optimal solution is still
lacking. The target signal is filtered by wavelet transforms to reduce the noise created by 1/f
diffused noise. The signal is then processed in a series of computational steps to generate a
power spectral density estimation graph. Exons are approximated with reference to
discrimination measure between intron and exons. The PSD’s graph glimpses a clear picture
of exons boundaries comparable with the standard NCBI range. The results have been
compared with existing approaches and significance was found in the exons regions
identification.
Statistical signal processing traditionally focuses on extraction of information from noisy
measurements. Typically, parameters or states are estimated by various filtering operations.
The quality of signal processing operations is assessed by evaluating the statistical
uncertainty of the result. The processing could for instance simulate, correct, modulate,
evaluate or control the response of a physical system. A statistical model of the parameters
describing to which degree the dynamic model is known and accurate will be assumed
given, instead of being the target of investigation as in system identification. Model
uncertainty (of parameters) is then propagated to model-ing uncertainty (of the result). |
