Abstract: This paper deals with the extraction of information from the experts to automatically identify and recognize Ganoderma infection in oil palm stem using tomography images. Expert-s knowledge are used as rules in a Fuzzy Inference Systems to classify each individual patterns observed in he tomography image. The classification is done by defining membership functions which assigned a set of three possible hypotheses : Ganoderma infection (G), non Ganoderma infection (N) or intact stem tissue (I) to every abnormalities pattern found in the tomography image. A complete comparison between Mamdani and Sugeno style,triangular, trapezoids and mixed triangular-trapezoids membership functions and different methods of aggregation and defuzzification is also presented and analyzed to select suitable Fuzzy Inference System methods to perform the above mentioned task. The results showed that seven out of 30 initial possible combination of available Fuzzy Inference methods in MATLAB Fuzzy Toolbox were observed giving result close to the experts estimation.
Abstract: Does the spatial perspective provide a common thread for rural sociology? Have rural sociologists succeeded in bringing order to their data using spatial analysis models and techniques? A trial answer to such questions, as touchstones of theoretical and applied sociological studies in rural areas, is the point at issue in the present paper. Spatial analyses have changed the way rural sociologists approach scientific problems. Rural sociology is spatial by nature because much, if not most, of its research topics has a spatial “awareness." However, such spatial awareness is not quite the same as spatial analysis because it is not typically associated with underlying theories and hypotheses about spatial patterns that are designed to be tested for their specific spatial content. This paper presents pressing issues for future research to reintroduce mainstream rural sociology to the concept of space.
Abstract: techniques are examined to overcome the
performance degradation caused by the channel dispersion using
slow frequency hopping (SFH) with dynamic frequency hopping
(DFH) pattern adaptation. In DFH systems, the frequency slots are
selected by continuous quality monitoring of all frequencies available
in a system and modification of hopping patterns for each individual
link based on replacing slots which its signal to interference ratio
(SIR) measurement is below a required threshold. Simulation results
will show the improvements in BER obtained by DFH in comparison
with matched frequency hopping (MFH), random frequency hopping
(RFH) and multi-carrier code division multiple access (MC-CDMA)
in multipath slowly fading dispersive channels using a generalized
bandpass two-path transfer function model, and will show the
improvement obtained according to the threshold selection.
Abstract: The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag Of Words representation of an email is widely used to stop this unwanted flood as it combines good performance with simplicity of the training and classification processes. However, facing the constantly changing patterns of spam, it is necessary to assure online adaptability of the classifier. This work proposes combining such a classifier with another NBC (naïve Bayesian classifier) based on pairs of adjacent words. Only the latter will be retrained with examples of spam reported by users. Tests are performed on considerable sets of mails both from public spam archives and CERN mailboxes. They suggest that this architecture can increase spam recall without affecting the classifier precision as it happens when only the NBC based on single words is retrained.
Abstract: This paper presents a new system developed in Java®
for pattern recognition and pattern summarisation in multi-band
(RGB) satellite images. The system design is described in some
detail. Results of testing the system to analyse and summarise
patterns in SPOT MS images and LANDSAT images are also
discussed.
Abstract: This paper looks into areas not covered by prominent
Agent-Oriented Software Engineering (AOSE) methodologies.
Extensive paper review led to the identification of two issues, first
most of these methodologies almost neglect semantic web and
ontology. Second, as expected, each one has its strength and
weakness and may focus on some phases of the development
lifecycle but not all of the phases. The work presented here builds
extensions to a highly regarded AOSE methodology (MaSE) in order
to cover the areas that this methodology does not concentrate on. The
extensions include introducing an ontology stage for semantic
representation and integrating early requirement specification from a
methodology which mainly focuses on that. The integration involved
developing transformation rules (with the necessary handling of nonmatching
notions) between the two sets of representations and
building the software which automates the transformation. The
application of this integration on a case study is also presented in the
paper. The main flow of MaSE stages was changed to smoothly
accommodate the new additions.
Abstract: Hybrid algorithm is the hot issue in Computational
Intelligence (CI) study. From in-depth discussion on Simulation
Mechanism Based (SMB) classification method and composite patterns,
this paper presents the Mamdani model based Adaptive Neural
Fuzzy Inference System (M-ANFIS) and weight updating formula in
consideration with qualitative representation of inference consequent
parts in fuzzy neural networks. M-ANFIS model adopts Mamdani
fuzzy inference system which has advantages in consequent part.
Experiment results of applying M-ANFIS to evaluate traffic Level
of service show that M-ANFIS, as a new hybrid algorithm in computational
intelligence, has great advantages in non-linear modeling,
membership functions in consequent parts, scale of training data and
amount of adjusted parameters.
Abstract: In this work we evaluate the possibility of predicting
the emotional state of a person based on the EEG. We investigate
the problem of classifying valence from EEG signals during
the presentation of affective pictures, utilizing the "frontal EEG
asymmetry" phenomenon. To distinguish positive and negative
emotions, we applied the Common Spatial Patterns algorithm.
In contrast to our expectations, the affective pictures did not
reliably elicit changes in frontal asymmetry. The classifying task
thereby becomes very hard as reflected by the poor classifier
performance. We suspect that the masking of the source of the
brain activity related to emotions, coming mostly from deeper
structures in the brain, and the insufficient emotional engagement
are among main reasons why it is difficult to predict the emotional
state of a person.
Abstract: A neuron can emit spikes in an irregular time basis and by averaging over a certain time window one would ignore a lot of information. It is known that in the context of fast information processing there is no sufficient time to sample an average firing rate of the spiking neurons. The present work shows that the spiking neurons are capable of computing the radial basis functions by storing the relevant information in the neurons' delays. One of the fundamental findings of the this research also is that when using overlapping receptive fields to encode the data patterns it increases the network-s clustering capacity. The clustering algorithm that is discussed here is interesting from computer science and neuroscience point of view as well as from a perspective.
Abstract: Textures are replications, symmetries and
combinations of various basic patterns, usually with some random
variation one of the gray-level statistics. This article proposes a
new approach to Segment texture images. The proposed approach
proceeds in 2 stages. First, in this method, local texture information
of a pixel is obtained by fuzzy texture unit and global texture
information of an image is obtained by fuzzy texture spectrum.
The purpose of this paper is to demonstrate the usefulness of fuzzy
texture spectrum for texture Segmentation.
The 2nd Stage of the method is devoted to a decision process,
applying a global analysis followed by a fine segmentation,
which is only focused on ambiguous points. The above Proposed
approach was applied to brain image to identify the components
of brain in turn, used to locate the brain tumor and its Growth
rate.
Abstract: Software reliability, defined as the probability of a
software system or application functioning without failure or errors
over a defined period of time, has been an important area of research
for over three decades. Several research efforts aimed at developing
models to improve reliability are currently underway. One of the
most popular approaches to software reliability adopted by some of
these research efforts involves the use of operational profiles to
predict how software applications will be used. Operational profiles
are a quantification of usage patterns for a software application. The
research presented in this paper investigates an innovative multiagent
framework for automatic creation and management of
operational profiles for generic distributed systems after their release
into the market. The architecture of the proposed Operational Profile
MAS (Multi-Agent System) is presented along with detailed
descriptions of the various models arrived at following the analysis
and design phases of the proposed system. The operational profile in
this paper is extended to comprise seven different profiles. Further,
the criticality of operations is defined using a new composed metrics
in order to organize the testing process as well as to decrease the time
and cost involved in this process. A prototype implementation of the
proposed MAS is included as proof-of-concept and the framework is
considered as a step towards making distributed systems intelligent
and self-managing.
Abstract: Face detection and recognition has many applications
in a variety of fields such as security system, videoconferencing and
identification. Face classification is currently implemented in
software. A hardware implementation allows real-time processing,
but has higher cost and time to-market.
The objective of this work is to implement a classifier based on
neural networks MLP (Multi-layer Perceptron) for face detection.
The MLP is used to classify face and non-face patterns. The systm is
described using C language on a P4 (2.4 Ghz) to extract weight
values. Then a Hardware implementation is achieved using VHDL
based Methodology. We target Xilinx FPGA as the implementation
support.
Abstract: Different forms of interaction are an integral part of
modern courses. Traditional courses held on-campus might focus on
teacher-student interaction, or student-student interaction, or both.
However when these traditional on-campus courses are to be held as
distance courses there is a risk that these well-designed interactions
will be difficult or impossible to uphold. For example, studentstudent
interaction in traditional project assignments might not work
well if the students are scattered across the world. Thus, even a welldesigned
traditional on-site course cannot without modification be
turned into a distance course. Traditional on-site courses simply have
to be redesigned to become true distance courses. This paper
describes a structured approach which facilitates the redesign of a
traditional course into a distance course. The approach is based on
that the desired forms of course flexibility are identified, and
thereafter that the course activities are redesigned to facilitate
interaction in a distance course. The approach is making use of
known patterns of pedagogic interaction and existing guidelines for
distance education design. The approach is illustrated with an
example course in the field of information systems design.
Abstract: In this paper a fast motion estimation method for
H.264/AVC named Triplet Search Motion Estimation (TS-ME) is
proposed. Similar to some of the traditional fast motion estimation
methods and their improved proposals which restrict the search points
only to some selected candidates to decrease the computation
complexity, proposed algorithm separate the motion search process to
several steps but with some new features. First, proposed algorithm try
to search the real motion area using proposed triplet patterns instead of
some selected search points to avoid dropping into the local minimum.
Then, in the localized motion area a novel 3-step motion search
algorithm is performed. Proposed search patterns are categorized into
three rings on the basis of the distance from the search center. These
three rings are adaptively selected by referencing the surrounding
motion vectors to early terminate the motion search process. On the
other hand, computation reduction for sub pixel motion search is also
discussed considering the appearance probability of the sub pixel
motion vector. From the simulation results, motion estimation speed
improved by a factor of up to 38 when using proposed algorithm than
that of the reference software of H.264/AVC with ignorable picture
quality loss.
Abstract: The purpose of this research was to design costume by the inspiration from the configurations, colors and decorations of Thai Royal Barges. The researcher investigated the bibliographies and the important of the Thai Royal Water-Course Procession, configurations and decoration techniques of four Royal Barges history. Furthermore, the researcher combined the contemporary architecture which became part of the four costumes with four patterns in this research. The four costumes designed by applied the physical configuration of the Royal Barge with the fold techniques which create the geometry pattern that are part of the Royal Barge-s decoration and contemporary architecture. Therefore, the researcher united each identity color of the barges with each costume composed with the original patterns by adjusted new layout and resized. Lastly, the new attractive patterns appeared. Nevertheless, the beauty of Thai traditional still remain by using Thai painting figure with black and white color which are the prevalent colors for the contemporary architectures.
Abstract: Static analysis of source code is used for auditing web
applications to detect the vulnerabilities. In this paper, we propose a
new algorithm to analyze the PHP source code for detecting LFI and
RFI potential vulnerabilities. In our approach, we first define some
patterns for finding some functions which have potential to be abused
because of unhandled user inputs. More precisely, we use regular
expression as a fast and simple method to define some patterns for
detection of vulnerabilities. As inclusion functions could be also used
in a safe way, there could occur many false positives (FP). The first
cause of these FP-s could be that the function does not use a usersupplied
variable as an argument. So, we extract a list of usersupplied
variables to be used for detecting vulnerable lines of code.
On the other side, as vulnerability could spread among the variables
like by multi-level assignment, we also try to extract the hidden usersupplied
variables. We use the resulted list to decrease the false
positives of our method. Finally, as there exist some ways to prevent
the vulnerability of inclusion functions, we define also some patterns
to detect them and decrease our false positives.
Abstract: This paper presents an experimental case using sensory thermography to describe temperatures behavior on median nerve once an activity of repetitive motion was done. Thermography is a noninvasive technique without biological hazard and not harm at all times and has been applied in many experiments to seek for temperature patterns that help to understand diseases like cancer and cumulative trauma disorders (CTD’s). An infrared sensory thermography technology was developed to execute this study. Three women in good shape were selected for the repetitive motion tests for 4 days, two right-handed women and 1 left handed woman, two sensory thermographers were put on both median nerve wrists to get measures. The evaluation time was of 3 hours 30 minutes in a controlled temperature, 20 minutes of stabilization time at the beginning and end of the operation. Temperatures distributions are statistically evaluated and showed similar temperature patterns behavior.
Abstract: Electrophysiological signals were recorded from primary cultures of dissociated rat cortical neurons coupled to Micro-Electrode Arrays (MEAs). The neuronal discharge patterns may change under varying physiological and pathological conditions. For this reason, we developed a new burst detection method able to identify bursts with peculiar features in different experimental conditions (i.e. spontaneous activity and under the effect of specific drugs). The main feature of our algorithm (i.e. Burst On Hurst), based on the auto-similarity or fractal property of the recorded signal, is the independence from the chosen spike detection method since it works directly on the raw data.
Abstract: Clustering techniques have been used by many intelligent software agents to group similar access patterns of the Web users into high level themes which express users intentions and interests. However, such techniques have been mostly focusing on one salient feature of the Web document visited by the user, namely the extracted keywords. The major aim of these techniques is to come up with an optimal threshold for the number of keywords needed to produce more focused themes. In this paper we focus on both keyword and similarity thresholds to generate themes with concentrated themes, and hence build a more sound model of the user behavior. The purpose of this paper is two fold: use distance based clustering methods to recognize overall themes from the Proxy log file, and suggest an efficient cut off levels for the keyword and similarity thresholds which tend to produce more optimal clusters with better focus and efficient size.
Abstract: During the last few decades in the academic field, the
debate has increased on the effects of social geography on the
opportunities of socioeconomic integration. On one hand, it has been
discussed how the contents of the urban structure and social
geography affect not only the way people interact, but also their
chances of social and economic integration. On the other hand, it has
also been discussed how the urban structure is also constrained and
transformed by the action of social actors. Without questioning the
powerful influence of structural factors, related to the logic of the
production system, labor markets, education and training, the
research has shown the role played by place of residence in shaping
individual outcomes such as unemployment. In the context of this
debate the importance of territory of residence with respect to the
problem of unemployment has been highlighted.
Although statistics of unemployment have already demonstrated
the unequal incidence of the phenomenon in social groups, the issue
of uneven territorial impact on the phenomenon at intra-urban level
remains relatively unknown.
The purpose of this article is to show and to interpret the spatial
patterns of unemployment in the city of Porto using GIS (Geographic
Information System - GIS) technology. Under this analysis the
overlap of the spatial patterns of unemployment with the spatial
distribution of social housing, allows the discussion of the
relationship that occurs between these patterns and the reasons that
might explain the relative immutability of socioeconomic problems in
some neighborhoods.