Abstract: To compress, improve bit error performance and also enhance 2D images, a new scheme, called Iterative Cellular-Turbo System (IC-TS) is introduced. In IC-TS, the original image is partitioned into 2N quantization levels, where N is denoted as bit planes. Then each of the N-bit-plane is coded by Turbo encoder and transmitted over Additive White Gaussian Noise (AWGN) channel. At the receiver side, bit-planes are re-assembled taking into consideration of neighborhood relationship of pixels in 2-D images. Each of the noisy bit-plane values of the image is evaluated iteratively using IC-TS structure, which is composed of equalization block; Iterative Cellular Image Processing Algorithm (ICIPA) and Turbo decoder. In IC-TS, there is an iterative feedback link between ICIPA and Turbo decoder. ICIPA uses mean and standard deviation of estimated values of each pixel neighborhood. It has extra-ordinary satisfactory results of both Bit Error Rate (BER) and image enhancement performance for less than -1 dB Signal-to-Noise Ratio (SNR) values, compared to traditional turbo coding scheme and 2-D filtering, applied separately. Also, compression can be achieved by using IC-TS systems. In compression, less memory storage is used and data rate is increased up to N-1 times by simply choosing any number of bit slices, sacrificing resolution. Hence, it is concluded that IC-TS system will be a compromising approach in 2-D image transmission, recovery of noisy signals and image compression.
Abstract: Scene interpretation systems need to match (often ambiguous)
low-level input data to concepts from a high-level ontology.
In many domains, these decisions are uncertain and benefit greatly
from proper context. This paper demonstrates the use of decision
trees for estimating class probabilities for regions described by feature
vectors, and shows how context can be introduced in order to improve
the matching performance.
Abstract: In literature, there are metrics for identifying the
quality of reusable components but the framework that makes use of
these metrics to precisely predict reusability of software components
is still need to be worked out. These reusability metrics if identified
in the design phase or even in the coding phase can help us to reduce
the rework by improving quality of reuse of the software component
and hence improve the productivity due to probabilistic increase in
the reuse level. As CK metric suit is most widely used metrics for
extraction of structural features of an object oriented (OO) software;
So, in this study, tuned CK metric suit i.e. WMC, DIT, NOC, CBO
and LCOM, is used to obtain the structural analysis of OO-based
software components. An algorithm has been proposed in which the
inputs can be given to K-Means Clustering system in form of
tuned values of the OO software component and decision tree is
formed for the 10-fold cross validation of data to evaluate the in
terms of linguistic reusability value of the component. The developed
reusability model has produced high precision results as desired.
Abstract: This paper presents and evaluates a new classification
method that aims to improve classifiers performances and speed up
their training process. The proposed approach, called labeled
classification, seeks to improve convergence of the BP (Back
propagation) algorithm through the addition of an extra feature
(labels) to all training examples. To classify every new example, tests
will be carried out each label. The simplicity of implementation is the
main advantage of this approach because no modifications are
required in the training algorithms. Therefore, it can be used with
others techniques of acceleration and stabilization. In this work, two
models of the labeled classification are proposed: the LMLP
(Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro
Fuzzy Classifier). These models are tested using Iris, wine, texture
and human thigh databases to evaluate their performances.
Abstract: Although automotive industry has brought different beneficiaries to human life, it is being pointed out as one of the major cause of global air pollution which resulted in climate change, smog, green house gases (GHGs), and human diseases by many reasons. Since auto industry is one of the largest consumers of fossil fuels, the realization of green innovations is becoming a crucial choice to meet the challenges towards sustainable development. Recently, many auto manufacturers have embarked on green technology initiatives to gain a competitive advantage in the global market; however, innovative manufacturing systems and technologies can enhance operational performance only if the human resource management is in place to elicit the motivation of the employees and develop their organizational expertise. No organization can perform at peak levels unless each employee is committed to the company goals and works as an effective team member. Strategic human resource practices are the primary means by which firms can shape the skills, attitudes, and behavior of individuals to align with the business strategic objectives. This study investigates on the comprehensive approach of multiple advanced technology innovations and human resource management at Toyota Motor Corporation as the market leader of full hybrid technology in the automotive industry. Then, HRM framework of the company is described and three sets of human resource practices that support the innovation-oriented HR system, presented. Finally, a conceptual framework for innovativeness in green technology in automotive industry by applying a deliberate strategic HR management system and knowledge management with the intervening factors of organizational culture, knowledge application and knowledge sharing is proposed.
Abstract: The logistical requirements placed on industrial manufacturing companies are steadily increasing. In order to meet those requirements, a consistent and efficient concept is necessary for production control. Set up properly, production control offers considerable potential with respect to achieving the logistical targets. As experience with the many production control methods already in existence and their compatibility is, however, often inadequate, this article describes a systematic approach to the configuration of production control based on the Lödding model. This model enables production control to be set up individually to suit a company and the requirements. It therefore permits today-s demands regarding logistical performance to be met.
Abstract: Maximal Ratio Combining (MRC) is considered the most complex combining technique as it requires channel coefficients estimation. It results in the lowest bit error rate (BER) compared to all other combining techniques. However the BER starts to deteriorate as errors are introduced in the channel coefficients estimation. A novel combining technique, termed Generalized Maximal Ratio Combining (GMRC) with a polynomial kernel, yields an identical BER as MRC with perfect channel estimation and a lower BER in the presence of channel estimation errors. We show that GMRC outperforms the optimal MRC scheme in general and we hereinafter introduce it to the scientific community as a new “supraoptimal" algorithm. Since diversity combining is especially effective in small femto- and pico-cells, internet-associated wireless peripheral systems are to benefit most from GMRC. As a result, many spinoff applications can be made to IP-based 4th generation networks.
Abstract: The work presented in this paper focus on Knowledge Management services enabling CSCW (Computer Supported Cooperative Work) applications to provide an appropriate adaptation to the user and the situation in which the user is working. In this paper, we explain how a knowledge management system can be designed to support users in different situations exploiting contextual data, users' preferences, and profiles of involved artifacts (e.g., documents, multimedia files, mockups...). The presented work roots in the experience we had in the MILK project and early steps made in the MAIS project.
Abstract: Today, Genetic Algorithm has been used to solve
wide range of optimization problems. Some researches conduct on
applying Genetic Algorithm to text classification, summarization
and information retrieval system in text mining process. This
researches show a better performance due to the nature of Genetic
Algorithm. In this paper a new algorithm for using Genetic
Algorithm in concept weighting and topic identification, based on
concept standard deviation will be explored.
Abstract: Texture information plays increasingly an important
role in remotely sensed imagery classification and many pattern
recognition applications. However, the selection of relevant textural
features to improve this classification accuracy is not a straightforward
task. This work investigates the effectiveness of two Mutual
Information Feature Selector (MIFS) algorithms to select salient
textural features that contain highly discriminatory information for
multispectral imagery classification. The input candidate features are
extracted from a SPOT High Resolution Visible(HRV) image using
Wavelet Transform (WT) at levels (l = 1,2).
The experimental results show that the selected textural features
according to MIFS algorithms make the largest contribution to
improve the classification accuracy than classical approaches such
as Principal Components Analysis (PCA) and Linear Discriminant
Analysis (LDA).
Abstract: This paper presents strategies for dynamically creating, managing and removing mesh cells during computations in the context of the Material Point Method (MPM). The dynamic meshing approach has been developed to help address problems involving motion of a finite size body in unbounded domains in which the extent of material travel and deformation is unknown a priori, such as in the case of landslides and debris flows. The key idea is to efficiently instantiate and search only cells that contain material points, thereby avoiding unneeded storage and computation. Mechanisms for doing this efficiently are presented, and example problems are used to demonstrate the effectiveness of dynamic mesh management relative to alternative approaches.
Abstract: The aim of this retrospective study was to evaluate the
parameters of dental implants such as patient gender, number of
implant, failed implant before prosthetic restorations and failed
implant after implantation and failed implant after prosthetic
restorations. 135 male and 99 female patients, total 234 implant
patients which have been treated with 450 implant between 2005-
2009 years in GATA Haydarpasa Training Hospital Dental Service.
Twelve implants were failed before prosthetic restorations. Four
implant were failed after fixed prosthetic restorations. Cumulative
survival rate after prostheses were 97.56 % during 6 years period.
Abstract: The Petri net tool INA is a well known tool by the
Petri net community. However, it lacks a graphical environment to
cerate and analyse INA models. Building a modelling tool for the
design and analysis from scratch (for INA tool for example) is
generally a prohibitive task. Meta-Modelling approach is useful to
deal with such problems since it allows the modelling of the
formalisms themselves. In this paper, we propose an approach based
on the combined use of Meta-modelling and Graph Grammars to
automatically generate a visual modelling tool for INA for analysis
purposes. In our approach, the UML Class diagram formalism is
used to define a meta-model of INA models. The meta-modelling
tool ATOM3 is used to generate a visual modelling tool according to
the proposed INA meta-model. We have also proposed a graph
grammar to automatically generate INA description of the
graphically specified Petri net models. This allows the user to avoid
the errors when this description is done manually. Then the INA tool
is used to perform the simulation and the analysis of the resulted INA
description. Our environment is illustrated through an example.
Abstract: This paper describes part of a project about Learningby-
Modeling (LbM). Studying complex systems is increasingly
important in teaching and learning many science domains. Many
features of complex systems make it difficult for students to develop
deep understanding. Previous research indicates that involvement
with modeling scientific phenomena and complex systems can play a
powerful role in science learning. Some researchers argue with this
view indicating that models and modeling do not contribute to
understanding complexity concepts, since these increases the
cognitive load on students. This study will investigate the effect of
different modes of involvement in exploring scientific phenomena
using computer simulation tools, on students- mental model from the
perspective of structure, behavior and function. Quantitative and
qualitative methods are used to report about 121 freshmen students
that engaged in participatory simulations about complex phenomena,
showing emergent, self-organized and decentralized patterns. Results
show that LbM plays a major role in students' concept formation
about complexity concepts.
Abstract: Pressures for urban redevelopment are intensifying in
all large cities. A new logic for urban development is required –
green urbanism – that provides a spatial framework for directing
population and investment inwards to brownfields and greyfields
precincts, rather than outwards to the greenfields. This represents
both a major opportunity and a major challenge for city planners in
pluralist liberal democracies. However, plans for more compact
forms of urban redevelopment are stalling in the face of community
resistance. A new paradigm and spatial planning platform is required
that will support timely multi-level and multi-actor stakeholder
engagement, resulting in the emergence of consensus plans for
precinct-level urban regeneration capable of more rapid
implementation. Using Melbourne, Australia as a case study, this
paper addresses two of the urban intervention challenges – where and
how – via the application of a 21st century planning tool ENVISION
created for this purpose.
Abstract: In this work a dual laser triangulation system is presented for fast building of 2.5D textured models of objects within a production line. This scanner is designed to produce data suitable for 3D completeness inspection algorithms. For this purpose two laser projectors have been used in order to considerably reduce the problem of occlusions in the camera movement direction. Results of reconstruction of electronic boards are presented, together with a comparison with a commercial system.
Abstract: The purpose of this paper is to contribute to the body
of knowledge in the area of management accounting, particularly
performance measurement systems within the BSC framework, by
investigating empirically the extent of multiple performance
measures usage and their effects on the financial performance of
Jordanian banks in the branches level. Nevertheless, the result of this
study shows that the non-financial measures usages, particularly,
customer oriented indicators and product/ service oriented indicators,
appears to be important as it enhances firm performance.
Remarkably, the findings reveal that there is positive relationship
between the usages of multiple performance measures via overall
BSC measures and financial performance in the branches level.
Abstract: One of the main problems of suspended cable structures is initial shape change under the action of non uniform load. The problem can be solved by increasing of weight of construction or by using of prestressing. But this methods cause increasing of materials consumption of suspended cable structure. The cable truss usage is another way how the problem of shape change under the action of non uniform load can be fixed. The cable trusses with the vertical and inclined suspensions, cross web and single cable were analyzed as the main load-bearing structures of suspension bridge. It was shown, that usage of cable truss allows to reduce the vertical displacements up to 32% in comparison with the single cable in case of non uniformly distributed load. In case of uniformly distributed load single cable is preferable.
Abstract: Plants are rich sources of bioactive compounds. In this study the photochemical screening of hexane, ethanolic and aqueous extracts of roots and latex of jojoba (Simmondsia chinensis) plant revealed the presence of saponins, tannins, alkaloids, steroids and glycosides. Ethanolic extract was found to be richer in these metabolites than hexane, aqueous extracts and latex. The extracts and latex displayed effective antimicrobial activity against Salmonella typhimurium, Bacillus cereus, Clostridium perfringens, Staphylococcus aureus, Escherichia coli, Candida albicans and Aspergillus flavus. The increase in volume of the extracts and latex caused more activity, as shown by zones of inhibition. Candida albicans growth was inhibited only by hexane extract. Jojoba latex was not effective against Candida albicans at 0.1 and 0.5 ml extracts concentration but showed 5mm zone of inhibition at (1.0 ml). Lower volume (0.1ml) of latex encouraged Aspergillus flavus growth, while at (1.00 ml) reduced its mycelial growth. Thus, jojoba root extracts and latex can be of potential natural antimicrobial agents.
Abstract: In this paper the Analytic Network Process (ANP) is
applied to the selection of photovoltaic (PV) solar power projects.
These projects follow a long management and execution process
from plant site selection to plant start-up. As a consequence, there are
many risks of time delays and even of project stoppage.
In the case study presented in this paper a top manager of an
important Spanish company that operates in the power market has to
decide on the best PV project (from four alternative projects) to
invest based on risk minimization. The manager identified 50 project
execution delay and/or stoppage risks.
The influences among elements of the network (groups of risks
and alternatives) were identified and analyzed using the ANP
multicriteria decision analysis method. After analyzing the results the
main conclusion is that the network model can manage all the
information of the real-world problem and thus it is a decision
analysis model recommended by the authors. The strengths and
weaknesses ANP as a multicriteria decision analysis tool are also
described in the paper.