Abstract: Subspace channel estimation methods have been
studied widely, where the subspace of the covariance matrix is
decomposed to separate the signal subspace from noise subspace. The
decomposition is normally done by using either the eigenvalue
decomposition (EVD) or the singular value decomposition (SVD) of
the auto-correlation matrix (ACM). However, the subspace
decomposition process is computationally expensive. This paper
considers the estimation of the multipath slow frequency hopping
(FH) channel using noise space based method. In particular, an
efficient method is proposed to estimate the multipath time delays by
applying multiple signal classification (MUSIC) algorithm which is
based on the null space extracted by the rank revealing LU (RRLU)
factorization. As a result, precise information is provided by the
RRLU about the numerical null space and the rank, (i.e., important
tool in linear algebra). The simulation results demonstrate the
effectiveness of the proposed novel method by approximately
decreasing the computational complexity to the half as compared
with RRQR methods keeping the same performance.
Abstract: In this paper, we investigate the effect of friendly
jamming power allocation strategies on the achievable average
secrecy rate over a bank of parallel fading wiretap channels.
We investigate the achievable average secrecy rate in parallel
fading wiretap channels subject to Rayleigh and Rician fading.
The achievable average secrecy rate, due to the presence of a
line-of-sight component in the jammer channel is also evaluated.
Moreover, we study the detrimental effect of correlation across the
parallel sub-channels, and evaluate the corresponding decrease in the
achievable average secrecy rate for the various fading configurations.
We also investigate the tradeoff between the transmission power
and the jamming power for a fixed total power budget. Our
results, which are applicable to current orthogonal frequency division
multiplexing (OFDM) communications systems, shed further light on
the achievable average secrecy rates over a bank of parallel fading
channels in the presence of friendly jammers.
Abstract: A Smart Building Controller (SBC) is a server
software that offers secured access to a pool of building specific
resources, executes monitoring tasks and performs automatic
administration of a building, thus optimizing the exploitation cost and
maximizing comfort. This paper brings to discussion the issues that
arise with the secure exploitation of the SBC administered resources
and proposes a technical solution to implement a robust secure access
system based on roles, individual rights and privileges (special
rights).
Abstract: The effects of hypertension are often lethal thus its
early detection and prevention is very important for everybody. In
this paper, a neural network (NN) model was developed and trained
based on a dataset of hypertension causative parameters in order to
forecast the likelihood of occurrence of hypertension in patients. Our
research goal was to analyze the potential of the presented NN to
predict, for a period of time, the risk of hypertension or the risk of
developing this disease for patients that are or not currently
hypertensive. The results of the analysis for a given patient can
support doctors in taking pro-active measures for averting the
occurrence of hypertension such as recommendations regarding the
patient behavior in order to lower his hypertension risk. Moreover,
the paper envisages a set of three example scenarios in order to
determine the age when the patient becomes hypertensive, i.e.
determine the threshold for hypertensive age, to analyze what
happens if the threshold hypertensive age is set to a certain age and
the weight of the patient if being varied, and, to set the ideal weight
for the patient and analyze what happens with the threshold of
hypertensive age.
Abstract: In this paper, we discuss the performance of applying
hybrid spiral dynamic bacterial chemotaxis (HSDBC) optimisation
algorithm on an intelligent controller for a differential drive robot. A
unicycle class of differential drive robot is utilised to serve as a basis
application to evaluate the performance of the HSDBC algorithm. A
hybrid fuzzy logic controller is developed and implemented for the
unicycle robot to follow a predefined trajectory. Trajectories of
various frictional profiles and levels were simulated to evaluate the
performance of the robot at different operating conditions. Controller
gains and scaling factors were optimised using HSDBC and the
performance is evaluated in comparison to previously adopted
optimisation algorithms. The HSDBC has proven its feasibility in
achieving a faster convergence toward the optimal gains and resulted
in a superior performance.
Abstract: Attributes and methods are the basic contents of an
object-oriented class. The connectivity among these class members
and the relationship between the class and other classes play an
important role in determining the quality of an object-oriented
system. Class cohesion evaluates the degree of relatedness of class
attributes and methods, whereas class coupling refers to the degree to
which a class is related to other classes. Researchers have proposed
several class cohesion and class coupling measures. However, the
correlation between class coupling and class cohesion measures has
not been thoroughly studied. In this paper, using classes of three
open-source Java systems, we empirically investigate the correlation
between several measures of connectivity-based class cohesion and
coupling. Four connectivity-based cohesion measures and eight
coupling measures are considered in the empirical study. The
empirical study results show that class connectivity-based cohesion
and coupling internal quality attributes are inversely correlated. The
strength of the correlation depends highly on the cohesion and
coupling measurement approaches.
Abstract: Nowadays, huge amount of multimedia repositories
make the browsing, retrieval and delivery of video contents very slow
and even difficult tasks. Video summarization has been proposed to
improve faster browsing of large video collections and more efficient
content indexing and access. In this paper, we focus on approaches to
video summarization. The video summaries can be generated in many
different forms. However, two fundamentals ways to generate
summaries are static and dynamic. We present different techniques
for each mode in the literature and describe some features used for
generating video summaries. We conclude with perspective for
further research.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.