Abstract: Feature selection has been used in many fields such as
classification, data mining and object recognition and proven to be
effective for removing irrelevant and redundant features from the
original dataset. In this paper, a new design of distributed intrusion
detection system using a combination feature selection model based
on bees and decision tree. Bees algorithm is used as the search
strategy to find the optimal subset of features, whereas decision tree
is used as a judgment for the selected features. Both the produced
features and the generated rules are used by Decision Making Mobile
Agent to decide whether there is an attack or not in the networks.
Decision Making Mobile Agent will migrate through the networks,
moving from node to another, if it found that there is an attack on one
of the nodes, it then alerts the user through User Interface Agent or
takes some action through Action Mobile Agent. The KDD Cup 99
dataset is used to test the effectiveness of the proposed system. The
results show that even if only four features are used, the proposed
system gives a better performance when it is compared with the
obtained results using all 41 features.
Abstract: As the Silicon oxide scaled down in MOSFET
technology to few nanometers, gate Direct Tunneling (DT) in
Floating gate (FGMOSFET) devices has become a major concern for
analog designers. FGMOSFET has been used in many low-voltage
and low-power applications, however, there is no accurate model that
account for DT gate leakage in nano-scale. This paper studied and
analyzed different simulation models for FGMOSFET using TSMC
90-nm technology. The simulation results for FGMOSFET cascade
current mirror shows the impact of DT on circuit performance in
terms of current and voltage without the need for fabrication. This
works shows the significance of using an accurate model for
FGMOSFET in nan-scale technologies.
Abstract: DNA analysis has been widely accepted as providing
valuable evidence concerning the identity of the source of biological
traces. Our work has showed that DNA samples can survive on
cartridges even after firing. The study also raised the possibility of
determining other information such as the age of the donor. Such
information may be invaluable in certain cases where spent cartridges
from automatic weapons are left behind at the scene of a crime. In
spite of the nature of touch evidence and exposure to high chamber
temperatures during shooting, we were still capable to retrieve
enough DNA for profile typing. In order to estimate age of
contributor, DNA methylation levels were analyzed using EpiTect
system for retrieved DNA. However, results were not conclusive, due
to low amount of input DNA.
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: File sharing in networks is generally achieved using
Peer-to-Peer (P2P) applications. Structured P2P approaches are
widely used in adhoc networks due to its distributed and scalability
features. Efficient mechanisms are required to handle the huge
amount of data distributed to all peers. The intrinsic characteristics of
P2P system makes for easier content distribution when compared to
client-server architecture. All the nodes in a P2P network act as both
client and server, thus, distributing data takes lesser time when
compared to the client-server method. CHORD protocol is a resource
routing based where nodes and data items are structured into a 1-
dimensional ring. The structured lookup algorithm of Chord is
advantageous for distributed P2P networking applications. However,
structured approach improves lookup performance in a high
bandwidth wired network it could contribute to unnecessary overhead
in overlay networks leading to degradation of network performance.
In this paper, the performance of existing CHORD protocol on
Wireless Mesh Network (WMN) when nodes are static and dynamic
is investigated.
Abstract: Workflow scheduling is an important part of cloud
computing and based on different criteria it decides cost, execution
time, and performances. A cloud workflow system is a platform
service facilitating automation of distributed applications based on
new cloud infrastructure. An aspect which differentiates cloud
workflow system from others is market-oriented business model, an
innovation which challenges conventional workflow scheduling
strategies. Time and Cost optimization algorithm for scheduling
Hybrid Clouds (TCHC) algorithm decides which resource should be
chartered from public providers is combined with a new De-De
algorithm considering that every instance of single and multiple
workflows work without deadlocks. To offset this, two new concepts
- De-De Dodging Algorithm and Priority Based Decisive Algorithm -
combine with conventional deadlock avoidance issues by proposing
one algorithm that maximizes active (not just allocated) resource use
and reduces Makespan.
Abstract: Particle size distribution, the most important
characteristics of aerosols, is obtained through electrical
characterization techniques. The dynamics of charged nanoparticles
under the influence of electric field in Electrical Mobility
Spectrometer (EMS) reveals the size distribution of these particles.
The accuracy of this measurement is influenced by flow conditions,
geometry, electric field and particle charging process, therefore by
the transfer function (transfer matrix) of the instrument. In this work,
a wire-cylinder corona charger was designed and the combined fielddiffusion
charging process of injected poly-disperse aerosol particles
was numerically simulated as a prerequisite for the study of a
multichannel EMS. The result, a cloud of particles with no uniform
charge distribution, was introduced to the EMS. The flow pattern and
electric field in the EMS were simulated using Computational Fluid
Dynamics (CFD) to obtain particle trajectories in the device and
therefore to calculate the reported signal by each electrometer.
According to the output signals (resulted from bombardment of
particles and transferring their charges as currents), we proposed a
modification to the size of detecting rings (which are connected to
electrometers) in order to evaluate particle size distributions more
accurately. Based on the capability of the system to transfer
information contents about size distribution of the injected particles,
we proposed a benchmark for the assessment of optimality of the
design. This method applies the concept of Von Neumann entropy
and borrows the definition of entropy from information theory
(Shannon entropy) to measure optimality. Entropy, according to the
Shannon entropy, is the ''average amount of information contained in
an event, sample or character extracted from a data stream''.
Evaluating the responses (signals) which were obtained via various
configurations of detecting rings, the best configuration which gave
the best predictions about the size distributions of injected particles,
was the modified configuration. It was also the one that had the
maximum amount of entropy. A reasonable consistency was also
observed between the accuracy of the predictions and the entropy
content of each configuration. In this method, entropy is extracted
from the transfer matrix of the instrument for each configuration.
Ultimately, various clouds of particles were introduced to the
simulations and predicted size distributions were compared to the
exact size distributions.
Abstract: The objective of the study is to assess the
implementation of LED lighting into forest machine work in the dark.
In addition, the paper includes a wide variety of important and
relevant safety and health parameters. In modern, computerized work
in the cab of forest machines, artificial illumination is a demanding
task when performing duties, such as the visual inspections of wood
and computer calculations. We interviewed entrepreneurs and
gathered the following as the most pertinent themes: (1) safety, (2)
practical problems, and (3) work with LED lighting. The most
important comments were in regards to the practical problems of
LED lighting. We found indications of technical problems in
implementing LED lighting, like snow and dirt on the surfaces of
lamps that dim the emission of light. Moreover, service work in the
dark forest is dangerous and increases the risks of on-site accidents.
We also concluded that the amount of blue light to the eyes should be
assessed, especially, when the drivers are working in a semi-dark cab.
Abstract: The design of Reverse logistics Network has attracted
growing attention with the stringent pressures from both
environmental awareness and business sustainability. Reverse
logistical activities include return, remanufacture, disassemble and
dispose of products can be quite complex to manage. In addition,
demand can be difficult to predict, and decision making is one of the
challenges task in such network. This complexity has amplified the
need to develop an integrated architecture for product return as an
enterprise system. The main purpose of this paper is to design Multi
Agent System (MAS) architecture using the Prometheus
methodology to efficiently manage reverse logistics processes. The
proposed MAS architecture includes five types of agents: Gate
keeping Agent, Collection Agent, Sorting Agent, Processing Agent
and Disposal Agent which act respectively during the five steps of
reverse logistics Network.
Abstract: This article presents a vibration diagnostic method
designed for Permanent Magnets (PM) electrical machines–traction
motors and generators. Those machines are commonly used in traction
drives of electrical vehicles and small wind or water systems. The
described method is very innovative and unique. Specific structural
properties of machines excited by permanent magnets are used in this
method - electromotive force (EMF) generated due to vibrations. There
was analyzed number of publications, which describe vibration
diagnostic methods, and tests of electrical machines and there was no
method found to determine the technical condition of such machine
basing on their own signals. This work presents field-circuit model,
results of static tests, results of calculations and simulations.
Abstract: In this paper, we provided a literature survey on the
artificial stock problem (ASM). The paper began by exploring the
complexity of the stock market and the needs for ASM. ASM
aims to investigate the link between individual behaviors (micro
level) and financial market dynamics (macro level). The variety of
patterns at the macro level is a function of the AFM complexity. The
financial market system is a complex system where the relationship
between the micro and macro level cannot be captured analytically.
Computational approaches, such as simulation, are expected to
comprehend this connection. Agent-based simulation is a simulation
technique commonly used to build AFMs. The paper proceeds by
discussing the components of the ASM. We consider the roles
of behavioral finance (BF) alongside the traditionally risk-averse
assumption in the construction of agent’s attributes. Also, the
influence of social networks in the developing of agents interactions is
addressed. Network topologies such as a small world, distance-based,
and scale-free networks may be utilized to outline economic
collaborations. In addition, the primary methods for developing
agents learning and adaptive abilities have been summarized.
These incorporated approach such as Genetic Algorithm, Genetic
Programming, Artificial neural network and Reinforcement Learning.
In addition, the most common statistical properties (the stylized facts)
of stock that are used for calibration and validation of ASM are
discussed. Besides, we have reviewed the major related previous
studies and categorize the utilized approaches as a part of these
studies. Finally, research directions and potential research questions
are argued. The research directions of ASM may focus on the macro
level by analyzing the market dynamic or on the micro level by
investigating the wealth distributions of the agents.
Abstract: Bloom’s Taxonomy has been changed during the
years. The idea of this writing is about the revision that has happened
in both facts and terms. It also contains case studies of using
cognitive Bloom’s taxonomy in teaching geometric solids to the
secondary school students, affective objectives in a creative
workshop for adults and psychomotor objectives in fixing a
malfunctioned refrigerator lamp. There is also pointed to the
important role of classification objectives in adult education as a way
to prevent memory loss.
Abstract: Nowadays, several research studies point up that an
active lifestyle is essential for physical and mental health benefits.
Mobile phones have greatly influenced people’s habits and attitudes
also in the way they exercise. Our research work is mainly focused on
investigating how to exploit mobile technologies to favour people’s
exertion experience. To this end, we developed an exertion framework
users can exploit through a real world mobile application, called
EverywhereSport Run (EWRun), designed to act as a virtual personal
trainer to support runners during their trainings. In this work, inspired
by both previous findings in the field of interaction design for people
with visual impairments, feedback gathered from real users of our
framework, and positive results obtained from two experimentations,
we present some new interaction facilities we designed to enhance
the interaction experience during a training. The positive obtained
results helped us to derive some interaction design recommendations
we believe will be a valid support for designers of future mobile
systems conceived to be used in circumstances where there are limited
possibilities of interaction.
Abstract: The development of electric vehicle batteries have
resulted in very high energy density lithium-ion batteries. However,
this progress is accompanied by the risk of thermal runaway, which
can result in serious accidents. Heat pipes are heat exchangers that
are suitable to be applied in electric vehicle battery thermal
management for their lightweight, compact size and do not require
external power supply. This paper aims to examine experimentally a
Flat Plate Loop Heat Pipe (FPLHP) performance as a heat exchanger
in thermal management system of lithium-ion battery for electric
vehicle application. The heat generation of the battery was simulated
using a cartridge heater. Stainless steel screen mesh was used as the
capillary wick. Distilled water, alcohol and acetone were used as
working fluids with a filling ratio of 60%. It was found that acetone
gives the best performance that produces thermal resistance of 0.22
W/°C with 50°C evaporator temperature at heat flux load of 1.61
W/cm2.
Abstract: Mobile Adhoc Networks (MANETs) are
infrastructure-less, dynamic network of collections of wireless mobile
nodes communicating with each other without any centralized
authority. A MANET is a mobile device of interconnections through
wireless links, forming a dynamic topology. Routing protocols have a
big role in data transmission across a network. Routing protocols,
two major classifications are unipath and multipath. This study
evaluates performance of an on-demand multipath routing protocol
named Adhoc On-demand Multipath Distance Vector routing
(AOMDV). This study proposes Energy Aware AOMDV (EAAOMDV)
an extension of AOMDV which decreases energy
consumed on a route.
Abstract: Voting algorithms are extensively used to make
decisions in fault tolerant systems where each redundant module
gives inconsistent outputs. Popular voting algorithms include
majority voting, weighted voting, and inexact majority voters. Each
of these techniques suffers from scenarios where agreements do not
exist for the given voter inputs. This has been successfully overcome
in literature using fuzzy theory. Our previous work concentrated on a
neuro-fuzzy algorithm where training using the neuro system
substantially improved the prediction result of the voting system.
Weight training of Neural Network is sub-optimal. This study
proposes to optimize the weights of the Neural Network using
Artificial Bee Colony algorithm. Experimental results show the
proposed system improves the decision making of the voting
algorithms.
Abstract: Based on application requirements, nodes are static or
mobile in Wireless Sensor Networks (WSNs). Mobility poses
challenges in protocol design, especially at the link layer requiring
mobility adaptation algorithms to localize mobile nodes and predict
link quality to be established with them. This study implements
XMAC and Berkeley Media Access Control (BMAC) routing
protocols to evaluate performance under WSN’s static and mobility
conditions. This paper gives a comparative study of mobility-aware
MAC protocols. Routing protocol performance, based on Average
End to End Delay, Average Packet Delivery Ratio, Average Number
of hops, and Jitter is evaluated.
Abstract: Phonocardiography is important in appraisal of
congenital heart disease and pulmonary hypertension as it reflects the
duration of right ventricular systoles. The systolic murmur in patients
with intra-cardiac shunt decreases as pulmonary hypertension
develops and may eventually disappear completely as the pulmonary
pressure reaches systemic level. Phonocardiography and auscultation
are non-invasive, low-cost, and accurate methods to assess heart
disease. In this work an objective signal processing tool to extract
information from phonocardiography signal using Wavelet is
proposed to classify the murmur as normal or abnormal. Since the
feature vector is large, a Binary Particle Swarm Optimization (PSO)
with mutation for feature selection is proposed. The extracted
features improve the classification accuracy and were tested across
various classifiers including Naïve Bayes, kNN, C4.5, and SVM.
Abstract: To tackle the air pollution issues, Plug-in Hybrid
Electric Vehicles (PHEVs) are proposed as an appropriate solution.
Charging a large amount of PHEV batteries, if not controlled, would
have negative impacts on the distribution system. The control process
of charging of these vehicles can be centralized in parking lots that
may provide a chance for better coordination than the individual
charging in houses. In this paper, an optimization-based approach is
proposed to determine the optimum PHEV parking capacities in
candidate nodes of the distribution system. In so doing, a profile for
charging and discharging of PHEVs is developed in order to flatten
the network load profile. Then, this profile is used in solving an
optimization problem to minimize the distribution system losses. The
outputs of the proposed method are the proper place for PHEV
parking lots and optimum capacity for each parking. The application
of the proposed method on the IEEE-34 node test feeder verifies the
effectiveness of the method.
Abstract: ANDASA is a knowledge management platform for
the capitalization of knowledge and cultural assets for the artistic and
cultural sectors. It was built based on the priorities expressed by the
participating artists. Through mapping artistic activities and
specificities, it enables to highlight various aspects of the artistic
research and production. Such instrument will contribute to create
networks and partnerships, as it enables to evidentiate who does
what, in what field, using which methodology. The platform is
accessible to network participants and to the general public.