Abstract: In the current decade, wireless sensor networks are
emerging as a peculiar multi-disciplinary research area. By this
way, energy efficiency is one of the fundamental research themes
in the design of Medium Access Control (MAC) protocols for
wireless sensor networks. Thus, in order to optimize the energy
consumption in these networks, a variety of MAC protocols are
available in the literature. These schemes were commonly evaluated
under simple network density and a few results are published on
their robustness in realistic network-s size. We, in this paper, provide
an analytical study aiming to highlight the energy waste sources in
wireless sensor networks. Then, we experiment three energy efficient
hybrid CSMA/CA based MAC protocols optimized for wireless
sensor networks: Sensor-MAC (SMAC), Time-out MAC (TMAC)
and Traffic aware Energy Efficient MAC (TEEM). We investigate
these protocols with different network densities in order to discuss
the end-to-end performances of these schemes (i.e. in terms of energy
efficiency, delay and throughput). Through Network Simulator (NS-
2) implementations, we explore the behaviors of these protocols with
respect to the network density. In fact, this study may help the multihops
sensor networks designers to design or select the MAC layer
which matches better their applications aims.
Abstract: Banyan networks are really attractive for serving as
the optical switching architectures due to their unique properties of
small depth and absolute signal loss uniformity. The fact has been
established that the limitations of blocking nature and the nonavailability
of proper connections due to non-rearrangeable property
can be easily ruled out using electro-optic MZI switches as basic
switching elements. Combination of the horizontal expansion and
vertical stacking of optical banyan networks is an appropriate scheme
for constructing non-blocking banyan-based optical switching
networks. The interconnected banyan switching fabrics (IBSF) have
been considered and analyzed to best serve the purpose of optical
switching with electro-optic MZI basic elements. The cross/bar state
interchange for the switches has been facilitated by appropriate
voltage switching or the by the switching of operating wavelength.
The paper is dedicated to the modification of the basic switching
element being used as well as the architecture of the switching
network.
Abstract: The most common forensic activity is searching a hard
disk for string of data. Nowadays, investigators and analysts are
increasingly experiencing large, even terabyte sized data sets when
conducting digital investigations. Therefore consecutive searching can
take weeks to complete successfully. There are two primary search
methods: index-based search and bitwise search. Index-based
searching is very fast after the initial indexing but initial indexing
takes a long time. In this paper, we discuss a high speed bitwise search
model for large-scale digital forensic investigations. We used pattern
matching board, which is generally used for network security, to
search for string and complex regular expressions. Our results indicate
that in many cases, the use of pattern matching board can substantially
increase the performance of digital forensic search tools.
Abstract: The success of an electronic system in a System-on- Chip is highly dependent on the efficiency of its interconnection network, which is constructed from routers and channels (the routers move data across the channels between nodes). Since neither classical bus based nor point to point architectures can provide scalable solutions and satisfy the tight power and performance requirements of future applications, the Network-on-Chip (NoC) approach has recently been proposed as a promising solution. Indeed, in contrast to the traditional solutions, the NoC approach can provide large bandwidth with moderate area overhead. The selected topology of the components interconnects plays prime rule in the performance of NoC architecture as well as routing and switching techniques that can be used. In this paper, we present two generic NoC architectures that can be customized to the specific communication needs of an application in order to reduce the area with minimal degradation of the latency of the system. An experimental study is performed to compare these structures with basic NoC topologies represented by 2D mesh, Butterfly-Fat Tree (BFT) and SPIN. It is shown that Cluster mesh (CMesh) and MinRoot schemes achieves significant improvements in network latency and energy consumption with only negligible area overhead and complexity over existing architectures. In fact, in the case of basic NoC topologies, CMesh and MinRoot schemes provides substantial savings in area as well, because they requires fewer routers. The simulation results show that CMesh and MinRoot networks outperforms MESH, BFT and SPIN in main performance metrics.
Abstract: The back-propagation algorithm calculates the weight
changes of an artificial neural network, and a two-term algorithm
with a dynamically optimal learning rate and a momentum factor
is commonly used. Recently the addition of an extra term, called a
proportional factor (PF), to the two-term BP algorithm was proposed.
The third term increases the speed of the BP algorithm. However,
the PF term also reduces the convergence of the BP algorithm, and
optimization approaches for evaluating the learning parameters are
required to facilitate the application of the three terms BP algorithm.
This paper considers the optimization of the new back-propagation
algorithm by using derivative information. A family of approaches
exploiting the derivatives with respect to the learning rate, momentum
factor and proportional factor is presented. These autonomously
compute the derivatives in the weight space, by using information
gathered from the forward and backward procedures. The three-term
BP algorithm and the optimization approaches are evaluated using
the benchmark XOR problem.
Abstract: A simple network model is developed in OPNET to
study the performance of the Wi-Fi protocol. The model is simulated
in OPNET and performance factors such as load, throughput and delay
are analysed from the model. Four applications such as oracle, http, ftp
and voice are applied over the Wireless LAN network to determine the
throughput. The voice application utilises a considerable amount of
bandwidth of up to 5Mbps, as a result the 802.11g standard of the
Wi-Fi protocol was chosen which can support a data rate of up to
54Mbps. Results indicate that when the load in the Wi-Fi network is
increased the queuing delay on the point-to-point links in the Wi-Fi
network significantly reduces until it is comparable to that of WiMAX.
In conclusion, the queuing delay of the Wi-Fi protocol for the network
model simulated was about 0.00001secs comparable to WiMAX
network values.
Abstract: In this paper we describes the authentication for DHCP
(Dynamic Host Configuration Protocol) message which provides the
efficient key management and reduces the danger replay attack without
an additional packet for a replay attack. And the authentication for
DHCP message supports mutual authentication and provides both
entity authentication and message authentication. We applied the
authentication for DHCP message to the home network environments
and tested through a home gateway.
Abstract: Quantum cryptography offers a way of key agreement,
which is unbreakable by any external adversary. Authentication is
of crucial importance, as perfect secrecy is worthless if the identity
of the addressee cannot be ensured before sending important information.
Message authentication has been studied thoroughly, but no
approach seems to be able to explicitly counter meet-in-the-middle
impersonation attacks. The goal of this paper is the development of
an authentication scheme being resistant against active adversaries
controlling the communication channel. The scheme is built on top
of a key-establishment protocol and is unconditionally secure if built
upon quantum cryptographic key exchange. In general, the security
is the same as for the key-agreement protocol lying underneath.
Abstract: Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.
Abstract: In this paper, an intelligent algorithm for optimal
document archiving is presented. It is kown that electronic archives
are very important for information system management. Minimizing
the size of the stored data in electronic archive is a main issue to
reduce the physical storage area. Here, the effect of different types of
Arabic fonts on electronic archives size is discussed. Simulation
results show that PDF is the best file format for storage of the Arabic
documents in electronic archive. Furthermore, fast information
detection in a given PDF file is introduced. Such approach uses fast
neural networks (FNNs) implemented in the frequency domain. The
operation of these networks relies on performing cross correlation in
the frequency domain rather than spatial one. It is proved
mathematically and practically that the number of computation steps
required for the presented FNNs is less than that needed by
conventional neural networks (CNNs). Simulation results using
MATLAB confirm the theoretical computations.
Abstract: In wireless networks, bandwidth is scare resource and it is essential to utilize it effectively. This paper analyses effects of using different bandwidth management techniques on the network performances of the Wireless Local Area Networks (WLANs) that use hybrid load balancing scheme. In particular, we study three bandwidth management schemes, namely Complete Sharing (CS), Complete Partitioning (CP), and Partial Sharing (PS). Performances of these schemes are evaluated by simulation experiments in term of percentage of network association blocking. Our results show that the CS scheme can provide relatively low blocking percentage in various network traffic scenarios whereas the PS scheme can enhance quality of services of the multimedia traffic with rather small expenses on the blocking percentage of the best effort traffic.
Abstract: In this paper, we propose a routing scheme that guarantees
the residual lifetime of wireless sensor networks where each
sensor node operates with a limited budget of battery energy. The
scheme maximizes the communications QoS while sustaining the
residual battery lifetime of the network for a specified duration.
Communication paths of wireless nodes are translated into a directed
acyclic graph(DAG) and the maximum-flow algorithm is applied to
the graph. The found maximum flow are assigned to sender nodes, so
as to maximize their communication QoS. Based on assigned flows,
the scheme determines the routing path and the transmission rate of
data packet so that any sensor node on the path would not exhaust
its battery energy before a specified duration.
Abstract: Today automobile and aerospace industries realise Laser Beam Welding for a clean and non contact source of heating and fusion for joining of sheets. The welding performance is mainly based on by the laser welding parameters. Some concepts related to Artificial Neural Networks and how can be applied to model weld bead geometry and mechanical properties in terms of equipment parameters are reported in order to evaluate the accuracy and compare it with traditional modeling schemes. This review reveals the output features of Titanium and Aluminium weld bead geometry and mechanical properties such as ultimate tensile strength, yield strength, elongation and reduction of the area of the weld using Artificial Neural Network.
Abstract: Until recently, researchers have developed various
tools and methodologies for effective clinical decision-making.
Among those decisions, chest pain diseases have been one of
important diagnostic issues especially in an emergency department. To
improve the ability of physicians in diagnosis, many researchers have
developed diagnosis intelligence by using machine learning and data
mining. However, most of the conventional methodologies have been
generally based on a single classifier for disease classification and
prediction, which shows moderate performance. This study utilizes an
ensemble strategy to combine multiple different classifiers to help
physicians diagnose chest pain diseases more accurately than ever.
Specifically the ensemble strategy is applied by using the integration
of decision trees, neural networks, and support vector machines. The
ensemble models are applied to real-world emergency data. This study
shows that the performance of the ensemble models is superior to each
of single classifiers.
Abstract: Optimum communication and performance in
Wireless Sensor Networks, constitute multi-facet challenges due to
the specific networking characteristics as well as the scarce resource
availability. Furthermore, it is becoming increasingly apparent that
isolated layer based approaches often do not meet the demands posed
by WSNs applications due to omission of critical inter-layer
interactions and dependencies. As a counterpart, cross-layer is
receiving high interest aiming to exploit these interactions and
increase network performance. However, in order to clearly identify
existing dependencies, comprehensive performance studies are
required evaluating the effect of different critical network parameters
on system level performance and behavior.This paper-s main
objective is to address the need for multi-parametric performance
evaluations considering critical network parameters using a well
known network simulator, offering useful and practical conclusions
and guidelines. The results reveal strong dependencies among
considered parameters which can be utilized by and drive future
research efforts, towards designing and implementing highly efficient
protocols and architectures.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: This paper presents a simple approach for load
flow analysis of a radial distribution network. The proposed
approach utilizes forward and backward sweep algorithm
based on Kirchoff-s current law (KCL) and Kirchoff-s voltage
law (KVL) for evaluating the node voltages iteratively. In this
approach, computation of branch current depends only on the
current injected at the neighbouring node and the current in
the adjacent branch. This approach starts from the end nodes
of sub lateral line, lateral line and main line and moves
towards the root node during branch current computation. The
node voltage evaluation begins from the root node and moves
towards the nodes located at the far end of the main, lateral
and sub lateral lines. The proposed approach has been tested
using four radial distribution systems of different size and
configuration and found to be computationally efficient.
Abstract: In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.
Abstract: Application of neural networks in execution of
programmed pulse width modulation (PPWM) of a voltage source
inverter (VSI) is studied in this paper. Using the proposed method it is
possible to cancel out the desired harmonics in output of VSI in
addition to control the magnitude of fundamental harmonic,
contineously. By checking the non-trained values and a performance
index, the most appropriate neural network is proposed. It is shown
that neural networks may solve the custom difficulties of practical
utilization of PPWM such as large size of memory, complex digital
circuits and controlling the magnitude of output voltage in a discrete
manner.
Abstract: An effective approach for unbalanced three-phase
distribution power flow solutions is proposed in this paper. The
special topological characteristics of distribution networks have been
fully utilized to make the direct solution possible. Two matrices–the
bus-injection to branch-current matrix and the branch-current to busvoltage
matrix– and a simple matrix multiplication are used to
obtain power flow solutions. Due to the distinctive solution
techniques of the proposed method, the time-consuming LU
decomposition and forward/backward substitution of the Jacobian
matrix or admittance matrix required in the traditional power flow
methods are no longer necessary. Therefore, the proposed method is
robust and time-efficient. Test results demonstrate the validity of the
proposed method. The proposed method shows great potential to be
used in distribution automation applications.