Abstract: Efforts to secure supervisory control and data acquisition
(SCADA) systems must be supported under the guidance of
sound security policies and mechanisms to enforce them. Critical
elements of the policy must be systematically translated into a format
that can be used by policy enforcement components. Ideally, the
goal is to ensure that the enforced policy is a close reflection of
the specified policy. However, security controls commonly used to
enforce policies in the IT environment were not designed to satisfy
the specific needs of the SCADA environment. This paper presents
a language, based on the well-known XACML framework, for the
expression of authorization policies for SCADA systems.
Abstract: The present work is motivated by the idea that the
layer deformation in anisotropic elasticity can be estimated from the
theory of interfacial dislocations. In effect, this work which is an
extension of a previous approach given by one of the authors
determines the anisotropic displacement fields and the critical
thickness due to a complex biperiodic network of MDs lying just
below the free surface in view of the arrangement of dislocations.
The elastic fields of such arrangements observed along interfaces
play a crucial part in the improvement of the physical properties of
epitaxial systems. New results are proposed in anisotropic elasticity
for hexagonal networks of MDs which contain intrinsic and extrinsic
stacking faults. We developed, using a previous approach based on
the relative interfacial displacement and a Fourier series formulation
of the displacement fields, the expressions of elastic fields when
there is a possible dissociation of MDs. The numerical investigations
in the case of the observed system Si/(111)Si with low twist angles
show clearly the effect of the anisotropy and thickness when the
misfit networks are dissociated.
Abstract: We present an explicit expression to estimate driving voltage attenuation through RC networks representation of an ultrahigh- speed image sensor. Elmore delay metric for a fundamental RC chain is employed as the first-order approximation. By application of dimensional analysis to SPICE simulation data, we found a simple expression that significantly improves the accuracy of the approximation. Estimation error of the resultant expression for uniform RC networks is less than 2%. Similarly, another simple closed-form model to estimate 50 % delay through fundamental RC networks is also derived with sufficient accuracy. The framework of this analysis can be extended to address delay or attenuation issues of other VLSI structures.
Abstract: This paper address the network reliability optimization
problem in the optical access network design for the 3G cellular
systems. We presents a novel 0-1 integer programming model for
designing optical access network topologies comprised of multi-rings
with common-edge in order to guarantee always-on services. The
results show that the proposed model yields access network
topologies with the optimal reliablity and satisfies both network cost
limitations and traffic demand requirements.
Abstract: One of the most important applications of
wireless sensor networks is data collection. This paper
proposes as efficient approach for data collection in wireless
sensor networks by introducing Member Forward List. This list
includes the nodes with highest priority for forwarding the data.
When a node fails or dies, this list is used to select the next node
with higher priority. The benefit of this node is that it prevents
the algorithm from repeating when a node fails or dies. The
results show that Member Forward List decreases power
consumption and latency in wireless sensor networks.
Abstract: In this research study, an intelligent detection system
to support medical diagnosis and detection of abnormal lesions by
processing endoscopic images is presented. The images used in this
study have been obtained using the M2A Swallowable Imaging
Capsule - a patented, video color-imaging disposable capsule.
Schemes have been developed to extract texture features from the
fuzzy texture spectra in the chromatic and achromatic domains for a
selected region of interest from each color component histogram of
endoscopic images. The implementation of an advanced fuzzy
inference neural network which combines fuzzy systems and
artificial neural networks and the concept of fusion of multiple
classifiers dedicated to specific feature parameters have been also
adopted in this paper. The achieved high detection accuracy of the
proposed system has provided thus an indication that such intelligent
schemes could be used as a supplementary diagnostic tool in
endoscopy.
Abstract: This paper proposes a new methodology for the
optimal allocation and sizing of Embedded Generation (EG)
employing Real Coded Genetic Algorithm (RCGA) to minimize the
total power losses and to improve voltage profiles in the radial
distribution networks. RCGA is a method that uses continuous
floating numbers as representation which is different from
conventional binary numbers. The RCGA is used as solution tool,
which can determine the optimal location and size of EG in radial
system simultaneously. This method is developed in MATLAB. The
effect of EG units- installation and their sizing to the distribution
networks are demonstrated using 24 bus system.
Abstract: In this paper, we propose a supervised method for
color image classification based on a multilevel sigmoidal neural
network (MSNN) model. In this method, images are classified into
five categories, i.e., “Car", “Building", “Mountain", “Farm" and
“Coast". This classification is performed without any segmentation
processes. To verify the learning capabilities of the proposed method,
we compare our MSNN model with the traditional Sigmoidal Neural
Network (SNN) model. Results of comparison have shown that the
MSNN model performs better than the traditional SNN model in the
context of training run time and classification rate. Both color
moments and multi-level wavelets decomposition technique are used
to extract features from images. The proposed method has been
tested on a variety of real and synthetic images.
Abstract: MultiProtocol Label Switching (MPLS) is an
emerging technology that aims to address many of the existing issues
associated with packet forwarding in today-s Internetworking
environment. It provides a method of forwarding packets at a high
rate of speed by combining the speed and performance of Layer 2
with the scalability and IP intelligence of Layer 3. In a traditional IP
(Internet Protocol) routing network, a router analyzes the destination
IP address contained in the packet header. The router independently
determines the next hop for the packet using the destination IP
address and the interior gateway protocol. This process is repeated at
each hop to deliver the packet to its final destination. In contrast, in
the MPLS forwarding paradigm routers on the edge of the network
(label edge routers) attach labels to packets based on the forwarding
Equivalence class (FEC). Packets are then forwarded through the
MPLS domain, based on their associated FECs , through swapping
the labels by routers in the core of the network called label switch
routers. The act of simply swapping the label instead of referencing
the IP header of the packet in the routing table at each hop provides
a more efficient manner of forwarding packets, which in turn allows
the opportunity for traffic to be forwarded at tremendous speeds and
to have granular control over the path taken by a packet. This paper
deals with the process of MPLS forwarding mechanism,
implementation of MPLS datapath , and test results showing the
performance comparison of MPLS and IP routing. The discussion
will focus primarily on MPLS IP packet networks – by far the
most common application of MPLS today.
Abstract: This paper presents the results of enhancing images from a left and right stereo pair in order to increase the resolution of a 3D representation of a scene generated from that same pair. A new neural network structure known as a Self Delaying Dynamic Network (SDN) has been used to perform the enhancement. The advantage of SDNs over existing techniques such as bicubic interpolation is their ability to cope with motion and noise effects. SDNs are used to generate two high resolution images, one based on frames taken from the left view of the subject, and one based on the frames from the right. This new high resolution stereo pair is then processed by a disparity map generator. The disparity map generated is compared to two other disparity maps generated from the same scene. The first is a map generated from an original high resolution stereo pair and the second is a map generated using a stereo pair which has been enhanced using bicubic interpolation. The maps generated using the SDN enhanced pairs match more closely the target maps. The addition of extra noise into the input images is less problematic for the SDN system which is still able to out perform bicubic interpolation.
Abstract: Wireless Sensor Networks (WSN) are emerging
because of the developments in wireless communication technology and miniaturization of the hardware. WSN consists of a large number of low-cost, low-power, multifunctional sensor nodes to monitor physical conditions, such as temperature, sound, vibration, pressure,
motion, etc. The MAC protocol to be used in the sensor networks must be energy efficient and this should aim at conserving the energy during its operation. In this paper, with the focus of analyzing the
MAC protocols used in wireless Adhoc networks to WSN, simulation
experiments were conducted in Global Mobile Simulator
(GloMoSim) software. Number of packets sent by regular nodes, and received by sink node in different deployment strategies, total energy
spent, and the network life time have been chosen as the metric for comparison. From the results of simulation, it is evident that the IEEE 802.11 protocol performs better compared to CSMA and MACA protocols.
Abstract: This paper proposes new hybrid approaches for face
recognition. Gabor wavelets representation of face images is an
effective approach for both facial action recognition and face
identification. Perform dimensionality reduction and linear
discriminate analysis on the down sampled Gabor wavelet faces can
increase the discriminate ability. Nearest feature space is extended to
various similarity measures. In our experiments, proposed Gabor
wavelet faces combined with extended neural net feature space
classifier shows very good performance, which can achieve 93 %
maximum correct recognition rate on ORL data set without any preprocessing
step.
Abstract: Wireless Mesh Networking is a promising proposal
for broadband data transmission in a large area with low cost and
acceptable QoS. These features- trade offs in WMNs is a hot research
field nowadays. In this paper a mathematical optimization framework
has been developed to maximize throughput according to upper
bound delay constraints. IEEE 802.11 based infrastructure
backhauling mode of WMNs has been considered to formulate the
MINLP optimization problem. Proposed method gives the full
routing and scheduling procedure in WMN in order to obtain
mentioned goals.
Abstract: Minimization methods for training feed-forward networks with Backpropagation are compared. Feedforward network training is a special case of functional minimization, where no explicit model of the data is assumed. Therefore due to the high dimensionality of the data, linearization of the training problem through use of orthogonal basis functions is not desirable. The focus is functional minimization on any basis. A number of methods based on local gradient and Hessian matrices are discussed. Modifications of many methods of first and second order training methods are considered. Using share rates data, experimentally it is proved that Conjugate gradient and Quasi Newton?s methods outperformed the Gradient Descent methods. In case of the Levenberg-Marquardt algorithm is of special interest in financial forecasting.
Abstract: Movable power sources of proton exchange
membrane fuel cells (PEMFC) are the important research done in the
current fuel cells (FC) field. The PEMFC system control influences
the cell performance greatly and it is a control system for industrial
complex problems, due to the imprecision, uncertainty and partial
truth and intrinsic nonlinear characteristics of PEMFCs. In this paper
an adaptive PI control strategy using neural network adaptive Morlet
wavelet for control is proposed. It is based on a single layer feed
forward neural networks with hidden nodes of adaptive morlet
wavelet functions controller and an infinite impulse response (IIR)
recurrent structure. The IIR is combined by cascading to the network
to provide double local structure resulting in improving speed of
learning. The proposed method is applied to a typical 1 KW PEMFC
system and the results show the proposed method has more accuracy
against to MLP (Multi Layer Perceptron) method.
Abstract: Although many researchers have studied the flow
hydraulics in compound channels, there are still many complicated problems in determination of their flow rating curves. Many different
methods have been presented for these channels but extending them
for all types of compound channels with different geometrical and
hydraulic conditions is certainly difficult. In this study, by aid of nearly 400 laboratory and field data sets of geometry and flow rating
curves from 30 different straight compound sections and using artificial neural networks (ANNs), flow discharge in compound channels was estimated. 13 dimensionless input variables including relative depth, relative roughness, relative width, aspect ratio, bed
slope, main channel side slopes, flood plains side slopes and berm
inclination and one output variable (flow discharge), have been used
in ANNs. Comparison of ANNs model and traditional method
(divided channel method-DCM) shows high accuracy of ANNs model results. The results of Sensitivity analysis showed that the relative depth with 47.6 percent contribution, is the most effective input parameter for flow discharge prediction. Relative width and
relative roughness have 19.3 and 12.2 percent of importance, respectively. On the other hand, shape parameter, main channel and
flood plains side slopes with 2.1, 3.8 and 3.8 percent of contribution, have the least importance.
Abstract: There exists an injective, information-preserving function
that maps a semantic network (i.e a directed labeled network)
to a directed network (i.e. a directed unlabeled network). The edge
label in the semantic network is represented as a topological feature
of the directed network. Also, there exists an injective function that
maps a directed network to an undirected network (i.e. an undirected
unlabeled network). The edge directionality in the directed network
is represented as a topological feature of the undirected network.
Through function composition, there exists an injective function that
maps a semantic network to an undirected network. Thus, aside from
space constraints, the semantic network construct does not have any
modeling functionality that is not possible with either a directed
or undirected network representation. Two proofs of this idea will
be presented. The first is a proof of the aforementioned function
composition concept. The second is a simpler proof involving an
undirected binary encoding of a semantic network.
Abstract: Text categorization - the assignment of natural language documents to one or more predefined categories based on their semantic content - is an important component in many information organization and management tasks. Performance of neural networks learning is known to be sensitive to the initial weights and architecture. This paper discusses the use multilayer neural network initialization with decision tree classifier for improving text categorization accuracy. An adaptation of the algorithm is proposed in which a decision tree from root node until a final leave is used for initialization of multilayer neural network. The experimental evaluation demonstrates this approach provides better classification accuracy with Reuters-21578 corpus, one of the standard benchmarks for text categorization tasks. We present results comparing the accuracy of this approach with multilayer neural network initialized with traditional random method and decision tree classifiers.
Abstract: The wireless mesh networks (WMNs) are emerging technology in wireless networking as they can serve large scale high speed internet access. Due to its wireless multi-hop feature, wireless mesh network is prone to suffer from many attacks, such as denial of service attack (DoS). We consider a special case of DoS attack which is selective forwarding attack (a.k.a. gray hole attack). In such attack, a misbehaving mesh router selectively drops the packets it receives rom its predecessor mesh router. It is very hard to detect that packet loss is due to medium access collision, bad channel quality or because of selective forwarding attack. In this paper, we present a review of detection algorithms of selective forwarding attack and discuss their advantage & disadvantage. Finally we conclude this paper with open research issues and challenges.
Abstract: Recent scientific investigations indicate that
multimodal biometrics overcome the technical limitations of
unimodal biometrics, making them ideally suited for everyday life
applications that require a reliable authentication system. However,
for a successful adoption of multimodal biometrics, such systems
would require large heterogeneous datasets with complex multimodal
fusion and privacy schemes spanning various distributed
environments. From experimental investigations of current
multimodal systems, this paper reports the various issues related to
speed, error-recovery and privacy that impede the diffusion of such
systems in real-life. This calls for a robust mechanism that caters to
the desired real-time performance, robust fusion schemes,
interoperability and adaptable privacy policies.
The main objective of this paper is to present a framework that
addresses the abovementioned issues by leveraging on the
heterogeneous resource sharing capacities of Grid services and the
efficient machine learning capabilities of artificial neural networks
(ANN). Hence, this paper proposes a Grid-based neural network
framework for adopting multimodal biometrics with the view of
overcoming the barriers of performance, privacy and risk issues that
are associated with shared heterogeneous multimodal data centres.
The framework combines the concept of Grid services for reliable
brokering and privacy policy management of shared biometric
resources along with a momentum back propagation ANN (MBPANN)
model of machine learning for efficient multimodal fusion and
authentication schemes. Real-life applications would be able to adopt
the proposed framework to cater to the varying business requirements
and user privacies for a successful diffusion of multimodal
biometrics in various day-to-day transactions.