Abstract: Lately management strategy that put Industrial Design (ID) in its core is recognized more important, as technology and price alone cannot differentiate a product. The needs to shorten the time to develop a product also shorten the development period of ID, and it necessitates the ID process management. This research analyzes the status of integration process of ID and Engineering Design (ED) of office equipment that requires the collaboration of ID and ED to clarify the issues for the efficiency of the development and to propose solutions.
Abstract: Fault-proneness of a software module is the
probability that the module contains faults. A correlation exists
between the fault-proneness of the software and the measurable
attributes of the code (i.e. the static metrics) and of the testing (i.e.
the dynamic metrics). Early detection of fault-prone software
components enables verification experts to concentrate their time and
resources on the problem areas of the software system under
development. This paper introduces Genetic Algorithm based
software fault prediction models with Object-Oriented metrics. The
contribution of this paper is that it has used Metric values of JEdit
open source software for generation of the rules for the classification
of software modules in the categories of Faulty and non faulty
modules and thereafter empirically validation is performed. The
results shows that Genetic algorithm approach can be used for
finding the fault proneness in object oriented software components.
Abstract: In this paper, we demonstrate the adaptive
least-mean-square (LMS) filter modeling using SystemC. SystemC is
a modeling language that allows designer to model both hardware and
software component and makes it possible to design from high level
system of abstraction to low level system of abstraction. We produced
five adaptive least-mean-square filter models that are classed as five
abstraction levels using SystemC proceeding from the abstract model
to the more concrete model.
Abstract: Until recently, researchers have developed various
tools and methodologies for effective clinical decision-making.
Among those decisions, chest pain diseases have been one of
important diagnostic issues especially in an emergency department. To
improve the ability of physicians in diagnosis, many researchers have
developed diagnosis intelligence by using machine learning and data
mining. However, most of the conventional methodologies have been
generally based on a single classifier for disease classification and
prediction, which shows moderate performance. This study utilizes an
ensemble strategy to combine multiple different classifiers to help
physicians diagnose chest pain diseases more accurately than ever.
Specifically the ensemble strategy is applied by using the integration
of decision trees, neural networks, and support vector machines. The
ensemble models are applied to real-world emergency data. This study
shows that the performance of the ensemble models is superior to each
of single classifiers.
Abstract: This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.
Abstract: Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). Classically it is believed that IS strives to discriminate between self and non-self. Most of the existing AIS research is based on this approach. Danger Theory (DT) argues this approach and proposes that IS fights against danger producing elements and tolerates others. We, the computational researchers, are not concerned with the arguments among immunologists but try to extract from it novel abstractions for intelligent computation. This paper aims to follow DT inspiration for intelligent data processing. The approach may introduce new avenue in intelligent processing. The data used is system calls data that is potentially significant in intrusion detection applications.
Abstract: An application framework provides a reusable design
and implementation for a family of software systems. Application
developers extend the framework to build their particular
applications using hooks. Hooks are the places identified to show
how to use and customize the framework. Hooks define Framework
Interface Classes (FICs) and their possible specifications, which
helps in building reusable test cases for the implementations of these
classes. In applications developed using gray-box frameworks, FICs
inherit framework classes or use them without inheritance. In this
paper, a test-case generation technique is extended to build test cases
for FICs built for gray-box frameworks. A tool is developed to
automate the introduced technique.
Abstract: This paper addresses the problem of asymptotic tracking
control of a linear parabolic partial differential equation with indomain
point actuation. As the considered model is a non-standard
partial differential equation, we firstly developed a map that allows
transforming this problem into a standard boundary control problem
to which existing infinite-dimensional system control methods can
be applied. Then, a combination of energy multiplier and differential
flatness methods is used to design an asymptotic tracking controller.
This control scheme consists of stabilizing state-feedback derived
from the energy multiplier method and feed-forward control based
on the flatness property of the system. This approach represents
a systematic procedure to design tracking control laws for a class
of partial differential equations with in-domain point actuation. The
applicability and system performance are assessed by simulation
studies.
Abstract: Purpose: To explore the use of Curvelet transform to
extract texture features of pulmonary nodules in CT image and support
vector machine to establish prediction model of small solitary
pulmonary nodules in order to promote the ratio of detection and
diagnosis of early-stage lung cancer. Methods: 2461 benign or
malignant small solitary pulmonary nodules in CT image from 129
patients were collected. Fourteen Curvelet transform textural features
were as parameters to establish support vector machine prediction
model. Results: Compared with other methods, using 252 texture
features as parameters to establish prediction model is more proper.
And the classification consistency, sensitivity and specificity for the
model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based
on texture features extracted from Curvelet transform, support vector
machine prediction model is sensitive to lung cancer, which can
promote the rate of diagnosis for early-stage lung cancer to some
extent.
Abstract: A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dimensional fuzzy decision region is approximated by union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision regions are determined by estimating the parameters of each hyperellipsoid.Genetic Algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the learned decision region can be arbitrarily complex.
Abstract: Within the last years, several technologies have been developed to help building e-learning portals. Most of them follow approaches that deliver a vast amount of functionalities, suitable for class-like learning. The SuGI project, as part of the D-Grid (funded by the BMBF), targets on delivering a highly scalable and sustainable learning solution to provide materials (e.g. learning modules, training systems, webcasts, tutorials, etc.) containing knowledge about Grid computing to the D-Grid community. In this article, the process of the development of an e-learning portal focused on the requirements of this special user group is described. Furthermore, it deals with the conceptual and technical design of an e-learning portal, addressing the special needs of heterogeneous target groups. The main focus lies on the quality management of the software development process, Web templates for uploading new contents, the rich search and filter functionalities which will be described from a conceptual as well as a technical point of view. Specifically, it points out best practices as well as concepts to provide a sustainable solution to a relatively unknown and highly heterogeneous community.
Abstract: This paper introduces a new instantaneous frequency
computation approach -Counting Instantaneous Frequency for a
general class of signals called simple waves. The classsimple wave
contains a wide range of continuous signals for which the concept
instantaneous frequency has a perfect physical sense. The concept of
-Counting Instantaneous Frequency also applies to all the discrete data.
For all the simple wave signals and the discrete data, -Counting
instantaneous frequency can be computed directly without signal
decomposition process. The intrinsic mode functions obtained through
empirical mode decomposition belongs to simple wave. So
-Counting instantaneous frequency can be used together with
empirical mode decomposition.
Abstract: A new class of fuzzy closed sets, namely fuzzy weakly closed set in a fuzzy topological space is introduced and it is established that this class of fuzzy closed sets lies between fuzzy closed sets and fuzzy generalized closed sets. Alongwith the study of fundamental results of such closed sets, we define and characterize fuzzy weakly compact space and fuzzy weakly closed space.
Abstract: Acoustical properties of speech have been shown to
be related to mental states of speaker with symptoms: depression
and remission. This paper describes way to address the issue of
distinguishing depressed patients from remitted subjects based on
measureable acoustics change of their spoken sound. The vocal-tract
related frequency characteristics of speech samples from female
remitted and depressed patients were analyzed via speech
processing techniques and consequently, evaluated statistically by
cross-validation with Support Vector Machine. Our results
comparatively show the classifier's performance with effectively
correct separation of 93% determined from testing with the subjectbased
feature model and 88% from the frame-based model based on
the same speech samples collected from hospital visiting interview
sessions between patients and psychiatrists.
Abstract: Mitochondria are dynamic organelles, capable to
interact with each other. While the number of mitochondria in a cell
varies, their quality and functionality depends on the operation of
fusion, fission, motility and mitophagy. Nowadays, several
researches declare as an important factor in neurogenerative diseases
the disruptions in the regulation of mitochondrial dynamics. In this
paper a stochastic model in BioAmbients calculus is presented,
concerning mitochondrial fusion and its distribution in the renewal of
mitochondrial population in a cell. This model describes the
successive and dependent stages of protein synthesis, protein-s
activation and merging of two independent mitochondria.
Abstract: Acoustic function plays an important role in
aerodynamic mechanical engineering. It can classify the kind of
air-vehicle such as subsonic or supersonic. Acoustic velocity
relates with velocity and Mach number. Mach number relates
again acoustic stability or instability condition. Mach number
plays an important role in growth or decay in energy system.
Acoustic is a function of temperature and temperature is directly
proportional to pressure. If we control the pressure, we can control
acoustic function. To get pressure stability condition, we apply
Navier-Stokes equations.
Abstract: An automated wood recognition system is designed to
classify tropical wood species.The wood features are extracted based
on two feature extractors: Basic Grey Level Aura Matrix (BGLAM)
technique and statistical properties of pores distribution (SPPD)
technique. Due to the nonlinearity of the tropical wood species
separation boundaries, a pre classification stage is proposed which
consists ofKmeans clusteringand kernel discriminant analysis (KDA).
Finally, Linear Discriminant Analysis (LDA) classifier and KNearest
Neighbour (KNN) are implemented for comparison purposes.
The study involves comparison of the system with and without pre
classification using KNN classifier and LDA classifier.The results
show that the inclusion of the pre classification stage has improved
the accuracy of both the LDA and KNN classifiers by more than
12%.
Abstract: This paper presents an evaluation for a wavelet-based
digital watermarking technique used in estimating the quality of
video sequences transmitted over Additive White Gaussian Noise
(AWGN) channel in terms of a classical objective metric, such as
Peak Signal-to-Noise Ratio (PSNR) without the need of the original
video. In this method, a watermark is embedded into the Discrete
Wavelet Transform (DWT) domain of the original video frames
using a quantization method. The degradation of the extracted
watermark can be used to estimate the video quality in terms of
PSNR with good accuracy. We calculated PSNR for video frames
contaminated with AWGN and compared the values with those
estimated using the Watermarking-DWT based approach. It is found
that the calculated and estimated quality measures of the video
frames are highly correlated, suggesting that this method can provide
a good quality measure for video frames transmitted over AWGN
channel without the need of the original video.
Abstract: After the Terengganu state government decided to give a boost in teaching and learning through the allocation of free ebooks to all Primary five and six students; it was time to examine the presence of e-books in the classrooms. A survey was conducted on 101 students to determine how they felt about using the e-book and their experiences. It was discovered that a majority of these students liked using the e-book. However, although they had little problems using the e-book and the e-book helped to lighten the schoolbags, these new-age textbooks were not fully utilized. It is implied that perhaps the school administrators, teachers and students may not be able to overcome the unfamiliar characteristics of the e-book and its limitations.
Abstract: EPC Class-1 Generation-2 UHF tags, one of Radio
frequency identification or RFID tag types, is expected that most
companies are planning to use it in the supply chain in the short term
and in consumer packaging in the long term due to its inexpensive
cost. Because of the very cost, however, its resources are extremely
scarce and it is hard to have any valuable security algorithms in it. It
causes security vulnerabilities, in particular cloning the tags for
counterfeits. In this paper, we propose a product authentication
solution for anti-counterfeiting at application level in the supply chain
and mobile RFID environment. It aims to become aware of
distribution of spurious products with fake RFID tags and to provide a
product authentication service to general consumers with mobile
RFID devices like mobile phone or PDA which has a mobile RFID
reader. We will discuss anti-counterfeiting mechanisms which are
required to our proposed solution and address requirements that the
mechanisms should have.