Abstract: This paper presents the design and implementation of CASTE, a Cloud-based automatic software test environment. We first present the architecture of CASTE, then the main packages and classes of it are described in detail. CASTE is built upon a private Infrastructure as a Service platform. Through concentrated resource management of virtualized testing environment and automatic execution control of test scripts, we get a better solution to the testing resource utilization and test automation problem. Experiments on CASTE give very appealing results.
Abstract: In this research study, an intelligent detection system
to support medical diagnosis and detection of abnormal lesions by
processing endoscopic images is presented. The images used in this
study have been obtained using the M2A Swallowable Imaging
Capsule - a patented, video color-imaging disposable capsule.
Schemes have been developed to extract texture features from the
fuzzy texture spectra in the chromatic and achromatic domains for a
selected region of interest from each color component histogram of
endoscopic images. The implementation of an advanced fuzzy
inference neural network which combines fuzzy systems and
artificial neural networks and the concept of fusion of multiple
classifiers dedicated to specific feature parameters have been also
adopted in this paper. The achieved high detection accuracy of the
proposed system has provided thus an indication that such intelligent
schemes could be used as a supplementary diagnostic tool in
endoscopy.
Abstract: Existing ground movement surveillance technologies
at airports are subjected to limitations due to shadowing effects or
multiple reflections. Therefore, there is a strong demand for a new
sensing technology, which will be cost effective and will provide
detection of non-cooperative targets under any weather conditions.
This paper aims to present a new intelligent system, developed
within the framework of the EC-funded ISMAEL project, which is
based on a new magnetic sensing technology and provides detection,
tracking and automatic classification of targets moving on the airport
surface. The system is currently being installed at two European
airports. Initial experimental results under real airport traffic
demonstrate the great potential of the proposed system.
Abstract: Everyday the usages of the Internet increase and simply a world of the data become accessible. Network providers do not want to let the provided services to be used in harmful or terrorist affairs, so they used a variety of methods to protect the special regions from the harmful data. One of the most important methods is supposed to be the firewall. Firewall stops the transfer of such packets through several ways, but in some cases they do not use firewall because of its blind packet stopping, high process power needed and expensive prices. Here we have proposed a method to find a discriminate function to distinguish between usual packets and harmful ones by the statistical processing on the network router logs. So an administrator can alarm to the user. This method is very fast and can be used simply in adjacent with the Internet routers.
Abstract: In this paper, we propose a supervised method for
color image classification based on a multilevel sigmoidal neural
network (MSNN) model. In this method, images are classified into
five categories, i.e., “Car", “Building", “Mountain", “Farm" and
“Coast". This classification is performed without any segmentation
processes. To verify the learning capabilities of the proposed method,
we compare our MSNN model with the traditional Sigmoidal Neural
Network (SNN) model. Results of comparison have shown that the
MSNN model performs better than the traditional SNN model in the
context of training run time and classification rate. Both color
moments and multi-level wavelets decomposition technique are used
to extract features from images. The proposed method has been
tested on a variety of real and synthetic images.
Abstract: This research adapts experimental design to investigate
the effect of conditioning or not and pre-exposure or not on brand
attitude, so it is a 2×2=4 factorial design. The results show that the
brand attitude of conditioning group is significantly higher than that of
unconditioning group. The brand attitude with pre-exposure is
significantly higher than that without pre-exposure. Conditioning or
not and pre-exposure or not have significant interaction. No matter the
celebrity is pre-exposure or not, the brand attitude is higher under
conditioning process.
Abstract: MultiProtocol Label Switching (MPLS) is an
emerging technology that aims to address many of the existing issues
associated with packet forwarding in today-s Internetworking
environment. It provides a method of forwarding packets at a high
rate of speed by combining the speed and performance of Layer 2
with the scalability and IP intelligence of Layer 3. In a traditional IP
(Internet Protocol) routing network, a router analyzes the destination
IP address contained in the packet header. The router independently
determines the next hop for the packet using the destination IP
address and the interior gateway protocol. This process is repeated at
each hop to deliver the packet to its final destination. In contrast, in
the MPLS forwarding paradigm routers on the edge of the network
(label edge routers) attach labels to packets based on the forwarding
Equivalence class (FEC). Packets are then forwarded through the
MPLS domain, based on their associated FECs , through swapping
the labels by routers in the core of the network called label switch
routers. The act of simply swapping the label instead of referencing
the IP header of the packet in the routing table at each hop provides
a more efficient manner of forwarding packets, which in turn allows
the opportunity for traffic to be forwarded at tremendous speeds and
to have granular control over the path taken by a packet. This paper
deals with the process of MPLS forwarding mechanism,
implementation of MPLS datapath , and test results showing the
performance comparison of MPLS and IP routing. The discussion
will focus primarily on MPLS IP packet networks – by far the
most common application of MPLS today.
Abstract: The Integrated Management of Child illnesses (IMCI) and the surveillance Health Information Systems (HIS) are related strategies that are designed to manage child illnesses and community practices of diseases. However, both strategies do not function well together because of classification incompatibilities and, as such, are difficult to use by health care personnel in rural areas where a majority of people lack the basic knowledge of interpreting disease classification from these methods. This paper discusses a single approach on how a stand-alone expert system can be used as a prompt diagnostic tool for all cases of illnesses presented. The system combines the action-oriented IMCI and the disease-oriented HIS approaches to diagnose malaria and typhoid fever in the rural areas of the Niger-delta region.
Abstract: In this paper, we use a one-step iteration scheme to approximate common fixed points of two quasi-asymptotically nonexpansive mappings. We prove weak and strong convergence theorems in a uniformly convex Banach space. Our results generalize the corresponding results of Yao and Chen [15] to a wider class of mappings while extend those of Khan, Abbas and Khan [4] to an improved one-step iteration scheme without any condition and improve upon many others in the literature.
Abstract: In process control applications, above 90% of the
controllers are of PID type. This paper proposed a robust PI
controller with fractional-order integrator. The PI parameters were
obtained using classical Ziegler-Nichols rules but enhanced with the
application of error filter cascaded to the fractional-order PI. The
controller was applied on steam temperature process that was
described by FOPDT transfer function. The process can be classified
as lag dominating process with very small relative dead-time. The
proposed control scheme was compared with other PI controller
tuned using Ziegler-Nichols and AMIGO rules. Other PI controller
with fractional-order integrator known as F-MIGO was also
considered. All the controllers were subjected to set point change and
load disturbance tests. The performance was measured using Integral
of Squared Error (ISE) and Integral of Control Signal (ICO). The
proposed controller produced best performance for all the tests with
the least ISE index.
Abstract: In this paper, a pipelined version of genetic algorithm,
called PLGA, and a corresponding hardware platform are described.
The basic operations of conventional GA (CGA) are made pipelined
using an appropriate selection scheme. The selection operator, used
here, is stochastic in nature and is called SA-selection. This helps
maintaining the basic generational nature of the proposed pipelined
GA (PLGA). A number of benchmark problems are used to compare
the performances of conventional roulette-wheel selection and the
SA-selection. These include unimodal and multimodal functions with
dimensionality varying from very small to very large. It is seen that
the SA-selection scheme is giving comparable performances with
respect to the classical roulette-wheel selection scheme, for all the
instances, when quality of solutions and rate of convergence are considered.
The speedups obtained by PLGA for different benchmarks
are found to be significant. It is shown that a complete hardware
pipeline can be developed using the proposed scheme, if parallel
evaluation of the fitness expression is possible. In this connection
a low-cost but very fast hardware evaluation unit is described.
Results of simulation experiments show that in a pipelined hardware
environment, PLGA will be much faster than CGA. In terms of
efficiency, PLGA is found to outperform parallel GA (PGA) also.
Abstract: This paper proposes new hybrid approaches for face
recognition. Gabor wavelets representation of face images is an
effective approach for both facial action recognition and face
identification. Perform dimensionality reduction and linear
discriminate analysis on the down sampled Gabor wavelet faces can
increase the discriminate ability. Nearest feature space is extended to
various similarity measures. In our experiments, proposed Gabor
wavelet faces combined with extended neural net feature space
classifier shows very good performance, which can achieve 93 %
maximum correct recognition rate on ORL data set without any preprocessing
step.
Abstract: The Chinese Postman Problem (CPP) is one of the
classical problems in graph theory and is applicable in a wide range
of fields. With the rapid development of hybrid systems and model
based testing, Chinese Postman Problem with Time Dependent Travel
Times (CPPTDT) becomes more realistic than the classical problems.
In the literature, we have proposed the first integer programming
formulation for the CPPTDT problem, namely, circuit formulation,
based on which some polyhedral results are investigated and a cutting
plane algorithm is also designed. However, there exists a main drawback:
the circuit formulation is only available for solving the special
instances with all circuits passing through the origin. Therefore, this
paper proposes a new integer programming formulation for solving
all the general instances of CPPTDT. Moreover, the size of the circuit
formulation is too large, which is reduced dramatically here. Thus, it
is possible to design more efficient algorithm for solving the CPPTDT
in the future research.
Abstract: Environment both endowed and built are essential for
tourism. However tourism and environment maintains a complex
relationship, where in most cases environment is at the receiving end.
Many tourism development activities have adverse environmental
effects, mainly emanating from construction of general infrastructure
and tourism facilities. These negative impacts of tourism can lead to
the destruction of precious natural resources on which it depends.
These effects vary between locations; and its effect on a hill
destination is highly critical. This study aims at developing a
Sustainable Tourism Planning Model for an environmentally
sensitive tourism destination in Kerala, India. Being part of the
Nilgiri mountain ranges, Munnar falls in the Western Ghats, one of
the biological hotspots in the world. Endowed with a unique high
altitude environment Munnar inherits highly significant ecological
wealth. Giving prime importance to the protection of this ecological
heritage, the study proposes a tourism planning model with resource
conservation and sustainability as the paramount focus. Conceiving a
novel approach towards sustainable tourism planning, the study
proposes to assess tourism attractions using Ecological Sensitivity
Index (ESI) and Tourism Attractiveness Index (TAI). Integration of
these two indices will form the Ecology – Tourism Matrix (ETM),
outlining the base for tourism planning in an environmentally
sensitive destination. The ETM Matrix leads to a classification of
tourism nodes according to its Conservation Significance and
Tourism Significance. The spatial integration of such nodes based on
the Hub & Spoke Principle constitutes sub – regions within the STZ.
Ensuing analyses lead to specific guidelines for the STZ as a whole,
specific tourism nodes, hubs and sub-regions. The study results in a
multi – dimensional output, viz., (1) Classification system for tourism
nodes in an environmentally sensitive region/ destination (2)
Conservation / Tourism Development Strategies and Guidelines for
the micro and macro regions and (3) A Sustainable Tourism Planning
Tool particularly for Ecologically Sensitive Destinations, which can
be adapted for other destinations as well.
Abstract: This paper focuses on analyzing medical diagnostic data using classification rules in data mining and context reduction in formal concept analysis. It helps in finding redundancies among the various medical examination tests used in diagnosis of a disease. Classification rules have been derived from positive and negative association rules using the Concept lattice structure of the Formal Concept Analysis. Context reduction technique given in Formal Concept Analysis along with classification rules has been used to find redundancies among the various medical examination tests. Also it finds out whether expensive medical tests can be replaced by some cheaper tests.
Abstract: Dynamic models of power converters are normally
time-varying because of their switching actions. Several approaches
are applied to analyze the power converters to achieve the timeinvariant
models suitable for system analysis and design via the
classical control theory. The paper presents how to derive dynamic
models of the power system consisting of a three-phase controlled
rectifier feeding an uncontrolled buck converter by using the
combination between the well known techniques called the DQ and
the generalized state-space averaging methods. The intensive timedomain
simulations of the exact topology model are used to support
the accuracies of the reported model. The results show that the
proposed model can provide good accuracies in both transient and
steady-state responses.
Abstract: The study investigates the relationship between
education level, workplace learning behaviors, psychological
empowerment and burnout in a sample of 191 teachers. We
hypothesized that education level will positively affect psychological
state of increased empowerment and decreased burnout, and we
purposed that these effects will be mediated by workplace learning
behaviors. We used multiple regression analyses to test the model
that included also the 6 following control variables: The teachers'
age, gender, and teaching tenure; the schools' religious level, the
pupils' needs: regular/ special needs, and the class level: elementary/
high school. The results support the purposed mediating model.
Abstract: By taking advantage of both k-NN which is highly
accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less
subclass number, stability and bounded time of classification with respect to the variable data size. We find between 96% and 99.7 % of accuracy in the lassification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.
Abstract: In the context of channel coding, the Generalized Belief Propagation (GBP) is an iterative algorithm used to recover the transmission bits sent through a noisy channel. To ensure a reliable transmission, we apply a map on the bits, that is called a code. This code induces artificial correlations between the bits to send, and it can be modeled by a graph whose nodes are the bits and the edges are the correlations. This graph, called Tanner graph, is used for most of the decoding algorithms like Belief Propagation or Gallager-B. The GBP is based on a non unic transformation of the Tanner graph into a so called region-graph. A clear advantage of the GBP over the other algorithms is the freedom in the construction of this graph. In this article, we explain a particular construction for specific graph topologies that involves relevant performance of the GBP. Moreover, we investigate the behavior of the GBP considered as a dynamic system in order to understand the way it evolves in terms of the time and in terms of the noise power of the channel. To this end we make use of classical measures and we introduce a new measure called the hyperspheres method that enables to know the size of the attractors.
Abstract: Classifying data hierarchically is an efficient approach
to analyze data. Data is usually classified into multiple categories, or
annotated with a set of labels. To analyze multi-labeled data, such
data must be specified by giving a set of labels as a semantic range.
There are some certain purposes to analyze data. This paper shows
which multi-labeled data should be the target to be analyzed for
those purposes, and discusses the role of a label against a set of
labels by investigating the change when a label is added to the set of
labels. These discussions give the methods for the advanced analysis
of multi-labeled data, which are based on the role of a label against
a semantic range.