Abstract: Medical image segmentation based on image smoothing followed by edge detection assumes a great degree of importance in the field of Image Processing. In this regard, this paper proposes a novel algorithm for medical image segmentation based on vigorous smoothening by identifying the type of noise and edge diction ideology which seems to be a boom in medical image diagnosis. The main objective of this algorithm is to consider a particular medical image as input and make the preprocessing to remove the noise content by employing suitable filter after identifying the type of noise and finally carrying out edge detection for image segmentation. The algorithm consists of three parts. First, identifying the type of noise present in the medical image as additive, multiplicative or impulsive by analysis of local histograms and denoising it by employing Median, Gaussian or Frost filter. Second, edge detection of the filtered medical image is carried out using Canny edge detection technique. And third part is about the segmentation of edge detected medical image by the method of Normalized Cut Eigen Vectors. The method is validated through experiments on real images. The proposed algorithm has been simulated on MATLAB platform. The results obtained by the simulation shows that the proposed algorithm is very effective which can deal with low quality or marginal vague images which has high spatial redundancy, low contrast and biggish noise, and has a potential of certain practical use of medical image diagnosis.
Abstract: This paper makes a detailed analysis regarding the definition of the intrinsic mode function and proves that Condition 1 of the intrinsic mode function can really be deduced from Condition 2. Finally, an improved definition of the intrinsic mode function is given.
Abstract: Postgraduate education is generally aimed at providing in-depth knowledge and understanding that include general philosophy in the world sciences, management, technologies, applications and other elements closely related to specific areas. In most universities, besides core and non-core subjects, a thesis is one of the requirements for the postgraduate student to accomplish before graduating. This paper reports on the empirical investigation into attributes that are associated with the obstacles to thesis accomplishment among postgraduate students. Using the quantitative approach the experiences of postgraduate students were tapped. Findings clearly revealed that information seeking, writing skills and other factors which refer to supervisor and time management, in particular, are recognized as contributory factors which positively or negatively influence postgraduates’ thesis accomplishment. Among these, writing skills dimensions were found to be the most difficult process in thesis accomplishment compared to information seeking and other factors. This pessimistic indication has provided some implications not only for the students but supervisors and institutions as a whole.
Abstract: This paper investigates the problem of spreading
sequence and receiver code synchronization techniques for satellite
based CDMA communications systems. The performance of CDMA
system depends on the autocorrelation and cross-correlation
properties of the used spreading sequences. In this paper we propose
the uses of chaotic Lu system to generate binary sequences for
spreading codes in a direct sequence spread CDMA system. To
minimize multiple access interference (MAI) we propose the use of
genetic algorithm for optimum selection of chaotic spreading
sequences. To solve the problem of transmitter-receiver
synchronization, we use the passivity controls. The concept of
semipassivity is defined to find simple conditions which ensure
boundedness of the solutions of coupled Lu systems. Numerical
results are presented to show the effectiveness of the proposed
approach.
Abstract: Consider a mass production of HDD arms where
hundreds of CNC machines are used to manufacturer the HDD arms.
According to an overwhelming number of machines and models of
arm, construction of separate control chart for monitoring each HDD
arm model by each machine is not feasible. This research proposed a
strategy to optimize the SPC management on shop floor. The
procedure started from identifying the clusters of the machine with
similar manufacturing performance using clustering technique. The
three way control chart ( I - MR - R ) is then applied to each
clustered group of machine. This proposed research has
advantageous to the manufacturer in terms of not only better
performance of the SPC but also the quality management paradigm.
Abstract: Decision tree algorithms have very important place at
classification model of data mining. In literature, algorithms use
entropy concept or gini index to form the tree. The shape of the
classes and their closeness to each other some of the factors that
affect the performance of the algorithm. In this paper we introduce a
new decision tree algorithm which employs data (attribute) folding
method and variation of the class variables over the branches to be
created. A comparative performance analysis has been held between
the proposed algorithm and C4.5.
Abstract: AAM has been successfully applied to face alignment,
but its performance is very sensitive to initial values. In case the initial
values are a little far distant from the global optimum values, there
exists a pretty good possibility that AAM-based face alignment may
converge to a local minimum. In this paper, we propose a progressive
AAM-based face alignment algorithm which first finds the feature
parameter vector fitting the inner facial feature points of the face and
later localize the feature points of the whole face using the first
information. The proposed progressive AAM-based face alignment
algorithm utilizes the fact that the feature points of the inner part of the
face are less variant and less affected by the background surrounding
the face than those of the outer part (like the chin contour). The
proposed algorithm consists of two stages: modeling and relation
derivation stage and fitting stage. Modeling and relation derivation
stage first needs to construct two AAM models: the inner face AAM
model and the whole face AAM model and then derive relation matrix
between the inner face AAM parameter vector and the whole face
AAM model parameter vector. In the fitting stage, the proposed
algorithm aligns face progressively through two phases. In the first
phase, the proposed algorithm will find the feature parameter vector
fitting the inner facial AAM model into a new input face image, and
then in the second phase it localizes the whole facial feature points of
the new input face image based on the whole face AAM model using
the initial parameter vector estimated from using the inner feature
parameter vector obtained in the first phase and the relation matrix
obtained in the first stage. Through experiments, it is verified that the
proposed progressive AAM-based face alignment algorithm is more
robust with respect to pose, illumination, and face background than the
conventional basic AAM-based face alignment algorithm.
Abstract: In this work we present an efficient approach for face
recognition in the infrared spectrum. In the proposed approach
physiological features are extracted from thermal images in order to
build a unique thermal faceprint. Then, a distance transform is used
to get an invariant representation for face recognition. The obtained
physiological features are related to the distribution of blood vessels
under the face skin. This blood network is unique to each individual
and can be used in infrared face recognition. The obtained results are
promising and show the effectiveness of the proposed scheme.
Abstract: In this paper, we present symbolic recognition models to extract knowledge characterized by document structures. Focussing on the extraction and the meticulous exploitation of the semantic structure of documents, we obtain a meaningful contextual tagging corresponding to different unit types (title, chapter, section, enumeration, etc.).
Abstract: In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.
Abstract: An experimental study is presented on the effect
of microstructural change on the Portevin-Le Chatelier effect
behaviour of Al-2.5%Mg alloy. Tensile tests are performed on
the as received and heat treated (at 400 ºC for 16 hours)
samples for a wide range of strain rates. The serrations
observed in the stress-time curve are investigated from
statistical analysis point of view. Microstructures of the
samples are characterized by optical metallography and X-ray
diffraction. It is found that the excess vacancy generated due
to heat treatment leads to decrease in the strain rate sensitivity
and the increase in the number of stress drop occurrences per
unit time during the PLC effect. The microstructural
parameters like domain size, dislocation density have no
appreciable effect on the PLC effect as far as the statistical
behavior of the serrations is considered.
Abstract: In recent years, the number of the cases of information
leaks is increasing. Companies and Research Institutions make various
actions against information thefts and security accidents. One of the
actions is adoption of the crime prevention system, including the
monitoring system by surveillance cameras. In order to solve
difficulties of multiple cameras monitoring, we develop the automatic
human tracking system using mobile agents through multiple
surveillance cameras to track target persons. In this paper, we develop
the monitor which confirms mobile agents tracing target persons, and
the simulator of video picture analysis to construct the tracking
algorithm.
Abstract: The history of technology and banking is examined as
it relates to risk and technological determinism. It is proposed that
the services that banks offer are determined by technology and that
banks must adopt new technologies to be competitive. The adoption
of technologies paradoxically forces the adoption of other new
technologies to protect the bank from the increased risk of
technology. This cycle will lead to bank examiners and regulators to
focus on human behavior, not on the ever changing technology.
Abstract: In this study, the theoretical relationship between pressure and density was investigated on cylindrical hollow fuel briquettes produced of a mixture of fibrous biomass material using a screw press without any chemical binder. The fuel briquettes were made of biomass and other waste material such as spent coffee beans, mielie husks, saw dust and coal fines under pressures of 0.878-2.2 Mega Pascals (MPa). The material was densified into briquettes of outer diameter of 100mm, inner diameter of 35mm and 50mm long. It was observed that manual screw compression action produces briquettes of relatively low density as compared to the ones made using hydraulic compression action. The pressure and density relationship was obtained in the form of power law and compare well with other cylindrical solid briquettes made using hydraulic compression action. The produced briquettes have a dry density of 989 kg/m3 and contain 26.30% fixed carbon, 39.34% volatile matter, 10.9% moisture and 10.46% ash as per dry proximate analysis. The bomb calorimeter tests have shown the briquettes yielding a gross calorific value of 18.9MJ/kg.
Abstract: An approach for experimental measurement of the
dynamic characteristics of linear electromagnet actuators is
presented. It uses accelerometer sensor to register the armature
acceleration. The velocity and displacement of the moving parts can
be obtained by integration of the acceleration results. The armature
movement of permanent magnet linear actuator is acquired using this
technique. The results are analyzed and the performance of the
supposed approach is compared with the most commonly used
experimental setup where the displacement of the armature vs. time
is measured instead of its acceleration.
Abstract: In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Abstract: Scale Invariant Feature Transform (SIFT) has been
widely applied, but extracting SIFT feature is complicated and
time-consuming. In this paper, to meet the demand of the real-time
applications, SIFT is parallelized and optimized on cluster system,
which is named pSIFT. Redundancy storage and communication are
used for boundary data to improve the performance, and before
representation of feature descriptor, data reallocation is adopted to
keep load balance in pSIFT. Experimental results show that pSIFT
achieves good speedup and scalability.
Abstract: This study aims to identify cellular phone users- shopping motivating factors towards online shopping. 100 university students located in Klang Valley, Malaysia were involved as the respondents. They were required to complete a set of questionnaire and had to own a cellular phone in order to be selected as sample in this study. Three from five proposed hypotheses were supported: purchasing information, shopping utilities and service quality. As a result, marketers and retailers should concentrate more on the less important factors in order to encourage and create willingness of the consumers to purchase online. Recommendation for future research is also presented.
Abstract: The paper presents a space-vector pulse width modulation (SVPWM) inverter feeding a permanent-magnet synchronous motor (PMSM). The SVPWM inverter enables to feed the motor with a higher voltage with low harmonic distortions than the conventional sinusoidal PWM inverter. The control strategy of the inverter is the voltage / frequency control method, which is based on the space-vector modulation technique. The proposed PMSM drive system involving the field-oriented control scheme not only decouples the torque and flux which provides faster response but also makes the control task easy. The performance of the proposed drive is simulated. The advantages of the proposed drive are confirmed by the simulation results.
Abstract: In this paper, three types of defected ground structure
(DGS) units which are triangular-head (TH), rectangular-head (RH)
and U-shape (US) are investigated. They are further used to low-pass
and band-pass filters designs (LPF and BPF) and the obtained
performances are examined. The LPF employing RH-DGS geometry
presents the advantages of compact size, low-insertion loss and wide
stopband compared to the other filters. It provides cutoff frequency of
2.5 GHz, largest rejection band width of 20 dB from 2.98 to 8.76
GHz, smallest transition region and smallest sharpness of the cutoff
frequency. The BPF based on RH-DGS has the highest bandwidth
(BW) of about 0.74 GHz and the lowest center frequency of 3.24
GHz, whereas the other BPFs have BWs less than 0.7 GHz.