Abstract: The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.
Abstract: Later life loneliness is a social issue that is increasing alongside an upward global population trend. As a society, one way that we have responded to this social challenge is through developing non-pharmacological interventions such as befriending services, activity clubs, meet-ups, etc. Through a systematic literature review, this paper suggests that currently there is an underrepresentation of radical innovation, and underutilization of digital technologies in developing loneliness interventions for older adults. This paper examines intervention studies that were published in English language, within peer reviewed journals between January 2005 and December 2014 across 4 electronic databases. In addition to academic databases, interventions found in grey literature in the form of websites, blogs, and Twitter were also included in the overall review. This approach yielded 129 interventions that were included in the study. A systematic approach allowed the minimization of any bias dictating the selection of interventions to study. A coding strategy based on a pattern analysis approach was devised to be able to compare and contrast the loneliness interventions. Firstly, interventions were categorized on the basis of their objective to identify whether they were preventative, supportive, or remedial in nature. Secondly, depending on their scope, they were categorized as one-to-one, community-based, or group based. It was also ascertained whether interventions represented an improvement, an incremental innovation, a major advance or a radical departure, in comparison to the most basic form of a loneliness intervention. Finally, interventions were also assessed on the basis of the extent to which they utilized digital technologies. Individual visualizations representing the four levels of coding were created for each intervention, followed by an aggregated visual to facilitate analysis. To keep the inquiry within scope and to present a coherent view of the findings, the analysis was primarily concerned the level of innovation, and the use of digital technologies. This analysis highlights a weak but positive correlation between the level of innovation and the use of digital technologies in designing and deploying loneliness interventions, and also emphasizes how certain existing interventions could be tweaked to enable their migration from representing incremental innovation to radical innovation for example. This analysis also points out the value of including grey literature, especially from Twitter, in systematic literature reviews to get a contemporary view of latest work in the area under investigation.
Abstract: The traditional rhythms of the West African country
of Guinea have played a centuries-long role in defining the different
people groups that make up the country. Throughout their history,
before and since colonization by the French, the different ethnicities
have used their traditional music as a distinct part of their historical
identities. That is starting to change. Guinea is an impoverished
nation created in the early twentieth-century with little regard for the
history and cultures of the people who were included. The traditional
rhythms of the different people groups and their heritages have
remained. Fifteen individual traditional Guinean rhythms were
chosen to represent popular rhythms from the four geographical
regions of Guinea. Each rhythm was traced back to its native village
and video recorded on-site by as many different local performing
groups as could be located. The cyclical patterns rhythms were
transcribed via a circular, spatial design and then copied into a box
notation system where sounds happening at the same time could be
studied. These rhythms were analyzed for their consistency-overperformance
in a Fundamental Rhythm Pattern analysis so rhythms
could be compared for how they are changing through different
performances. The analysis showed that the traditional rhythm
performances of the Middle and Forest Guinea regions were the most
cohesive and showed the least evidence of change between
performances. The role of music in each of these regions is both
limited and focused. The Coastal and High Guinea regions have
much in common historically through their ethnic history and
modern-day trade connections, but the rhythm performances seem to
be less consistent and demonstrate more changes in how they are
performed today. In each of these regions the role and usage of music
is much freer and wide-spread. In spite of advances being made as a
country, different ethnic groups still frequently only respond and
participate (dance and sing) to the music of their native ethnicity.
There is some evidence that this self-imposed musical barrier is
beginning to change and evolve, partially through the development of
better roads, more access to electricity and technology, the nationwide
Ebola health crisis, and a growing self-identification as a
unified nation.
Abstract: The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns.
Abstract: Vertex configuration for a vertex in an orthogonal
pseudo-polyhedron is an identity of a vertex that is determined by the
number of edges, dihedral angles, and non-manifold properties
meeting at the vertex. There are up to sixteen vertex configurations
for any orthogonal pseudo-polyhedron (OPP). Understanding the
relationship between these vertex configurations will give us insight
into the structure of an OPP and help us design better algorithms for
many 3-dimensional geometric problems. In this paper, 16 vertex
configurations for OPP are described first. This is followed by a
number of formulas giving insight into the relationship between
different vertex configurations in an OPP. These formulas
will be useful as an extension of orthogonal polyhedra usefulness on
pattern analysis in 3D-digital images.
Abstract: In this paper a new method is suggested for risk
management by the numerical patterns in data-mining. These patterns
are designed using probability rules in decision trees and are cared to
be valid, novel, useful and understandable. Considering a set of
functions, the system reaches to a good pattern or better objectives.
The patterns are analyzed through the produced matrices and some
results are pointed out. By using the suggested method the direction
of the functionality route in the systems can be controlled and best
planning for special objectives be done.
Abstract: Point quad tree is considered as one of the most
common data organizations to deal with spatial data & can be used to
increase the efficiency for searching the point features. As the
efficiency of the searching technique depends on the height of the
tree, arbitrary insertion of the point features may make the tree
unbalanced and lead to higher time of searching. This paper attempts
to design an algorithm to make a nearly balanced quad tree. Point
pattern analysis technique has been applied for this purpose which
shows a significant enhancement of the performance and the results
are also included in the paper for the sake of completeness.
Abstract: Weblogs are resource of social structure to discover and track the various type of information written by blogger. In this paper, we proposed to use mining weblogs technique for identifying the trends of influenza where blogger had disseminated their opinion for the anomaly disease. In order to identify the trends, web crawler is applied to perform a search and generated a list of visited links based on a set of influenza keywords. This information is used to implement the analytics report system for monitoring and analyzing the pattern and trends of influenza (H1N1). Statistical and graphical analysis reports are generated. Both types of the report have shown satisfactory reports that reflect the awareness of Malaysian on the issue of influenza outbreak through blogs.
Abstract: Properly sized capacitor banks are connected across induction motors for several reasons including power factor correction, reducing distortions, increasing capacity, etc. Total harmonic distortion (THD) and power factor (PF) are used in such cases to quantify the improvements obtained through connection of the external capacitor banks. On the other hand, one of the methods for assessing the motor internal condition is by the use of Park-s pattern analysis. In spite of taking adequate precautionary measures, the capacitor banks may sometimes malfunction. Such a minor fault in the capacitor bank is often not apparently discernible. This may however, give rise to substantial degradation of power factor correction performance and may also damage the supply profile. The case is more severe with the fact that the Park-s pattern gets distorted due to such external capacitor faults, and can give anomalous results about motor internal fault analyses. The aim of this paper is to present simulation and hardware laboratory test results to have an understanding of the anomalies in harmonic distortion and Park-s pattern analyses in induction motors due to capacitor bank defects.
Abstract: This paper proposes a method of adaptively generating a gait pattern of biped robot. The gait synthesis is based on human's gait pattern analysis. The proposed method can easily be applied to generate the natural and stable gait pattern of any biped robot. To analyze the human's gait pattern, sequential images of the human's gait on the sagittal plane are acquired from which the gait control values are extracted. The gait pattern of biped robot on the sagittal plane is adaptively generated by a genetic algorithm using the human's gait control values. However, gait trajectories of the biped robot on the sagittal plane are not enough to construct the complete gait pattern because the biped robot moves on 3-dimension space. Therefore, the gait pattern on the frontal plane, generated from Zero Moment Point (ZMP), is added to the gait one acquired on the sagittal plane. Consequently, the natural and stable walking pattern for the biped robot is obtained.
Abstract: We present analysis of spatial patterns of generic
disease spread simulated by a stochastic long-range correlation SIR
model, where individuals can be infected at long distance in a power
law distribution. We integrated various tools, namely perimeter,
circularity, fractal dimension, and aggregation index to characterize
and investigate spatial pattern formations. Our primary goal was to
understand for a given model of interest which tool has an advantage
over the other and to what extent. We found that perimeter and
circularity give information only for a case of strong correlation–
while the fractal dimension and aggregation index exhibit the growth
rule of pattern formation, depending on the degree of the correlation
exponent (β). The aggregation index method used as an alternative
method to describe the degree of pathogenic ratio (α). This study may
provide a useful approach to characterize and analyze the pattern
formation of epidemic spreading
Abstract: The changes in quality properties and nutritional
components in two fermented mugworts (Artemisia capillaries
Thumberg, Artemisiaeasiaticae Nakai) were characterized followed
by the rapid pattern analysis of volatile flavor compounds by Electric
Nose based on SAW(Surface Acoustic Wave) sensor in GC system.
There were remarkable decreases in the pH and small changes in the
total soluble solids after fermentation. The L (lightness) and b
(yellowness) values in Hunter's color system were shown to be
decreased, whilst the a (redness) value was increased by fermentation.
The HPLC analysis demonstrated that total amino acids were
increased in quantity and the essential amino acids were contained
higher in A. asiaticaeNakai than in A. capillaries Thumberg. While
the total polyphenol contents were not affected by fermentation, the
total sugar contents were dramatically decreased. Scopoletinwere
highly abundant in A. capillarisThumberg, however, it was not
detected in A. asiaticaeNakai. Volatile flavor compounds by Electric
Nose showed that the intensity of several peaks were increased much
and seven additional flavor peaks were newly produced after
fermentation. The flavor differences of two mugworts were clearly
distinguished from the image patterns of VaporPrintTM which indicate
that the fermentation enables the two mugworts to have subtle flavor
differences.
Abstract: Intelligent systems based on machine learning
techniques, such as classification, clustering, are gaining wide spread
popularity in real world applications. This paper presents work on
developing a software system for predicting crop yield, for example
oil-palm yield, from climate and plantation data. At the core of our
system is a method for unsupervised partitioning of data for finding
spatio-temporal patterns in climate data using kernel methods which
offer strength to deal with complex data. This work gets inspiration
from the notion that a non-linear data transformation into some high
dimensional feature space increases the possibility of linear
separability of the patterns in the transformed space. Therefore, it
simplifies exploration of the associated structure in the data. Kernel
methods implicitly perform a non-linear mapping of the input data
into a high dimensional feature space by replacing the inner products
with an appropriate positive definite function. In this paper we
present a robust weighted kernel k-means algorithm incorporating
spatial constraints for clustering the data. The proposed algorithm
can effectively handle noise, outliers and auto-correlation in the
spatial data, for effective and efficient data analysis by exploring
patterns and structures in the data, and thus can be used for
predicting oil-palm yield by analyzing various factors affecting the
yield.