Abstract: The purpose of this study is to determine the
circumstances affecting elementary school students in their family
and school lives and what kind of emotions children may feel
because of these circumstances. The study was carried out according
to the survey model. Four Turkish elementary schools provided 123
fourth grade students for participation in the study. The study-s data
were collected by using worksheets for the activity titled “Important
Days in Our Lives", which was part of the Elementary School Social
Sciences Course 4th Grade Education Program. Data analysis was
carried out according to the content analysis technique used in
qualitative research. The study detected that circumstances of their
family and school lives caused children to feel emotions such as
happiness, sadness, anger, fear and jealousy. The circumstances and
the emotions caused by these circumstances were analyzed according
to gender and interpreted by presenting them with their frequencies.
Abstract: A novel file splitting technique for the reduction of the nth-order entropy of text files is proposed. The technique is based on mapping the original text file into a non-ASCII binary file using a new codeword assignment method and then the resulting binary file is split into several subfiles each contains one or more bits from each codeword of the mapped binary file. The statistical properties of the subfiles are studied and it is found that they reflect the statistical properties of the original text file which is not the case when the ASCII code is used as a mapper. The nth-order entropy of these subfiles are determined and it is found that the sum of their entropies is less than that of the original text file for the same values of extensions. These interesting statistical properties of the resulting subfiles can be used to achieve better compression ratios when conventional compression techniques are applied to these subfiles individually and on a bit-wise basis rather than on character-wise basis.
Abstract: Network coding has recently attracted attention as an efficient technique in multicast/broadcast services. The problem of finding the optimal network coding mechanism maximizing the bandwidth efficiency is hard to solve and hard to approximate. Lots of network coding-based schemes have been suggested in the literature to improve the bandwidth efficiency, especially network coding-based automatic repeat request (NCARQ) schemes. However, existing schemes have several limitations which cause the performance degradation in resource limited systems. To improve the performance in resource limited systems, we propose NCARQ with overlapping selection (OS-NCARQ) scheme. The advantages of OS-NCARQ scheme over the traditional ARQ scheme and existing NCARQ schemes are shown through the analysis and simulations.
Abstract: This paper presents a new hardware interface using a
microcontroller which processes audio music signals to standard
MIDI data. A technique for processing music signals by extracting
note parameters from music signals is described. An algorithm to
convert the voice samples for real-time processing without complex
calculations is proposed. A high frequency microcontroller as the
main processor is deployed to execute the outlined algorithm. The
MIDI data generated is transmitted using the EIA-232 protocol. The
analyses of data generated show the feasibility of using
microcontrollers for real-time MIDI generation hardware interface.
Abstract: This article is devoted to the numerical solution of
large-scale quadratic eigenvalue problems. Such problems arise in
a wide variety of applications, such as the dynamic analysis of
structural mechanical systems, acoustic systems, fluid mechanics,
and signal processing. We first introduce a generalized second-order
Krylov subspace based on a pair of square matrices and two initial
vectors and present a generalized second-order Arnoldi process for
constructing an orthonormal basis of the generalized second-order
Krylov subspace. Then, by using the projection technique and the
refined projection technique, we propose a restarted generalized
second-order Arnoldi method and a restarted refined generalized
second-order Arnoldi method for computing some eigenpairs of largescale
quadratic eigenvalue problems. Some theoretical results are also
presented. Some numerical examples are presented to illustrate the
effectiveness of the proposed methods.
Abstract: Agriculture products are being more demanding in
market today. To increase its productivity, automation to produce
these products will be very helpful. The purpose of this work is to
measure and determine the ripeness and quality of watermelon. The
textures on watermelon skin will be captured using digital camera.
These images will be filtered using image processing technique. All
these information gathered will be trained using ANN to determine
the watermelon ripeness accuracy. Initial results showed that the best
model has produced percentage accuracy of 86.51%, when measured
at 32 hidden units with a balanced percentage rate of training dataset.
Abstract: This paper may be considered as combination of both pervasive computing and Differential GPS (global positioning satellite) which relates to control automatic traffic signals in such a
way as to pre-empt normal signal operation and permit lifesaving vehicles. Before knowing the arrival of the lifesaving vehicles from
the signal there is a chance of clearing the traffic. Traffic signal
preemption system includes a vehicle equipped with onboard computer system capable of capturing diagnostic information and
estimated location of the lifesaving vehicle using the information provided by GPS receiver connected to the onboard computer system
and transmitting the information-s using a wireless transmitter via a
wireless network. The fleet management system connected to a
wireless receiver is capable of receiving the information transmitted
by the lifesaving vehicle .A computer is also located at the
intersection uses corrected vehicle position, speed & direction
measurements, in conjunction with previously recorded data defining
approach routes to the intersection, to determine the optimum time to
switch a traffic light controller to preemption mode so that lifesaving
vehicles can pass safely. In case when the ambulance need to take a
“U" turn in a heavy traffic area we suggest a solution. Now we are
going to make use of computerized median which uses LINKED
BLOCKS (removable) to solve the above problem.
Abstract: The Globally Asynchronous Locally Synchronous Network on Chip (GALS NoC) is the most efficient solution that provides low latency transfers and power efficient System on Chip (SoC) interconnect. This study presents a GALS and generic NoC architecture based on a configurable router. This router integrates a sophisticated dynamic arbiter, the wormhole routing technique and can be configured in a manner that allows it to be used in many possible NoC topologies such as Mesh 2-D, Tree and Polygon architectures. This makes it possible to improve the quality of service (QoS) required by the proposed NoC. A comparative performances study of the proposed NoC architecture, Tore architecture and of the most used Mesh 2D architecture is performed. This study shows that Spidergon architecture is characterised by the lower latency and the later saturation. It is also shown that no matter what the number of used links is raised; the Links×Diameter product permitted by the Spidergon architecture remains always the lower. The only limitation of this architecture comes from it-s over cost in term of silicon area.
Abstract: This paper proposes a novel architecture for developing decision support systems. Unlike conventional decision support systems, the proposed architecture endeavors to reveal the decision-making process such that humans' subjectivity can be incorporated into a computerized system and, at the same time, to preserve the capability of the computerized system in processing information objectively. A number of techniques used in developing the decision support system are elaborated to make the decisionmarking process transparent. These include procedures for high dimensional data visualization, pattern classification, prediction, and evolutionary computational search. An artificial data set is first employed to compare the proposed approach with other methods. A simulated handwritten data set and a real data set on liver disease diagnosis are then employed to evaluate the efficacy of the proposed approach. The results are analyzed and discussed. The potentials of the proposed architecture as a useful decision support system are demonstrated.
Abstract: Keystroke authentication is a new access control system
to identify legitimate users via their typing behavior. In this paper,
machine learning techniques are adapted for keystroke authentication.
Seven learning methods are used to build models to differentiate user
keystroke patterns. The selected classification methods are Decision
Tree, Naive Bayesian, Instance Based Learning, Decision Table, One
Rule, Random Tree and K-star. Among these methods, three of them
are studied in more details. The results show that machine learning
is a feasible alternative for keystroke authentication. Compared to
the conventional Nearest Neighbour method in the recent research,
learning methods especially Decision Tree can be more accurate. In
addition, the experiment results reveal that 3-Grams is more accurate
than 2-Grams and 4-Grams for feature extraction. Also, combination
of attributes tend to result higher accuracy.
Abstract: Topology Optimization is a defined as the method of
determining optimal distribution of material for the assumed design
space with functionality, loads and boundary conditions [1].
Topology optimization can be used to optimize shape for the
purposes of weight reduction, minimizing material requirements or
selecting cost effective materials [2]. Topology optimization has been
implemented through the use of finite element methods for the
analysis, and optimization techniques based on the method of moving
asymptotes, genetic algorithms, optimality criteria method, level sets
and topological derivatives. Case study of Typical “Fuselage design"
is considered for this paper to explain the benefits of Topology
Optimization in the design cycle. A cylindrical shell is assumed as
the design space and aerospace standard pay loads were applied on
the fuselage with wing attachments as constraints. Then topological
optimization is done using Finite Element (FE) based software. This
optimization results in the structural concept design which satisfies
all the design constraints using minimum material.
Abstract: This paper explores the extent of the gap in poverty rates between immigrant and native households in Spanish regions and assess to what extent regional differences in individual and contextual characteristics can explain the divergences in such a gap. By using multilevel techniques and European Union Survey on Income and Living Conditions, we estimate immigrant households experiments an increase of 76 per cent in the odds of being poor compared with a native one when we control by individual variables. In relation to regional differences in the risk of poverty, regionallevel variables have higher effect in the reduction of these differences than individual variables.
Abstract: The objective of this paper is to characterize the spontaneous Electroencephalogram (EEG) signals of four different motor imagery tasks and to show hereby a possible solution for the present binary communication between the brain and a machine ora Brain-Computer Interface (BCI). The processing technique used in this paper was the fractal analysis evaluated by the Critical Exponent Method (CEM). The EEG signal was registered in 5 healthy subjects,sampling 15 measuring channels at 1024 Hz.Each channel was preprocessed by the Laplacian space ltering so as to reduce the space blur and therefore increase the spaceresolution. The EEG of each channel was segmented and its Fractaldimension (FD) calculated. The FD was evaluated in the time interval corresponding to the motor imagery and averaged out for all the subjects (each channel). In order to characterize the FD distribution,the linear regression curves of FD over the electrodes position were applied. The differences FD between the proposed mental tasks are quantied and evaluated for each experimental subject. The obtained results of the proposed method are a substantial fractal dimension in the EEG signal of motor imagery tasks and can be considerably utilized as the multiple-states BCI applications.
Abstract: Exploring an autistic child in Elementary school is a
difficult task that must be fully thought out and the teachers should
be aware of the many challenges they face raising their child
especially the behavioral problems of autistic children. Hence there
arises a need for developing Artificial intelligence (AI)
Contemporary Techniques to help diagnosis to discover autistic
people.
In this research, we suggest designing architecture of expert
system that combine Cognitive Maps (CM) with Case Based
Reasoning technique (CBR) in order to reduce time and costs of
traditional diagnosis process for the early detection to discover
autistic children. The teacher is supposed to enter child's information
for analyzing by CM module. Then, the reasoning processor would
translate the output into a case to be solved a current problem by
CBR module. We will implement a prototype for the model as a
proof of concept using java and MYSQL.
This will be provided a new hybrid approach that will achieve new
synergies and improve problem solving capabilities in AI. And we
will predict that will reduce time, costs, the number of human errors
and make expertise available to more people who want who want to
serve autistic children and their families.
Abstract: In diversity rich environments, such as in Ultra-
Wideband (UWB) applications, the a priori determination of the
number of strong diversity branches is difficult, because of the considerably large number of diversity paths, which are characterized
by a variety of power delay profiles (PDPs). Several
Rake implementations have been proposed in the past, in order to reduce the number of the estimated and combined paths. To this
aim, we introduce two adaptive Rake receivers, which combine
a subset of the resolvable paths considering simultaneously the
quality of both the total combining output signal-to-noise ratio (SNR) and the individual SNR of each path. These schemes achieve
better adaptation to channel conditions compared to other known receivers, without further increasing the complexity. Their performance
is evaluated in different practical UWB channels, whose models are based on extensive propagation measurements. The
proposed receivers compromise between the power consumption,
complexity and performance gain for the additional paths, resulting in important savings in power and computational resources.
Abstract: This paper presents an adaptive technique for generation
of data required for construction of artificial neural network-based
performance model of nano-scale CMOS inverter circuit. The training
data are generated from the samples through SPICE simulation. The
proposed algorithm has been compared to standard progressive sampling
algorithms like arithmetic sampling and geometric sampling.
The advantages of the present approach over the others have been
demonstrated. The ANN predicted results have been compared with
actual SPICE results. A very good accuracy has been obtained.
Abstract: In this study concept of experimental design is
successfully applied for the determination of optimum condition to
produce PP/SWCNT (Polypropylene/Single wall carbon nanotube)
nanocomposite. Central composite design as one of experimental
design techniques is employed for the optimization and statistical
determination of the significant factors influencing on the tensile
modulus and yield stress as mechanical properties of this
nanocomposite. The significant factors are SWCNT weight fraction
and acid treatment time for functionalizing the nanoparticles.
Optimum conditions are in 0.7 % of SWCNT weight fraction and 210
min as acid treatment time for 1112.75 ± 28 MPa as maximum tensile
modulus and in 216 min and 0.65 % as acid treatment time and
SWCNT weight fraction respectively for 40.26 ± 0.3 MPa as
maximum yield stress. Also after setting new experiments for test
these optimum conditions, found excelent agreement with predicted
values.
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: Adaptive control involves modifying the control law
used by the controller to cope with the fact that the parameters of the
system being controlled change drastically due to change in
environmental conditions or in system itself. This technique is based
on the fundamental characteristic of adaptation of living organism.
The adaptive control process is one that continuously and
automatically measures the dynamic behavior of plant, compares it
with the desired output and uses the difference to vary adjustable
system parameters or to generate an actuating signal in such a way so
that optimal performance can be maintained regardless of system
changes. This paper deals with application of model reference
adaptive control scheme in first order system. The rule which is used
for this application is MIT rule. This paper also shows the effect of
adaptation gain on the system performance. Simulation is done in
MATLAB and results are discussed in detail.
Abstract: Nanostructured materials have attracted many
researchers due to their outstanding mechanical and physical
properties. For example, carbon nanotubes (CNTs) or carbon
nanofibres (CNFs) are considered to be attractive reinforcement
materials for light weight and high strength metal matrix composites.
These composites are being projected for use in structural
applications for their high specific strength as well as functional
materials for their exciting thermal and electrical characteristics. The
critical issues of CNT-reinforced MMCs include processing
techniques, nanotube dispersion, interface, strengthening mechanisms
and mechanical properties. One of the major obstacles to the effective
use of carbon nanotubes as reinforcements in metal matrix
composites is their agglomeration and poor distribution/dispersion
within the metallic matrix. In order to tap into the advantages of the
properties of CNTs (or CNFs) in composites, the high dispersion of
CNTs (or CNFs) and strong interfacial bonding are the key issues
which are still challenging. Processing techniques used for synthesis
of the composites have been studied with an objective to achieve
homogeneous distribution of carbon nanotubes in the matrix.
Modified mechanical alloying (ball milling) techniques have emerged
as promising routes for the fabrication of carbon nanotube (CNT)
reinforced metal matrix composites. In order to obtain a
homogeneous product, good control of the milling process, in
particular control of the ball movement, is essential. The control of
the ball motion during the milling leads to a reduction in grinding
energy and a more homogeneous product. Also, the critical inner
diameter of the milling container at a particular rotational speed can
be calculated. In the present work, we use conventional and modified
mechanical alloying to generate a homogenous distribution of 2 wt.
% CNT within Al powders. 99% purity Aluminium powder (Acros,
200mesh) was used along with two different types of multiwall
carbon nanotube (MWCNTs) having different aspect ratios to
produce Al-CNT composites. The composite powders were processed
into bulk material by compaction, and sintering using a cylindrical
compaction and tube furnace. Field Emission Scanning electron
microscopy (FESEM), X-Ray diffraction (XRD), Raman
spectroscopy and Vickers macro hardness tester were used to
evaluate CNT dispersion, powder morphology, CNT damage, phase
analysis, mechanical properties and crystal size determination.
Despite the success of ball milling in dispersing CNTs in Al powder,
it is often accompanied with considerable strain hardening of the Al
powder, which may have implications on the final properties of the
composite. The results show that particle size and morphology vary
with milling time. Also, by using the mixing process and sonication
before mechanical alloying and modified ball mill, dispersion of the
CNTs in Al matrix improves.