Abstract: The IEEE 802.11e which is an enhanced version of the 802.11 WLAN standards incorporates the Quality of Service (QoS) which makes it a better choice for multimedia and real time applications. In this paper we study various aspects concerned with 802.11e standard. Further, the analysis results for this standard are compared with the legacy 802.11 standard. Simulation results show that IEEE 802.11e out performs legacy IEEE 802.11 in terms of quality of service due to its flow differentiated channel allocation and better queue management architecture. We also propose a method to improve the unfair allocation of bandwidth for downlink and uplink channels by varying the medium access priority level.
Abstract: Fisheries management all around the world is
hampered by the lack, or poor quality, of critical data on fish
resources and fishing operations. The main reasons for the chronic
inability to collect good quality data during fishing operations is the
culture of secrecy common among fishers and the lack of modern
data gathering technology onboard most fishing vessels. In response,
OLRAC-SPS, a South African company, developed fisheries datalogging
software (eLog in short) and named it Olrac. The Olrac eLog
solution is capable of collecting, analysing, plotting, mapping,
reporting, tracing and transmitting all data related to fishing
operations. Olrac can be used by skippers, fleet/company managers,
offshore mariculture farmers, scientists, observers, compliance
inspectors and fisheries management authorities. The authors believe
that using eLog onboard fishing vessels has the potential to
revolutionise the entire process of data collection and reporting
during fishing operations and, if properly deployed and utilised,
could transform the entire commercial fleet to a provider of good
quality data and forever change the way fish resources are managed.
In addition it will make it possible to trace catches back to the actual
individual fishing operation, to improve fishing efficiency and to
dramatically improve control of fishing operations and enforcement
of fishing regulations.
Abstract: The main purpose of the dam is to control the surface
streams and rivers across the country. Dam construction and
formation of river and big water reservoirs and resources happen in
the glen is a big incident which would change its surrounding area
considerably. In fact, constructing a dam the glen width is close and
fishes don't migrate from upstream to downstream and ultimately it
would led to their death. To resolve this, it seems necessity to create a
passage for fishes during the construction of dam. It is provided
establishing a set of stepped pools overlooking each other as a fish
way or fish ladder a proper pathway for moving fishes. In this article
we first examine the surrounding environment and then Ghazal Ozon
River and preserving the aquatics.
Abstract: We have defined two suites of metrics, which cover
static and dynamic aspects of component assembly. The static
metrics measure complexity and criticality of component assembly,
wherein complexity is measured using Component Packing Density
and Component Interaction Density metrics. Further, four criticality
conditions namely, Link, Bridge, Inheritance and Size criticalities
have been identified and quantified. The complexity and criticality
metrics are combined to form a Triangular Metric, which can be used
to classify the type and nature of applications. Dynamic metrics are
collected during the runtime of a complete application. Dynamic
metrics are useful to identify super-component and to evaluate the
degree of utilisation of various components. In this paper both static
and dynamic metrics are evaluated using Weyuker-s set of properties.
The result shows that the metrics provide a valid means to measure
issues in component assembly. We relate our metrics suite with
McCall-s Quality Model and illustrate their impact on product
quality and to the management of component-based product
development.
Abstract: Faced with social and health system capacity
constraints and rising and changing demand for welfare services,
governments and welfare providers are increasingly relying on
innovation to help support and enhance services. However, the
evidence reported by several studies indicates that the realization of
that potential is not an easy task. Innovations can be deemed
inherently complex to implement and operate, because many of them
involve a combination of technological and organizational renewal
within an environment featuring a diversity of stakeholders. Many
public welfare service innovations are markedly systemic in their
nature, which means that they emerge from, and must address, the
complex interplay between political, administrative, technological,
institutional and legal issues. This paper suggests that stakeholders
dealing with systemic innovation in welfare services must deal with
ambiguous and incomplete information in circumstances of
uncertainty. Employing a literature review methodology and case
study, this paper identifies, categorizes and discusses different
aspects of the uncertainty of systemic innovation in public welfare
services, and argues that uncertainty can be classified into eight
categories: technological uncertainty, market uncertainty,
regulatory/institutional uncertainty, social/political uncertainty,
acceptance/legitimacy uncertainty, managerial uncertainty, timing
uncertainty and consequence uncertainty.
Abstract: Computer languages are usually lumped together
into broad -paradigms-, leaving us in want of a finer classification
of kinds of language. Theories distinguishing between -genuine
differences- in language has been called for, and we propose that
such differences can be observed through a notion of expressive mode.
We outline this concept, propose how it could be operationalized and
indicate a possible context for the development of a corresponding
theory. Finally we consider a possible application in connection
with evaluation of language revision. We illustrate this with a case,
investigating possible revisions of the relational algebra in order to
overcome weaknesses of the division operator in connection with
universal queries.
Abstract: Irradiated material is a typical example of a complex
system with nonlinear coupling between its elements. During
irradiation the radiation damage is developed and this development
has bifurcations and qualitatively different kinds of behavior.
The accumulation of primary defects in irradiated crystals is
considered in frame work of nonlinear evolution of complex system.
The thermo-concentration nonlinear feedback is carried out as a
mechanism of self-oscillation development.
It is shown that there are two ways of the defect density evolution
under stationary irradiation. The first is the accumulation of defects;
defect density monotonically grows and tends to its stationary state
for some system parameters. Another way that takes place for
opportune parameters is the development of self-oscillations of the
defect density.
The stationary state, its stability and type are found. The
bifurcation values of parameters (environment temperature, defect
generation rate, etc.) are obtained. The frequency of the selfoscillation
and the conditions of their development is found and
rated. It is shown that defect density, heat fluxes and temperature
during self-oscillations can reach much higher values than the
expected steady-state values. It can lead to a change of typical
operation and an accident, e.g. for nuclear equipment.
Abstract: LABVIEW is a graphical programming language that has its roots in automation control and data acquisition. In this paper we have utilized this platform to provide a powerful toolset for process identification and control of nonlinear systems based on artificial neural networks (ANN). This tool has been applied to the monitoring and control of a lab-scale distillation column DELTALAB DC-SP. The proposed control scheme offers high speed of response for changes in set points and null stationary error for dual composition control and shows robustness in presence of externally imposed disturbance.
Abstract: In this paper we present a hybrid search algorithm for
solving constraint satisfaction and optimization problems. This
algorithm combines ideas of two basic approaches: complete and
incomplete algorithms which also known as systematic search and
local search algorithms. Different characteristics of systematic search
and local search methods are complementary. Therefore we have
tried to get the advantages of both approaches in the presented
algorithm. The major advantage of presented algorithm is finding
partial sound solution for complicated problems which their complete
solution could not be found in a reasonable time. This algorithm
results are compared with other algorithms using the well known
n-queens problem.
Abstract: A new digital watermarking technique for images that
are sensitive to blocking artifacts is presented. Experimental results
show that the proposed MDCT based approach produces highly
imperceptible watermarked images and is robust to attacks such as
compression, noise, filtering and geometric transformations. The
proposed MDCT watermarking technique is applied to fingerprints
for ensuring security. The face image and demographic text data of
an individual are used as multiple watermarks. An AFIS system was
used to quantitatively evaluate the matching performance of the
MDCT-based watermarked fingerprint. The high fingerprint
matching scores show that the MDCT approach is resilient to
blocking artifacts. The quality of the extracted face and extracted text
images was computed using two human visual system metrics and
the results show that the image quality was high.
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: Ontology is widely being used as a tool for organizing
information, creating the relation between the subjects within the
defined knowledge domain area. Various fields such as Civil,
Biology, and Management have successful integrated ontology in
decision support systems for managing domain knowledge and to
assist their decision makers. Gross pollutant traps (GPT) are devices
used in trapping and preventing large items or hazardous particles in
polluting and entering our waterways. However choosing and
determining GPT is a challenge in Malaysia as there are inadequate
GPT data repositories being captured and shared. Hence ontology is
needed to capture, organize and represent this knowledge into
meaningful information which can be contributed to the efficiency of
GPT selection in Malaysia urbanization. A GPT Ontology framework
is therefore built as the first step to capture GPT knowledge which
will then be integrated into the decision support system. This paper
will provide several examples of the GPT ontology, and explain how
it is constructed by using the Protégé tool.
Abstract: The third phase of web means semantic web requires many web pages which are annotated with metadata. Thus, a crucial question is where to acquire these metadata. In this paper we propose our approach, a semi-automatic method to annotate the texts of documents and web pages and employs with a quite comprehensive knowledge base to categorize instances with regard to ontology. The approach is evaluated against the manual annotations and one of the most popular annotation tools which works the same as our tool. The approach is implemented in .net framework and uses the WordNet for knowledge base, an annotation tool for the Semantic Web.
Abstract: The complexity of today-s software systems makes
collaborative development necessary to accomplish tasks.
Frameworks are necessary to allow developers perform their tasks
independently yet collaboratively. Similarity detection is one of the
major issues to consider when developing such frameworks. It allows
developers to mine existing repositories when developing their own
views of a software artifact, and it is necessary for identifying the
correspondences between the views to allow merging them and
checking their consistency. Due to the importance of the
requirements specification stage in software development, this paper
proposes a framework for collaborative development of Object-
Oriented formal specifications along with a similarity detection
approach to support the creation, merging and consistency checking
of specifications. The paper also explores the impact of using
additional concepts on improving the matching results. Finally, the
proposed approach is empirically evaluated.
Abstract: We present a simplified equalization technique for a
π/4 differential quadrature phase shift keying ( π/4 -DQPSK) modulated
signal in a multipath fading environment. The proposed equalizer is
realized as a fractionally spaced adaptive decision feedback equalizer
(FS-ADFE), employing exponential step-size least mean square
(LMS) algorithm as the adaptation technique. The main advantage of
the scheme stems from the usage of exponential step-size LMS algorithm
in the equalizer, which achieves similar convergence behavior
as that of a recursive least squares (RLS) algorithm with significantly
reduced computational complexity. To investigate the finite-precision
performance of the proposed equalizer along with the π/4 -DQPSK
modem, the entire system is evaluated on a 16-bit fixed point digital
signal processor (DSP) environment. The proposed scheme is found
to be attractive even for those cases where equalization is to be
performed within a restricted number of training samples.
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: Tofurther advance research on immune-related genes
from T. molitor, we constructed acDNA library and analyzed
expressed sequence taq (EST) sequences from 1,056 clones. After
removing vector sequence and quality checkingthrough thePhred
program (trim_alt 0.05 (P-score>20), 1039 sequences were generated.
The average length of insert was 792 bp. In addition, we identified 162
clusters, 167 contigs and 391 contigs after clustering and assembling
process using a TGICL package. EST sequences were searchedagainst
NCBI nr database by local BLAST (blastx, E
Abstract: The use of machine vision to inspect the outcome of
surgical tasks is investigated, with the aim of incorporating this
approach in robotic surgery systems. Machine vision is a non-contact
form of inspection i.e. no part of the vision system is in direct contact
with the patient, and is therefore well suited for surgery where
sterility is an important consideration,. As a proof-of-concept, three
primary surgical tasks for a common neurosurgical procedure were
inspected using machine vision. Experiments were performed on
cadaveric pig heads to simulate the two possible outcomes i.e.
satisfactory or unsatisfactory, for tasks involved in making a burr
hole, namely incision, retraction, and drilling. We identify low level
image features to distinguish the two outcomes, as well as report on
results that validate our proposed approach. The potential of using
machine vision in a surgical environment, and the challenges that
must be addressed, are identified and discussed.
Abstract: This paper outlines the development of a learning retrieval agent. Task of this agent is to extract knowledge of the Active Semantic Network in respect to user-requests. Based on a reinforcement learning approach, the agent learns to interpret the user-s intention. Especially, the learning algorithm focuses on the retrieval of complex long distant relations. Increasing its learnt knowledge with every request-result-evaluation sequence, the agent enhances his capability in finding the intended information.