Iraqi Journal for Electrical And Electronic Engineering

In this paper new semi-empirical formulas are developed to evaluate the variation of both real
and imaginary parts of soil complex permittivity with depth inside the earth's surface. Computed
values using these models show good agreement with published measured values for soils of the
same textures and same frequency band. Use of these models may serve to handle more accurate
results especially in the ground probing radar (GPR) applications and other applications relating the
detection of buried objects inside the earth's surface, where the use of a single average value of the
soil complex permittivity had not necessarily led, for most of the times, to accurate results for the
electromagnetic fields propagated inside the earth's surface

Brain machine interface provides a communication channel between the human brain and an
external device. Brain interfaces are studied to provide rehabilitation to patients with
neurodegenerative diseases; such patients loose all communication pathways except for their
sensory and cognitive functions. One of the possible rehabilitation methods for these patients is
to provide a brain machine interface (BMI) for communication; the BMI uses the electrical
activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted
during mental tasks is a technique for designing a BMI. In this paper a BMI design using five
mental tasks from two subjects were studied, a combination of two tasks is studied per subject.
An Elman recurrent neural network is proposed for classification of EEG signals. Two feature
extraction algorithms using overlapped and non overlapped signal segments are analyzed.
Principal component analysis is used for extracting features from the EEG signal segments.
Classification performance of overlapping EEG signal segments is observed to be better in
terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG
signal segments show better classification in terms of maximum classifications

This work presents aneural and fuzzy based ECG signal recognition system based on wavelet
transform. The suitable coefficients that can be used as a feature for each fuzzy network or neural
network is found using a proposed best basis technique. Using the proposed best bases reduces the
dimension of the input vector and hence reduces the complexity of the classifier. The fuzzy network and
the neural network parameters are learned using back propagation algorithm

In order to reduce the impact of watermark embedding on the perceptual fidelity of the
marked signal, watermarking systems process the generated watermark to match it to the local
properties of the underlying host signal prior to embedding. However, this adaptation process
could distort the watermark, affecting its robustness and information content. In this paper, a
new watermark coding technique is proposed, that enables the application of some marknondistorting
host-adaptation processing,
where the intensity of
the watermark
could be
redistributed
according
to the local properties
of
the underlying host without
changing
the

way
of
interpreting the watermark
to be embedded.
This completely
eliminates
the
need to
equalize
adaptation distortions prior
to decoding, and hence, to
pass any side information

about
the adaptation processing
to the decoder, too.