Abstract: In this paper we presented a new method for tracking
flying targets in color video sequences based on contour and kernel.
The aim of this work is to overcome the problem of losing target in
changing light, large displacement, changing speed, and occlusion.
The proposed method is made in three steps, estimate the target
location by particle filter, segmentation target region using neural
network and find the exact contours by greedy snake algorithm. In
the proposed method we have used both region and contour
information to create target candidate model and this model is
dynamically updated during tracking. To avoid the accumulation of
errors when updating, target region given to a perceptron neural
network to separate the target from background. Then its output used
for exact calculation of size and center of the target. Also it is used as
the initial contour for the greedy snake algorithm to find the exact
target's edge. The proposed algorithm has been tested on a database
which contains a lot of challenges such as high speed and agility of
aircrafts, background clutter, occlusions, camera movement, and so
on. The experimental results show that the use of neural network
increases the accuracy of tracking and segmentation.
Abstract: This paper considers people’s driving skills
diagnosis under real driving conditions. In that sense, this research
presents an approach that uses GPS signals which have a direct
correlation with driving maneuvers. Besides, it is presented a novel
expert-driving-criteria approximation using fuzzy logic which
seeks to analyze GPS signals in order to issue an intelligent driving
diagnosis.
Based on above, this works presents in the first section the
intelligent driving diagnosis system approach in terms of its own
characteristics properties, explaining in detail significant
considerations about how an expert-driving-criteria approximation
must be developed. In the next section, the implementation of our
developed system based on the proposed fuzzy logic approach is
explained. Here, a proposed set of rules which corresponds to a
quantitative abstraction of some traffics laws and driving secure
techniques seeking to approach an expert-driving- criteria
approximation is presented.
Experimental testing has been performed in real driving
conditions. The testing results show that the intelligent driving
diagnosis system qualifies driver’s performance quantitatively with
a high degree of reliability.
Abstract: This study aims at being acquainted with the using the
body fat percentage (%BF) with body Mass Index (BMI) as input
parameters in fuzzy logic decision support system to predict properly
the lifted weight for students at weightlifting class lift according to
his abilities instead of traditional manner. The sample included 53
male students (age = 21.38 ± 0.71 yrs, height (Hgt) = 173.17 ± 5.28
cm, body weight (BW) = 70.34 ± 7.87.6 kg, Body mass index (BMI)
23.42 ± 2.06 kg.m-2, fat mass (FM) = 9.96 ± 3.15 kg and fat
percentage (% BF) = 13.98 ± 3.51 %.) experienced the weightlifting
class as a credit and has variance at BW, Hgt and BMI and FM. BMI
and % BF were taken as input parameters in FUZZY logic whereas
the output parameter was the lifted weight (LW). There were
statistical differences between LW values before and after using
fuzzy logic (Diff 3.55± 2.21, P > 0.001). The percentages of the LW
categories proposed by fuzzy logic were 3.77% of students to lift 1.0
fold of their bodies; 50.94% of students to lift 0.95 fold of their
bodies; 33.96% of students to lift 0.9 fold of their bodies; 3.77% of
students to lift 0.85 fold of their bodies and 7.55% of students to lift
0.8 fold of their bodies. The study concluded that the characteristic
changes in body composition experienced by students when
undergoing weightlifting could be utilized side by side with the
Fuzzy logic decision support system to determine the proper
workloads consistent with the abilities of students.
Abstract: Mobile Ad hoc Network is a set of self-governing
nodes which communicate through wireless links. Dynamic topology
MANETs makes routing a challenging task. Various routing
protocols are there, but due to various fundamental characteristic
open medium, changing topology, distributed collaboration and
constrained capability, these protocols are tend to various types of
security attacks. Black hole is one among them. In this attack,
malicious node represents itself as having the shortest path to the
destination but that path not even exists. In this paper, we aim to
develop a routing protocol for detection and prevention of black hole
attack by modifying AODV routing protocol. This protocol is able to
detect and prevent the black hole attack. Simulation is done using
NS-2, which shows the improvement in network performance.
Abstract: Search engine plays an important role in internet, to
retrieve the relevant documents among the huge number of web
pages. However, it retrieves more number of documents, which are
all relevant to your search topics. To retrieve the most meaningful
documents related to search topics, ranking algorithm is used in
information retrieval technique. One of the issues in data miming is
ranking the retrieved document. In information retrieval the ranking
is one of the practical problems. This paper includes various Page
Ranking algorithms, page segmentation algorithms and compares
those algorithms used for Information Retrieval. Diverse Page Rank
based algorithms like Page Rank (PR), Weighted Page Rank (WPR),
Weight Page Content Rank (WPCR), Hyperlink Induced Topic
Selection (HITS), Distance Rank, Eigen Rumor, Distance Rank Time
Rank, Tag Rank, Relational Based Page Rank and Query Dependent
Ranking algorithms are discussed and compared.
Abstract: Different services based on different switching
techniques in wireless networks leads to drastic changes in the
properties of network traffic. Because of these diversities in services,
network traffic is expected to undergo qualitative and quantitative
variations. Hence, assumption of traffic characteristics and the
prediction of network events become more complex for the wireless
networks. In this paper, the traffic characteristics have been studied
by collecting traces from the mobile switching centre (MSC). The
traces include initiation and termination time, originating node, home
station id, foreign station id. Traffic parameters namely, call interarrival
and holding times were estimated statistically. The results
show that call inter-arrival and distribution time in this wireless
network is heavy-tailed and follow gamma distributions. They are
asymptotically long-range dependent. It is also found that the call
holding times are best fitted with lognormal distribution. Based on
these observations, an analytical model for performance estimation is
also proposed.
Abstract: Recent advances in wireless networking technologies
introduce several energy aware routing protocols in sensor networks.
Such protocols aim to extend the lifetime of network by reducing the
energy consumption of nodes. Many researchers are looking for
certain challenges that are predominant in the grounds of energy
consumption. One such protocol that addresses this energy
consumption issue is ‘Cluster based hierarchical routing protocol’. In
this paper, we intend to discuss some of the major hierarchical
routing protocols adhering towards sensor networks. Furthermore, we
examine and compare several aspects and characteristics of few
widely explored hierarchical clustering protocols, and its operations
in wireless sensor networks (WSN). This paper also presents a
discussion on the future research topics and the challenges of
hierarchical clustering in WSNs.
Abstract: Opportunistic routing is used, where the network has
the features like dynamic topology changes and intermittent network
connectivity. In Delay tolerant network or Disruption tolerant
network opportunistic forwarding technique is widely used. The key
idea of opportunistic routing is selecting forwarding nodes to forward
data packets and coordination among these nodes to avoid duplicate
transmissions. This paper gives the analysis of pros and cons of
various opportunistic routing techniques used in MANET.
Abstract: One of the major goals of Spoken Dialog Systems
(SDS) is to understand what the user utters.
In the SDS domain, the Spoken Language Understanding (SLU)
Module classifies user utterances by means of a pre-definite
conceptual knowledge. The SLU module is able to recognize only the
meaning previously included in its knowledge base. Due the vastity
of that knowledge, the information storing is a very expensive
process.
Updating and managing the knowledge base are time-consuming
and error-prone processes because of the rapidly growing number of
entities like proper nouns and domain-specific nouns. This paper
proposes a solution to the problem of Name Entity Recognition
(NER) applied to a SDS domain. The proposed solution attempts to
automatically recognize the meaning associated with an utterance by
using the PANKOW (Pattern based Annotation through Knowledge
On the Web) method at runtime.
The method being proposed extracts information from the Web to
increase the SLU knowledge module and reduces the development
effort. In particular, the Google Search Engine is used to extract
information from the Facebook social network.
Abstract: The emergence of the Semantic Web technology
increases day by day due to the rapid growth of multiple web pages.
Many standard formats are available to store the semantic web data.
The most popular format is the Resource Description Framework
(RDF). Querying large RDF graphs becomes a tedious procedure
with a vast increase in the amount of data. The problem of query
optimization becomes an issue in querying large RDF graphs.
Choosing the best query plan reduces the amount of query execution
time. To address this problem, nature inspired algorithms can be used
as an alternative to the traditional query optimization techniques. In
this research, the optimal query plan is generated by the proposed
SAPSO algorithm which is a hybrid of Simulated Annealing (SA)
and Particle Swarm Optimization (PSO) algorithms. The proposed
SAPSO algorithm has the ability to find the local optimistic result
and it avoids the problem of local minimum. Experiments were
performed on different datasets by changing the number of predicates
and the amount of data. The proposed algorithm gives improved
results compared to existing algorithms in terms of query execution
time.
Abstract: ‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. In this paper, a novel feature based image steganalysis technique is proposed. Various statistical moments have been used along with some similarity metric. The proposed steganalysis technique has been designed based on transformation in four wavelet domains, which include Haar, Daubechies, Symlets and Biorthogonal. Each domain is being subjected to various classifiers, namely K-nearest-neighbor, K* Classifier, Locally weighted learning, Naive Bayes classifier, Neural networks, Decision trees and Support vector machines. The experiments are performed on a large set of pictures which are available freely in image database. The system also predicts the different message length definitions.
Abstract: An extensive amount of work has been done in data
clustering research under the unsupervised learning technique in Data
Mining during the past two decades. Moreover, several approaches
and methods have been emerged focusing on clustering diverse data
types, features of cluster models and similarity rates of clusters.
However, none of the single clustering algorithm exemplifies its best
nature in extracting efficient clusters. Consequently, in order to
rectify this issue, a new challenging technique called Cluster
Ensemble method was bloomed. This new approach tends to be the
alternative method for the cluster analysis problem. The main
objective of the Cluster Ensemble is to aggregate the diverse
clustering solutions in such a way to attain accuracy and also to
improve the eminence the individual clustering algorithms. Due to
the massive and rapid development of new methods in the globe of
data mining, it is highly mandatory to scrutinize a vital analysis of
existing techniques and the future novelty. This paper shows the
comparative analysis of different cluster ensemble methods along
with their methodologies and salient features. Henceforth this
unambiguous analysis will be very useful for the society of clustering
experts and also helps in deciding the most appropriate one to resolve
the problem in hand.
Abstract: This paper proposes a method to automatic transformation of CIM level to PIM level respecting the MDA approach. Our proposal is based on creating a good CIM level through well-defined rules allowing as achieving rich models that contain relevant information to facilitate the task of the transformation to the PIM level. We define, thereafter, an appropriate PIM level through various UML diagram. Next, we propose set rules to move from CIM to PIM. Our method follows the MDA approach by considering the business dimension in the CIM level through the use BPMN, standard modeling business of OMG, and the use of UML in PIM advocated by MDA in this level.
Abstract: Service Oriented Architecture (SOA) allows modeling of dynamic interaction between incongruous providers, which enables governing the development of complex applications. However, implementation of SOA comes with some challenges, including its adaptability and robustness. Dynamism is inherent to the nature of service based applications and of their running environment. These factors lead to necessity for dynamic adaptation. In this paper we try to describe basics and main structure of SOA adaptation process with a conceptual view to this issue. In this survey we will review the relevant adaptation approaches. This paper allows studying how different approaches deal with service oriented architecture adaptation life-cycle and provides basic guidelines for their analysis, evaluation and comparison.
Abstract: Compared with traditional distributed environment, the
net-centric environment brings on more demanding challenges for
information sharing with the characteristics of ultra-large scale and
strong distribution, dynamic, autonomy, heterogeneity, redundancy.
This paper realizes an information sharing model and a series of core
services, through which provides an open, flexible and scalable
information sharing platform.
Abstract: When it comes to last, it is regarded as the critical foundation of shoe design and development. Not only the last relates to the comfort of shoes wearing but also it aids the production of shoe styling and manufacturing. In order to enhance the efficiency and application of last development, a computer aided methodology for customized last form designs is proposed in this study. The reverse engineering is mainly applied to the process of scanning for the last form. Then the minimum energy is used for the revision of surface continuity, the surface of the last is reconstructed with the feature curves of the scanned last. When the surface of a last is reconstructed, based on the foundation of the proposed last form reconstruction module, the weighted arithmetic mean method is applied to the calculation on the shape morphing which differs from the grading for the control mesh of last, and the algorithm of subdivision is used to create the surface of last mesh, thus the feet-fitting 3D last form of different sizes is generated from its original form feature with functions remained. Finally, the practicability of the proposed methodology is verified through later case studies.
Abstract: Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when digital image is resized on a diagnostic monitor. In this paper we propose an automated grid artifactsdetection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches.
Abstract: Analysis of EEG brainwave provides information on mental or emotional states. One of the particular states that can have various applications in human machine interface (HMI) is concentration. 8-channel EEG signals were measured and analyzed. The concentration index was compared during resting and concentrating periods. Among eight channels, locations the frontal lobe (Fp1 and Fp2) showed a clear increase of the concentration index during concentration regardless of subjects. The rest six channels produced conflicting observations depending on subjects. At this time, it is not clear whether individual difference or how to concentrate made these results for the rest six channels. Nevertheless, it is expected that Fp1 and Fp2 are promising locations for extracting control signal for HMI applications.
Abstract: In a human tracking system, expanding a monitoring
range of one system is complicating the management of devices and
increasing its cost. Therefore, we propose a method to realize a
wide-range human tracking by connecting small systems. In this paper,
we examined an agent deploy method and information contents across
the heterogeneous human tracking systems. By implementing the
proposed method, we can construct a human tracking system across
heterogeneous systems, and the system can track a target continuously
between systems.
Abstract: In this paper, we propose a method that allows faster and more accurate detection of traffic lights by a vision sensor during driving, DGPS is used to obtain physical location of a traffic light, extract from the image information of the vision sensor only the traffic light area at this location and ascertain if the sign is in operation and determine its form. This method can solve the problem in existing research where low visibility at night or reflection under bright light makes it difficult to recognize the form of traffic light, thus making driving unstable. We compared our success rate of traffic light recognition in day and night road environments. Compared to previous researches, it showed similar performance during the day but 50% improvement at night.