Abstract: The uses of road map in daily activities are numerous
but it is a hassle to construct and update a road map whenever there
are changes. In Universiti Malaysia Sarawak, research on Automatic
Road Extraction (ARE) was explored to solve the difficulties in
updating road map. The research started with using Satellite Image
(SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space
Segmentation & Edge Detection (Hybrid SCSS-EDGE) algorithm
was developed to extract roads automatically from satellite-taken
images. In order to extract the road network accurately, the satellite
image must be analyzed prior to the extraction process. The
characteristics of these elements are analyzed and consequently the
relationships among them are determined. In this study, the road
regions are extracted based on colour space elements and edge details
of roads. Besides, edge detection method is applied to further filter
out the non-road regions. The extracted road regions are validated by
using a segmentation method. These results are valuable for building
road map and detecting the changes of the existing road database.
The proposed Hybrid Simple Colour Space Segmentation and Edge
Detection (Hybrid SCSS-EDGE) algorithm can perform the tasks
fully automatic, where the user only needs to input a high-resolution
satellite image and wait for the result. Moreover, this system can
work on complex road network and generate the extraction result in
seconds.
Abstract: In order to implement flexibility as well as survivable
capacities over passive optical network (PON), a new automatic
random fault-recovery mechanism with array-waveguide-grating
based (AWG-based) optical switch (OSW) is presented. Firstly,
wavelength-division-multiplexing and optical code-division
multiple-access (WDM/OCDMA) scheme are configured to meet the
various geographical locations requirement between optical network
unit (ONU) and optical line terminal (OLT). The AWG-base optical
switch is designed and viewed as central star-mesh topology to
prohibit/decrease the duplicated redundant elements such as fiber and
transceiver as well. Hence, by simple monitoring and routing switch
algorithm, random fault-recovery capacity is achieved over
bi-directional (up/downstream) WDM/OCDMA scheme. When error
of distribution fiber (DF) takes place or bit-error-rate (BER) is higher
than 10-9 requirement, the primary/slave AWG-based OSW are
adjusted and controlled dynamically to restore the affected ONU
groups via the other working DFs immediately.
Abstract: Research into the problem of classification of sonar signals has been taken up as a challenging task for the neural networks. This paper investigates the design of an optimal classifier using a Multi layer Perceptron Neural Network (MLP NN) and Support Vector Machines (SVM). Results obtained using sonar data sets suggest that SVM classifier perform well in comparison with well-known MLP NN classifier. An average classification accuracy of 91.974% is achieved with SVM classifier and 90.3609% with MLP NN classifier, on the test instances. The area under the Receiver Operating Characteristics (ROC) curve for the proposed SVM classifier on test data set is found as 0.981183, which is very close to unity and this clearly confirms the excellent quality of the proposed classifier. The SVM classifier employed in this paper is implemented using kernel Adatron algorithm is seen to be robust and relatively insensitive to the parameter initialization in comparison to MLP NN.
Abstract: Networked schools have become a feature of
education systems in countries that seek to provide learning
opportunities in schools located beyond major centres of population.
The internet and e-learning have facilitated the development of
virtual educational structures that complement traditional schools,
encouraging collaborative teaching and learning to proceed. In rural
New Zealand and in the Atlantic Canadian province of
Newfoundland and Labrador, e-learning is able to provide new ways
of organizing teaching, learning and the management of educational
opportunities. However, the future of e-teaching and e-learning in
networked schools depends on the development of professional
education programs that prepare teachers for collaborative teaching
and learning environments in which both virtual and traditional face
to face instruction co-exist.
Abstract: this paper presented a survey analysis subjected on
network bandwidth management from published papers referred in
IEEE Explorer database in three years from 2009 to 2011. Network
Bandwidth Management is discussed in today-s issues for computer
engineering applications and systems. Detailed comparison is
presented between published papers to look further in the IP based
network critical research area for network bandwidth management.
Important information such as the network focus area, a few
modeling in the IP Based Network and filtering or scheduling used in
the network applications layer is presented. Many researches on
bandwidth management have been done in the broad network area
but fewer are done in IP Based network specifically at the
applications network layer. A few researches has contributed new
scheme or enhanced modeling but still the issue of bandwidth
management still arise at the applications network layer. This survey
is taken as a basic research towards implementations of network
bandwidth management technique, new framework model and
scheduling scheme or algorithm in an IP Based network which will
focus in a control bandwidth mechanism in prioritizing the network
traffic the applications layer.
Abstract: A common way to elude the signature-based Network Intrusion Detection System is based upon changing a recognizable attack to an unrecognizable one via the IDS. For example, in order to evade sign accommodation with intrusion detection system markers, a hacker spilt the payload packet into many small pieces or hides them within messages. In this paper we try to model the main fragmentation attack and create a new module in the intrusion detection architecture system which recognizes the main fragmentation attacks through verification of integrity checking of TCP packet in order to prevent elusion of the system and also to announce the necessary alert to the system administrator.
Abstract: In this paper the development of a heat exchanger as a
pilot plant for educational purpose is discussed and the use of neural
network for controlling the process is being presented. The aim of the
study is to highlight the need of a specific Pseudo Random Binary
Sequence (PRBS) to excite a process under control. As the neural
network is a data driven technique, the method for data generation
plays an important role. In light of this a careful experimentation
procedure for data generation was crucial task. Heat exchange is a
complex process, which has a capacity and a time lag as process
elements. The proposed system is a typical pipe-in- pipe type heat
exchanger. The complexity of the system demands careful selection,
proper installation and commissioning. The temperature, flow, and
pressure sensors play a vital role in the control performance. The
final control element used is a pneumatically operated control valve.
While carrying out the experimentation on heat exchanger a welldrafted
procedure is followed giving utmost attention towards safety
of the system. The results obtained are encouraging and revealing
the fact that if the process details are known completely as far as
process parameters are concerned and utilities are well stabilized then
feedback systems are suitable, whereas neural network control
paradigm is useful for the processes with nonlinearity and less
knowledge about process. The implementation of NN control
reinforces the concepts of process control and NN control paradigm.
The result also underlined the importance of excitation signal
typically for that process. Data acquisition, processing, and
presentation in a typical format are the most important parameters
while validating the results.
Abstract: Nowadays, several techniques such as; Fuzzy
Inference System (FIS) and Neural Network (NN) are employed for
developing of the predictive models to estimate parameters of water
quality. The main objective of this study is to compare between the
predictive ability of the Adaptive Neuro-Fuzzy Inference System
(ANFIS) model and Artificial Neural Network (ANN) model to
estimate the Biochemical Oxygen Demand (BOD) on data from 11
sampling sites of Saen Saep canal in Bangkok, Thailand. The data is
obtained from the Department of Drainage and Sewerage, Bangkok
Metropolitan Administration, during 2004-2011. The five parameters
of water quality namely Dissolved Oxygen (DO), Chemical Oxygen
Demand (COD), Ammonia Nitrogen (NH3N), Nitrate Nitrogen
(NO3N), and Total Coliform bacteria (T-coliform) are used as the
input of the models. These water quality indices affect the
biochemical oxygen demand. The experimental results indicate that
the ANN model provides a higher correlation coefficient (R=0.73)
and a lower root mean square error (RMSE=4.53) than the
corresponding ANFIS model.
Abstract: Fast forecasting of stock market prices is very important for
strategic planning. In this paper, a new approach for fast forecasting of
stock market prices is presented. Such algorithm uses new high speed
time delay neural networks (HSTDNNs). The operation of these
networks relies on performing cross correlation in the frequency
domain between the input data and the input weights of neural
networks. It is proved mathematically and practically that the number
of computation steps required for the presented HSTDNNs is less
than that needed by traditional time delay neural networks
(TTDNNs). Simulation results using MATLAB confirm the
theoretical computations.
Abstract: In recent years, the research in wireless sensor
network has increased steadily, and many studies were focusing on
reducing energy consumption of sensor nodes to extend their lifetimes.
In this paper, the issue of energy consumption is investigated and two
adaptive mechanisms are proposed to extend the network lifetime.
This study uses high-energy-first scheme to determine cluster heads
for data transmission. Thus, energy consumption in each cluster is
balanced and network lifetime can be extended. In addition, this study
uses cluster merging and dynamic routing mechanisms to further
reduce energy consumption during data transmission. The simulation
results show that the proposed method can effectively extend the
lifetime of wireless sensor network, and it is suitable for different base
station locations.
Abstract: The hospital and the health-care center of a
community, as a place for people-s life-care and health-care settings,
must provide more and better services for patients or residents. After
Establishing Electronic Medical Record (EMR) system -which is a
necessity- in the hospital, providing pervasive services is a further
step. Our objective in this paper is to use pervasive computing in a
case study of healthcare, based on EMR database that coordinates
application services over network to form a service environment for
medical and health-care. Our method also categorizes the hospital
spaces into 3 spaces: Public spaces, Private spaces and Isolated
spaces. Although, there are many projects about using pervasive
computing in healthcare, but all of them concentrate on the disease
recognition, designing smart cloths, or provide services only for
patient. The proposed method is implemented in a hospital. The
obtained results show that it is suitable for our purpose.
Abstract: This paper describes studies carried out to investigate
the viability of using wireless cameras as a tool in monitoring
changes in air quality. A camera is used to monitor the change in
colour of a chemically responsive polymer within view of the camera
as it is exposed to varying chemical species concentration levels. The
camera captures this image and the colour change is analyzed by
averaging the RGB values present. This novel chemical sensing
approach is compared with an established chemical sensing method
using the same chemically responsive polymer coated onto LEDs. In
this way, the concentration levels of acetic acid in the air can be
tracked using both approaches. These approaches to chemical plume
tracking have many applications for air quality monitoring.
Abstract: Development of artificial neural network (ANN) for
prediction of aluminum workpieces' surface roughness in ultrasonicvibration
assisted turning (UAT) has been the subject of the present
study. Tool wear as the main cause of surface roughness was also
investigated. ANN was trained through experimental data obtained
on the basis of full factorial design of experiments. Various
influential machining parameters were taken into consideration. It
was illustrated that a multilayer perceptron neural network could
efficiently model the surface roughness as the response of the
network, with an error less than ten percent. The performance of the
trained network was verified by further experiments. The results of
UAT were compared with the results of conventional turning
experiments carried out with similar machining parameters except for
the vibration amplitude whence considerable reduction was observed
in the built-up edge and the surface roughness.
Abstract: In this paper, a method for deriving a group priority vector in the Fuzzy Analytic Network Process (FANP) is proposed. By introducing importance weights of multiple decision makers (DMs) based on their experiences, the Fuzzy Preferences Programming Method (FPP) is extended to a fuzzy group prioritization problem in the FANP. Additionally, fuzzy pair-wise comparison judgments are presented rather than exact numerical assessments in order to model the uncertainty and imprecision in the DMs- judgments and then transform the fuzzy group prioritization problem into a fuzzy non-linear programming optimization problem which maximize the group satisfaction. Unlike the known fuzzy prioritization techniques, the new method proposed in this paper can easily derive crisp weights from incomplete and inconsistency fuzzy set of comparison judgments and does not require additional aggregation producers. Detailed numerical examples are used to illustrate the implement of our approach and compare with the latest fuzzy prioritization method.
Abstract: In this study a neural network (NN) was proposed to
predict the sorption of binary mixture of copper-cobalt ions into
clinoptilolite as ion-exchanger. The configuration of the
backpropagation neural network giving the smallest mean square
error was three-layer NN with tangent sigmoid transfer function at
hidden layer with 10 neurons, linear transfer function at output layer
and Levenberg-Marquardt backpropagation training algorithm.
Experiments have been carried out in the batch reactor to obtain
equilibrium data of the individual sorption and the mixture of coppercobalt
ions. The obtained modeling results have shown that the used
of neural network has better adjusted the equilibrium data of the
binary system when compared with the conventional sorption
isotherm models.
Abstract: The paper is concerned with developing stochastic delay mechanisms for efficient multicast protocols and for smooth mobile handover processes which are capable of preserving a given Quality of Service (QoS). In both applications the participating entities (receiver nodes or subscribers) sample a stochastic timer and generate load after a random delay. In this way, the load on the networking resources is evenly distributed which helps to maintain QoS communication. The optimal timer distributions have been sought in different p.d.f. families (e.g. exponential, power law and radial basis function) and the optimal parameter have been found in a recursive manner. Detailed simulations have demonstrated the improvement in performance both in the case of multicast and mobile handover applications.
Abstract: Access control is a critical security service in Wire- less
Sensor Networks (WSNs). To prevent malicious nodes from joining
the sensor network, access control is required. On one hand, WSN
must be able to authorize and grant users the right to access to the
network. On the other hand, WSN must organize data collected by
sensors in such a way that an unauthorized entity (the adversary)
cannot make arbitrary queries. This restricts the network access only
to eligible users and sensor nodes, while queries from outsiders will
not be answered or forwarded by nodes. In this paper we presentee
different access control schemes so as to ?nd out their objectives,
provision, communication complexity, limits, etc. Using the node
density parameter, we also provide a comparison of these proposed
access control algorithms based on the network topology which can
be flat or hierarchical.
Abstract: This paper describes a new supervised fusion (hybrid)
electrocardiogram (ECG) classification solution consisting of a new
QRS complex geometrical feature extraction as well as a new version
of the learning vector quantization (LVQ) classification algorithm
aimed for overcoming the stability-plasticity dilemma. Toward this
objective, after detection and delineation of the major events of ECG
signal via an appropriate algorithm, each QRS region and also its
corresponding discrete wavelet transform (DWT) are supposed as
virtual images and each of them is divided into eight polar sectors.
Then, the curve length of each excerpted segment is calculated
and is used as the element of the feature space. To increase the
robustness of the proposed classification algorithm versus noise,
artifacts and arrhythmic outliers, a fusion structure consisting of
five different classifiers namely as Support Vector Machine (SVM),
Modified Learning Vector Quantization (MLVQ) and three Multi
Layer Perceptron-Back Propagation (MLP–BP) neural networks with
different topologies were designed and implemented. The new proposed
algorithm was applied to all 48 MIT–BIH Arrhythmia Database
records (within–record analysis) and the discrimination power of the
classifier in isolation of different beat types of each record was
assessed and as the result, the average accuracy value Acc=98.51%
was obtained. Also, the proposed method was applied to 6 number
of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging
to 20 different records of the aforementioned database (between–
record analysis) and the average value of Acc=95.6% was achieved.
To evaluate performance quality of the new proposed hybrid learning
machine, the obtained results were compared with similar peer–
reviewed studies in this area.
Abstract: As networking has become popular, Web-learning
tends to be a trend while designing a tool. Moreover, five-axis
machining has been widely used in industry recently; however, it has
potential axial table colliding problems. Thus this paper aims at
proposing an efficient web-learning collision detection tool on
five-axis machining. However, collision detection consumes heavy
resource that few devices can support, thus this research uses a
systematic approach based on web knowledge to detect collision. The
methodologies include the kinematics analyses for five-axis motions,
separating axis method for collision detection, and computer
simulation for verification. The machine structure is modeled as STL
format in CAD software. The input to the detection system is the
g-code part program, which describes the tool motions to produce the
part surface. This research produced a simulation program with C
programming language and demonstrated a five-axis machining
example with collision detection on web site. The system simulates the
five-axis CNC motion for tool trajectory and detects for any collisions
according to the input g-codes and also supports high-performance
web service benefiting from C. The result shows that our method
improves 4.5 time of computational efficiency, comparing to the
conventional detection method.
Abstract: The main goal of the present work is to decrease the
computational burden for optimum design of steel frames with
frequency constraints using a new type of neural networks called
Wavelet Neural Network. It is contested to train a suitable neural
network for frequency approximation work as the analysis program.
The combination of wavelet theory and Neural Networks (NN)
has lead to the development of wavelet neural networks.
Wavelet neural networks are feed-forward networks using
wavelet as activation function. Wavelets are mathematical
functions within suitable inner parameters, which help them to
approximate arbitrary functions. WNN was used to predict the
frequency of the structures. In WNN a RAtional function with
Second order Poles (RASP) wavelet was used as a transfer
function. It is shown that the convergence speed was faster
than other neural networks. Also comparisons of WNN with
the embedded Artificial Neural Network (ANN) and with
approximate techniques and also with analytical solutions are
available in the literature.