Abstract: Random and natural textures classification is still
one of the biggest challenges in the field of image processing and
pattern recognition. In this paper, texture feature extraction using
Slant Hadamard Transform was studied and compared to other
signal processing-based texture classification schemes. A
parametric SHT was also introduced and employed for natural
textures feature extraction. We showed that a subtly modified
parametric SHT can outperform ordinary Walsh-Hadamard
transform and discrete cosine transform. Experiments were carried
out on a subset of Vistex random natural texture images using a
kNN classifier.
Abstract: The notion of Next Generation Network (NGN) is
based on the Network Convergence concept which refers to
integration of services (such as IT and communication services) over
IP layer. As the most popular implementation of Service Oriented
Architecture (SOA), Web Services technology is known to be the
base for service integration. In this paper, we present a platform to
deliver communication services as web services. We also implement
a sample service to show the simplicity of making composite web
and communication services using this platform. A Service Logic
Execution Environment (SLEE) is used to implement the
communication services. The proposed architecture is in agreement
with Service Oriented Architecture (SOA) and also can be integrated
to an Enterprise Service Bus to make a base for NGN Service
Delivery Platform (SDP).
Abstract: An artificial neural network (ANN) model is
presented for the prediction of kinematic viscosity of binary mixtures
of poly (ethylene glycol) (PEG) in water as a function of temperature,
number-average molecular weight and mass fraction. Kinematic
viscosities data of aqueous solutions for PEG (0.55419×10-6 –
9.875×10-6 m2/s) were obtained from the literature for a wide range
of temperatures (277.15 - 338.15 K), number-average molecular
weight (200 -10000), and mass fraction (0.0 – 1.0). A three layer
feed-forward artificial neural network was employed. This model
predicts the kinematic viscosity with a mean square error (MSE) of
0.281 and the coefficient of determination (R2) of 0.983. The results
show that the kinematic viscosity of binary mixture of PEG in water
could be successfully predicted using an artificial neural network
model.