الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
This thesis addresses and studies the problem of Blind Source Separation BSS. In
recent years, the importance of BSS techniques has been grown due to limited
knowledge of their properties and how they contribute to each sensor output and
the explosive growth of using Multiple-Input Multiple-Output MIMO systems in
different applications and cases. The term blind refers to the fact that there is no
explicit information about the mixing process or about source signals.
Applications of BSS are numerous such as biomedical signal analysis, speech
separation, image processing and feature extraction, wireless communication and
sensor array processing, geophysical data processing, data mining and financial
applications. Depending on the application, multiple antennas, multiple
microphones or multiple biomedical sensors are used for the data acquisition.This thesis addresses and studies the problem of Blind Source Separation BSS. In
recent years, the importance of BSS techniques has been grown due to limited
knowledge of their properties and how they contribute to each sensor output and
the explosive growth of using Multiple-Input Multiple-Output MIMO systems in
different applications and cases. The term blind refers to the fact that there is no
explicit information about the mixing process or about source signals.
Applications of BSS are numerous such as biomedical signal analysis, speech
separation, image processing and feature extraction, wireless communication and
sensor array processing, geophysical data processing, data mining and financial
applications. Depending on the application, multiple antennas, multiple
microphones or multiple biomedical sensors are used for the data acquisition.In this thesis, the de-nosing technique is applied to separate mixed signals or
images. Preprocessing for mixed signals and images will be investigate and study
its effect in the performance of de-noise process. Moreover, post processing of
the extracted signals or images will be investigate and compared to the
preprocessing technique for improvement the quality of separated signals or
images. The curvelet de-nosing is applied as pre-processing and post processing.
Also, BSS algorithm using different frequency transforms are applied and studied
their performance Ridgelet Transform RT, Discrete Cosine Transform DCT,
Discrete Sine Transform DST and Discrete Wavelet Transform DWT. These
transforms are proposed and compared to the time domain using ICA
performance the separation of mixed signals and images.
Moreover, a wavelet based ICA approach using Kurtosis for BSS is proposed. In
this approach, the observations are transformed into elementary forms using
Wavelet Packets Decomposition WPD and choose the best band by Kurtosis
criteria.