Abstract: Smart Dust particles, are small smart materials used for generating weather maps. We investigate question of the optimal number of Smart Dust particles necessary for generating precise, computationally feasible and cost effective 3–D weather maps. We also give an optimal matching algorithm for the generalized scenario, when there are N Smart Dust particles and M ground receivers.
Abstract: Models are placed by modeling paradigm at the center of development process. These models are represented by languages, like UML the language standardized by the OMG which became necessary for development. Moreover the ontology engineering paradigm places ontologies at the center of development process; in this paradigm we find OWL the principal language for knowledge representation. Building ontologies from scratch is generally a difficult task. The bridging between UML and OWL appeared on several regards such as the classes and associations. In this paper, we have to profit from convergence between UML and OWL to propose an approach based on Meta-Modelling and Graph Grammars and registered in the MDA architecture for the automatic generation of OWL ontologies from UML class diagrams. The transformation is based on transformation rules; the level of abstraction in these rules is close to the application in order to have usable ontologies. We illustrate this approach by an example.
Abstract: This paper demonstrates the bus location system for
the route bus through the experiment in the real environment. A
bus location system is a system that provides information such as
the bus delay and positions. This system uses actual services and
positions data of buses, and those information should match data
on the database. The system has two possible problems. One, the
system could cost high in preparing devices to get bus positions.
Two, it could be difficult to match services data of buses. To avoid
these problems, we have developed this system at low cost and short
time by using the smart phone with GPS and the bus route system.
This system realizes the path planning considering bus delay and
displaying position of buses on the map. The bus location system
was demonstrated on route buses with smart phones for two months.
Abstract: There are three approaches to complete Bayesian
Network (BN) model construction: total expert-centred, total datacentred,
and semi data-centred. These three approaches constitute the
basis of the empirical investigation undertaken and reported in this
paper. The objective is to determine, amongst these three
approaches, which is the optimal approach for the construction of a
BN-based model for the performance assessment of students-
laboratory work in a virtual electronic laboratory environment. BN
models were constructed using all three approaches, with respect to
the focus domain, and compared using a set of optimality criteria. In
addition, the impact of the size and source of the training, on the
performance of total data-centred and semi data-centred models was
investigated. The results of the investigation provide additional
insight for BN model constructors and contribute to literature
providing supportive evidence for the conceptual feasibility and
efficiency of structure and parameter learning from data. In addition,
the results highlight other interesting themes.
Abstract: In order to make surfing the internet faster, and to save redundant processing load with each request for the same web page, many caching techniques have been developed to reduce latency of retrieving data on World Wide Web. In this paper we will give a quick overview of existing web caching techniques used for dynamic web pages then we will introduce a design and implementation model that take advantage of “URL Rewriting" feature in some popular web servers, e.g. Apache, to provide an effective approach of caching dynamic web pages.
Abstract: The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the pre-processing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this pre-processing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.
Abstract: The ability of UML to handle the modeling process of complex industrial software applications has increased its popularity to the extent of becoming the de-facto language in serving the design purpose. Although, its rich graphical notation naturally oriented towards the object-oriented concept, facilitates the understandability, it hardly successes to report all domainspecific aspects in a satisfactory way. OCL, as the standard language for expressing additional constraints on UML models, has great potential to help improve expressiveness. Unfortunately, it suffers from a weak formalism due to its poor semantic resulting in many obstacles towards the build of tools support and thus its application in the industry field. For this reason, many researches were established to formalize OCL expressions using a more rigorous approach. Our contribution join this work in a complementary way since it focuses specifically on OCL predefined properties which constitute an important part in the construction of OCL expressions. Using formal methods, we mainly succeed in expressing rigorously OCL predefined functions.
Abstract: With the advent of emerging personal computing paradigms such as ubiquitous and mobile computing, Web contents are becoming accessible from a wide range of mobile devices. Since these devices do not have the same rendering capabilities, Web contents need to be adapted for transparent access from a variety of client agents. Such content adaptation is exploited for either an individual element or a set of consecutive elements in a Web document and results in better rendering and faster delivery to the client device. Nevertheless, Web content adaptation sets new challenges for semantic markup. This paper presents an advanced components platform, called SMC, enabling the development of mobility applications and services according to a channel model based on the principles of Services Oriented Architecture (SOA). It then goes on to describe the potential for integration with the Semantic Web through a novel framework of external semantic annotation that prescribes a scheme for representing semantic markup files and a way of associating Web documents with these external annotations. The role of semantic annotation in this framework is to describe the contents of individual documents themselves, assuring the preservation of the semantics during the process of adapting content rendering. Semantic Web content adaptation is a way of adding value to Web contents and facilitates repurposing of Web contents (enhanced browsing, Web Services location and access, etc).
Abstract: Retrieval image by shape similarity, given a template
shape is particularly challenging, owning to the difficulty to derive a
similarity measurement that closely conforms to the common
perception of similarity by humans. In this paper, a new method for the
representation and comparison of shapes is present which is based on
the shape matrix and snake model. It is scaling, rotation, translation
invariant. And it can retrieve the shape images with some missing or
occluded parts. In the method, the deformation spent by the template
to match the shape images and the matching degree is used to evaluate
the similarity between them.
Abstract: The increasing importance of data stream arising in a
wide range of advanced applications has led to the extensive study of
mining frequent patterns. Mining data streams poses many new
challenges amongst which are the one-scan nature, the unbounded
memory requirement and the high arrival rate of data streams. In this
paper, we propose a new approach for mining itemsets on data
stream. Our approach SFIDS has been developed based on FIDS
algorithm. The main attempts were to keep some advantages of the
previous approach and resolve some of its drawbacks, and
consequently to improve run time and memory consumption. Our
approach has the following advantages: using a data structure similar
to lattice for keeping frequent itemsets, separating regions from each
other with deleting common nodes that results in a decrease in search
space, memory consumption and run time; and Finally, considering
CPU constraint, with increasing arrival rate of data that result in
overloading system, SFIDS automatically detect this situation and
discard some of unprocessing data. We guarantee that error of results
is bounded to user pre-specified threshold, based on a probability
technique. Final results show that SFIDS algorithm could attain
about 50% run time improvement than FIDS approach.
Abstract: This paper presents a system overview of Mobile to Server Face Recognition, which is a face recognition application developed specifically for mobile phones. Images taken from mobile phone cameras lack of quality due to the low resolution of the cameras. Thus, a prototype is developed to experiment the chosen method. However, this paper shows a result of system backbone without the face recognition functionality. The result demonstrated in this paper indicates that the interaction between mobile phones and server is successfully working. The result shown before the database is completely ready. The system testing is currently going on using real images and a mock-up database to test the functionality of the face recognition algorithm used in this system. An overview of the whole system including screenshots and system flow-chart are presented in this paper. This paper also presents the inspiration or motivation and the justification in developing this system.
Abstract: In this paper, different approaches to solve the
forward kinematics of a three DOF actuator redundant hydraulic
parallel manipulator are presented. On the contrary to series
manipulators, the forward kinematic map of parallel manipulators
involves highly coupled nonlinear equations, which are almost
impossible to solve analytically. The proposed methods are using
neural networks identification with different structures to solve the
problem. The accuracy of the results of each method is analyzed in
detail and the advantages and the disadvantages of them in
computing the forward kinematic map of the given mechanism is
discussed in detail. It is concluded that ANFIS presents the best
performance compared to MLP, RBF and PNN networks in this
particular application.
Abstract: In recent years, a number of works proposing the
combination of multiple classifiers to produce a single
classification have been reported in remote sensing literature. The
resulting classifier, referred to as an ensemble classifier, is
generally found to be more accurate than any of the individual
classifiers making up the ensemble. As accuracy is the primary
concern, much of the research in the field of land cover
classification is focused on improving classification accuracy. This
study compares the performance of four ensemble approaches
(boosting, bagging, DECORATE and random subspace) with a
univariate decision tree as base classifier. Two training datasets,
one without ant noise and other with 20 percent noise was used to
judge the performance of different ensemble approaches. Results
with noise free data set suggest an improvement of about 4% in
classification accuracy with all ensemble approaches in
comparison to the results provided by univariate decision tree
classifier. Highest classification accuracy of 87.43% was achieved
by boosted decision tree. A comparison of results with noisy data
set suggests that bagging, DECORATE and random subspace
approaches works well with this data whereas the performance of
boosted decision tree degrades and a classification accuracy of
79.7% is achieved which is even lower than that is achieved (i.e.
80.02%) by using unboosted decision tree classifier.
Abstract: With the rapid development in the field of life
sciences and the flooding of genomic information, the need for faster
and scalable searching methods has become urgent. One of the
approaches that were investigated is indexing. The indexing methods
have been categorized into three categories which are the lengthbased
index algorithms, transformation-based algorithms and mixed
techniques-based algorithms. In this research, we focused on the
transformation based methods. We embedded the N-gram method
into the transformation-based method to build an inverted index
table. We then applied the parallel methods to speed up the index
building time and to reduce the overall retrieval time when querying
the genomic database. Our experiments show that the use of N-Gram
transformation algorithm is an economical solution; it saves time and
space too. The result shows that the size of the index is smaller than
the size of the dataset when the size of N-Gram is 5 and 6. The
parallel N-Gram transformation algorithm-s results indicate that the
uses of parallel programming with large dataset are promising which
can be improved further.
Abstract: The data exchanged on the Web are of different nature
from those treated by the classical database management systems;
these data are called semi-structured data since they do not have a
regular and static structure like data found in a relational database;
their schema is dynamic and may contain missing data or types.
Therefore, the needs for developing further techniques and
algorithms to exploit and integrate such data, and extract relevant
information for the user have been raised. In this paper we present
the system OSIX (Osiris based System for Integration of XML
Sources). This system has a Data Warehouse model designed for the
integration of semi-structured data and more precisely for the
integration of XML documents. The architecture of OSIX relies on
the Osiris system, a DL-based model designed for the representation
and management of databases and knowledge bases. Osiris is a viewbased
data model whose indexing system supports semantic query
optimization. We show that the problem of query processing on a
XML source is optimized by the indexing approach proposed by
Osiris.
Abstract: EGOTHOR is a search engine that indexes the Web
and allows us to search the Web documents. Its hit list contains URL
and title of the hits, and also some snippet which tries to shortly
show a match. The snippet can be almost always assembled by an
algorithm that has a full knowledge of the original document (mostly
HTML page). It implies that the search engine is required to store
the full text of the documents as a part of the index.
Such a requirement leads us to pick up an appropriate compression
algorithm which would reduce the space demand. One of the solutions
could be to use common compression methods, for instance gzip or
bzip2, but it might be preferable if we develop a new method which
would take advantage of the document structure, or rather, the textual
character of the documents.
There already exist a special compression text algorithms and
methods for a compression of XML documents. The aim of this
paper is an integration of the two approaches to achieve an optimal
level of the compression ratio
Abstract: Tool Tracker is a client-server based application. It is essentially a catalogue of various network monitoring and management tools that are available online. There is a database maintained on the server side that contains the information about various tools. Several clients can access this information simultaneously and utilize this information. The various categories of tools considered are packet sniffers, port mappers, port scanners, encryption tools, and vulnerability scanners etc for the development of this application. This application provides a front end through which the user can invoke any tool from a central repository for the purpose of packet sniffing, port scanning, network analysis etc. Apart from the tool, its description and the help files associated with it would also be stored in the central repository. This facility will enable the user to view the documentation pertaining to the tool without having to download and install the tool. The application would update the central repository with the latest versions of the tools. The application would inform the user about the availability of a newer version of the tool currently being used and give the choice of installing the newer version to the user. Thus ToolTracker provides any network administrator that much needed abstraction and ease-ofuse with respect to the tools that he can use to efficiently monitor a network.
Abstract: Crime is a major societal problem for most of the
world's nations. Consequently, the police need to develop new
methods to improve their efficiency in dealing with these ever increasing crime rates. Two of the common difficulties that the police
face in crime control are crime investigation and the provision of crime information to the general public to help them protect themselves. Crime control in police operations involves the use of
spatial data, crime data and the related crime data from different organizations (depending on the nature of the analysis to be made).
These types of data are collected from several heterogeneous sources
in different formats and from different platforms, resulting in a lack of standardization. Moreover, there is no standard framework for
crime data collection, integration and dissemination through mobile
devices. An investigation into the current situation in crime control was carried out to identify the needs to resolve these issues. This
paper proposes and investigates the use of service oriented
architecture (SOA) and the mobile spatial information service in crime control. SOA plays an important role in crime control as an
appropriate way to support data exchange and model sharing from
heterogeneous sources. Crime control also needs to facilitate mobile
spatial information services in order to exchange, receive, share and release information based on location to mobile users anytime and
anywhere.
Abstract: In this paper, we propose a Perceptually Optimized Embedded ZeroTree Image Coder (POEZIC) that introduces a perceptual weighting to wavelet transform coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to the coding quality obtained using the SPIHT algorithm only. The paper also, introduces a new objective quality metric based on a Psychovisual model that integrates the properties of the HVS that plays an important role in our POEZIC quality assessment. Our POEZIC coder is based on a vision model that incorporates various masking effects of human visual system HVS perception. Thus, our coder weights the wavelet coefficients based on that model and attempts to increase the perceptual quality for a given bit rate and observation distance. The perceptual weights for all wavelet subbands are computed based on 1) luminance masking and Contrast masking, 2) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting, 3) the Wavelet Error Sensitivity WES used to reduce the perceptual quantization errors. The new perceptually optimized codec has the same complexity as the original SPIHT techniques. However, the experiments results show that our coder demonstrates very good performance in terms of quality measurement.
Abstract: Image Searching was always a problem specially when these images are not properly managed or these are distributed over different locations. Currently different techniques are used for image search. On one end, more features of the image are captured and stored to get better results. Storing and management of such features is itself a time consuming job. While on the other extreme if fewer features are stored the accuracy rate is not satisfactory. Same image stored with different visual properties can further reduce the rate of accuracy. In this paper we present a new concept of using polynomials of sorted histogram of the image. This approach need less overhead and can cope with the difference in visual features of image.