Abstract: The objective of this research is to develop an advanced driver assistance system characterized with the functions of lane departure warning (LDW), forward collision warning (FCW) and adaptive front-lighting system (AFS). The system is mainly configured a CCD/CMOS camera to acquire the images of roadway ahead in association with the analysis made by an image-processing unit concerning the lane ahead and the preceding vehicles. The input image captured by a camera is used to recognize the lane and the preceding vehicle positions by image detection and DROI (Dynamic Range of Interesting) algorithms. Therefore, the system is able to issue real-time auditory and visual outputs of warning when a driver is departing the lane or driving too close to approach the preceding vehicle unwittingly so that the danger could be prevented from occurring. During the nighttime, in addition to the foregoing warning functions, the system is able to control the bending light of headlamp to provide an immediate light illumination when making a turn at a curved lane and adjust the level automatically to reduce the lighting interference against the oncoming vehicles driving in the opposite direction by the curvature of lane and the vanishing point estimations. The experimental results show that the integrated vehicle image system is robust to most environments such as the lane detection and preceding vehicle detection average accuracy performances are both above 90 %.
Abstract: In this paper, the main principles of text-to-speech synthesis system are presented. Associated problems which arise when developing speech synthesis system are described. Used approaches and their application in the speech synthesis systems for Azerbaijani language are shown.
Abstract: Liposomal magnetofection is a simple, highly efficient
technology for cell transfection, demonstrating better outcome than a
number of other common gene delivery methods. However,
aggregate complexes distribution over the cell surface is non-uniform
due to the gradient of the permanent magnetic field. The aim of this
study was to estimate the efficiency of liposomal magnetofection for
prostate carcinoma PC3 cell line using newly designed device,
“DynaFECTOR", ensuring magnetofection in a dynamic gradient
magnetic field. Liposomal magnetofection in a dynamic gradient
magnetic field demonstrated the highest transfection efficiency for
PC3 cells – it increased for 21% in comparison with liposomal
magnetofection and for 42% in comparison with lipofection alone.
The optimal incubation time under dynamic magnetic field for PC3
cell line was 5 minutes and the optimal rotation frequency of
magnets – 5 rpm. The new approach also revealed lower cytotoxic
effect to cells than liposomal magnetofection.
Abstract: E-learning is not restricted to the use of new technologies for the online content, but also induces the adoption of new approaches to improve the quality of education. This quality depends on the ability of these approaches (technical and pedagogical) to provide an adaptive learning environment. Thus, the environment should include features that convey intentions and meeting the educational needs of learners by providing a customized learning path to acquiring a competency concerned In our proposal, we believe that an individualized learning path requires knowledge of the learner. Therefore, it must pass through a personalization of diagnosis to identify precisely the competency gaps to fill, and reduce the cognitive load To personalize the diagnosis and pertinently measure the competency gap, we suggest implementing the formative assessment in the e-learning environment and we propose the introduction of a pre-regulation process in the area of formative assessment, involving its individualization and implementation in e-learning.
Abstract: We present the results of a case study aiming to assess the reflection of the tourism community in the Web and its usability to propose new ways to communicate visually. The wealth of information contained in the Web and the clear facilities to communicate personals points of view makes of the social web a new space of exploration. In this way, social web allow the sharing of information between communities with similar interests. However, the tourism community remains unexplored as is the case of the information covered in travel stories. Along the Web, we find multiples sites allowing the users to communicate their experiences and personal points of view of a particular place of the world. This cultural heritage is found in multiple documents, usually very little supplemented with photos, so they are difficult to explore due to the lack of visual information. This paper explores the possibility of analyzing travel stories to display them visually on maps and generate new knowledge such as patterns of travel routes. This way, travel narratives published in electronic formats can be very important especially to the tourism community because of the great amount of knowledge that can be extracted. Our approach is based on the use of a Geoparsing Web Service to extract geographic coordinates from travel narratives in order to draw the geo-positions and link the documents into a map image.
Abstract: In CMOS integrated circuit design there is a trade-off between static power consumption and technology scaling. Recently, the power density has increased due to combination of higher clock speeds, greater functional integration, and smaller process geometries. As a result static power consumption is becoming more dominant. This is a challenge for the circuit designers. However, the designers do have a few methods which they can use to reduce this static power consumption. But all of these methods have some drawbacks. In order to achieve lower static power consumption, one has to sacrifice design area and circuit performance. In this paper, we propose a new method to reduce static power in the CMOS VLSI circuit using Variable Body Biasing technique without being penalized in area requirement and circuit performance.
Abstract: Purpose of this paper is two-folded. At first it explains
the major problems that are causing stagnation in brownfield
redevelopment. In addition, these problems given the context of the
present multi-actor built environment are becoming more complex to
observe. Therefore, this paper suggests also a prospective decisionmaking
approach that is the most appropriate to observe and react on
the given stagnation problems. Such an approach should be regarded
as prescriptive-interactive decision-making approach, a barely
established branch. This approach should offer models that have
prescriptive as well as an interactive component enabling them to
successfully cope with the multi-actor environment. Overall, this
paper provides up-to-date insight on the brownfield stagnation by
gradually introducing the nowadays major problems and offers a
prospective decision-making approach how these problems could be
tackled.
Abstract: Recognizing behavioral patterns of financial markets
is essential for traders. Japanese candlestick chart is a common tool to
visualize and analyze such patterns in an economic time series. Since
the world was introduced to Japanese candlestick charting, traders
saw how combining this tool with intelligent technical approaches
creates a powerful formula for the savvy investors.
This paper propose a generalization to box counting method of
Grassberger-Procaccia, which is based on computing the correlation
dimension of Japanese candlesticks instead commonly used 'close'
points. The results of this method applied on several foreign
exchange rates vs. IRR (Iranian Rial). Satisfactorily show lower
chaotic dimension of Japanese candlesticks series than regular
Grassberger-Procaccia method applied merely on close points of
these same candles. This means there is some valuable information
inside candlesticks.
Abstract: The development and application of wind power for
renewable energy has attracted growing interest in recent years. Renewable energy sources are attracting much alteration as they can
reduce both environmental damage and dependence on fossil fuels. With the growing need for sustainable energy supplies, a case is made
for decentralized, stand-alone power supplies (SAPS) as an alternative to power grids. In the era which traditional petroleum energy resource
decreasing and the green house affect significant increasing, the development and usage of regenerative resources is inevitable. Due to the contribution of the pioneers, the development of regenerative resources already has a remarkable achievement; however, in the view of economy and quantity, it is still a long road for regenerative energy
to replace traditional petroleum energy. In our prospective, in stead of
investigate larger regenerative energy equipment, it is much wiser to
think about the blind side and breakthrough of the current technique.
Abstract: This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, to effectively extract spotlight of interest, a segmentation process based on automatic multi-level threshold method is applied on the road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process based on light tracking and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with near infrared mono-camera and tested it in the urban and rural roads. Through the test, classification performances are above 97% of true positive rate evaluated on real-time environment. Our method also has good performance in the case of clear, fog and rain weather.
Abstract: Support Vector Machine (SVM) is a statistical
learning tool that was initially developed by Vapnik in 1979 and later
developed to a more complex concept of structural risk minimization
(SRM). SVM is playing an increasing role in applications to
detection problems in various engineering problems, notably in
statistical signal processing, pattern recognition, image analysis, and
communication systems. In this paper, SVM was applied to the
detection of SAR (synthetic aperture radar) images in the presence of
partially developed speckle noise. The simulation was done for single
look and multi-look speckle models to give a complete overlook and
insight to the new proposed model of the SVM-based detector. The
structure of the SVM was derived and applied to real SAR images
and its performance in terms of the mean square error (MSE) metric
was calculated. We showed that the SVM-detected SAR images have
a very low MSE and are of good quality. The quality of the
processed speckled images improved for the multi-look model.
Furthermore, the contrast of the SVM detected images was higher
than that of the original non-noisy images, indicating that the SVM
approach increased the distance between the pixel reflectivity levels
(the detection hypotheses) in the original images.
Abstract: Collected data must be organized to be utilized efficiently, and hierarchical classification of data is efficient approach to organize data. When data is classified to multiple categories or annotated with a set of labels, users request multi-labeled data by giving a set of labels. There are several interpretations of the data expressed by a set of labels. This paper discusses which data is expressed by a set of labels by introducing orders for sets of labels and shows that there are four types of orders, which are characterized by whether the labels of expressed data includes every label of the given set of labels within the range of the set. Desirable properties of the orders, data is also expressed by the higher set of labels and different sets of labels express different data, are discussed for the orders.
Abstract: This paper presents an information retrieval model on
XML documents based on tree matching. Queries and documents are
represented by extended trees. An extended tree is built starting from
the original tree, with additional weighted virtual links between each
node and its indirect descendants allowing to directly reach each
descendant. Therefore only one level separates between each node
and its indirect descendants. This allows to compare the user query
and the document with flexibility and with respect to the structural
constraints of the query. The content of each node is very important to
decide weither a document element is relevant or not, thus the content
should be taken into account in the retrieval process. We separate
between the structure-based and the content-based retrieval processes.
The content-based score of each node is commonly based on the
well-known Tf × Idf criteria. In this paper, we compare between
this criteria and another one we call Tf × Ief. The comparison
is based on some experiments into a dataset provided by INEX1 to
show the effectiveness of our approach on one hand and those of
both weighting functions on the other.
Abstract: Use of the Internet and the World-Wide-Web
(WWW) has become widespread in recent years and mobile agent
technology has proliferated at an equally rapid rate. In this scenario
load balancing becomes important for P2P systems. Beside P2P
systems can be highly heterogeneous, i.e., they may consists of peers
that range from old desktops to powerful servers connected to
internet through high-bandwidth lines. There are various loads
balancing policies came into picture. Primitive one is Message
Passing Interface (MPI). Its wide availability and portability make it
an attractive choice; however the communication requirements are
sometimes inefficient when implementing the primitives provided by
MPI. In this scenario we use the concept of mobile agent because
Mobile agent (MA) based approach have the merits of high
flexibility, efficiency, low network traffic, less communication
latency as well as highly asynchronous. In this study we present
decentralized load balancing scheme using mobile agent technology
in which when a node is overloaded, task migrates to less utilized
nodes so as to share the workload. However, the decision of which
nodes receive migrating task is made in real-time by defining certain
load balancing policies. These policies are executed on PMADE (A
Platform for Mobile Agent Distribution and Execution) in
decentralized manner using JuxtaNet and various load balancing
metrics are discussed.
Abstract: Online Communities are an example of sociallyaware,
self-organising, complex adaptive computing systems.
The multi-agent systems (MAS) paradigm coordinated by
self-organisation mechanisms has been used as an effective
way for the simulation and modeling of such systems. In this
paper, we propose a model for simulating an online health
community using a situated multi-agent system approach,
governed by the co-evolution of the social and spatial
organisations of the agents.
Abstract: This paper examines the modeling and analysis of a
cruise control system using a Petri net based approach, task graphs,
invariant analysis and behavioral properties. It shows how the
structures used can be verified and optimized.
Abstract: In the last few years, the Semantic Web gained scientific acceptance as a means of relationships identification in knowledge base, widely known by semantic association. Query about complex relationships between entities is a strong requirement for many applications in analytical domains. In bioinformatics for example, it is critical to extract exchanges between proteins. Currently, the widely known result of such queries is to provide paths between connected entities from data graph. However, they do not always give good results while facing the user need by the best association or a set of limited best association, because they only consider all existing paths but ignore the path evaluation. In this paper, we present an approach for supporting association discovery queries. Our proposal includes (i) a query language PmSPRQL which provides a multiparadigm query expressions for association extraction and (ii) some quantification measures making easy the process of association ranking. The originality of our proposal is demonstrated by a performance evaluation of our approach on real world datasets.
Abstract: Tandem mass spectrometry (MS/MS) is the engine
driving high-throughput protein identification. Protein mixtures possibly
representing thousands of proteins from multiple species are
treated with proteolytic enzymes, cutting the proteins into smaller
peptides that are then analyzed generating MS/MS spectra. The
task of determining the identity of the peptide from its spectrum
is currently the weak point in the process. Current approaches to de
novo sequencing are able to compute candidate peptides efficiently.
The problem lies in the limitations of current scoring functions. In this
paper we introduce the concept of proteome signature. By examining
proteins and compiling proteome signatures (amino acid usage) it is
possible to characterize likely combinations of amino acids and better
distinguish between candidate peptides. Our results strongly support
the hypothesis that a scoring function that considers amino acid usage
patterns is better able to distinguish between candidate peptides. This
in turn leads to higher accuracy in peptide prediction.
Abstract: This research presents a system for post processing of
data that takes mined flat rules as input and discovers crisp as well as
fuzzy hierarchical structures using Learning Classifier System
approach. Learning Classifier System (LCS) is basically a machine
learning technique that combines evolutionary computing,
reinforcement learning, supervised or unsupervised learning and
heuristics to produce adaptive systems. A LCS learns by interacting
with an environment from which it receives feedback in the form of
numerical reward. Learning is achieved by trying to maximize the
amount of reward received. Crisp description for a concept usually
cannot represent human knowledge completely and practically. In the
proposed Learning Classifier System initial population is constructed
as a random collection of HPR–trees (related production rules) and
crisp / fuzzy hierarchies are evolved. A fuzzy subsumption relation is
suggested for the proposed system and based on Subsumption Matrix
(SM), a suitable fitness function is proposed. Suitable genetic
operators are proposed for the chosen chromosome representation
method. For implementing reinforcement a suitable reward and
punishment scheme is also proposed. Experimental results are
presented to demonstrate the performance of the proposed system.
Abstract: Bootstrapping has gained popularity in different tests of hypotheses as an alternative in using asymptotic distribution if one is not sure of the distribution of the test statistic under a null hypothesis. This method, in general, has two variants – the parametric and the nonparametric approaches. However, issues on reliability of this method always arise in many applications. This paper addresses the issue on reliability by establishing a reliability measure in terms of quantiles with respect to asymptotic distribution, when this is approximately correct. The test of hypotheses used is Ftest. The simulated results show that using nonparametric bootstrapping in F-test gives better reliability than parametric bootstrapping with relatively higher degrees of freedom.