Abstract: A concept of switched beam antennas consisting of
2×2 rectangular array spaced by λ/4 accompanied with a null locating
has been proposed in the previous work. In this letter, the
performance evaluations of its prototype are presented. The benefits
of using proposed system have been clearly measured in term of
signal quality, throughput and delays. Also, the impact of position
shift which mesh router is not located on the expected beam direction
has also been investigated.
Abstract: In this study, a classification-based video
super-resolution method using artificial neural network (ANN) is
proposed to enhance low-resolution (LR) to high-resolution (HR)
frames. The proposed method consists of four main steps:
classification, motion-trace volume collection, temporal adjustment,
and ANN prediction. A classifier is designed based on the edge
properties of a pixel in the LR frame to identify the spatial information.
To exploit the spatio-temporal information, a motion-trace volume is
collected using motion estimation, which can eliminate unfathomable
object motion in the LR frames. In addition, temporal lateral process is
employed for volume adjustment to reduce unnecessary temporal
features. Finally, ANN is applied to each class to learn the complicated
spatio-temporal relationship between LR and HR frames. Simulation
results show that the proposed method successfully improves both
peak signal-to-noise ratio and perceptual quality.
Abstract: In this paper, an artificial neural network simulator is
employed to carry out diagnosis and prognosis on electric motor as
rotating machinery based on predictive maintenance. Vibration data
of the primary failed motor including unbalance, misalignment and
bearing fault were collected for training the neural network. Neural
network training was performed for a variety of inputs and the motor
condition was used as the expert training information. The main
purpose of applying the neural network as an expert system was to
detect the type of failure and applying preventive maintenance. The
advantage of this study is for machinery Industries by providing
appropriate maintenance that has an essential activity to keep the
production process going at all processes in the machinery industry.
Proper maintenance is pivotal in order to prevent the possible failures
in operating system and increase the availability and effectiveness of
a system by analyzing vibration monitoring and developing expert
system.
Abstract: Many attempts have been made to strengthen Feistel based block ciphers. Among the successful proposals is the key- dependent S-box which was implemented in some of the high-profile ciphers. In this paper a key-dependent permutation box is proposed and implemented on DES as a case study. The new modified DES, MDES, was tested against Diehard Tests, avalanche test, and performance test. The results showed that in general MDES is more resistible to attacks than DES with negligible overhead. Therefore, it is believed that the proposed key-dependent permutation should be considered as a valuable primitive that can help strengthen the security of Substitution-Permutation Network which is a core design in many Feistel based block ciphers.
Abstract: The small interfering RNA (siRNA) alters the
regulatory role of mRNA during gene expression by translational
inhibition. Recent studies show that upregulation of mRNA because
serious diseases like cancer. So designing effective siRNA with good
knockdown effects plays an important role in gene silencing. Various
siRNA design tools had been developed earlier. In this work, we are
trying to analyze the existing good scoring second generation siRNA
predicting tools and to optimize the efficiency of siRNA prediction
by designing a computational model using Artificial Neural Network
and whole stacking energy (%G), which may help in gene silencing
and drug design in cancer therapy. Our model is trained and tested
against a large data set of siRNA sequences. Validation of our results
is done by finding correlation coefficient of experimental versus
observed inhibition efficacy of siRNA. We achieved a correlation
coefficient of 0.727 in our previous computational model and we
could improve the correlation coefficient up to 0.753 when the
threshold of whole tacking energy is greater than or equal to -32.5
kcal/mol.
Abstract: In this paper, Land Marks for Unique Addressing( LMUA) algorithm is develped to generate unique ID for each and every node which leads to the formation of overlapping/Non overlapping clusters based on unique ID. To overcome the draw back of the developed LMUA algorithm, the concept of clustering is introduced. Based on the clustering concept a Land Marks for Unique Addressing and Clustering(LMUAC) Algorithm is developed to construct strictly non-overlapping clusters and classify those nodes in to Cluster Heads, Member Nodes, Gate way nodes and generating the Hierarchical code for the cluster heads to operate in the level one hierarchy for wireless communication switching. The expansion of the existing network can be performed or not without modifying the cost of adding the clusterhead is shown. The developed algorithm shows one way of efficiently constructing the
Abstract: To determine the presence and location of faults in a transmission by the adaptation of protective distance relay based on the measurement of fixed settings as line impedance is achieved by several different techniques. Moreover, a fast, accurate and robust technique for real-time purposes is required for the modern power systems. The appliance of radial basis function neural network in transmission line protection is demonstrated in this paper. The method applies the power system via voltage and current signals to learn the hidden relationship presented in the input patterns. It is experiential that the proposed technique is competent to identify the particular fault direction more speedily. System simulations studied show that the proposed approach is able to distinguish the direction of a fault on a transmission line swiftly and correctly, therefore suitable for the real-time purposes.
Abstract: Distributed denial-of-service (DDoS) attacks pose a
serious threat to network security. There have been a lot of
methodologies and tools devised to detect DDoS attacks and reduce
the damage they cause. Still, most of the methods cannot
simultaneously achieve (1) efficient detection with a small number of
false alarms and (2) real-time transfer of packets. Here, we introduce
a method for proactive detection of DDoS attacks, by classifying the
network status, to be utilized in the detection stage of the proposed
anti-DDoS framework. Initially, we analyse the DDoS architecture
and obtain details of its phases. Then, we investigate the procedures
of DDoS attacks and select variables based on these features. Finally,
we apply the k-nearest neighbour (k-NN) method to classify the
network status into each phase of DDoS attack. The simulation result
showed that each phase of the attack scenario is classified well and
we could detect DDoS attack in the early stage.
Abstract: In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.
Abstract: This paper presents an analysis of the localization accuracy of indoor positioning systems using Cramer-s rule via IEEE 802.15.4 wireless sensor networks. The objective is to study the impact of the methods used to convert the received signal strength into the distance that is used to compute the object location in the wireless indoor positioning system. Various methods were tested and the localization accuracy was analyzed. The experimental results show that the method based on the empirical data measured in the non line-of-sight (NLOS) environment yield the highest localization accuracy; with the minimum error distance less than 3 m.
Abstract: The authors have been developing several models
based on artificial neural networks, linear regression models, Box-
Jenkins methodology and ARIMA models to predict the time series
of tourism. The time series consist in the “Monthly Number of Guest
Nights in the Hotels" of one region. Several comparisons between the
different type models have been experimented as well as the features
used at the entrance of the models. The Artificial Neural Network
(ANN) models have always had their performance at the top of the
best models. Usually the feed-forward architecture was used due to
their huge application and results. In this paper the author made a
comparison between different architectures of the ANNs using
simply the same input. Therefore, the traditional feed-forward
architecture, the cascade forwards, a recurrent Elman architecture and
a radial based architecture were discussed and compared based on the
task of predicting the mentioned time series.
Abstract: This paper is aimed at describing a delay-based endto-
end (e2e) congestion control algorithm, called Very FAST TCP
(VFAST), which is an enhanced version of FAST TCP. The main
idea behind this enhancement is to smoothly estimate the Round-Trip
Time (RTT) based on a nonlinear filter, which eliminates throughput
and queue oscillation when RTT fluctuates. In this context, an evaluation
of the suggested scheme through simulation is introduced, by
comparing our VFAST prototype with FAST in terms of throughput,
queue behavior, fairness, stability, RTT and adaptivity to changes in
network. The achieved simulation results indicate that the suggested
protocol offer better performance than FAST TCP in terms of RTT
estimation and throughput.
Abstract: Cloud Computing is an approach that provides computation and storage services on-demand to clients over the network, independent of device and location. In the last few years, cloud computing became a trend in information technology with many companies that transfer their business processes and applications in the cloud. Cloud computing with service oriented architecture has contributed to rapid development of Geographic Information Systems. Open Geospatial Consortium with its standards provides the interfaces for hosted spatial data and GIS functionality to integrated GIS applications. Furthermore, with the enormous processing power, clouds provide efficient environment for data intensive applications that can be performed efficiently, with higher precision, and greater reliability. This paper presents our work on the geospatial data services within the cloud computing environment and its technology. A cloud computing environment with the strengths and weaknesses of the geographic information system will be introduced. The OGC standards that solve our application interoperability are highlighted. Finally, we outline our system architecture with utilities for requesting and invoking our developed data intensive applications as a web service.
Abstract: Developing techniques for mobile robot navigation constitutes one of the major trends in the current
research on mobile robotics. This paper develops a local
model network (LMN) for mobile robot navigation. The
LMN represents the mobile robot by a set of locally valid
submodels that are Multi-Layer Perceptrons (MLPs).
Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular
region. The submodels then are combined in a unified
structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This
proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the
proposed LMN reflect the soundness of the proposed
scheme.
Abstract: In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot-s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervised learning. A modified A* algorithm is used to find the best path using this internal representation between starting and goal points. This method can be used with good performance for both known and unknown environments.
Abstract: Project selection problems on management
information system (MIS) are often considered a multi-criteria
decision-making (MCDM) for a solving method. These problems
contain two aspects, such as interdependencies among criteria and
candidate projects and qualitative and quantitative factors of projects.
However, most existing methods reported in literature consider these
aspects separately even though these two aspects are simultaneously
incorporated. For this reason, we proposed a hybrid method using
analytic network process (ANP) and fuzzy logic in order to represent
both aspects. We then propose a goal programming model to conduct
an optimization for the project selection problems interpreted by a
hybrid concept. Finally, a numerical example is conducted as
verification purposes.
Abstract: In today-s information age, numbers of organizations
are still arguing on capitalizing the values of Information Technology
(IT) and Knowledge Management (KM) to which individuals can
benefit from and effective communication among the individuals can
be established. IT exists in enabling positive improvement for
communication among knowledge workers (k-workers) with a
number of social network technology domains at workplace. The
acceptance of digital discourse in sharing of knowledge and
facilitating the knowledge and information flows at most of the
organizations indeed impose the culture of knowledge sharing in
Digital Social Networks (DSN). Therefore, this study examines
whether the k-workers with IT background would confer an effect on
the three knowledge characteristics -- conceptual, contextual, and
operational. Derived from these three knowledge characteristics, five
potential factors will be examined on the effects of knowledge
exchange via e-mail domain as the chosen query. It is expected, that
the results could provide such a parameter in exploring how DSN
contributes in supporting the k-workers- virtues, performance and
qualities as well as revealing the mutual point between IT and KM.
Abstract: A new approach for protection of power transformer is
presented using a time-frequency transform known as Wavelet transform.
Different operating conditions such as inrush, Normal, load,
External fault and internal fault current are sampled and processed
to obtain wavelet coefficients. Different Operating conditions provide
variation in wavelet coefficients. Features like energy and Standard
deviation are calculated using Parsevals theorem. These features
are used as inputs to PNN (Probabilistic neural network) for fault
classification. The proposed algorithm provides more accurate results
even in the presence of noise inputs and accurately identifies inrush
and fault currents. Overall classification accuracy of the proposed
method is found to be 96.45%. Simulation of the fault (with and
without noise) was done using MATLAB AND SIMULINK software
taking 2 cycles of data window (40 m sec) containing 800 samples.
The algorithm was evaluated by using 10 % Gaussian white noise.
Abstract: To meet the demands of wireless sensor networks
(WSNs) where data are usually aggregated at a single source prior to
transmitting to any distant user, there is a need to establish a tree
structure inside any given event region. In this paper , a novel
technique to create one such tree is proposed .This tree preserves the
energy and maximizes the lifetime of event sources while they are
constantly transmitting for data aggregation. The term Decentralized
Lifetime Maximizing Tree (DLMT) is used to denote this tree.
DLMT features in nodes with higher energy tend to be chosen as data
aggregating parents so that the time to detect the first broken tree link
can be extended and less energy is involved in tree maintenance. By
constructing the tree in such a way, the protocol is able to reduce the
frequency of tree reconstruction, minimize the amount of data loss
,minimize the delay during data collection and preserves the energy.
Abstract: Telemedicine is brought to life by contemporary changes of our world and summarizes the entire range of services that are at the crossroad of traditional healthcare and information technology. It is believed that eHealth can help in solving critical issues of rising costs, care for ageing and housebound population, staff shortage. It is a feasible tool to provide routine as well as specialized health service as it has the potential to improve both the access to and the standard of care. eHealth is no more an optional choice. It has already made quite a way but it still remains a fantastic challenge for the future requiring cooperation and coordination at all possible levels. The strategic objectives of this paper are: 1. To start with an attempt to clarify the mass of terms used nowadays; 2. To answer the question “Who needs eHealth"; 3. To focus on the necessity of bridging telemedicine and medical (health) informatics as well as on the dual relationship between them; as well as 4. To underline the need of networking in understanding, developing and implementing eHealth.