Abstract: This paper describes a blind algorithm for estimating a time varying and frequency selective fading channel. In order to identify blindly the impulse response of these channels, we have used Higher Order Statistics (HOS) to build our algorithm. In this paper, we have selected two theoretical frequency selective channels as the Proakis-s 'B' channel and the Macchi-s channel, and one practical frequency selective fading channel called Broadband Radio Access Network (BRAN A). The simulation results in noisy environment and for different data input channel, demonstrate that the proposed method could estimate the phase and magnitude of these channels blindly and without any information about the input, except that the input excitation is i.i.d (Identically and Independent Distributed) and non-Gaussian.
Abstract: A new dynamic clustering approach (DCPSO), based
on Particle Swarm Optimization, is proposed. This approach is
applied to unsupervised image classification. The proposed approach
automatically determines the "optimum" number of clusters and
simultaneously clusters the data set with minimal user interference.
The algorithm starts by partitioning the data set into a relatively large
number of clusters to reduce the effects of initial conditions. Using
binary particle swarm optimization the "best" number of clusters is
selected. The centers of the chosen clusters is then refined via the Kmeans
clustering algorithm. The experiments conducted show that
the proposed approach generally found the "optimum" number of
clusters on the tested images.
Abstract: Learning is the acquisition of new mental schemata, knowledge, abilities and skills which can be used to solve problems potentially more successfully. The learning process is optimum when it is assisted and personalized. Learning is not a single activity, but should involve many possible activities to make learning become meaningful. Many e-learning applications provide facilities to support teaching and learning activities. One way to identify whether the e-learning system is being used by the learners is through the number of hits that can be obtained from the e-learning system's log data. However, we cannot rely solely to the number of hits in order to determine whether learning had occurred meaningfully. This is due to the fact that meaningful learning should engage five characteristics namely active, constructive, intentional, authentic and cooperative. This paper aims to analyze the e-learning activities that is meaningful to learning. By focusing on the meaningful learning characteristics, we match it to the corresponding Moodle e-learning activities. This analysis discovers the activities that have high impact to meaningful learning, as well as activities that are less meaningful. The high impact activities is given high weights since it become important to meaningful learning, while the low impact has less weight and said to be supportive e-learning activities. The result of this analysis helps us categorize which e-learning activities that are meaningful to learning and guide us to measure the effectiveness of e-learning usage.
Abstract: This study investigated the effect of cross sectional
geometry on sediment transport rate. The processes of sediment
transport are generally associated to environmental management,
such as pollution caused by the forming of suspended sediment in the
channel network of a watershed and preserving physical habitats and
native vegetations, and engineering applications, such as the
influence of sediment transport on hydraulic structures and flood
control design. Many equations have been proposed for computing
the sediment transport, the influence of many variables on sediment
transport has been understood; however, the effect of other variables
still requires further research. For open channel flow, sediment
transport capacity is recognized to be a function of friction slope,
flow velocity, grain size, grain roughness and form roughness, the
hydraulic radius of the bed section and the type and quantity of
vegetation cover. The effect of cross sectional geometry of the
channel on sediment transport is one of the variables that need
additional investigation. The width-depth ratio (W/d) is a
comparative indicator of the channel shape. The width is the total
distance across the channel and the depth is the mean depth of the
channel. The mean depth is best calculated as total cross-sectional
area divided by the top width. Channels with high W/d ratios tend to
be shallow and wide, while channels with low (W/d) ratios tend to be
narrow and deep. In this study, the effects of the width-depth ratio on
sediment transport was demonstrated theoretically by inserting the
shape factor in sediment continuity equation and analytically by
utilizing the field data sets for Yalobusha River. It was found by
utilizing the two approaches as a width-depth ratio increases the
sediment transport decreases.
Abstract: Since 2004, we have been developing an in-situ storage image sensor (ISIS) that captures more than 100 consecutive images at a frame rate of 10 Mfps with ultra-high sensitivity as well as the video camera for use with this ISIS. Currently, basic research is continuing in an attempt to increase the frame rate up to 100 Mfps and above. In order to suppress electro-magnetic noise at such high frequency, a digital-noiseless imaging transfer scheme has been developed utilizing solely sinusoidal driving voltages. This paper presents highly efficient-yet-accurate expressions to estimate attenuation as well as phase delay of driving voltages through RC networks of an ultra-high-speed image sensor. Elmore metric for a fundamental RC chain is employed as the first-order approximation. By application of dimensional analysis to SPICE data, we found a simple expression that significantly improves the accuracy of the approximation. Similarly, another simple closed-form model to estimate phase delay through fundamental RC networks is also obtained. Estimation error of both expressions is much less than previous works, only less 2% for most of the cases . The framework of this analysis can be extended to address similar issues of other VLSI structures.
Abstract: Augmented Reality (AR) shows great promises for
its usage as a tool for simulation and verification of design proposal
of new technological systems. Main advantage of augmented reality
application usage is possibility of creation and simulation of new
technological unit before its realization. This may contribute to
increasing of safety and ergonomics and decreasing of economical
aspects of new proposed unit. Virtual model of proposed workcell
could reveal hidden errors which elimination in later stage of new
workcell creation should cause great difficulties. Paper describes
process of such virtual model creation and possibilities of its
simulation and verification by augmented reality tools.
Abstract: In social network analysis the mean nodal degree and
density of the graph can be considered as a measure of the activity of
all actors in the network and this is an important property of a graph
and for making comparisons among networks. Since subjects in a
family or organization are subject to common environment factors, it
is prime interest to study the association between responses.
Therefore, we study the distribution of the mean nodal degree and
density of the graph under correlated binary units. The cross product
ratio is used to capture the intra-units association among subjects.
Computer program and an application are given to show the benefits
of the method.
Abstract: Increasing number of vehicles and lack of awareness among road users may lead to road accidents. However no specific literature was found to rank vehicles involved in accidents based on fuzzy variables of road users. This paper proposes a ranking of four selected motor vehicles involved in road accidents. Human and non-human factors that normally linked with road accidents are considered for ranking. The imprecision or vagueness inherent in the subjective assessment of the experts has led the application of fuzzy sets theory to deal with ranking problems. Data in form of linguistic variables were collected from three authorised personnel of three Malaysian Government agencies. The Multi Criteria Decision Making, fuzzy TOPSIS was applied in computational procedures. From the analysis, it shows that motorcycles vehicles yielded the highest closeness coefficient at 0.6225. A ranking can be drawn using the magnitude of closeness coefficient. It was indicated that the motorcycles recorded the first rank.
Abstract: Here we report on the utilization of Laser-Induced
Breakdown Spectroscopy (LIBS) for determination of Quantum Dots
(QDs) in liquid solution. The process of optimization of experimental
conditions from choosing the carrier medium to application of colloid
QDs is described. The main goal was to get the best possible signal to
noise ratio.
The results obtained from the measurements confirmed the capability
of LIBS technique for qualitative and afterwards quantitative
determination of QDs in liquid solution.
Abstract: Numerical design optimization is a powerful tool that
can be used by engineers during any stage of the design process.
There are many different applications for structural optimization. A
specific application that will be discussed in the following paper is
experimental data matching. Data obtained through tests on a physical
structure will be matched with data from a numerical model of that
same structure. The data of interest will be the dynamic characteristics
of an antenna structure focusing on the mode shapes and modal
frequencies. The structure used was a scaled and simplified model of
the Karoo Array Telescope-7 (KAT-7) antenna structure.
This kind of data matching is a complex and difficult task. This
paper discusses how optimization can assist an engineer during the
process of correlating a finite element model with vibration test data.
Abstract: Crypto System Identification is one of the challenging tasks in Crypt analysis. The paper discusses the possibility of employing Neural Networks for identification of Cipher Systems from cipher texts. Cascade Correlation Neural Network and Back Propagation Network have been employed for identification of Cipher Systems. Very large collection of cipher texts were generated using a Block Cipher (Enhanced RC6) and a Stream Cipher (SEAL). Promising results were obtained in terms of accuracy using both the Neural Network models but it was observed that the Cascade Correlation Neural Network Model performed better compared to Back Propagation Network.
Abstract: Recent years have witnessed the rapid development of
the Internet and telecommunication techniques. Information security
is becoming more and more important. Applications such as covert
communication, copyright protection, etc, stimulate the research of
information hiding techniques. Traditionally, encryption is used to
realize the communication security. However, important information
is not protected once decoded. Steganography is the art and science
of communicating in a way which hides the existence of the communication.
Important information is firstly hidden in a host data, such
as digital image, video or audio, etc, and then transmitted secretly
to the receiver.In this paper a data hiding model with high security
features combining both cryptography using finite state sequential
machine and image based steganography technique for communicating
information more securely between two locations is proposed.
The authors incorporated the idea of secret key for authentication
at both ends in order to achieve high level of security. Before the
embedding operation the secret information has been encrypted with
the help of finite-state sequential machine and segmented in different
parts. The cover image is also segmented in different objects through
normalized cut.Each part of the encoded secret information has been
embedded with the help of a novel image steganographic method
(PMM) on different cuts of the cover image to form different stego
objects. Finally stego image is formed by combining different stego
objects and transmit to the receiver side. At the receiving end different
opposite processes should run to get the back the original secret
message.
Abstract: This paper proposes a direct power control for
doubly-fed induction machine for variable speed wind power
generation. It provides decoupled regulation of the primary side
active and reactive power and it is suitable for both electric energy
generation and drive applications. In order to control the power
flowing between the stator of the DFIG and the network, a decoupled
control of active and reactive power is synthesized using PI
controllers.The obtained simulation results show the feasibility
and the effectiveness of the suggested method
Abstract: Paper is dealing with vulnerability concerning elements of hydrological structures and elements of technological equipments which are acceptable for groundwater resources. The vulnerability assessment stems from the application of the register of hazards and a potential threat to individual water source elements within each type of hazard. The proposed procedure is pattern for assessing the risks of disturbance, damage, or destruction of water source by the identified natural or technological hazards and consequently for classification of these risks in relation to emergency water supply. Using of this procedure was verified on selected groundwater resource in particular region, which seems to be as potentially useful for crisis planning system.
Abstract: In this study, a double-sided linear switched reluctance
motor (LSRM) drive was investigated as an alternative actuator for
vertical linear transportation applications such as a linear elevator
door, hospital and subway doors which move linearly and where
accurate position control and rapid response is requested. A prototype
sliding elevator door that is focused on a home elevator with LSRMs
is designed. The motor has 6/4 poles, 3 phases, 8A, 24V, 250 W and
250 N pull forces. Air gap between rotor and translator poles of the
designed motor and phase coil-s ideal inductance profile are obtained
in compliance with the geometric dimensions. Operation and
switching sections as motor and generator has been determined from
the inductance profile.
Abstract: The various applications of VLSI circuits in highperformance
computing, telecommunications, and consumer
electronics has been expanding progressively, and at a very hasty
pace. This paper describes a new model for partitioning a circuit
using DBSCAN and fuzzy ARTMAP neural network. The first step
is concerned with feature extraction, where we had make use
DBSCAN algorithm. The second step is the classification and is
composed of a fuzzy ARTMAP neural network. The performance of
both approaches is compared using benchmark data provided by
MCNC standard cell placement benchmark netlists. Analysis of the
investigational results proved that the fuzzy ARTMAP with
DBSCAN model achieves greater performance then only fuzzy
ARTMAP in recognizing sub-circuits with lowest amount of
interconnections between them The recognition rate using fuzzy
ARTMAP with DBSCAN is 97.7% compared to only fuzzy
ARTMAP.
Abstract: Ability of accurate and reliable location estimation in
indoor environment is the key issue in developing great number of
context aware applications and Location Based Services (LBS).
Today, the most viable solution for localization is the Received
Signal Strength (RSS) fingerprinting based approach using wireless
local area network (WLAN). This paper presents two RSS
fingerprinting based approaches – first we employ widely used
WLAN based positioning as a reference system and then investigate
the possibility of using GSM signals for positioning. To compare
them, we developed a positioning system in real world environment,
where realistic RSS measurements were collected. Multi-Layer
Perceptron (MLP) neural network was used as the approximation
function that maps RSS fingerprints and locations. Experimental
results indicate advantage of WLAN based approach in the sense of
lower localization error compared to GSM based approach, but GSM
signal coverage by far outreaches WLAN coverage and for some
LBS services requiring less precise accuracy our results indicate that
GSM positioning can also be a viable solution.
Abstract: Scaffolds play a key role in tissue engineering and can be produced in many different ways depending on the applications and the materials used. Most researchers used an experimental trialand- error approach into new biomaterials but computer simulation applied to tissue engineering can offer a more exhaustive approach to test and screen out biomaterials. This paper develops the model of scaffolds and Computational Fluid Dynamics that show the value of computer simulations in determining the influence of the geometrical scaffold parameter porosity, pore size and shape on the permeability of scaffolds, magnitude of velocity, drop pressure, shear stress distribution and level and the proper design of the geometry of the scaffold. This creates a need for more advanced studies that include aspects of dynamic conditions of a micro fluid passing through the scaffold were characterized for tissue engineering applications and differentiation of tissues within scaffolds.
Abstract: Sensor Network are emerging as a new tool for
important application in diverse fields like military surveillance,
habitat monitoring, weather, home electrical appliances and others.
Technically, sensor network nodes are limited in respect to energy
supply, computational capacity and communication bandwidth. In
order to prolong the lifetime of the sensor nodes, designing efficient
routing protocol is very critical. In this paper, we illustrate the
existing routing protocol for wireless sensor network using data
centric approach and present performance analysis of these protocols.
The paper focuses in the performance analysis of specific protocol
namely Directed Diffusion and SPIN. This analysis reveals that the
energy usage is important features which need to be taken into
consideration while designing routing protocol for wireless sensor
network.
Abstract: In this paper, a comparative study of application of
supervised and unsupervised learning algorithms on illumination
invariant face recognition has been carried out. The supervised
learning has been carried out with the help of using a bi-layered
artificial neural network having one input, two hidden and one output
layer. The gradient descent with momentum and adaptive learning
rate back propagation learning algorithm has been used to implement
the supervised learning in a way that both the inputs and
corresponding outputs are provided at the time of training the
network, thus here is an inherent clustering and optimized learning of
weights which provide us with efficient results.. The unsupervised
learning has been implemented with the help of a modified
Counterpropagation network. The Counterpropagation network
involves the process of clustering followed by application of Outstar
rule to obtain the recognized face. The face recognition system has
been developed for recognizing faces which have varying
illumination intensities, where the database images vary in lighting
with respect to angle of illumination with horizontal and vertical
planes. The supervised and unsupervised learning algorithms have
been implemented and have been tested exhaustively, with and
without application of histogram equalization to get efficient results.