Abstract: Sheet-metal parts have been widely applied in electronics, communication and mechanical industries in recent decades; but the advancement in sheet-metal part design and manufacturing is still behind in comparison with the increasing importance of sheet-metal parts in modern industry. This paper presents a methodology for automatic extraction of some common 2D internal sheet metal features. The features used in this study are taken from Unipunch ™ catalogue. The extraction process starts with the data extraction from STEP file using an object oriented approach and with the application of suitable algorithms and rules, all features contained in the catalogue are automatically extracted. Since the extracted features include geometry and engineering information, they will be effective for downstream application such as feature rebuilding and process planning.
Abstract: Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification.
Abstract: The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.
Abstract: In the recent years, a considerable level of interest has been developed on the use of earth in construction, led by its rediscovery as an environmentally building material. The Stabilized Earth Concrete (SEC) is a good alternative to the cement concrete, thanks to its thermal and moisture regulating features. Many parameters affect the behavior of stabilized earth concrete. This article presents research results related to the influence of the compacting nature on some SEC properties namely: The mechanical behavior, capillary absorption, shrinkage and sustainability to water erosion, and this, basing on two types of compacting: Manual and semi-automatic.
Abstract: Today, wine quality is only evaluated by wine experts with their own different personal tastes, even if they may agree on some common features. So producers do not have any unbiased way to independently assess the quality of their products. A tool is here proposed to evaluate wine quality by an objective ranking based upon the variables entering wine elaboration, and analysed through principal component analysis (PCA) method. Actual climatic data are compared by measuring the relative distance between each considered wine, out of which the general ranking is performed.
Abstract: Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.
Abstract: In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.
Abstract: Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.
Abstract: In the deep south of Thailand, checkpoints for people
verification are necessary for the security management of risk zones,
such as official buildings in the conflict area. In this paper, we
propose an automatic checkpoint system that verifies persons using
information from ID cards and facial features. The methods for a
person’s information abstraction and verification are introduced
based on useful information such as ID number and name, extracted
from official cards, and facial images from videos. The proposed
system shows promising results and has a real impact on the local
society.
Abstract: In order to retrieve images efficiently from a large
database, a unique method integrating color and texture features
using genetic programming has been proposed. Opponent color
histogram which gives shadow, shade, and light intensity invariant
property is employed in the proposed framework for extracting color
features. For texture feature extraction, fast discrete curvelet
transform which captures more orientation information at different
scales is incorporated to represent curved like edges. The recent
scenario in the issues of image retrieval is to reduce the semantic gap
between user’s preference and low level features. To address this
concern, genetic algorithm combined with relevance feedback is
embedded to reduce semantic gap and retrieve user’s preference
images. Extensive and comparative experiments have been conducted
to evaluate proposed framework for content based image retrieval on
two databases, i.e., COIL-100 and Corel-1000. Experimental results
clearly show that the proposed system surpassed other existing
systems in terms of precision and recall. The proposed work achieves
highest performance with average precision of 88.2% on COIL-100
and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3%
on Corel. Thus, the experimental results confirm that the proposed
content based image retrieval system architecture attains better
solution for image retrieval.
Abstract: In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
Abstract: The acceptance of sustainable products by the final
consumer is still one of the challenges of the industry, which
constantly seeks alternative approaches to successfully be accepted in
the global market. A large set of methods and approaches have been
discussed and analysed throughout the literature. Considering the current need for sustainable development and the
current pace of consumption, the need for a combined solution
towards the development of new products became clear, forcing
researchers in product development to propose alternatives to the
previous standard product development models. This paper presents, through a systemic analysis of the literature
on product development, eco-design and consumer involvement, a set
of alternatives regarding consumer involvement towards the
development of sustainable products and how these approaches could
help improve the sustainable industry’s establishment in the general
market. Still being developed in the course of the author’s PhD, the initial
findings of the research show that the understanding of the benefits of
sustainable behaviour lead to a more conscious acquisition and
eventually to the implementation of sustainable change in the
consumer. Thus this paper is the initial approach towards the
development of new sustainable products using the fashion industry
as an example of practical implementation and acceptance by the
consumers. By comparing the existing literature and critically analysing it, this
paper concluded that the consumer involvement is strategic to
improve the general understanding of sustainability and its features.
The use of consumers and communities has been studied since the
early 90s in order to exemplify uses and to guarantee a fast
comprehension. The analysis done also includes the importance of
this approach for the increase of innovation and ground breaking
developments, thus requiring further research and practical
implementation in order to better understand the implications and
limitations of this methodology.
Abstract: Brain-Computer Interfaces (BCIs) measure brain
signals activity, intentionally and unintentionally induced by users,
and provides a communication channel without depending on the
brain’s normal peripheral nerves and muscles output pathway.
Feature Selection (FS) is a global optimization machine learning
problem that reduces features, removes irrelevant and noisy data
resulting in acceptable recognition accuracy. It is a vital step
affecting pattern recognition system performance. This study presents
a new Binary Particle Swarm Optimization (BPSO) based feature
selection algorithm. Multi-layer Perceptron Neural Network
(MLPNN) classifier with backpropagation training algorithm and
Levenberg-Marquardt training algorithm classify selected features.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: This paper suggests a new internal architecture of
holon based on feature selection model using the combination of
Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is
used to generate features while ANN is used as a classifier to
evaluate the produced features. Proposed system is applied on the
Wine dataset, the statistical result proves that the proposed system is
effective and has the ability to choose informative features with high
accuracy.
Abstract: Polymeric micro-cantilevers (Cs) are rapidly
becoming popular for MEMS applications such as chemo- and biosensing
as well as purely electromechanical applications such as
microrelays. Polymer materials present suitable physical and
chemical properties combined with low-cost mass production. Hence,
micro-cantilevers made of polymers indicate much more
biocompatibility and adaptability of rapid prototyping along with
mechanical properties. This research studies the effects of three
process and one size factors on the filling behaviour in micro cavity,
and the role of each in the replication of micro parts using different
polymer materials i.e. polypropylene (PP) SABIC 56M10 and
acrylonitrile butadiene styrene (ABS) Magnum 8434 . In particular,
the following factors are considered: barrel temperature, mould
temperature, injection speed and the thickness of micro features. The
study revealed that the barrel temperature and the injection speed are
the key factors affecting the flow length of micro features replicated
in PP and ABS. For both materials, an increase of feature sizes
improves the melt flow. However, the melt fill of micro features does
not increase linearly with the increase of their thickness.
Abstract: This work is on decision tree-based classification for
the disbursement of scholarship. Tree-based data mining
classification technique is used in other to determine the generic rule
to be used to disburse the scholarship. The system based on the
defined rules from the tree is able to determine the class (status) to
which an applicant shall belong whether Granted or Not Granted. The
applicants that fall to the class of granted denote a successful
acquirement of scholarship while those in not granted class are
unsuccessful in the scheme. An algorithm that can be used to classify
the applicants based on the rules from tree-based classification was
also developed. The tree-based classification is adopted because of its
efficiency, effectiveness, and easy to comprehend features. The
system was tested with the data of National Information Technology
Development Agency (NITDA) Abuja, a Parastatal of Federal
Ministry of Communication Technology that is mandated to develop
and regulate information technology in Nigeria. The system was
found working according to the specification. It is therefore
recommended for all scholarship disbursement organizations.
Abstract: A large amount of software products offer a wide
range and number of features. This is called featuritis or creeping
featurism and tends to rise with each release of the product. Feautiris
often adds unnecessary complexity to software, leading to longer
learning curves and overall confusing the users and degrading their
experience. We take a look to a new design approach tendency that
has been coming up, the so-called “What You Get is What You
Need” concept that argues that products should be very focused,
simple and with minimalistic interfaces in order to help users conduct
their tasks in distraction-free ambiences. This isn’t as simple to
implement as it might sound and the developers need to cut down
features. Our contribution illustrates and evaluates this design method
through a novel distraction-free diagramming tool named Delineato
Pro for Mac OS X in which the user is confronted with an empty
canvas when launching the software and where tools only show up
when really needed.
Abstract: Feature selection has been used in many fields such as
classification, data mining and object recognition and proven to be
effective for removing irrelevant and redundant features from the
original dataset. In this paper, a new design of distributed intrusion
detection system using a combination feature selection model based
on bees and decision tree. Bees algorithm is used as the search
strategy to find the optimal subset of features, whereas decision tree
is used as a judgment for the selected features. Both the produced
features and the generated rules are used by Decision Making Mobile
Agent to decide whether there is an attack or not in the networks.
Decision Making Mobile Agent will migrate through the networks,
moving from node to another, if it found that there is an attack on one
of the nodes, it then alerts the user through User Interface Agent or
takes some action through Action Mobile Agent. The KDD Cup 99
dataset is used to test the effectiveness of the proposed system. The
results show that even if only four features are used, the proposed
system gives a better performance when it is compared with the
obtained results using all 41 features.
Abstract: File sharing in networks is generally achieved using
Peer-to-Peer (P2P) applications. Structured P2P approaches are
widely used in adhoc networks due to its distributed and scalability
features. Efficient mechanisms are required to handle the huge
amount of data distributed to all peers. The intrinsic characteristics of
P2P system makes for easier content distribution when compared to
client-server architecture. All the nodes in a P2P network act as both
client and server, thus, distributing data takes lesser time when
compared to the client-server method. CHORD protocol is a resource
routing based where nodes and data items are structured into a 1-
dimensional ring. The structured lookup algorithm of Chord is
advantageous for distributed P2P networking applications. However,
structured approach improves lookup performance in a high
bandwidth wired network it could contribute to unnecessary overhead
in overlay networks leading to degradation of network performance.
In this paper, the performance of existing CHORD protocol on
Wireless Mesh Network (WMN) when nodes are static and dynamic
is investigated.