Abstract: An image compression method has been developed
using fuzzy edge image utilizing the basic Block Truncation Coding
(BTC) algorithm. The fuzzy edge image has been validated with
classical edge detectors on the basis of the results of the well-known
Canny edge detector prior to applying to the proposed method. The
bit plane generated by the conventional BTC method is replaced with
the fuzzy bit plane generated by the logical OR operation between
the fuzzy edge image and the corresponding conventional BTC bit
plane. The input image is encoded with the block mean and standard
deviation and the fuzzy bit plane. The proposed method has been
tested with test images of 8 bits/pixel and size 512×512 and found to
be superior with better Peak Signal to Noise Ratio (PSNR) when
compared to the conventional BTC, and adaptive bit plane selection
BTC (ABTC) methods. The raggedness and jagged appearance, and
the ringing artifacts at sharp edges are greatly reduced in
reconstructed images by the proposed method with the fuzzy bit
plane.
Abstract: Most of the Question Answering systems
composed of three main modules: question processing,
document processing and answer processing. Question
processing module plays an important role in QA systems. If
this module doesn't work properly, it will make problems for
other sections. Moreover answer processing module is an
emerging topic in Question Answering, where these systems
are often required to rank and validate candidate answers.
These techniques aiming at finding short and precise answers
are often based on the semantic classification.
This paper discussed about a new model for question
answering which improved two main modules, question
processing and answer processing.
There are two important components which are the bases
of the question processing. First component is question
classification that specifies types of question and answer.
Second one is reformulation which converts the user's
question into an understandable question by QA system in a
specific domain. Answer processing module, consists of
candidate answer filtering, candidate answer ordering
components and also it has a validation section for interacting
with user. This module makes it more suitable to find exact
answer. In this paper we have described question and answer
processing modules with modeling, implementing and
evaluating the system. System implemented in two versions.
Results show that 'Version No.1' gave correct answer to 70%
of questions (30 correct answers to 50 asked questions) and
'version No.2' gave correct answers to 94% of questions (47
correct answers to 50 asked questions).
Abstract: The mosques have been appearance in Thailand since
Ayutthaya Kingdom (1350 to 1767 A.D.) Until today, more than 400 years later; there are many styles of art form behind their structure.
This research intended to identify Islamic Art in Thai mosques. A framework was applied using qualitative research methods; Thai
Muslims with dynamic roles in Islamic culture were interviewed. In
addition, a field survey of 40 selected mosques from 175 Thai
mosques was studied. Data analysis will be according to the pattern
of each period. The identification of Islamic Art in Thai Mosques are
1) the image of Thai identity: with Thai traditional art style and Government policy. 2) The image of the Ethnological identity: with
the traditional culture of Asian Muslims in Thailand. 3) The image of
the Nostalgia identity: with Islamic and Arabian conservative style.
4) The image of the Neo Classic identity: with Neo – Classic and
Contemporary art. 5) The image of the new identity: with Post
Modern and Deconstruction art.
Abstract: The paper describes a knowledge based system for
analysis of microscopic wear particles. Wear particles contained in
lubricating oil carry important information concerning machine
condition, in particular the state of wear. Experts (Tribologists) in the
field extract this information to monitor the operation of the machine
and ensure safety, efficiency, quality, productivity, and economy of
operation. This procedure is not always objective and it can also be
expensive. The aim is to classify these particles according to their
morphological attributes of size, shape, edge detail, thickness ratio,
color, and texture, and by using this classification thereby predict
wear failure modes in engines and other machinery. The attribute
knowledge links human expertise to the devised Knowledge Based
Wear Particle Analysis System (KBWPAS). The system provides an
automated and systematic approach to wear particle identification
which is linked directly to wear processes and modes that occur in
machinery. This brings consistency in wear judgment prediction
which leads to standardization and also less dependence on
Tribologists.
Abstract: A lot of research has been done in the past decade in the field of audio content analysis for extracting various information from audio signal. One such significant information is the "perceived mood" or the "emotions" related to a music or audio clip. This information is extremely useful in applications like creating or adapting the play-list based on the mood of the listener. This information could also be helpful in better classification of the music database. In this paper we have presented a method to classify music not just based on the meta-data of the audio clip but also include the "mood" factor to help improve the music classification. We propose an automated and efficient way of classifying music samples based on the mood detection from the audio data. We in particular try to classify the music based on mood for Indian bollywood music. The proposed method tries to address the following problem statement: Genre information (usually part of the audio meta-data) alone does not help in better music classification. For example the acoustic version of the song "nothing else matters by Metallica" can be classified as melody music and thereby a person in relaxing or chill out mood might want to listen to this track. But more often than not this track is associated with metal / heavy rock genre and if a listener classified his play-list based on the genre information alone for his current mood, the user shall miss out on listening to this track. Currently methods exist to detect mood in western or similar kind of music. Our paper tries to solve the issue for Indian bollywood music from an Indian cultural context
Abstract: We proposed a new class of asymmetric turbo encoder for 3G systems that performs well in both “water fall" and “error floor" regions in [7]. In this paper, a modified (optimal) power allocation scheme for the different bits of new class of asymmetric turbo encoder has been investigated to enhance the performance. The simulation results and performance bound for proposed asymmetric turbo code with modified Unequal Power Allocation (UPA) scheme for the frame length, N=400, code rate, r=1/3 with Log-MAP decoder over Additive White Gaussian Noise (AWGN) channel are obtained and compared with the system with typical UPA and without UPA. The performance tests are extended over AWGN channel for different frame size to verify the possibility of implementation of the modified UPA scheme for the proposed asymmetric turbo code. From the performance results, it is observed that the proposed asymmetric turbo code with modified UPA performs better than the system without UPA and with typical UPA and it provides a coding gain of 0.4 to 0.52dB.
Abstract: We compare three categorical data clustering
algorithms with respect to the problem of classifying cultural data
related to the aesthetic judgment of comics artists. Such a
classification is very important in Comics Art theory since the
determination of any classes of similarities in such kind of data will
provide to art-historians very fruitful information of Comics Art-s
evolution. To establish this, we use a categorical data set and we
study it by employing three categorical data clustering algorithms.
The performances of these algorithms are compared each other,
while interpretations of the clustering results are also given.
Abstract: In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases.
Abstract: In this paper, we propose a method of resolving dependency ambiguities of Korean subordinate clauses based on Support Vector Machines (SVMs). Dependency analysis of clauses is well known to be one of the most difficult tasks in parsing sentences, especially in Korean. In order to solve this problem, we assume that the dependency relation of Korean subordinate clauses is the dependency relation among verb phrase, verb and endings in the clauses. As a result, this problem is represented as a binary classification task. In order to apply SVMs to this problem, we selected two kinds of features: static and dynamic features. The experimental results on STEP2000 corpus show that our system achieves the accuracy of 73.5%.
Abstract: For future Broad band ISDN, Asynchronous Transfer
Mode (ATM) is designed not only to support a wide range of traffic
classes with diverse flow characteristics, but also to guarantee the
different quality of service QOS requirements. The QOS may be
measured in terms of cell loss probability and maximum cell delay.
In this paper, ATM networks in which the virtual path (VP)
concept is implemented are considered. By applying the Markov
Deterministic process method, an efficient algorithm to compute the
minimum capacity required to satisfy the QOS requirements when
multiple classes of on-off are multiplexed on to a single VP. Using
the result, we then proposed a simple algorithm to determine different
combinations of VP to achieve the optimum of the total capacity
required for satisfying the individual QOS requirements (loss- delay).
Abstract: In this paper, our concern is the management of mobile transactions in the shared area among many servers, when the mobile user moves from one cell to another in online partiallyreplicated distributed mobile database environment. We defined the concept of transaction and classified the different types of transactions. Based on this analysis, we propose an algorithm that handles the disconnection due to moving among sites.
Abstract: An early and accurate detection of Alzheimer's disease (AD) is an important stage in the treatment of individuals suffering from AD. We present an approach based on the use of structural magnetic resonance imaging (sMRI) phase images to distinguish between normal controls (NC), mild cognitive impairment (MCI) and AD patients with clinical dementia rating (CDR) of 1. Independent component analysis (ICA) technique is used for extracting useful features which form the inputs to the support vector machines (SVM), K nearest neighbour (kNN) and multilayer artificial neural network (ANN) classifiers to discriminate between the three classes. The obtained results are encouraging in terms of classification accuracy and effectively ascertain the usefulness of phase images for the classification of different stages of Alzheimer-s disease.
Abstract: Microaneurysm is a key indicator of diabetic retinopathy that can potentially cause damage to retina. Early detection and automatic quantification are the keys to prevent further damage. In this paper, which focuses on automatic microaneurysm detection in images acquired through non-dilated pupils, we present a series of experiments on feature selection and automatic microaneurysm pixel classification. We found that the best feature set is a combination of 10 features: the pixel-s intensity of shade corrected image, the pixel hue, the standard deviation of shade corrected image, DoG4, the area of the candidate MA, the perimeter of the candidate MA, the eccentricity of the candidate MA, the circularity of the candidate MA, the mean intensity of the candidate MA on shade corrected image and the ratio of the major axis length and minor length of the candidate MA. The overall sensitivity, specificity, precision, and accuracy are 84.82%, 99.99%, 89.01%, and 99.99%, respectively.
Abstract: The cuticular hydrocarbons of Pamphagus elephas
(Orthoptera: Pamphagidae) has been analysed by gas
chromatography and by combined gas chromatograph-mass
spectrometry. The following hydrocarbon classes have been
identified in insect cuticular hydrocarbons are: n-alkanes and
methylalkanes comprising Monomethyl-, dimethyl-and
trimethylalkanes. Sexual dimorphism is observed in long chain
alkanes (C24-C36) present on male and female. The cuticulars
hydrocarbons of P.elephas ranged from 24 to 36 carbons and
incluted n-alkanes, Dimethylalkanes and Trimethylalkanes. nalkanes
represented by (C24-C36,72,7% on male and 79,2% on
female), internally branched Monomethylalkanes identified were
(C25, C30-C32,C35-C37;11% on male and 9,4% on female),
Dimethylalkanes detected are (C31-C32, C36; 2,2% on male and
2,06% on female) and Trimethylalkanes detected are (C32, C36;
3,1% on male and 4, 97 on female). Larvae male and female (stage
7) showed the same quality of n-alkanes observed in adults.
However a difference quantity is noted.
Abstract: A Reading Comprehend (RC) Platform has been
constructed and developed to facilitate children-s English reading
comprehension. Like a learning bridge, the RC Platform focuses on
the integration of rich media and picture-book texts. The study is to
examine the effects of the project within the RC Platform for children.
Two classes of fourth graders were selected from a public elementary
school in an urban area of central Taiwan. The findings taken from the
survey showed that the students demonstrated high interest in the RC
Platform. The students benefited greatly and enjoyed reading via the
technology-enhanced project within the RC Platform. This Platform is
a good reading bridge to enrich students- learning experiences and
enhance their performance in English reading comprehension.
Abstract: As the number of networked computers grows,
intrusion detection is an essential component in keeping networks
secure. Various approaches for intrusion detection are currently
being in use with each one has its own merits and demerits. This
paper presents our work to test and improve the performance of a
new class of decision tree c-fuzzy decision tree to detect intrusion.
The work also includes identifying best candidate feature sub set to
build the efficient c-fuzzy decision tree based Intrusion Detection
System (IDS). We investigated the usefulness of c-fuzzy decision
tree for developing IDS with a data partition based on horizontal
fragmentation. Empirical results indicate the usefulness of our
approach in developing the efficient IDS.
Abstract: The refueling of a transparent rectangular fuel tank
fitted with a standard filler pipe and roll-over valve was
experimentally studied. A fuel-conditioning cart, capable of
handling fuels of different Reid vapor pressure at a constant
temperature, was used to dispense fuel at the desired rate. The
experimental protocol included transient recording of the tank and
filler tube pressures while video recording the flow patterns in the
filler tube and tank during the refueling process. This information
was used to determine the effect of changes in the vent tube
diameter, fuel-dispense flow rate and fuel Reid vapor pressure on the
pressure-time characteristics and the occurrence of premature fuel
filling shut-off and fuel spill-back. Pressure-time curves for the case
of normal shut-off demonstrated the classic, three-phase
characteristic noted in the literature. The variation of the maximum
values of tank dome and filler tube pressures are analyzed in relation
to the occurrence of premature shut-off.
Abstract: In this paper a Pattern Recognition algorithm based on
a constrained version of the k-means clustering algorithm will be
presented. The proposed algorithm is a non parametric supervised
statistical pattern recognition algorithm, i.e. it works under very mild
assumptions on the dataset. The performance of the algorithm will
be tested, togheter with a feature extraction technique that captures
the information on the closed two-dimensional contour of an image,
on images of industrial mineral ores.
Abstract: A welded structure must be inspected to guarantee that the weld quality meets the design requirements to assure safety and reliability. However, X-ray image analyses and defect recognition with the computer vision techniques are very complex. Most difficulties lie in finding the small, irregular defects in poor contrast images which requires pre processing to image, extract, and classify features from strong background noise. This paper addresses the issue of designing methodology to extract defect from noisy background radiograph with image processing. Based on the use of actives contours this methodology seems to give good results
Abstract: In this note first we define the notions of intuitionistic
fuzzy dual positive implicative hyper K-ideals of types
1,2,3,4 and intuitionistic fuzzy dual hyper K-ideals. Then we
give some classifications about these notions according to the
level subsets. Also by given some examples we show that these
notions are not equivalent, however we prove some theorems
which show that there are some relationships between these
notions. Finally we define the notions of product and antiproduct
of two fuzzy subsets and then give some theorems
about the relationships between the intuitionistic fuzzy dual
positive implicative hyper K-ideal of types 1,2,3,4 and their
(anti-)products, in particular we give a main decomposition
theorem.