Abstract: The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.
Abstract: Users of computer systems may often require the
private transfer of messages/communications between parties across
a network. Information warfare and the protection and dominance of
information in the military context is a prime example of an
application area in which the confidentiality of data needs to be
maintained. The safe transportation of critical data is therefore often
a vital requirement for many private communications. However,
unwanted interception/sniffing of communications is also a
possibility. An elementary stealthy transfer scheme is therefore
proposed by the authors. This scheme makes use of encoding,
splitting of a message and the use of a hashing algorithm to verify the
correctness of the reconstructed message. For this proof-of-concept
purpose, the authors have experimented with the random sending of
encoded parts of a message and the construction thereof to
demonstrate how data can stealthily be transferred across a network
so as to prevent the obvious retrieval of data.
Abstract: This paper presents the determination of the proper
quality costs parameters which provide the optimum return. The
system dynamics simulation was applied. The simulation model was
constructed by the real data from a case of the electronic devices
manufacturer in Thailand. The Steepest Descent algorithm was
employed to optimise. The experimental results show that the
company should spend on prevention and appraisal activities for 850
and 10 Baht/day respectively. It provides minimum cumulative total
quality cost, which is 258,000 Baht in twelve months. The effect of
the step size in the stage of improving the variables to the optimum
was also investigated. It can be stated that the smaller step size
provided a better result with more experimental runs. However, the
different yield in this case is not significant in practice. Therefore, the
greater step size is recommended because the region of optima could
be reached more easily and rapidly.
Abstract: Diabetes is one of the high prevalence diseases
worldwide with increased number of complications, with retinopathy
as one of the most common one. This paper describes how data
mining and case-based reasoning were integrated to predict
retinopathy prevalence among diabetes patients in Malaysia. The
knowledge base required was built after literature reviews and
interviews with medical experts. A total of 140 diabetes patients- data
were used to train the prediction system. A voting mechanism selects
the best prediction results from the two techniques used. It has been
successfully proven that both data mining and case-based reasoning
can be used for retinopathy prediction with an improved accuracy of
85%.
Abstract: The amount of the information being churned out by the field of biology has jumped manifold and now requires the extensive use of computer techniques for the management of this information. The predominance of biological information such as protein sequence similarity in the biological information sea is key information for detecting protein evolutionary relationship. Protein sequence similarity typically implies homology, which in turn may imply structural and functional similarities. In this work, we propose, a learning method for detecting remote protein homology. The proposed method uses a transformation that converts protein sequence into fixed-dimensional representative feature vectors. Each feature vector records the sensitivity of a protein sequence to a set of amino acids substrings generated from the protein sequences of interest. These features are then used in conjunction with support vector machines for the detection of the protein remote homology. The proposed method is tested and evaluated on two different benchmark protein datasets and it-s able to deliver improvements over most of the existing homology detection methods.
Abstract: XML files contain data which is in well formatted manner. By studying the format or semantics of the grammar it will be helpful for fast retrieval of the data. There are many algorithms which describes about searching the data from XML files. There are no. of approaches which uses data structure or are related to the contents of the document. In these cases user must know about the structure of the document and information retrieval techniques using NLPs is related to content of the document. Hence the result may be irrelevant or not so successful and may take more time to search.. This paper presents fast XML retrieval techniques by using new indexing technique and the concept of RXML. When indexing an XML document, the system takes into account both the document content and the document structure and assigns the value to each tag from file. To query the system, a user is not constrained about fixed format of query.
Abstract: The security of computer networks plays a strategic
role in modern computer systems. Intrusion Detection Systems (IDS)
act as the 'second line of defense' placed inside a protected
network, looking for known or potential threats in network traffic
and/or audit data recorded by hosts. We developed an Intrusion
Detection System using LAMSTAR neural network to learn patterns
of normal and intrusive activities, to classify observed system
activities and compared the performance of LAMSTAR IDS with
other classification techniques using 5 classes of KDDCup99 data.
LAMSAR IDS gives better performance at the cost of high
Computational complexity, Training time and Testing time, when
compared to other classification techniques (Binary Tree classifier,
RBF classifier, Gaussian Mixture classifier). we further reduced the
Computational Complexity of LAMSTAR IDS by reducing the
dimension of the data using principal component analysis which in
turn reduces the training and testing time with almost the same
performance.
Abstract: This paper in essence presents comparative
experimental data on the mechanical performance of steel and
synthetic fibre-reinforced concrete under compression, tensile split
and flexure. URW1050 steel fibre and HPP45 synthetic fibre, both
with the same concrete design mix, have been used to make cube
specimens for a compression test, cylinders for a tensile split test and
beam specimens for a flexural test. The experimental data
demonstrated steel fibre reinforced concrete to be stronger in flexure
at early stages, whilst both fibre reinforced concrete types displayed
comparatively the same performance in compression, tensile splitting
and 28-day flexural strength. In terms of post-crack controlHPP45
was preferable.
Abstract: As many scientific applications require large data processing, the importance of parallel I/O has been increasingly recognized. Collective I/O is one of the considerable features of parallel I/O and enables application programmers to easily handle their large data volume. In this paper we measured and analyzed the performance of original collective I/O and the subgroup method, the way of using collective I/O of MPI effectively. From the experimental results, we found that the subgroup method showed good performance with small data size.
Abstract: Tolerance is a tool for achieving a social cohesion, particularly, among individuals and groups with different values. The aim is to study the characteristics of the ethnic tolerance, the inhabitants of Latvia. The ethnic tolerance is taught as a set of conscious and unconscious orientations of the individual in social interaction and inter-ethnic communication. It uses the tools of empirical studies of the ethnic tolerance which allows to identify the explicitly and implicitly levels of the emotional component of Latvia's residents. Explicit measurements were made using the techniques of self-report which revealed the index of the ethnic tolerance and the ethnic identity of the participants. The implicit component was studied using methods based on the effect of the emotional priming. During the processing of the results, there were calculated indicators of the positive and negative implicit attitudes towards members of their own and other ethnicity as well as the explicit parameters of the ethnic tolerance and the ethnic identity of Latvia-s residents. The implicit measurements of the ratio of neighboring ethnic groups against each other showed a mutual negative attitude whereas the explicit measurements indicate a neutral attitude. The data obtained contribute to a further study of the ethnic tolerance of Latvia's residents.
Abstract: The recognition of handwritten numeral is an
important area of research for its applications in post office, banks
and other organizations. This paper presents automatic recognition of
handwritten Kannada numerals based on structural features. Five
different types of features, namely, profile based 10-segment string,
water reservoir; vertical and horizontal strokes, end points and
average boundary length from the minimal bounding box are used in
the recognition of numeral. The effect of each feature and their
combination in the numeral classification is analyzed using nearest
neighbor classifiers. It is common to combine multiple categories of
features into a single feature vector for the classification. Instead,
separate classifiers can be used to classify based on each visual
feature individually and the final classification can be obtained based
on the combination of separate base classification results. One
popular approach is to combine the classifier results into a feature
vector and leaving the decision to next level classifier. This method
is extended to extract a better information, possibility distribution,
from the base classifiers in resolving the conflicts among the
classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy
k-NN) as base classifier for individual feature sets, the results of
which together forms the feature vector for the final k Nearest
Neighbor (k-NN) classifier. Testing is done, using different features,
individually and in combination, on a database containing 1600
samples of different numerals and the results are compared with the
results of different existing methods.
Abstract: VRML( The virtual reality modeling language) is a standard language used to build up 3D virtualized models. The quick development of internet technology and computer manipulation has promoted the commercialization of reality virtualization. VRML, thereof, is expected to be the most effective framework of building up virtual reality. This article has studied plans to build virtualized scenes based on the technology of virtual reality and Java programe, and introduced how to execute real-time data transactions of VRML file and Java programe by applying Script Node, in doing so we have the VRML interactivity being strengthened.
Abstract: Digital watermarking is a way to provide the facility of secure multimedia data communication besides its copyright protection approach. The Spread Spectrum modulation principle is widely used in digital watermarking to satisfy the robustness of multimedia signals against various signal-processing operations. Several SS watermarking algorithms have been proposed for multimedia signals but very few works have discussed on the issues responsible for secure data communication and its robustness improvement. The current paper has critically analyzed few such factors namely properties of spreading codes, proper signal decomposition suitable for data embedding, security provided by the key, successive bit cancellation method applied at decoder which have greater impact on the detection reliability, secure communication of significant signal under camouflage of insignificant signals etc. Based on the analysis, robust SS watermarking scheme for secure data communication is proposed in wavelet domain and improvement in secure communication and robustness performance is reported through experimental results. The reported result also shows improvement in visual and statistical invisibility of the hidden data.
Abstract: In this study, the use of silicon NAM (Non-Audible
Murmur) microphone in automatic speech recognition is presented.
NAM microphones are special acoustic sensors, which are attached
behind the talker-s ear and can capture not only normal (audible)
speech, but also very quietly uttered speech (non-audible murmur).
As a result, NAM microphones can be applied in automatic speech
recognition systems when privacy is desired in human-machine communication.
Moreover, NAM microphones show robustness against
noise and they might be used in special systems (speech recognition,
speech conversion etc.) for sound-impaired people. Using a small
amount of training data and adaptation approaches, 93.9% word
accuracy was achieved for a 20k Japanese vocabulary dictation
task. Non-audible murmur recognition in noisy environments is also
investigated. In this study, further analysis of the NAM speech has
been made using distance measures between hidden Markov model
(HMM) pairs. It has been shown the reduced spectral space of NAM
speech using a metric distance, however the location of the different
phonemes of NAM are similar to the location of the phonemes
of normal speech, and the NAM sounds are well discriminated.
Promising results in using nonlinear features are also introduced,
especially under noisy conditions.
Abstract: Climate change is a phenomenon has been based on
the available evidence from a very long time ago and now its
existence is very probable. The speed and nature of climate
parameters changes at the middle of twentieth century has been
different and its quickness more than the before and its trend changed
to some extent comparing to the past. Climate change issue now
regarded as not only one of the most common scientific topic but also
a social political one, is not a new issue. Climate change is a
complicated atmospheric oceanic phenomenon on a global scale and
long-term. Precipitation pattern change, fast decrease of snowcovered
resources and its rapid melting, increased evaporation, the
occurrence of destroying floods, water shortage crisis, severe
reduction at the rate of harvesting agricultural products and, so on are
all the significant of climate change. To cope with this phenomenon,
its consequences and events in which public instruction is the most
important but it may be climate that no significant cant and effective
action has been done so far. The present article is included a part of
one surrey about climate change in Fars. The study area having
annually mean temperature 14 and precipitation 320 mm .23 stations
inside the basin with a common 37 year statistical period have been
applied to the meteorology data (1974-2010). Man-kendal and
change factor methods are two statistical methods, applying them, the
trend of changes and the annual mean average temperature and the
annual minimum mean temperature were studied by using them.
Based on time series for each parameter, the annual mean average
temperature and the mean of annual maximum temperature have a
rising trend so that this trend is clearer to the mean of annual
maximum temperature.
Abstract: Knowledge is indispensable but voluminous knowledge becomes a bottleneck for efficient processing. A great challenge for data mining activity is the generation of large number of potential rules as a result of mining process. In fact sometimes result size is comparable to the original data. Traditional data mining pruning activities such as support do not sufficiently reduce the huge rule space. Moreover, many practical applications are characterized by continual change of data and knowledge, thereby making knowledge voluminous with each change. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. Michalski & Winston proposed Censored Production Rules (CPRs), as an extension of production rules, that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence, are tight or there is simply no information available as to whether it holds or not. Thus the 'If P Then D' part of the CPR expresses important information while the Unless C part acts only as a switch changes the polarity of D to ~D. In this paper a scheme based on Dempster-Shafer Theory (DST) interpretation of a CPR is suggested for discovering CPRs from the discovered flat PRs. The discovery of CPRs from flat rules would result in considerable reduction of the already discovered rules. The proposed scheme incrementally incorporates new knowledge and also reduces the size of knowledge base considerably with each episode. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested cumulative learning scheme would be useful in mining data streams.
Abstract: Most of fuzzy clustering algorithms have some
discrepancies, e.g. they are not able to detect clusters with convex
shapes, the number of the clusters should be a priori known, they
suffer from numerical problems, like sensitiveness to the
initialization, etc. This paper studies the synergistic combination of
the hierarchical and graph theoretic minimal spanning tree based
clustering algorithm with the partitional Gath-Geva fuzzy clustering
algorithm. The aim of this hybridization is to increase the robustness
and consistency of the clustering results and to decrease the number
of the heuristically defined parameters of these algorithms to
decrease the influence of the user on the clustering results. For the
analysis of the resulted fuzzy clusters a new fuzzy similarity measure
based tool has been presented. The calculated similarities of the
clusters can be used for the hierarchical clustering of the resulted
fuzzy clusters, which information is useful for cluster merging and
for the visualization of the clustering results. As the examples used
for the illustration of the operation of the new algorithm will show,
the proposed algorithm can detect clusters from data with arbitrary
shape and does not suffer from the numerical problems of the
classical Gath-Geva fuzzy clustering algorithm.
Abstract: Imaging is defined as the process of obtaining
geometric images either two dimensional or three dimensional by scanning or digitizing the existing objects or products. In this research, it applied to retrieve 3D information of the human skin
surface in medical application. This research focuses on analyzing
and determining volume of leg ulcers using imaging devices. Volume
determination is one of the important criteria in clinical assessment of leg ulcer. The volume and size of the leg ulcer wound will give the
indication on responding to treatment whether healing or worsening.
Different imaging techniques are expected to give different result (and accuracies) in generating data and images. Midpoint projection
algorithm was used to reconstruct the cavity to solid model and compute the volume. Misinterpretation of the results can affect the
treatment efficacy. The objectives of this paper is to compare the
accuracy between two 3D data acquisition method, which is laser
triangulation and structured light methods, It was shown that using models with known volume, that structured-light-based 3D technique
produces better accuracy compared with laser triangulation data
acquisition method for leg ulcer volume determination.
Abstract: The ubiquitous payment problems within construction
industry of China are notoriously hard to be resolved, thus lead to a
series of impacts to the industry chain. Among of them, the most direct
result is affecting the normal operation of contractors negatively. A
wealth of research has already discussed reasons of the payment
problems by introducing a number of possible improvement strategies.
But the causalities of these problems are still far from harsh reality. In
this paper, the authors propose a model for cash flow system of
construction projects by introducing System Dynamics techniques to
explore causal facets of the payment problem. The effects of payment
arrears on both cash flow and profitability of project are simulated into
four scenarios by using data from real projects. Simulating results
show visible clues to help contractors quantitatively determining the
consequences for the construction project that arise from payment
delay.