Abstract: This paper presents a new heuristic algorithm useful
for long-term planning of survivable WDM networks. A multi-period
model is formulated that combines network topology design and
capacity expansion. The ability to determine network expansion
schedules of this type becomes increasingly important to the
telecommunications industry and to its customers. The solution
technique consists of a Genetic Algorithm that allows generating
several network alternatives for each time period simultaneously and
shortest-path techniques to deduce from these alternatives a least-cost
network expansion plan over all time periods. The multi-period
planning approach is illustrated on a realistic network example.
Extensive simulations on a wide range of problem instances are
carried out to assess the cost savings that can be expected by
choosing a multi-period planning approach instead of an iterative
network expansion design method.
Abstract: This paper investigates the problem of designing a robust state-feedback controller for a class of uncertain Markovian jump nonlinear systems that guarantees the L2-gain from an exogenous input to a regulated output is less than or equal to a prescribed value. First, we approximate this class of uncertain Markovian jump nonlinear systems by a class of uncertain Takagi-Sugeno fuzzy models with Markovian jumps. Then, based on an LMI approach, LMI-based sufficient conditions for the uncertain Markovian jump nonlinear systems to have an H performance are derived. An illustrative example is used to illustrate the effectiveness of the proposed design techniques.
Abstract: The school / university orientation interests a broad and
often badly informed public. Technically, it is an important
multicriterion decision problem, which supposes the combination of
much academic professional and/or lawful knowledge, which in turn
justifies software resorting to the techniques of Artificial Intelligence.
CORUS is an expert system of the "Conseil et ORientation
Universitaire et Scolaire", based on a knowledge representation
language (KRL) with rules and objects, called/ known as Ibn Rochd.
CORUS was developed thanks to DéGSE, a workshop of cognitive
engineering which supports this LRC. CORUS works out many
acceptable solutions for the case considered, and retains the most
satisfactory among them. Several versions of CORUS have extended
its services gradually.
Abstract: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.
Abstract: Recently, as information industry and mobile
communication technology are developing, this study is conducted on
the new concept of intelligent structures and maintenance techniques
that applied wireless sensor network, USN (Ubiquitous Sensor
Network), to social infrastructures such as civil and architectural
structures on the basis of the concept of Ubiquitous Computing that
invisibly provides human life with computing, along with mutually
cooperating, compromising and connecting networks each other by
having computers within all objects around us.
Therefore, the purpose of this study is to investigate the capability
of wireless communication of sensor node embedded in reinforced
concrete structure with a basic experiment on an electric wave
permeability of sensor node by fabricating molding with variables of
concrete thickness and steel bars that are mostly used in constructing
structures to determine the feasibility of application to constructing
structures with USN.
At this time, with putting the pitches of steel bars, the thickness of
concrete placed, and the intensity of RF signal of a
transmitter-receiver as variables and when wireless communication
module was installed inside, the possible communication distance of
plain concrete and the possible communication distance by the pitches
of steel bars was measured in the horizontal and vertical direction
respectively. Besides, for the precise measurement of diminution of an
electric wave, the magnitude of an electric wave in the range of used
frequencies was measured by using Spectrum Analyzer. The
phenomenon of diminution of an electric wave was numerically
analyzed and the effect of the length of wavelength of frequencies was
analyzed by the properties of a frequency band area.
As a result of studying the feasibility of an application to
constructing structures with wireless sensor, in case of plain concrete,
it shows 45cm for the depth of permeability and in case of reinforced
concrete with the pitches of 5cm, it shows 37cm and 45cm for the
pitches of 15cm.
Abstract: The study investigated the 2011 TPGA Ever Rich
Championship – North Bay Open spectators- on-the-site spectating
motivations, experiences, and satisfactions. The research was
conducted on a convenience sample of the on-the-spot spectators at the
North Bay Golf and Country Club. A total of 200 questionnaires were
distributed, of which 185 valid questionnaires were collected,
approaching a 92.5% response rate. The data obtained was analyzed
with statistical techniques. First, the data showed significant
differences in motivations, experiences, and satisfactions relative to
demographic variables among the on-the-spot spectators. Second,
spectating motivation, experience, and satisfaction were significantly
related to one another.
Abstract: Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.
Abstract: In this paper, we present an analytical framework for the evaluation of the uplink performance of multihop cellular networks based on dynamic time division duplex (TDD). New wireless broadband protocols, such as WiMAX, WiBro, and 3G-LTE apply TDD, and mobile communication protocols under standardization (e.g., IEEE802.16j) are investigating mobile multihop relay (MMR) as a future technology. In this paper a novel MMR TDD scheme is presented, where the dynamic range of the frame is shared to traffic resources of asymmetric nature and multihop relaying. The mobile communication channel interference model comprises of inner and co-channel interference (CCI). The performance analysis focuses on the uplink due to the fact that the effects of dynamic resource allocation show significant performance degradation only in the uplink compared to time division multiple access (TDMA) schemes due to CCI [1-3], where the downlink results to be the same or better.The analysis was based on the signal to interference power ratio (SIR) outage probability of dynamic TDD (D-TDD) and TDMA systems,which are the most widespread mobile communication multi-user control techniques. This paper presents the uplink SIR outage probability with multihop results and shows that the dynamic TDD scheme applying MMR can provide a performance improvement compared to single hop applications if executed properly.
Abstract: The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.
Abstract: Nowadays, organizing a repository of documents and
resources for learning on a special field as Information Technology
(IT), together with search techniques based on domain knowledge or
document-s content is an urgent need in practice of teaching, learning
and researching. There have been several works related to methods of
organization and search by content. However, the results are still
limited and insufficient to meet user-s demand for semantic
document retrieval. This paper presents a solution for the
organization of a repository that supports semantic representation and
processing in search. The proposed solution is a model which
integrates components such as an ontology describing domain
knowledge, a database of document repository, semantic
representation for documents and a file system; with problems,
semantic processing techniques and advanced search techniques
based on measuring semantic similarity. The solution is applied to
build a IT learning materials management system of a university with
semantic search function serving students, teachers, and manager as
well. The application has been implemented, tested at the University
of Information Technology, Ho Chi Minh City, Vietnam and has
achieved good results.
Abstract: The paper proposes a way of parallel processing of
SURF and Optical Flow for moving object recognition and tracking.
The object recognition and tracking is one of the most important task
in computer vision, however disadvantage are many operations cause
processing speed slower so that it can-t do real-time object recognition
and tracking. The proposed method uses a typical way of feature
extraction SURF and moving object Optical Flow for reduce
disadvantage and real-time moving object recognition and tracking,
and parallel processing techniques for speed improvement. First
analyse that an image from DB and acquired through the camera using
SURF for compared to the same object recognition then set ROI
(Region of Interest) for tracking movement of feature points using
Optical Flow. Secondly, using Multi-Thread is for improved
processing speed and recognition by parallel processing. Finally,
performance is evaluated and verified efficiency of algorithm
throughout the experiment.
Abstract: Software projects are very dynamic and require
recurring adjustments of their project plans. These settings can be
understood as reconfigurations in the schedule, in the resources
allocation and other design elements. Yet, during the planning and
execution of a software project, the integration of specific activities
in the projects with the activities that take part in the organization-s
common activity flow should be considered. This article presents the
results from a systematic review of aspects related to software
projects- dynamic reconfiguration emphasizing the integration of
project management with the organizational flows. A series of studies
was analyzed from the year 2000 to the present. The results of this
work show that there is a diversity of techniques and strategies for
dynamic reconfiguration of software projects-. However, few
approaches consider the integration of software project activities with
the activities that take part in the organization-s common workflow.
Abstract: Since prestressed concrete members rely on the tensile
strength of the prestressing strands to resist loads, loss of even few
them could result catastrophic. Therefore, it is important to measure
present residual prestress force. Although there are some techniques
for obtaining present prestress force, some problems still remain. One
method is to install load cell in front of anchor head but this may
increase cost. Load cell is a transducer using the elastic material
property. Anchor head is also an elastic material and this might result
in monitoring monitor present prestress force. Features of fiber optic
sensor such as small size, great sensitivity, high durability can assign
sensing function to anchor head. This paper presents the concept of
smart anchor head which acts as load cell and experiment for the
applicability of it. Test results showed the smart anchor head worked
good and strong linear relationship between load and response.
Abstract: Continuation of an active call is one of the most important quality measurements in the cellular systems. Handoff process enables a cellular system to provide such a facility by transferring an active call from one cell to another. Different approaches are proposed and applied in order to achieve better handoff service. The principal parameters used to evaluate handoff techniques are: forced termination probability and call blocking probability. The mechanisms such as guard channels and queuing handoff calls decrease the forced termination probability while increasing the call blocking probability. In this paper we present an overview about the issues related to handoff initiation and decision and discuss about different types of handoff techniques available in the literature.
Abstract: Internet security attack could endanger the privacy of
World Wide Web users and the integrity of their data. The attack can
be carried out on today's most secure systems- browsers, including
Netscape Navigator and Microsoft Internet Explorer. There are too
many types, methods and mechanisms of attack where new attack
techniques and exploits are constantly being developed and
discovered. In this paper, various types of internet security attack
mechanisms are explored and it is pointed out that when different
types of attacks are combined together, network security can suffer
disastrous consequences.
Abstract: Fourty one strains of ESBL producing P.aeruginosa
which were previously isolated from burn patients in Kerman
University general hospital, Iran were subjected to PCR, RFLP and
sequencing in order to determine the type of extended spectrum β-
lactamases (ESBL), the restriction digestion pattern and possibility of
mutation among detected genes. DNA extraction was carried out by
phenol chloroform method. PCR for detection of bla genes was
performed using specific primer for each gene. Restriction Fragment
Length Polymorphism (RFLP) for ESBL genes was carried out using
EcoRI, NheI, PVUII, EcoRV, DdeI, and PstI restriction enzymes. The
PCR products were subjected to direct sequencing of both the strands
for identification of the ESBL genes.The blaCTX-M, blaVEB-1, blaPER-1,
blaGES-1, blaOXA-1, blaOXA-4 and blaOXA-10 genes were detected in the
(n=1) 2.43%, (n=41)100%, (n=28) 68.3%, (n=10) 24.4%, (n=29)
70.7%, (n=7)17.1% and (n=38) 92.7% of the ESBL producing isolates
respectively. The RFLP analysis showed that each ESBL gene has
identical pattern of digestion among the isolated strains. Sequencing
of the ESBL genes confirmed the genuinety of PCR products and
revealed no mutation in the restriction sites of the above genes. From
results of the present investigation it can be concluded that blaVEB-1
and blaCTX-M were the most and the least frequently isolated ESBL
genes among the P.aeruginosa strains isolated from burn patients. The
RFLP and sequencing analysis revealed that same clone of the bla
genes were indeed existed among the antibiotic resistant strains.
Abstract: The increasing interest on processing data created by
sensor networks has evolved into approaches to implement sensor
networks as databases. The aggregation operator, which calculates a
value from a large group of data such as computing averages or sums,
etc. is an essential function that needs to be provided when
implementing such sensor network databases. This work proposes to
add the DURING clause into TinySQL to calculate values during a
specific long period and suggests a way to implement the aggregation
service in sensor networks by applying materialized view and
incremental view maintenance techniques that is used in data
warehouses. In sensor networks, data values are passed from child
nodes to parent nodes and an aggregation value is computed at the root
node. As such root nodes need to be memory efficient and low
powered, it becomes a problem to recompute aggregate values from all
past and current data. Therefore, applying incremental view
maintenance techniques can reduce the memory consumption and
support fast computation of aggregate values.
Abstract: System-level design based on high-level abstractions
is becoming increasingly important in hardware and embedded
system design. This paper analyzes meta-design techniques oriented
at developing meta-programs and meta-models for well-understood
domains. Meta-design techniques include meta-programming and
meta-modeling. At the programming level of design process, metadesign
means developing generic components that are usable in a
wider context of application than original domain components. At the
modeling level, meta-design means developing design patterns that
describe general solutions to the common recurring design problems,
and meta-models that describe the relationship between different
types of design models and abstractions. The paper describes and
evaluates the implementation of meta-design in hardware design
domain using object-oriented and meta-programming techniques.
The presented ideas are illustrated with a case study.
Abstract: A novel path planning approach is presented to solve
optimal path in stochastic, time-varying networks under priori traffic
information. Most existing studies make use of dynamic programming
to find optimal path. However, those methods are proved to
be unable to obtain global optimal value, moreover, how to design
efficient algorithms is also another challenge.
This paper employs a decision theoretic framework for defining
optimal path: for a given source S and destination D in urban transit
network, we seek an S - D path of lowest expected travel time
where its link travel times are discrete random variables. To solve
deficiency caused by the methods of dynamic programming, such as
curse of dimensionality and violation of optimal principle, an integer
programming model is built to realize assignment of discrete travel
time variables to arcs. Simultaneously, pruning techniques are also
applied to reduce computation complexity in the algorithm. The final
experiments show the feasibility of the novel approach.
Abstract: By employing BS (Base Station) cooperation we can
increase substantially the spectral efficiency and capacity of cellular
systems. The signals received at each BS are sent to a central unit that
performs the separation of the different MT (Mobile Terminal) using
the same physical channel. However, we need accurate sampling and
quantization of those signals so as to reduce the backhaul
communication requirements.
In this paper we consider the optimization of the quantizers for BS
cooperation systems. Four different quantizer types are analyzed and
optimized to allow better SQNR (Signal-to-Quantization Noise
Ratio) and BER (Bit Error Rate) performance.