Abstract: Program slicing is the task of finding all statements in
a program that directly or indirectly influence the value of a variable
occurrence. The set of statements that can affect the value of a
variable at some point in a program is called a program backward
slice. In several software engineering applications, such as program
debugging and measuring program cohesion and parallelism, several
slices are computed at different program points. The existing
algorithms for computing program slices are introduced to compute a
slice at a program point. In these algorithms, the program, or the
model that represents the program, is traversed completely or
partially once. To compute more than one slice, the same algorithm
is applied for every point of interest in the program. Thus, the same
program, or program representation, is traversed several times.
In this paper, an algorithm is introduced to compute all forward
static slices of a computer program by traversing the program
representation graph once. Therefore, the introduced algorithm is
useful for software engineering applications that require computing
program slices at different points of a program. The program
representation graph used in this paper is called Program Dependence
Graph (PDG).
Abstract: In recent years, new product development became more and more competitive and globalized, and the designing phase is critical for the product success. The concept of modularity can provide the necessary foundation for organizations to design products that can respond rapidly to market needs. The paper describes data structures and algorithms of intelligent Web-based system for modular design taking into account modules compatibility relationship and given design requirements. The system intelligence is realized by developed algorithms for choice of modules reflecting all system restrictions and requirements. The proposed data structure and algorithms are illustrated by case study of personal computer configuration. The applicability of the proposed approach is tested through a prototype of Web-based system.
Abstract: This paper deals with modeling and parameter
identification of nonlinear systems described by Hammerstein model
having Piecewise nonlinear characteristics such as Dead-zone
nonlinearity characteristic. The simultaneous use of both an easy
decomposition technique and the triangular basis functions leads to a
particular form of Hammerstein model. The approximation by using
Triangular basis functions for the description of the static nonlinear
block conducts to a linear regressor model, so that least squares
techniques can be used for the parameter estimation. Singular Values
Decomposition (SVD) technique has been applied to separate the
coupled parameters. The proposed approach has been efficiently
tested on academic examples of simulation.
Abstract: Stipples are desired for pattern fillings and
transparency effects. In contrast, some graphics standards, including
OpenGL ES 1.1 and 2.0, omitted this feature. We represent details of
providing line stipples and polygon stipples, through combining
texture mapping and alpha blending functions. We start from the
OpenGL-specified stipple-related API functions. The details of
mathematical transformations are explained to get the correct texture
coordinates. Then, the overall algorithm is represented, and its
implementation results are followed. We accomplished both of line
and polygon stipples, and verified its result with conformance test
routines.
Abstract: In any trust model, the two information sources that a peer relies on to predict trustworthiness of another peer are direct experience as well as reputation. These two vital components evolve over time. Trust evolution is an important issue, where the objective is to observe a sequence of past values of a trust parameter and determine the future estimates. Unfortunately, trust evolution algorithms received little attention and the proposed algorithms in the literature do not comply with the conditions and the nature of trust. This paper contributes to this important problem in the following ways: (a) presents an algorithm that manages and models trust evolution in a P2P environment, (b) devises new mechanisms for effectively maintaining trust values based on the conditions that influence trust evolution , and (c) introduces a new methodology for incorporating trust-nurture incentives into the trust evolution algorithm. Simulation experiments are carried out to evaluate our trust evolution algorithm.
Abstract: Deoxyribonucleic Acid or DNA computing has
emerged as an interdisciplinary field that draws together chemistry,
molecular biology, computer science and mathematics. Thus, in this
paper, the possibility of DNA-based computing to solve an absolute
1-center problem by molecular manipulations is presented. This is
truly the first attempt to solve such a problem by DNA-based
computing approach. Since, part of the procedures involve with
shortest path computation, research works on DNA computing for
shortest path Traveling Salesman Problem, in short, TSP are reviewed.
These approaches are studied and only the appropriate one is adapted
in designing the computation procedures. This DNA-based
computation is designed in such a way that every path is encoded by
oligonucleotides and the path-s length is directly proportional to the
length of oligonucleotides. Using these properties, gel electrophoresis
is performed in order to separate the respective DNA molecules
according to their length. One expectation arise from this paper is that
it is possible to verify the instance absolute 1-center problem using
DNA computing by laboratory experiments.
Abstract: Sonogram images of normal and lymphocyte thyroid tissues have considerable overlap which makes it difficult to interpret and distinguish. Classification from sonogram images of thyroid gland is tackled in semiautomatic way. While making manual diagnosis from images, some relevant information need not to be recognized by human visual system. Quantitative image analysis could be helpful to manual diagnostic process so far done by physician. Two classes are considered: normal tissue and chronic lymphocyte thyroid (Hashimoto's Thyroid). Data structure is analyzed using K-nearest-neighbors classification. This paper is mentioned that unlike the wavelet sub bands' energy, histograms and Haralick features are not appropriate to distinguish between normal tissue and Hashimoto's thyroid.
Abstract: A robust control approach is proposed for a high speed manipulator using a hybrid computed torque control approach in the state space. The high-speed manipulator is driven by permanent magnet dc motors to track a trajectory in the joint space in the presence of disturbances. Tracking problem is analyzed in the state space where the completed models are considered for actuators. The proposed control approach can guarantee the stability and a satisfactory tracking performance. A two-link elbow manipulator driven by electrical actuators is simulated and results are shown to satisfy conditions under technical specifications.
Abstract: In this paper, we propose use of convolutional codes
for file dispersal. The proposed method is comparable in complexity
to the information Dispersal Algorithm proposed by M.Rabin and for
particular choices of (non-binary) convolutional codes, is almost as
efficient as that algorithm in terms of controlling expansion in the
total storage. Further, our proposed dispersal method allows string
search.
Abstract: The drastic increase in the usage of SMS technology
has led service providers to seek for a solution that enable users of
mobile devices to access services through SMSs. This has resulted in
the proposal of solutions towards SMS-based service invocation in
service oriented environments. However, the dynamic nature of
service-oriented environments coupled with sudden load peaks
generated by service request, poses performance challenges to
infrastructures for supporting SMS-based service invocation. To
address this problem we adopt load balancing techniques. A load
balancing model with adaptive load balancing and load monitoring
mechanisms as its key constructs is proposed. The load balancing
model then led to realization of Least Loaded Load Balancing
Framework (LLLBF). Evaluation of LLLBF benchmarked with round
robin (RR) scheme on the queuing approach showed LLLBF
outperformed RR in terms of response time and throughput.
However, LLLBF achieved better result in the cost of high
processing power.
Abstract: A serious problem on the WWW is finding reliable
information. Not everything found on the Web is true and the
Semantic Web does not change that in any way. The problem will be
even more crucial for the Semantic Web, where agents will be
integrating and using information from multiple sources. Thus, if an
incorrect premise is used due to a single faulty source, then any
conclusions drawn may be in error. Thus, statements published on
the Semantic Web have to be seen as claims rather than as facts, and
there should be a way to decide which among many possibly
inconsistent sources is most reliable. In this work, we propose a trust
model for the Semantic Web. The proposed model is inspired by the
use trust in human society. Trust is a type of social knowledge and
encodes evaluations about which agents can be taken as reliable
sources of information or services. Our proposed model allows
agents to decide which among different sources of information to
trust and thus act rationally on the semantic web.
Abstract: Abovepresented work deals with the new scope of application of information and communication technologies for the improvement of the election process in the biased environment. We are introducing a new concept of construction of the information-communication system for the election participant. It consists of four main components: Software, Physical Infrastructure, Structured Information and the Trained Stuff. The Structured Information is the bases of the whole system and is the collection of all possible events (irregularities among them) at the polling stations, which are structured in special templates, forms and integrated in mobile devices.The software represents a package of analytic modules, which operates with the dynamic database. The application of modern communication technologies facilities the immediate exchange of information and of relevant documents between the polling stations and the Server of the participant. No less important is the training of the staff for the proper functioning of the system. The e-training system with various modules should be applied in this respect. The presented methodology is primarily focused on the election processes in the countries of emerging democracies.It can be regarded as the tool for the monitoring of elections process by the political organization(s) and as one of the instruments to foster the spread of democracy in these countries.
Abstract: The integration between technology of remote
sensing, information from the data of digital image, and modeling
technology for the simulation of water quality will provide easiness
during the observation on the quality of water changes on the river
surface. For example, Ciliwung River which is contaminated with
non-point source pollutant from household wastes, particularly on its
downstream. This fact informed that the quality of water in this river
is getting worse. The land use for settlements and housing ranges
between 62.84% - 81.26% on the downstream of Ciliwung River,
give a significant picture in seeing factors that affected the water
quality of Ciliwung River.
Abstract: This paper proposes a system to extract images from web pages and then detect the skin color regions of these images. As part of the proposed system, using BandObject control, we built a Tool bar named 'Filter Tool Bar (FTB)' by modifying the Pavel Zolnikov implementation. The Yahoo! Team provides us with the Yahoo! SDK API, which also supports image search and is really useful. In the proposed system, we introduced three new methods for extracting images from the web pages (after loading the web page by using the proposed FTB, before loading the web page physically from the localhost, and before loading the web page from any server). These methods overcome the drawback of the regular expressions method for extracting images suggested by Ilan Assayag. The second part of the proposed system is concerned with the detection of the skin color regions of the extracted images. So, we studied two famous skin color detection techniques. The first technique is based on the RGB color space and the second technique is based on YUV and YIQ color spaces. We modified the second technique to overcome the failure of detecting complex image's background by using the saturation parameter to obtain an accurate skin detection results. The performance evaluation of the efficiency of the proposed system in extracting images before and after loading the web page from localhost or any server in terms of the number of extracted images is presented. Finally, the results of comparing the two skin detection techniques in terms of the number of pixels detected are presented.
Abstract: Steganography is the art of hiding and transmitting data
through apparently innocuous carriers in an effort to conceal the
existence of the data. A lot of steganography algorithms have been
proposed recently. Many of them use the digital image data as a carrier.
In data hiding scheme of halftoning and coordinate projection, still
image data is used as a carrier, and the data of carrier image are
modified for data embedding. In this paper, we present three features
for analysis of data hiding via halftoning and coordinate projection.
Also, we present a classifier using the proposed three features.
Abstract: Recognition of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. . In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.
Abstract: Along with the progress of our information society,
various risks are becoming increasingly common, causing multiple social problems. For this reason, risk communications for
establishing consensus among stakeholders who have different
priorities have become important. However, it is not always easy for the decision makers to agree on measures to reduce risks based on
opposing concepts, such as security, privacy and cost. Therefore, we previously developed and proposed the “Multiple Risk Communicator" (MRC) with the following functions: (1) modeling
the support role of the risk specialist, (2) an optimization engine, and (3) displaying the computed results. In this paper, MRC program
version 1.0 is applied to the personal information leakage problem. The application process and validation of the results are discussed.
Abstract: Bioprocesses are appreciated as difficult to control because their dynamic behavior is highly nonlinear and time varying, in particular, when they are operating in fed batch mode. The research objective of this study was to develop an appropriate control method for a complex bioprocess and to implement it on a laboratory plant. Hence, an intelligent control structure has been designed in order to produce biomass and to maximize the specific growth rate.
Abstract: The paper describes a knowledge based system for
analysis of microscopic wear particles. Wear particles contained in
lubricating oil carry important information concerning machine
condition, in particular the state of wear. Experts (Tribologists) in the
field extract this information to monitor the operation of the machine
and ensure safety, efficiency, quality, productivity, and economy of
operation. This procedure is not always objective and it can also be
expensive. The aim is to classify these particles according to their
morphological attributes of size, shape, edge detail, thickness ratio,
color, and texture, and by using this classification thereby predict
wear failure modes in engines and other machinery. The attribute
knowledge links human expertise to the devised Knowledge Based
Wear Particle Analysis System (KBWPAS). The system provides an
automated and systematic approach to wear particle identification
which is linked directly to wear processes and modes that occur in
machinery. This brings consistency in wear judgment prediction
which leads to standardization and also less dependence on
Tribologists.
Abstract: This paper presents the novel Rao-Blackwellised
particle filter (RBPF) for mobile robot simultaneous localization and
mapping (SLAM) using monocular vision. The particle filter is
combined with unscented Kalman filter (UKF) to extending the path
posterior by sampling new poses that integrate the current observation
which drastically reduces the uncertainty about the robot pose. The
landmark position estimation and update is also implemented through
UKF. Furthermore, the number of resampling steps is determined
adaptively, which seriously reduces the particle depletion problem,
and introducing the evolution strategies (ES) for avoiding particle
impoverishment. The 3D natural point landmarks are structured with
matching Scale Invariant Feature Transform (SIFT) feature pairs. The
matching for multi-dimension SIFT features is implemented with a
KD-Tree in the time cost of O(log2
N). Experiment results on real robot
in our indoor environment show the advantages of our methods over
previous approaches.