Abstract: This article is dedicated to development of
mathematical models for determining the dynamics of
concentration of hazardous substances in urban turbulent
atmosphere. Development of the mathematical models implied
taking into account the time-space variability of the fields of
meteorological items and such turbulent atmosphere data as vortex
nature, nonlinear nature, dissipativity and diffusivity. Knowing the
turbulent airflow velocity is not assumed when developing the
model. However, a simplified model implies that the turbulent and
molecular diffusion ratio is a piecewise constant function that
changes depending on vertical distance from the earth surface.
Thereby an important assumption of vertical stratification of urban
air due to atmospheric accumulation of hazardous substances
emitted by motor vehicles is introduced into the mathematical
model. The suggested simplified non-linear mathematical model of
determining the sought exhaust concentration at a priori unknown
turbulent flow velocity through non-degenerate transformation is
reduced to the model which is subsequently solved analytically.
Abstract: University websites are considered as one of the brand primary touch points for multiple stakeholders, but most of them did not have great designs to create favorable impressions. Some of the elements that web designers should carefully consider are the appearance, the content, the functionality, usability and search engine optimization. However, priority should be placed on website simplicity and negative space. In terms of content, previous research suggests that universities should include reputation, learning environment, graduate career prospects, image destination, cultural integration, and virtual tour on their websites. The study examines how top 200 world ranking science and technology-based universities present their brands online and whether the websites capture the content dimensions. Content analysis of the websites revealed that the top ranking universities captured these dimensions at varying degree. Besides, the UK-based university had better priority on website simplicity and negative space compared to the Malaysian-based university.
Abstract: The security of their network remains the priorities of almost all companies. Existing security systems have shown their limit; thus a new type of security systems was born: honeypots. Honeypots are defined as programs or intended servers which have to attract pirates to study theirs behaviours. It is in this context that the leurre.com project of gathering about twenty platforms was born. This article aims to specify a model of honeypots attack. Our model describes, on a given platform, the evolution of attacks according to theirs hours. Afterward, we show the most attacked services by the studies of attacks on the various ports. It is advisable to note that this article was elaborated within the framework of the research projects on honeyspots within the LABTIC (Laboratory of Information Technologies and Communication).
Abstract: Intrusion Detection System is significant in network
security. It detects and identifies intrusion behavior or intrusion
attempts in a computer system by monitoring and analyzing the
network packets in real time. In the recent year, intelligent algorithms
applied in the intrusion detection system (IDS) have been an
increasing concern with the rapid growth of the network security.
IDS data deals with a huge amount of data which contains irrelevant
and redundant features causing slow training and testing process,
higher resource consumption as well as poor detection rate. Since the
amount of audit data that an IDS needs to examine is very large even
for a small network, classification by hand is impossible. Hence, the
primary objective of this review is to review the techniques prior to
classification process suit to IDS data.
Abstract: This paper proposes a solution to the motion planning
and control problem of a point-mass robot which is required to move
safely to a designated target in a priori known workspace cluttered
with fixed elliptical obstacles of arbitrary position and sizes. A
tailored and unique algorithm for target convergence and obstacle
avoidance is proposed that will work for any number of fixed
obstacles. The control laws proposed in this paper also ensures that
the equilibrium point of the given system is asymptotically stable.
Computer simulations with the proposed technique and applications
to a planar (RP) manipulator will be presented.
Abstract: This paper presents the development of a Bayesian
belief network classifier for prediction of graft status and survival
period in renal transplantation using the patient profile information
prior to the transplantation. The objective was to explore feasibility
of developing a decision making tool for identifying the most suitable
recipient among the candidate pool members. The dataset was
compiled from the University of Toledo Medical Center Hospital
patients as reported to the United Network Organ Sharing, and had
1228 patient records for the period covering 1987 through 2009. The
Bayes net classifiers were developed using the Weka machine
learning software workbench. Two separate classifiers were induced
from the data set, one to predict the status of the graft as either failed
or living, and a second classifier to predict the graft survival period.
The classifier for graft status prediction performed very well with a
prediction accuracy of 97.8% and true positive values of 0.967 and
0.988 for the living and failed classes, respectively. The second
classifier to predict the graft survival period yielded a prediction
accuracy of 68.2% and a true positive rate of 0.85 for the class
representing those instances with kidneys failing during the first year
following transplantation. Simulation results indicated that it is
feasible to develop a successful Bayesian belief network classifier for
prediction of graft status, but not the graft survival period, using the
information in UNOS database.
Abstract: Tracing and locating the geographical location of users (Geolocation) is used extensively in todays Internet. Whenever we, e.g., request a page from google we are - unless there was a specific configuration made - automatically forwarded to the page with the relevant language and amongst others, dependent on our location identified, specific commercials are presented. Especially within the area of Network Security, Geolocation has a significant impact. Because of the way the Internet works, attacks can be executed from almost everywhere. Therefore, for an attribution, knowledge of the origination of an attack - and thus Geolocation - is mandatory in order to be able to trace back an attacker. In addition, Geolocation can also be used very successfully to increase the security of a network during operation (i.e. before an intrusion actually has taken place). Similar to greylisting in emails, Geolocation allows to (i) correlate attacks detected with new connections and (ii) as a consequence to classify traffic a priori as more suspicious (thus particularly allowing to inspect this traffic in more detail). Although numerous techniques for Geolocation are existing, each strategy is subject to certain restrictions. Following the ideas of Endo et al., this publication tries to overcome these shortcomings with a combined solution of different methods to allow improved and optimized Geolocation. Thus, we present our architecture for improved Geolocation, by designing a new algorithm, which combines several Geolocation techniques to increase the accuracy.
Abstract: Wireless sensor networks are consisted of hundreds or
thousands of small sensors that have limited resources.
Energy-efficient techniques are the main issue of wireless sensor
networks. This paper proposes an energy efficient agent-based
framework in wireless sensor networks. We adopt biologically
inspired approaches for wireless sensor networks. Agent operates
automatically with their behavior policies as a gene. Agent aggregates
other agents to reduce communication and gives high priority to nodes
that have enough energy to communicate. Agent behavior policies are
optimized by genetic operation at the base station. Simulation results
show that our proposed framework increases the lifetime of each node.
Each agent selects a next-hop node with neighbor information and
behavior policies. Our proposed framework provides self-healing,
self-configuration, self-optimization properties to sensor nodes.
Abstract: In the age of global communications, heterogeneous
networks are seen to be the best choice of strategy to ensure continuous and uninterruptible services. This will allow mobile
terminal to stay in connection even they are migrating into different segment coverage through the handoff process. With the increase of
teletraffic demands in mobile cellular system, hierarchical cellular systems have been adopted extensively for more efficient channel
utilization and better QoS (Quality of Service). This paper presents a
bidirectional call overflow scheme between two layers of microcells and macrocells, where handoffs are decided by the velocity of mobile
making the call. To ensure that handoff calls are given higher priorities, it is assumed that guard channels are assigned in both
macrocells and microcells. A hysteresis value introduced in mobile velocity is used to allow mobile roam in the same cell if its velocity
changes back within the set threshold values. By doing this the number of handoffs is reduced thereby reducing the processing overhead and enhancing the quality of service to the end user.
Abstract: Network security attacks are the violation of
information security policy that received much attention to the
computational intelligence society in the last decades. Data mining
has become a very useful technique for detecting network intrusions
by extracting useful knowledge from large number of network data
or logs. Naïve Bayesian classifier is one of the most popular data
mining algorithm for classification, which provides an optimal way
to predict the class of an unknown example. It has been tested that
one set of probability derived from data is not good enough to have
good classification rate. In this paper, we proposed a new learning
algorithm for mining network logs to detect network intrusions
through naïve Bayesian classifier, which first clusters the network
logs into several groups based on similarity of logs, and then
calculates the prior and conditional probabilities for each group of
logs. For classifying a new log, the algorithm checks in which cluster
the log belongs and then use that cluster-s probability set to classify
the new log. We tested the performance of our proposed algorithm by
employing KDD99 benchmark network intrusion detection dataset,
and the experimental results proved that it improves detection rates
as well as reduces false positives for different types of network
intrusions.
Abstract: Safety, river environment, and sediment utilization are the elements of the target of sediment management. As a change in an element by sediment management, may affect the other two elements, and the priority among three elements depends on stakeholders. It is necessary to develop a method to evaluate the effect of sediment management on each element and an integrated evaluation method for socio-economic effect. In this study, taking Mount Merapi basin as an investigation field, the method for an active volcanic basin was developed. An integrated evaluation method for sediment management was discussed from a socio-economic point on safety, environment, and sediment utilization and a case study of sediment management was evaluated by means of this method. To evaluate the effect of sediment management, some parameters on safety, utilization, and environment have been introduced. From a utilization point of view, job opportunity, additional income of local people, and tax income to local government were used to evaluate the effectiveness of sediment management. The risk degree of river infrastructure was used to describe the effect of sediment management on a safety aspect. To evaluate the effects of sediment management on environment, the mean diameter of grain size distribution of riverbed surface was used. On the coordinate system designating these elements, the direction of change in basin condition by sediment management can be predicted, so that the most preferable sediment management can be decided. The results indicate that the cases of sediment management tend to give the negative impacts on sediment utilization. However, these sediment managements will give positive impacts on safety and environment condition. Evaluation result from a social-economic point of view shows that the case study of sediment management reduces job opportunity and additional income for inhabitants as well as tax income for government. Therefore, it is necessary to make another policy for creating job opportunity for inhabitants to support these sediment managements.
Abstract: Image compression is one of the most important
applications Digital Image Processing. Advanced medical imaging
requires storage of large quantities of digitized clinical data. Due to
the constrained bandwidth and storage capacity, however, a medical
image must be compressed before transmission and storage. There
are two types of compression methods, lossless and lossy. In Lossless
compression method the original image is retrieved without any
distortion. In lossy compression method, the reconstructed images
contain some distortion. Direct Cosine Transform (DCT) and Fractal
Image Compression (FIC) are types of lossy compression methods.
This work shows that lossy compression methods can be chosen for
medical image compression without significant degradation of the
image quality. In this work DCT and Fractal Compression using
Partitioned Iterated Function Systems (PIFS) are applied on different
modalities of images like CT Scan, Ultrasound, Angiogram, X-ray
and mammogram. Approximately 20 images are considered in each
modality and the average values of compression ratio and Peak
Signal to Noise Ratio (PSNR) are computed and studied. The quality
of the reconstructed image is arrived by the PSNR values. Based on
the results it can be concluded that the DCT has higher PSNR values
and FIC has higher compression ratio. Hence in medical image
compression, DCT can be used wherever picture quality is preferred
and FIC is used wherever compression of images for storage and
transmission is the priority, without loosing picture quality
diagnostically.
Abstract: In the past few years, the use of wireless sensor networks (WSNs) potentially increased in applications such as intrusion detection, forest fire detection, disaster management and battle field. Sensor nodes are generally battery operated low cost devices. The key challenge in the design and operation of WSNs is to prolong the network life time by reducing the energy consumption among sensor nodes. Node clustering is one of the most promising techniques for energy conservation. This paper presents a novel clustering algorithm which maximizes the network lifetime by reducing the number of communication among sensor nodes. This approach also includes new distributed cluster formation technique that enables self-organization of large number of nodes, algorithm for maintaining constant number of clusters by prior selection of cluster head and rotating the role of cluster head to evenly distribute the energy load among all sensor nodes.
Abstract: Dill (Anethum graveolens L.) is a popular herb used in
many regions, including Baltic countries. Dill is widely used for
flavoring foods and beverages due to its pleasant spicy aroma. The
aim of this work was to determine the best blanching method for
processing of dill prior to microwave vacuum drying based on
sensory properties, color and volatile compounds in dried product.
Two blanching mediums were used – water and steam, and for part of
samples microwave pretreatment was additionally used. Evaluation of
dried dill volatile aroma compounds, color changes and sensory
attributes was performed. Results showed that blanching significantly
influences the quality of dried dill. After evaluation of volatile aroma
compounds, color and sensory properties of microwave vacuum dried
dill, as the best method for dill pretreatment was established
blanching at 90 °C for 30 s.
Abstract: Low power consumption is a major constraint for battery-powered system like computer notebook or PDA. In the past, specialists usually designed both specific optimized equipments and codes to relief this concern. Doing like this could work for quite a long time, however, in this era, there is another significant restraint, the time to market. To be able to serve along the power constraint while can launch products in shorter production period, objectoriented programming (OOP) has stepped in to this field. Though everyone knows that OOP has quite much more overhead than assembly and procedural languages, development trend still heads to this new world, which contradicts with the target of low power consumption. Most of the prior power related software researches reported that OOP consumed much resource, however, as industry had to accept it due to business reasons, up to now, no papers yet had mentioned about how to choose the best OOP practice in this power limited boundary. This article is the pioneer that tries to specify and propose the optimized strategy in writing OOP software under energy concerned environment, based on quantitative real results. The language chosen for studying is C# based on .NET Framework 2.0 which is one of the trendy OOP development environments. The recommendation gotten from this research would be a good roadmap that can help developers in coding that well balances between time to market and time of battery.
Abstract: Waiting times and queues are a daily problem for theme parks. Fast lines or priority queues appear as a solution for a specific segment of customers, that is, tourists who are willing to pay to avoid waiting. This paper analyzes the fast line system and explores the factors that affect the decision to purchase a fast line pass. A greater understanding of these factors may help companies to design appropriate products and services. This conceptual paper was based on a literature review in marketing and consumer behavior. Additional research was identified in related disciplines such as leisure studies, psychology, and sociology. A conceptual framework of the factors influencing the decision to purchase a fast line pass is presented.
Abstract: This paper presents Qmulus- a Cloud Based GPS
Model. Qmulus is designed to compute the best possible route which
would lead the driver to the specified destination in the shortest time
while taking into account real-time constraints. Intelligence
incorporated to Qmulus-s design makes it capable of generating and
assigning priorities to a list of optimal routes through customizable
dynamic updates. The goal of this design is to minimize travel and
cost overheads, maintain reliability and consistency, and implement
scalability and flexibility. The model proposed focuses on
reducing the bridge between a Client Application and a Cloud
service so as to render seamless operations. Qmulus-s system
model is closely integrated and its concept has the potential to be
extended into several other integrated applications making it capable
of adapting to different media and resources.
Abstract: In recent years, global warming has become a
worldwide problem. The reduction of carbon dioxide emissions is a
top priority for many companies in the manufacturing industry. In the
automobile industry as well, the reduction of carbon dioxide emissions
is one of the most important issues. Technology to reduce the weight
of automotive parts improves the fuel economy of automobiles, and is
an important technology for reducing carbon dioxide. Also, even if
this weight reduction technology is applied to electric automobiles
rather than gasoline automobiles, reducing energy consumption
remains an important issue. Plastic processing of hollow pipes is one
important technology for realizing the weight reduction of automotive
parts. Ohashi et al. [1],[2] present an example of research on pipe
formation in which a process was carried out to enlarge a pipe
diameter using a lost core, achieving the suppression of wall thickness
reduction and greater pipe expansion than hydroforming.
In this study, we investigated a method to increase the wall
thickness of a pipe through pipe compression using planetary rolls.
The establishment of a technology whereby the wall thickness of a
pipe can be controlled without buckling the pipe is an important
technology for the weight reduction of products. Using the finite
element analysis method, we predicted that it would be possible to
increase the compression of an aluminum pipe with a 3mm wall
thickness by approximately 20%, and wall thickness by approximately
20% by pressing the hollow pipe with planetary rolls.
Abstract: As a security mechanism, authorization is to provide access control to the system resources according to the polices and rules specified by the security strategies. Either by update or in the initial specification, conflicts in authorization is an issue needs to be solved. In this paper, we propose a new approach to solve conflict by using prioritized logic programs and discuss the uniqueness of its answer set. Addressing conflict resolution from logic programming viewpoint and the uniqueness analysis of the answer set provide a novel, efficient approach for authorization conflict resolution.
Abstract: In this paper performance of Puma 560
manipulator is being compared for hybrid gradient descent
and least square method learning based ANFIS controller with
hybrid Genetic Algorithm and Generalized Pattern Search
tuned radial basis function based Neuro-Fuzzy controller.
ANFIS which is based on Takagi Sugeno type Fuzzy
controller needs prior knowledge of rule base while in radial
basis function based Neuro-Fuzzy rule base knowledge is not
required. Hybrid Genetic Algorithm with generalized Pattern
Search is used for tuning weights of radial basis function
based Neuro- fuzzy controller. All the controllers are checked
for butterfly trajectory tracking and results in the form of
Cartesian and joint space errors are being compared. ANFIS
based controller is showing better performance compared to
Radial Basis Function based Neuro-Fuzzy Controller but rule
base independency of RBF based Neuro-Fuzzy gives it an
edge over ANFIS