Abstract: Morphological operators transform the original image
into another image through the interaction with the other image of
certain shape and size which is known as the structure element.
Mathematical morphology provides a systematic approach to analyze
the geometric characteristics of signals or images, and has been
applied widely too many applications such as edge detection,
objection segmentation, noise suppression and so on. Fuzzy
Mathematical Morphology aims to extend the binary morphological
operators to grey-level images. In order to define the basic
morphological operations such as fuzzy erosion, dilation, opening
and closing, a general method based upon fuzzy implication and
inclusion grade operators is introduced. The fuzzy morphological
operations extend the ordinary morphological operations by using
fuzzy sets where for fuzzy sets, the union operation is replaced by a
maximum operation, and the intersection operation is replaced by a
minimum operation.
In this work, it consists of two articles. In the first one, fuzzy set
theory, fuzzy Mathematical morphology which is based on fuzzy
logic and fuzzy set theory; fuzzy Mathematical operations and their
properties will be studied in details. As a second part, the application
of fuzziness in Mathematical morphology in practical work such as
image processing will be discussed with the illustration problems.
Abstract: Traditional object segmentation methods are time consuming and computationally difficult. In this paper, onedimensional object detection along the secant lines is applied. Statistical features of texture images are computed for the recognition process. Example matrices of these features and formulae for calculation of similarities between two feature patterns are expressed. And experiments are also carried out using these features.
Abstract: Gesture recognition is a challenging task for extracting
meaningful gesture from continuous hand motion. In this paper, we propose an automatic system that recognizes isolated gesture,
in addition meaningful gesture from continuous hand motion for Arabic numbers from 0 to 9 in real-time based on Hidden Markov Models (HMM). In order to handle isolated gesture, HMM using
Ergodic, Left-Right (LR) and Left-Right Banded (LRB) topologies is applied over the discrete vector feature that is extracted from stereo
color image sequences. These topologies are considered to different
number of states ranging from 3 to 10. A new system is developed to recognize the meaningful gesture based on zero-codeword detection
with static velocity motion for continuous gesture. Therefore, the
LRB topology in conjunction with Baum-Welch (BW) algorithm for
training and forward algorithm with Viterbi path for testing presents the best performance. Experimental results show that the proposed system can successfully recognize isolated and meaningful gesture and achieve average rate recognition 98.6% and 94.29% respectively.
Abstract: The paper deals with the comparison study of
harmonic detection methods for a shunt active power filter. The
%THD and the power factor value at the PCC point after
compensation are considered for the comparison. There are three
harmonic detection methods used in the paper that are synchronous
reference frame method, synchronous detection method, and DQ axis
with Fourier method. In addition, the ideal current source is used to
represent the active power filter by assuming an infinitely fast
controller action of the active power filter. The simulation results
show that the DQ axis with Fourier method provides the minimum
%THD after compensation compared with other methods. However,
the power factor value at the PCC point after compensation is slightly
lower than that of synchronous detection method.
Abstract: Iris localization is a very important approach in
biometric identification systems. Identification process usually is
implemented in three levels: iris localization, feature extraction, and
pattern matching finally. Accuracy of iris localization as the first step
affects all other levels and this shows the importance of iris
localization in an iris based biometric system. In this paper, we
consider Daugman iris localization method as a standard method,
propose a new method in this field and then analyze and compare the
results of them on a standard set of iris images. The proposed method
is based on the detection of circular edge of iris, and improved by
fuzzy circles and surface energy difference contexts. Implementation
of this method is so easy and compared to the other methods, have a
rather high accuracy and speed. Test results show that the accuracy of
our proposed method is about Daugman method and computation
speed of it is 10 times faster.
Abstract: A power measurement algorithm of the input mix components of the noise signal and narrowband interference is considered using functional transformations of the input mix in the postdetection processing channel. The algorithm efficiency analysis has been carried out for different interference-to-signal ratio. Algorithm performance features have been explored by numerical experiment results.
Abstract: This paper presents a simple and original method for
the generation of short monocycle pulses based on the transient
response of a passive band-pass filter. The recorded sub-nanosecond
pulses show a good symmetry and a small ringing (13 % of the peak
amplitude). Their spectral density covers the range 3.1 GHz to
10.6 GHz. The possibility to adapt the pulse spectral density to the
indoor FCC frequency mask is demonstrated with a prototype
working at a reduced frequency (FCC/1000). A detection technique is
proposed.
Abstract: Understanding road features such as lanes, the color
of lanes, and sidewalks in a live video captured from a moving
vehicle is essential to build video-based navigation systems. In this
paper, we present a novel idea to understand the road features using
support vector machines. Various feature vectors including color
components of road markings and the difference between two
regions, i.e., chosen AOIs, and so on are fed into SVM, deciding
colors of lanes and sidewalks robustly. Experimental results are
provided to show the robustness of the proposed idea.
Abstract: Hierarchical Mobile IPv6 (HMIPv6) was designed to
support IP micro-mobility management in the Next Generation
Networks (NGN) framework. The main design behind this protocol is
the usage of Mobility Anchor Point (MAP) located at any level router
of network to support hierarchical mobility management. However,
the distance MAP selection in HMIPv6 causes MAP overloaded and
increase frequent binding update as the network grows. Therefore, to
address the issue in designing MAP selection scheme, we propose a
dynamic load control mechanism integrates with a speed detection
mechanism (DMS-DLC). From the experimental results we obtain
that the proposed scheme gives better distribution in MAP load and
increase handover speed.
Abstract: A portable sensor for the analysis of phosphate in
aqueous samples has been developed. The sensor incorporates
microfluidic technology, colorimetric detection, and wireless
communications into a compact and rugged portable device. The
detection method used is the molybdenum yellow method, in which a
phosphate-containing sample is mixed with a reagent containing
ammonium metavanadate and ammonium molybdate in an acidic
medium. A yellow-coloured compound is generated and the
absorption of this compound is measured using a light emitting diode
(LED) light source and a photodiode detector. The absorption is
directly proportional to the phosphate concentration in the original
sample. In this paper we describe the application of this phosphate
sensor to the analysis of wastewater at a municipal wastewater
treatment plant in Co. Kildare, Ireland.
Abstract: This paper presents a formalisation of the different existing code mutation techniques (polymorphism and metamorphism) by means of formal grammars. While very few theoretical results are known about the detection complexity of viral mutation techniques, we exhaustively address this critical issue by considering the Chomsky classification of formal grammars. This enables us to determine which family of code mutation techniques are likely to be detected or on the contrary are bound to remain undetected. As an illustration we then present, on a formal basis, a proof-of-concept metamorphic mutation engine denoted PB MOT, whose detection has been proven to be undecidable.
Abstract: Early detection of dementia by testing the spatial
memory can be applied using a virtual environment. This paper
presents guidelines on how to design a virtual environment
specifically for elderly in early detection of dementia. The specific
design needs to be considered because the effectiveness of the
technology relies on the ability of the end user to use it. The primary
goal of these guidelines is to promote accessibility. Based on these
guidelines, a virtual simulation was developed and evaluated. The
results on usability of acceptance and satisfaction that are tested on
young (control group) and elderly participants indicate that these
guidelines are reliable and useful for use with elderly people.
Abstract: We present a label-free biosensor based on
electrochemical impedance spectroscopy for the detection of proinflammatory
cytokine Tumor Necrosis Factor (TNF-α). Secretion of
TNF-α has been correlated to the onset of various diseases including
rheumatoid arthritis, Crohn-s disease etc. Gold electrodes were
patterned on a silicon substrate and self assembled monolayer of
dithiobis-succinimidyl propionate was used to develop the biosensor
which achieved a detection limit of ~57fM. A linear relationship was
also observed between increasing TNF-α concentrations and chargetransfer
resistance within a dynamic range of 1pg/ml – 1ng/ml.
Abstract: This paper describes a new approach of classification
using genetic programming. The proposed technique consists of
genetically coevolving a population of non-linear transformations on
the input data to be classified, and map them to a new space with a
reduced dimension, in order to get a maximum inter-classes
discrimination. The classification of new samples is then performed
on the transformed data, and so become much easier. Contrary to the
existing GP-classification techniques, the proposed one use a
dynamic repartition of the transformed data in separated intervals, the
efficacy of a given intervals repartition is handled by the fitness
criterion, with a maximum classes discrimination. Experiments were
first performed using the Fisher-s Iris dataset, and then, the KDD-99
Cup dataset was used to study the intrusion detection and
classification problem. Obtained results demonstrate that the
proposed genetic approach outperform the existing GP-classification
methods [1],[2] and [3], and give a very accepted results compared to
other existing techniques proposed in [4],[5],[6],[7] and [8].
Abstract: In this paper, an analysis of a target location estimation
system using the best linear unbiased estimator (BLUE) for high
performance radar systems is presented. In synthetic environments,
we are here concerned with three key elements of radar system
modeling, which makes radar systems operates accurately in strategic
situation in virtual ground. Radar Cross Section (RCS) modeling
is used to determine the actual amount of electromagnetic waves
that are reflected from a tactical object. Pattern Propagation Factor
(PPF) is an attenuation coefficient of the radar equation that contains
the reflection from the surface of the earth, the diffraction, the
refraction and scattering by the atmospheric environment. Clutter is
the unwanted echoes of electronic systems. For the data fusion of
output results from radar detection in synthetic environment, BLUE
is used and compared with the mean values of each simulation results.
Simulation results demonstrate the performance of the radar system.
Abstract: For cognitive radio networks, there is a major
spectrum sensing problem, i.e. dynamic spectrum management. It is
an important issue to sense and identify the spectrum holes in
cognitive radio networks. The first-order derivative scheme is usually
used to detect the edge of the spectrum. In this paper, a novel
spectrum sensing technique for cognitive radio is presented. The
proposed algorithm offers efficient edge detection. Then, simulation
results show the performance of the first-order derivative scheme and
the proposed scheme and depict that the proposed scheme obtains
better performance than does the first-order derivative scheme.
Abstract: Detecting object in video sequence is a challenging
mission for identifying, tracking moving objects. Background
removal considered as a basic step in detected moving objects tasks.
Dual static cameras placed in front and rear moving platform
gathered information which is used to detect objects. Background
change regarding with speed and direction moving platform, so
moving objects distinguished become complicated. In this paper, we
propose framework allows detection moving object with variety of
speed and direction dynamically. Object detection technique built on
two levels the first level apply background removal and edge
detection to generate moving areas. The second level apply Moving
Areas Filter (MAF) then calculate Correlation Score (CS) for
adjusted moving area. Merging moving areas with closer CS and
marked as moving object. Experiment result is prepared on real scene
acquired by dual static cameras without overlap in sense. Results
showing accuracy in detecting objects compared with optical flow
and Mixture Module Gaussian (MMG), Accurate ratio produced to
measure accurate detection moving object.
Abstract: In this study, we present an advanced detection
technique for mass type breast cancer based on texture information
of organs. The proposed method detects the cancer areas in three
stages. In the first stage, the midpoints of mass area are determined
based on AHE (Adaptive Histogram Equalization). In the second
stage, we set the threshold coefficient of homogeneity by using
MLE (Maximum Likelihood Estimation) to compute the uniformity
of texture. Finally, mass type cancer tissues are extracted from the
original image. As a result, it was observed that the proposed
method shows an improved detection performance on dense breast
tissues of Korean women compared with the existing methods. It is
expected that the proposed method may provide additional
diagnostic information for detection of mass-type breast cancer.
Abstract: In order to monitor for traffic traversal, sensors can be
deployed to perform collaborative target detection. Such a sensor
network achieves a certain level of detection performance with the
associated costs of deployment and routing protocol. This paper
addresses these two points of sensor deployment and routing algorithm
in the situation where the absolute quantity of sensors or total energy
becomes insufficient. This discussion on the best deployment system
concluded that two kinds of deployments; Normal and Power law
distributions, show 6 and 3 times longer than Random distribution in
the duration of coverage, respectively. The other discussion on routing
algorithm to achieve good performance in each deployment system
was also addressed. This discussion concluded that, in place of the
traditional algorithm, a new algorithm can extend the time of coverage
duration by 4 times in a Normal distribution, and in the circumstance
where every deployed sensor operates as a binary model.
Abstract: Most of the collision warning systems currently
available in the automotive market are mainly designed to warn
against imminent rear-end and lane-changing collisions. No collision
warning system is commercially available to warn against imminent
turning collisions at intersections, especially for left-turn collisions
when a driver attempts to make a left-turn at either a signalized or
non-signalized intersection, conflicting with the path of other
approaching vehicles traveling on the opposite-direction traffic
stream. One of the major factors that lead to left-turn collisions is the
human error and misjudgment of the driver of the turning vehicle
when perceiving the speed and acceleration of other vehicles
traveling on the opposite-direction traffic stream; therefore, using a
properly-designed collision warning system will likely reduce, or
even eliminate, this type of collisions by reducing human error. This
paper introduces perceptual framework for a proposed collision
warning system that can detect imminent left-turn collisions at
intersections. The system utilizes a commercially-available detection
sensor (either a radar sensor or a laser detector) to detect approaching
vehicles traveling on the opposite-direction traffic stream and
calculate their speeds and acceleration rates to estimate the time-tocollision
and compare that time to the time required for the turning
vehicle to clear the intersection. When calculating the time required
for the turning vehicle to clear the intersection, consideration is given
to the perception-reaction time of the driver of the turning vehicle,
which is the time required by the driver to perceive the message
given by the warning system and react to it by engaging the throttle.
A regression model was developed to estimate perception-reaction
time based on age and gender of the driver of the host vehicle.
Desired acceleration rate selected by the driver of the turning vehicle,
when making the left-turn movement, is another human factor that is
considered by the system. Another regression model was developed
to estimate the acceleration rate selected by the driver of the turning
vehicle based on driver-s age and gender as well as on the location
and speed of the nearest approaching vehicle along with the
maximum acceleration rate provided by the mechanical
characteristics of the turning vehicle. By comparing time-to-collision
with the time required for the turning vehicle to clear the intersection,
the system displays a message to the driver of the turning vehicle
when departure is safe. An application example is provided to
illustrate the logic algorithm of the proposed system.