Abstract: One of the main image representations in Mathematical Morphology is the 3D Shape Decomposition Representation, useful for Image Compression and Representation,and Pattern Recognition. The 3D Morphological Shape Decomposition representation can be generalized a number of times,to extend the scope of its algebraic characteristics as much as possible. With these generalizations, the Morphological Shape Decomposition 's role to serve as an efficient image decomposition tool is extended to grayscale images.This work follows the above line, and further develops it. Anew evolutionary branch is added to the 3D Morphological Shape Decomposition's development, by the introduction of a 3D Multi Structuring Element Morphological Shape Decomposition, which permits 3D Morphological Shape Decomposition of 3D binary images (grayscale images) into "multiparameter" families of elements. At the beginning, 3D Morphological Shape Decomposition representations are based only on "1 parameter" families of elements for image decomposition.This paper addresses the gray scale inter frame interpolation by means of mathematical morphology. The new interframe interpolation method is based on generalized morphological 3D Shape Decomposition. This article will present the theoretical background of the morphological interframe interpolation, deduce the new representation and show some application examples.Computer simulations could illustrate results.
Abstract: The paper presents an on-line recognition machine
(RM) for continuous/isolated, dynamic and static gestures that arise
in Flight Deck Officer (FDO) training. RM is based on generic pattern
recognition framework. Gestures are represented as templates using
summary statistics. The proposed recognition algorithm exploits temporal
and spatial characteristics of gestures via dynamic programming
and Markovian process. The algorithm predicts corresponding index
of incremental input data in the templates in an on-line mode.
Accumulated consistency in the sequence of prediction provides a
similarity measurement (Score) between input data and the templates.
The algorithm provides an intuitive mechanism for automatic detection
of start/end frames of continuous gestures. In the present paper,
we consider isolated gestures. The performance of RM is evaluated
using four datasets - artificial (W TTest), hand motion (Yang) and
FDO (tracker, vision-based ). RM achieves comparable results which
are in agreement with other on-line and off-line algorithms such as
hidden Markov model (HMM) and dynamic time warping (DTW).
The proposed algorithm has the additional advantage of providing
timely feedback for training purposes.
Abstract: Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.
Abstract: Video watermarking is usually considered as watermarking of a set of still images. In frame-by-frame watermarking approach, each video frame is seen as a single watermarked image, so collusion attack is more critical in video watermarking. If the same or redundant watermark is used for embedding in every frame of video, the watermark can be estimated and then removed by watermark estimate remodolulation (WER) attack. Also if uncorrelated watermarks are used for every frame, these watermarks can be washed out with frame temporal filtering (FTF). Switching watermark system or so-called SS-N system has better performance against WER and FTF attacks. In this system, for each frame, the watermark is randomly picked up from a finite pool of watermark patterns. At first SS-N system will be surveyed and then a new collusion attack for SS-N system will be proposed using a new algorithm for separating video frame based on watermark pattern. So N sets will be built in which every set contains frames carrying the same watermark. After that, using WER attack in every set, N different watermark patterns will be estimated and removed later.
Abstract: Until recently, energy security and climate change
were considered separate issues to be dealt with by policymakers.
The two issues are now converging, challenging the security and
climate communities to develop a better understanding of how to deal
with both issues simultaneously. Although Egypt is not a major
contributor to the world's total GHG emissions, it is particularly
vulnerable to the potential effects of global climate change such as
rising sea levels and changed patterns of rainfall in the Nile Basin.
Climate change is a major threat to sustainable growth and
development in Egypt, and the achievement of the Millennium
Development Goals. Egypt-s capacity to respond to the challenges of
climate instability will be expanded by improving overall resilience,
integrating climate change goals into sustainable development
strategies, increasing the use of modern energy systems with reduced
carbon intensity, and strengthening international initiatives. This
study seeks to establish a framework for considering the complex and
evolving links between energy security and climate change,
applicable to Egypt.
Abstract: Modern applications realized onto FPGAs exhibit high connectivity demands. Throughout this paper we study the routing constraints of Virtex devices and we propose a systematic methodology for designing a novel general-purpose interconnection network targeting to reconfigurable architectures. This network consists of multiple segment wires and SB patterns, appropriately selected and assigned across the device. The goal of our proposed methodology is to maximize the hardware utilization of fabricated routing resources. The derived interconnection scheme is integrated on a Virtex style FPGA. This device is characterized both for its high-performance, as well as for its low-energy requirements. Due to this, the design criterion that guides our architecture selections was the minimal Energy×Delay Product (EDP). The methodology is fully-supported by three new software tools, which belong to MEANDER Design Framework. Using a typical set of MCNC benchmarks, extensive comparison study in terms of several critical parameters proves the effectiveness of the derived interconnection network. More specifically, we achieve average Energy×Delay Product reduction by 63%, performance increase by 26%, reduction in leakage power by 21%, reduction in total energy consumption by 11%, at the expense of increase of channel width by 20%.
Abstract: Many experimental results suggest that more precise spike timing is significant in neural information processing. We construct a self-organization model using the spatiotemporal pat-terns, where Spike-Timing Dependent Plasticity (STDP) tunes the conduction delays between neurons. We show that, for highly syn-chronized inputs, the fluctuation of conduction delays causes globally continuous and locally distributed firing patterns through the self-organization.
Abstract: SEMG (Surface Electromyogram) is one of the
bio-signals and is generated from the muscle. And there are many
research results that use forearm EMG to detect hand motions. In this
paper, we will talk about our developed the robot hand system that can
control grasping power by SEMG. In our system, we suppose that
muscle power is proportional to the amplitude of SEMG. The power is
estimated and the grip power of a robot hand is able to be controlled
using estimated muscle power in our system. In addition, to perform a
more precise control can be considered to build a closed loop feedback
system as an object to a subject to pressure from the edge of hand. Our
objectives of this study are the development of a method that makes
perfect detection of the hand grip force possible using SEMG patterns,
and applying this method to the man-machine interface.
Abstract: As the Internet continues to grow at a rapid pace as
the primary medium for communications and commerce and as
telecommunication networks and systems continue to expand their
global reach, digital information has become the most popular and
important information resource and our dependence upon the
underlying cyber infrastructure has been increasing significantly.
Unfortunately, as our dependency has grown, so has the threat to the
cyber infrastructure from spammers, attackers and criminal
enterprises. In this paper, we propose a new machine learning based
network intrusion detection framework for cyber security. The
detection process of the framework consists of two stages: model
construction and intrusion detection. In the model construction stage,
a semi-supervised machine learning algorithm is applied to a
collected set of network audit data to generate a profile of normal
network behavior and in the intrusion detection stage, input network
events are analyzed and compared with the patterns gathered in the
profile, and some of them are then flagged as anomalies should these
events are sufficiently far from the expected normal behavior. The
proposed framework is particularly applicable to the situations where
there is only a small amount of labeled network training data
available, which is very typical in real world network environments.
Abstract: In digital signal processing it is important to
approximate multi-dimensional data by the method called rank
reduction, in which we reduce the rank of multi-dimensional data from
higher to lower. For 2-dimennsional data, singular value
decomposition (SVD) is one of the most known rank reduction
techniques. Additional, outer product expansion expanded from SVD
was proposed and implemented for multi-dimensional data, which has
been widely applied to image processing and pattern recognition.
However, the multi-dimensional outer product expansion has behavior
of great computation complex and has not orthogonally between the
expansion terms. Therefore we have proposed an alterative method,
Third-order Orthogonal Tensor Product Expansion short for 3-OTPE.
3-OTPE uses the power method instead of nonlinear optimization
method for decreasing at computing time. At the same time the group
of B. D. Lathauwer proposed Higher-Order SVD (HOSVD) that is
also developed with SVD extensions for multi-dimensional data.
3-OTPE and HOSVD are similarly on the rank reduction of
multi-dimensional data. Using these two methods we can obtain
computation results respectively, some ones are the same while some
ones are slight different. In this paper, we compare 3-OTPE to
HOSVD in accuracy of calculation and computing time of resolution,
and clarify the difference between these two methods.
Abstract: Circle grid space filling plate is a flow conditioner with a fractal pattern and used to eliminate turbulence originating from pipe fittings in experimental fluid flow applications. In this paper, steady state, incompressible, swirling turbulent flow through circle grid space filling plate has been studied. The solution and the analysis were carried out using finite volume CFD solver FLUENT 6.2. Three turbulence models were used in the numerical investigation and their results were compared with the pressure drop correlation of BS EN ISO 5167-2:2003. The turbulence models investigated here are the standard k-ε, realizable k-ε, and the Reynolds Stress Model (RSM). The results showed that the RSM model gave the best agreement with the ISO pressure drop correlation. The effects of circle grids space filling plate thickness and Reynolds number on the flow characteristics have been investigated as well.
Abstract: With the enormous growth on the web, users get easily
lost in the rich hyper structure. Thus developing user friendly and
automated tools for providing relevant information without any
redundant links to the users to cater to their needs is the primary task
for the website owners. Most of the existing web mining algorithms
have concentrated on finding frequent patterns while neglecting the
less frequent one that are likely to contain the outlying data such as
noise, irrelevant and redundant data. This paper proposes new
algorithm for mining the web content by detecting the redundant
links from the web documents using set theoretical(classical
mathematics) such as subset, union, intersection etc,. Then the
redundant links is removed from the original web content to get the
required information by the user..
Abstract: A new technique to quantify the differential mode
delay (DMD) in multimode fiber (MMF) is been presented. The
technique measures DMD based on angular launch and
measurements of the difference in modal delay using variable
apertures at the fiber face. The result of the angular spatial filtering
revealed less excitation of higher order modes when the laser beam is
filtered at higher angles. This result would indicate that DMD
profiles would experience a data pattern dependency.
Abstract: There are multiple reasons to expect that detecting the
word order errors in a text will be a difficult problem, and detection
rates reported in the literature are in fact low. Although grammatical
rules constructed by computer linguists improve the performance of
grammar checker in word order diagnosis, the repairing task is still
very difficult. This paper presents an approach for repairing word
order errors in English text by reordering words in a sentence and
choosing the version that maximizes the number of trigram hits
according to a language model. The novelty of this method concerns
the use of an efficient confusion matrix technique for reordering the
words. The comparative advantage of this method is that works with
a large set of words, and avoids the laborious and costly process of
collecting word order errors for creating error patterns.
Abstract: The security of computer networks plays a strategic
role in modern computer systems. Intrusion Detection Systems (IDS)
act as the 'second line of defense' placed inside a protected
network, looking for known or potential threats in network traffic
and/or audit data recorded by hosts. We developed an Intrusion
Detection System using LAMSTAR neural network to learn patterns
of normal and intrusive activities, to classify observed system
activities and compared the performance of LAMSTAR IDS with
other classification techniques using 5 classes of KDDCup99 data.
LAMSAR IDS gives better performance at the cost of high
Computational complexity, Training time and Testing time, when
compared to other classification techniques (Binary Tree classifier,
RBF classifier, Gaussian Mixture classifier). we further reduced the
Computational Complexity of LAMSTAR IDS by reducing the
dimension of the data using principal component analysis which in
turn reduces the training and testing time with almost the same
performance.
Abstract: Arrack is one of the forms of alcoholic beverage or
liquor which is produced from palm or date juice and commonly
consumed by the lower social class of all religious/ethnic
communities in the north-western villages of Bangladesh. The
purpose of the study was to compare arrack drinking patterns
associated with socio-demographic status among the Muslim, Hindu,
Santal, and Oraon communities in the Rasulpur union of Bangladesh.
A total of 391 respondents (Muslim n-109, Hindu n-103, Santal n-89,
Oraon n-90) selected by cluster random sampling were interviewed
by ADP (Arrack Drinking Pattern) questionnaire. The results of
Pearson Chi-Squire test revealed that arrack drinking patterns were
significantly differed among the Muslim, Hindu, Santal, and Oraon
communities- drinkers. In addition, the results of Spearman-s
bivariate correlation coefficients also revealed that sociodemographic
characteristics of the communities- drinkers were the
significantly positive and negative associations with the arrack
drinking patterns in the Rasulpur union, Bangladesh. The study
suggests that further cross-cultural researches should be conducted
on the consequences of arrack drinking patterns on the communities-
drinkers.
Abstract: Climate change is a phenomenon has been based on
the available evidence from a very long time ago and now its
existence is very probable. The speed and nature of climate
parameters changes at the middle of twentieth century has been
different and its quickness more than the before and its trend changed
to some extent comparing to the past. Climate change issue now
regarded as not only one of the most common scientific topic but also
a social political one, is not a new issue. Climate change is a
complicated atmospheric oceanic phenomenon on a global scale and
long-term. Precipitation pattern change, fast decrease of snowcovered
resources and its rapid melting, increased evaporation, the
occurrence of destroying floods, water shortage crisis, severe
reduction at the rate of harvesting agricultural products and, so on are
all the significant of climate change. To cope with this phenomenon,
its consequences and events in which public instruction is the most
important but it may be climate that no significant cant and effective
action has been done so far. The present article is included a part of
one surrey about climate change in Fars. The study area having
annually mean temperature 14 and precipitation 320 mm .23 stations
inside the basin with a common 37 year statistical period have been
applied to the meteorology data (1974-2010). Man-kendal and
change factor methods are two statistical methods, applying them, the
trend of changes and the annual mean average temperature and the
annual minimum mean temperature were studied by using them.
Based on time series for each parameter, the annual mean average
temperature and the mean of annual maximum temperature have a
rising trend so that this trend is clearer to the mean of annual
maximum temperature.
Abstract: The objective was to determine the single gene and
interaction effect of composite genotype of beta-kappa casein and
DGAT1 gene on milk yield (MY) and milk composition, content of
milk fat (%FAT), milk protein (%PRO), solid not fat (%SNF), and
total solid (%TS) in crossbred Holstein cows. Two hundred and
thirty- one cows were genotyped with PCR-RFLP for DGAT1 and
composite genotype data of beta-kappa casein from previous work
were used. Two model, (1), and (2), was used to estimate single gene
effect, and interaction effect on the traits, respectively. The
significance of interaction effects on all traits were detected. Most
traits have consistent pattern of significant when model (1), and (2)
were compared, except the effect of composite genotype of betakappa
casein on %FAT, and the effect of DGAT1 on MY, which the
significant difference was detected in only model (1).The results
suggested that when the optimum of all traits was necessary,
interaction effect should be concerned.
Abstract: This paper presents a comparative study on
Vanadyl Phthalocyanine (VOPc) thin films deposited by thermal
evaporation and spin coating techniques. The samples
were prepared on cleaned glass substrates and annealed at
various temperatures ranging form 95oC to 155oC. To obtain
the morphological and structural properties of VOPc thin
films, X-ray diffraction (XRD) technique and atomic force
microscopy (AFM) have been implied. The AFM topographic
images show a very slight difference in the thermally grown
films, before and after annealing, however best results are
achieved for the spin-cast film annealed at 125oC. The XRD
spectra show no existence of the sharp peaks, suggesting the
material to be amorphous. The humps in the XRD patterns
indicate the presence of some crystallites.
Abstract: Hand gesture is an active area of research in the vision
community, mainly for the purpose of sign language recognition and
Human Computer Interaction. In this paper, we propose a system to
recognize alphabet characters (A-Z) and numbers (0-9) in real-time
from stereo color image sequences using Hidden Markov Models
(HMMs). Our system is based on three main stages; automatic segmentation
and preprocessing of the hand regions, feature extraction
and classification. In automatic segmentation and preprocessing stage,
color and 3D depth map are used to detect hands where the hand
trajectory will take place in further step using Mean-shift algorithm
and Kalman filter. In the feature extraction stage, 3D combined features
of location, orientation and velocity with respected to Cartesian
systems are used. And then, k-means clustering is employed for
HMMs codeword. The final stage so-called classification, Baum-
Welch algorithm is used to do a full train for HMMs parameters.
The gesture of alphabets and numbers is recognized using Left-Right
Banded model in conjunction with Viterbi algorithm. Experimental
results demonstrate that, our system can successfully recognize hand
gestures with 98.33% recognition rate.