Abstract: At present, web Service is the first choice to reuse the
legacy system for the implementation of SOA. According to the status
of the implementation of SOA and the status of the legacy systems, we propose four encapsulating strategies. Base on the strategies, we
proposal the service-oriented encapsulating framework, the legacy system can be encapsulated by the service-oriented encapsulating
layer in three aspects, communication protocols, data and program.
The reuse rate of the legacy systems can be increased by using this framework
Abstract: In distributed resource allocation a set of agents must assign their resources to a set of tasks. This problem arises in many real-world domains such as distributed sensor networks, disaster rescue, hospital scheduling and others. Despite the variety of approaches proposed for distributed resource allocation, a systematic formalization of the problem, explaining the different sources of difficulties, and a formal explanation of the strengths and limitations of key approaches is missing. We take a step towards this goal by using a formalization of distributed resource allocation that represents both dynamic and distributed aspects of the problem. In this paper we present a new idea for target tracking in sensor networks and compare it with previous approaches. The central contribution of the paper is a generalized mapping from distributed resource allocation to DDCSP. This mapping is proven to correctly perform resource allocation problems of specific difficulty. This theoretical result is verified in practice by a simulation on a realworld distributed sensor network.
Abstract: In this work we develop an object extraction method
and propose efficient algorithms for object motion characterization.
The set of proposed tools serves as a basis for development of objectbased
functionalities for manipulation of video content. The
estimators by different algorithms are compared in terms of quality
and performance and tested on real video sequences. The proposed
method will be useful for the latest standards of encoding and
description of multimedia content – MPEG4 and MPEG7.
Abstract: Fully customized hardware based technology provides high performance and low power consumption by specializing the tasks in hardware but lacks design flexibility since any kind of changes require re-design and re-fabrication. Software based solutions operate with software instructions due to which a great flexibility is achieved from the easy development and maintenance of the software code. But this execution of instructions introduces a high overhead in performance and area consumption. In past few decades the reconfigurable computing domain has been introduced which overcomes the traditional trades-off between flexibility and performance and is able to achieve high performance while maintaining a good flexibility. The dramatic gains in terms of chip performance and design flexibility achieved through the reconfigurable computing systems are greatly dependent on the design of their computational units being integrated with reconfigurable logic resources. The computational unit of any reconfigurable system plays vital role in defining its strength. In this research paper an RFU based computational unit design has been presented using the tightly coupled, multi-threaded reconfigurable cores. The proposed design has been simulated for VLIW based architectures and a high gain in performance has been observed as compared to the conventional computing systems.
Abstract: Semantic Web services will enable the semiautomatic
and automatic annotation, advertisement, discovery,
selection, composition, and execution of inter-organization business
logic, making the Internet become a common global platform where
organizations and individuals communicate with each other to carry
out various commercial activities and to provide value-added
services. There is a growing consensus that Web services alone will
not be sufficient to develop valuable solutions due the degree of
heterogeneity, autonomy, and distribution of the Web. This paper
deals with two of the hottest R&D and technology areas currently
associated with the Web – Web services and the Semantic Web. It
presents the synergies that can be created between Web Services and
Semantic Web technologies to provide a new generation of eservices.
Abstract: A comparison between the performance of Latin and
Arabic handwritten digits recognition problems is presented. The
performance of ten different classifiers is tested on two similar
Arabic and Latin handwritten digits databases. The analysis shows
that Arabic handwritten digits recognition problem is easier than that
of Latin digits. This is because the interclass difference in case of
Latin digits is smaller than in Arabic digits and variances in writing
Latin digits are larger. Consequently, weaker yet fast classifiers are
expected to play more prominent role in Arabic handwritten digits
recognition.
Abstract: Image data holds a large amount of different context
information. However, as of today, these resources remain largely
untouched. It is thus the aim of this paper to present a basic technical
framework which allows for a quick and easy exploitation of context
information from image data especially by non-expert users.
Furthermore, the proposed framework is discussed in detail
concerning important social and ethical issues which demand special
requirements in system design. Finally, a first sensor prototype is
presented which meets the identified requirements. Additionally,
necessary implications for the software and hardware design of the
system are discussed, rendering a sensor system which could be
regarded as a good, acceptable and justifiable technical and thereby
enabling the extraction of context information from image data.
Abstract: SoftBoost is a recently presented boosting algorithm,
which trades off the size of achieved classification margin and
generalization performance. This paper presents a performance
evaluation of SoftBoost algorithm on the generic object recognition
problem. An appearance-based generic object recognition
model is used. The evaluation experiments are performed using
a difficult object recognition benchmark. An assessment with respect
to different degrees of label noise as well as a comparison to
the well known AdaBoost algorithm is performed. The obtained
results reveal that SoftBoost is encouraged to be used in cases
when the training data is known to have a high degree of noise.
Otherwise, using Adaboost can achieve better performance.
Abstract: Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.
Abstract: In this paper, we have proposed a low cost optimized solution for the movement of a three-arm manipulator using Genetic Algorithm (GA) and Analytical Hierarchy Process (AHP). A scheme is given for optimizing the movement of robotic arm with the help of Genetic Algorithm so that the minimum energy consumption criteria can be achieved. As compared to Direct Kinematics, Inverse Kinematics evolved two solutions out of which the best-fit solution is selected with the help of Genetic Algorithm and is kept in search space for future use. The Inverse Kinematics, Fitness Value evaluation and Binary Encoding like tasks are simulated and tested. Although, three factors viz. Movement, Friction and Least Settling Time (or Min. Vibration) are used for finding the Fitness Function / Fitness Values, however some more factors can also be considered.
Abstract: This paper presents a design and prototype
implementation of new home automation system that uses WiFi
technology as a network infrastructure connecting its parts. The
proposed system consists of two main components; the first part is
the server (web server), which presents system core that manages,
controls, and monitors users- home. Users and system administrator
can locally (LAN) or remotely (internet) manage and control system
code. Second part is hardware interface module, which provides
appropriate interface to sensors and actuator of home automation
system. Unlike most of available home automation system in the
market the proposed system is scalable that one server can manage
many hardware interface modules as long as it exists on WiFi
network coverage. System supports a wide range of home
automation devices like power management components, and
security components. The proposed system is better from the
scalability and flexibility point of view than the commercially
available home automation systems.
Abstract: This paper introduces and studies new indexing techniques for content-based queries in images databases. Indexing is the key to providing sophisticated, accurate and fast searches for queries in image data. This research describes a new indexing approach, which depends on linear modeling of signals, using bases for modeling. A basis is a set of chosen images, and modeling an image is a least-squares approximation of the image as a linear combination of the basis images. The coefficients of the basis images are taken together to serve as index for that image. The paper describes the implementation of the indexing scheme, and presents the findings of our extensive evaluation that was conducted to optimize (1) the choice of the basis matrix (B), and (2) the size of the index A (N). Furthermore, we compare the performance of our indexing scheme with other schemes. Our results show that our scheme has significantly higher performance.
Abstract: Training neural networks to capture an intrinsic
property of a large volume of high dimensional data is a difficult
task, as the training process is computationally expensive. Input
attributes should be carefully selected to keep the dimensionality of
input vectors relatively small.
Technical indexes commonly used for stock market prediction
using neural networks are investigated to determine its effectiveness
as inputs. The feed forward neural network of Levenberg-Marquardt
algorithm is applied to perform one step ahead forecasting of
NASDAQ and Dow stock prices.
Abstract: Society has grown to rely on Internet services, and the
number of Internet users increases every day. As more and more
users become connected to the network, the window of opportunity
for malicious users to do their damage becomes very great and
lucrative. The objective of this paper is to incorporate different
techniques into classier system to detect and classify intrusion from
normal network packet. Among several techniques, Steady State
Genetic-based Machine Leaning Algorithm (SSGBML) will be used
to detect intrusions. Where Steady State Genetic Algorithm (SSGA),
Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and
Zeroth Level Classifier system are investigated in this research.
SSGA is used as a discovery mechanism instead of SGA. SGA
replaces all old rules with new produced rule preventing old good
rules from participating in the next rule generation. Zeroth Level
Classifier System is used to play the role of detector by matching
incoming environment message with classifiers to determine whether
the current message is normal or intrusion and receiving feedback
from environment. Finally, in order to attain the best results,
Modified SSGA will enhance our discovery engine by using Fuzzy
Logic to optimize crossover and mutation probability. The
experiments and evaluations of the proposed method were performed
with the KDD 99 intrusion detection dataset.
Abstract: In this paper multivariable predictive PID controller has
been implemented on a multi-inputs multi-outputs control problem
i.e., quadruple tank system, in comparison with a simple multiloop
PI controller. One of the salient feature of this system is an
adjustable transmission zero which can be adjust to operate in both
minimum and non-minimum phase configuration, through the flow
distribution to upper and lower tanks in quadruple tank system.
Stability and performance analysis has also been carried out for this
highly interactive two input two output system, both in minimum
and non-minimum phases. Simulations of control system revealed
that better performance are obtained in predictive PID design.
Abstract: In current common research reports, salient regions
are usually defined as those regions that could present the main
meaningful or semantic contents. However, there are no uniform
saliency metrics that could describe the saliency of implicit image
regions. Most common metrics take those regions as salient regions,
which have many abrupt changes or some unpredictable
characteristics. But, this metric will fail to detect those salient useful
regions with flat textures. In fact, according to human semantic
perceptions, color and texture distinctions are the main characteristics
that could distinct different regions. Thus, we present a novel saliency
metric coupled with color and texture features, and its corresponding
salient region extraction methods. In order to evaluate the
corresponding saliency values of implicit regions in one image, three
main colors and multi-resolution Gabor features are respectively used
for color and texture features. For each region, its saliency value is
actually to evaluate the total sum of its Euclidean distances for other
regions in the color and texture spaces. A special synthesized image
and several practical images with main salient regions are used to
evaluate the performance of the proposed saliency metric and other
several common metrics, i.e., scale saliency, wavelet transform
modulus maxima point density, and important index based metrics.
Experiment results verified that the proposed saliency metric could
achieve more robust performance than those common saliency
metrics.
Abstract: The growing influence of service industries has
prompted greater attention being paid to service operations
management. However, service managers often have difficulty
articulating the veritable effects of their service innovation. Especially,
the performance evaluation process of service innovation problems
generally involves uncertain and imprecise data. This paper presents a
2-tuple fuzzy linguistic computing approach to dealing with
heterogeneous information and information loss problems while the
processes of subjective evaluation integration. The proposed method
based on group decision-making scenario to assist business managers
in measuring performance of service innovation manipulates the
heterogeneity integration processes and avoids the information loss
effectively.
Abstract: In recent years there has been renewal of interest in the
relation between Green IT and Cloud Computing. The growing use of
computers in cloud platform has caused marked energy consumption,
putting negative pressure on electricity cost of cloud data center. This
paper proposes an effective mechanism to reduce energy utilization in
cloud computing environments. We present initial work on the
integration of resource and power management that aims at reducing
power consumption. Our mechanism relies on recalling virtualization
services dynamically according to user-s virtualization request and
temporarily shutting down the physical machines after finish in order
to conserve energy. Given the estimated energy consumption, this
proposed effort has the potential to positively impact power
consumption. The results from the experiment concluded that energy
indeed can be saved by powering off the idling physical machines in
cloud platforms.
Abstract: In this paper, we propose an energy efficient cluster
based communication protocol for wireless sensor network. Our
protocol considers both the residual energy of sensor nodes and the
distance of each node from the BS when selecting cluster-head. This
protocol can successfully prolong the network-s lifetime by 1)
reducing the total energy dissipation on the network and 2) evenly
distributing energy consumption over all sensor nodes. In this
protocol, the nodes with more energy and less distance from the BS
are probable to be selected as cluster-head. Simulation results with
MATLAB show that proposed protocol could increase the lifetime of
network more than 94% for first node die (FND), and more than 6%
for the half of the nodes alive (HNA) factor as compared with
conventional protocols.
Abstract: The users are now expecting higher level of
DSP(Digital Signal Processing) software quality than ever before.
Prevention and detection of defect are critical elements of software
quality assurance. In this paper, principles and rules for prevention and
detection of defect are suggested, which are not universal guidelines,
but are useful for both novice and experienced DSP software
developers.