Abstract: Detection, feature extraction and pose estimation of
people in images and video is made challenging by the variability of
human appearance, the complexity of natural scenes and the high
dimensionality of articulated body models and also the important
field in Image, Signal and Vision Computing in recent years. In this
paper, four types of people in 2D dimension image will be tested and
proposed. The system will extract the size and the advantage of them
(such as: tall fat, short fat, tall thin and short thin) from image. Fat
and thin, according to their result from the human body that has been
extract from image, will be obtained. Also the system extract every
size of human body such as length, width and shown them in output.
Abstract: The study of the variability of the postural strategies
in low back pain patients, as a criterion in evaluation of the
adaptability of this system to the environmental demands is the
purpose of this study. A cross-sectional case-control study was
performed on 21 recurrent non-specific low back pain patients and 21
healthy volunteers. The electromyography activity of Deltoid,
External Oblique (EO), Transverse Abdominis/Internal Oblique
(TrA/IO) and Erector Spine (ES) muscles of each person was
recorded in 75 rapid arm flexion with maximum acceleration.
Standard deviation of trunk muscles onset relative to deltoid muscle
onset were statistically analyzed by MANOVA . The results show
that chronic low back pain patients exhibit less variability in their
anticipatory postural adjustments (APAs) in comparison with the
control group. There is a decrease in variability of postural control
system of recurrent non-specific low back pain patients that can
result in the persistence of pain and chronicity by decreasing the
adaptability to environmental demands.
Abstract: Airport capacity has always been perceived in the
traditional sense as the number of aircraft operations during a
specified time corresponding to a tolerable level of average delay and
it mostly depends on the airside characteristics, on the fleet mix
variability and on the ATM. The adoption of the Directive
2002/30/EC in the EU countries drives the stakeholders to conceive
airport capacity in a different way though. Airport capacity in this
sense is fundamentally driven by environmental criteria, and since
acoustical externalities represent the most important factors, those are
the ones that could pose a serious threat to the growth of airports and
to aviation market itself in the short-medium term. The importance of
the regional airports in the deregulated market grew fast during the
last decade since they represent spokes for network carriers and a
preferential destination for low-fares carriers. Not only regional
airports have witnessed a fast and unexpected growth in traffic but
also a fast growth in the complaints for the nuisance by the people
living near those airports. In this paper the results of a study
conducted in cooperation with the airport of Bologna G. Marconi are
presented in order to investigate airport acoustical capacity as a defacto
constraint of airport growth.
Abstract: The importance of machining process in today-s
industry requires the establishment of more practical approaches to
clearly represent the intimate and severe contact on the tool-chipworkpiece
interfaces. Mathematical models are developed using the
measured force signals to relate each of the tool-chip friction
components on the rake face to the operating cutting parameters in
rough turning operation using multilayers coated carbide inserts.
Nonlinear modeling proved to have high capability to detect the
nonlinear functional variability embedded in the experimental data.
While feedrate is found to be the most influential parameter on the
friction coefficient and its related force components, both cutting
speed and depth of cut are found to have slight influence. Greater
deformed chip thickness is found to lower the value of friction
coefficient as the sliding length on the tool-chip interface is reduced.
Abstract: In recent years, the use of vector variance as a
measure of multivariate variability has received much attention in
wide range of statistics. This paper deals with a more economic
measure of multivariate variability, defined as vector variance minus
all duplication elements. For high dimensional data, this will increase
the computational efficiency almost 50 % compared to the original
vector variance. Its sampling distribution will be investigated to make
its applications possible.
Abstract: The linear methods of heart rate variability analysis
such as non-parametric (e.g. fast Fourier transform analysis) and
parametric methods (e.g. autoregressive modeling) has become an
established non-invasive tool for marking the cardiac health, but their
sensitivity and specificity were found to be lower than expected with
positive predictive value
Abstract: Soil organic carbon (SOC) plays a key role in soil
fertility, hydrology, contaminants control and acts as a sink or source
of terrestrial carbon content that can affect the concentration of
atmospheric CO2. SOC supports the sustainability and quality of
ecosystems, especially in semi-arid region. This study was
conducted to determine relative importance of 13 different
exploratory climatic, soil and geometric factors on the SOC contents
in one of the semiarid watershed zones in Iran. Two methods
canonical discriminate analysis (CDA) and feed-forward back
propagation neural networks were used to predict SOC. Stepwise
regression and sensitivity analysis were performed to identify
relative importance of exploratory variables. Results from sensitivity
analysis showed that 7-2-1 neural networks and 5 inputs in CDA
models output have highest predictive ability that explains %70 and
%65 of SOC variability. Since neural network models outperformed
CDA model, it should be preferred for estimating SOC.
Abstract: Longitudinal data typically have the characteristics of
changes over time, nonlinear growth patterns, between-subjects
variability, and the within errors exhibiting heteroscedasticity and
dependence. The data exploration is more complicated than that of
cross-sectional data. The purpose of this paper is to organize/integrate
of various visual-graphical techniques to explore longitudinal data.
From the application of the proposed methods, investigators can
answer the research questions include characterizing or describing the
growth patterns at both group and individual level, identifying the time
points where important changes occur and unusual subjects, selecting
suitable statistical models, and suggesting possible within-error
variance.
Abstract: The empirical mode decomposition (EMD) represents any time series into a finite set of basis functions. The bases are termed as intrinsic mode functions (IMFs) which are mutually orthogonal containing minimum amount of cross-information. The EMD successively extracts the IMFs with the highest local frequencies in a recursive way, which yields effectively a set low-pass filters based entirely on the properties exhibited by the data. In this paper, EMD is applied to explore the properties of the multi-year air temperature and to observe its effects on climate change under global warming. This method decomposes the original time-series into intrinsic time scale. It is capable of analyzing nonlinear, non-stationary climatic time series that cause problems to many linear statistical methods and their users. The analysis results show that the mode of EMD presents seasonal variability. The most of the IMFs have normal distribution and the energy density distribution of the IMFs satisfies Chi-square distribution. The IMFs are more effective in isolating physical processes of various time-scales and also statistically significant. The analysis results also show that the EMD method provides a good job to find many characteristics on inter annual climate. The results suggest that climate fluctuations of every single element such as temperature are the results of variations in the global atmospheric circulation.
Abstract: In this paper, a two factor scheme is proposed to
generate cryptographic keys directly from biometric data, which
unlike passwords, are strongly bound to the user. Hash value of the
reference iris code is used as a cryptographic key and its length
depends only on the hash function, being independent of any other
parameter. The entropy of such keys is 94 bits, which is much higher
than any other comparable system. The most important and distinct
feature of this scheme is that it regenerates the reference iris code by
providing a genuine iris sample and the correct user password. Since
iris codes obtained from two images of the same eye are not exactly
the same, error correcting codes (Hadamard code and Reed-Solomon
code) are used to deal with the variability. The scheme proposed here
can be used to provide keys for a cryptographic system and/or for
user authentication. The performance of this system is evaluated on
two publicly available databases for iris biometrics namely CBS and
ICE databases. The operating point of the system (values of False
Acceptance Rate (FAR) and False Rejection Rate (FRR)) can be set
by properly selecting the error correction capacity (ts) of the Reed-
Solomon codes, e.g., on the ICE database, at ts = 15, FAR is 0.096%
and FRR is 0.76%.
Abstract: Many recent electrophysiological studies have
revealed the importance of investigating meditation state in order to
achieve an increased understanding of autonomous control of
cardiovascular functions. In this paper, we characterize heart rate
variability (HRV) time series acquired during meditation using
nonlinear dynamical parameters. We have computed minimum
embedding dimension (MED), correlation dimension (CD), largest
Lyapunov exponent (LLE), and nonlinearity scores (NLS) from HRV
time series of eight Chi and four Kundalini meditation practitioners.
The pre-meditation state has been used as a baseline (control) state to
compare the estimated parameters. The chaotic nature of HRV during
both pre-meditation and meditation is confirmed by MED. The
meditation state showed a significant decrease in the value of CD and
increase in the value of LLE of HRV, in comparison with premeditation
state, indicating a less complex and less predictable nature
of HRV. In addition, it was shown that the HRV of meditation state
is having highest NLS than pre-meditation state. The study indicated
highly nonlinear dynamic nature of cardiac states as revealed by
HRV during meditation state, rather considering it as a quiescent
state.
Abstract: This paper presents a recognition system for isolated
words like robot commands. It’s carried out by Time Delay Neural
Networks; TDNN. To teleoperate a robot for specific tasks as turn,
close, etc… In industrial environment and taking into account the
noise coming from the machine. The choice of TDNN is based on its
generalization in terms of accuracy, in more it acts as a filter that
allows the passage of certain desirable frequency characteristics of
speech; the goal is to determine the parameters of this filter for
making an adaptable system to the variability of speech signal and to
noise especially, for this the back propagation technique was used in
learning phase. The approach was applied on commands pronounced
in two languages separately: The French and Arabic. The results for
two test bases of 300 spoken words for each one are 87%, 97.6% in
neutral environment and 77.67%, 92.67% when the white Gaussian
noisy was added with a SNR of 35 dB.
Abstract: An automatic method for the extraction of feature points for face based applications is proposed. The system is based upon volumetric feature descriptors, which in this paper has been extended to incorporate scale space. The method is robust to noise and has the ability to extract local and holistic features simultaneously from faces stored in a database. Extracted features are stable over a range of faces, with results indicating that in terms of intra-ID variability, the technique has the ability to outperform manual landmarking.
Abstract: A study was conducted to formally characterize
notebook computer performance under various environmental and
usage conditions. Software was developed to collect data from the
operating system of the computer. An experiment was conducted to
evaluate the performance parameters- variations, trends, and
correlations, as well as the extreme value they can attain in various
usage and environmental conditions. An automated software script
was written to simulate user activity. The variability of each
performance parameter was addressed by establishing the empirical
relationship between performance parameters. These equations were
presented as baseline estimates for performance parameters, which
can be used to detect system deviations from normal operation and
for prognostic assessment. The effect of environmental factors,
including different power sources, ambient temperatures, humidity,
and usage, on performance parameters of notebooks was studied.
Abstract: The Želazny Most tailing pond is one of the largest
facilities worldwide for waste disposal from the copper mines located
in South-West Poland. A potential failure of the dam would allow
more than 10 million cubic meters of contaminated slurry to flow to
the valley, causing immense environmental problems to the
surrounding area. Thus, the determination of the strength properties
of the dam's soils and their variability is of utmost importance.
An extensive site investigation consisting of more than 480 cone
penetration tests (CPTs) with or without pore water pressure
measurements were conducted within a period of 13 years to study
the mechanical properties of the tailings body. The present work
investigates the point variability of the soil strength parameters
(effective friction angle
Abstract: Facing the concern of the population to its environment and to climatic change, city planners are now considering the urban climate in their choices of planning. The urban climate, representing different urban morphologies across central Bangkok metropolitan area (BMA), are used to investigates the effects of both the composition and configuration of variables of urban morphology indicators on the summer diurnal range of urban climate, using correlation analyses and multiple linear regressions. Results show first indicate that approximately 92.6% of the variation in the average maximum daytime near-surface air temperature (Ta) was explained jointly by the two composition variables of urban morphology indicators including open space ratio (OSR) and floor area ratio (FAR). It has been possible to determine the membership of sample areas to the local climate zones (LCZs) using these urban morphology descriptors automatically computed with GIS and remote sensed data. Finally result found the temperature differences among zones of large separation, such as the city center could be respectively from 35.48±1.04ºC (Mean±S.D.) warmer than the outskirt of Bangkok on average for maximum daytime near surface temperature to 28.27±0.21ºC for extreme event and, can exceed as 8ºC. A spatially disaggregation of urban thermal responsiveness map would be helpful for several reasons. First, it would localize urban areas concerned by different climate behavior over summer daytime and be a good indicator of urban climate variability. Second, when overlaid with a land cover map, this map may contribute to identify possible urban management strategies to reduce heat wave effects in BMA.
Abstract: The effects of global warming on India vary from the
submergence of low-lying islands and coastal lands to the melting of
glaciers in the Indian Himalayas, threatening the volumetric flow rate
of many of the most important rivers of India and South Asia. In
India, such effects are projected to impact millions of lives. As a
result of ongoing climate change, the climate of India has become
increasingly volatile over the past several decades; this trend is
expected to continue.
Climate change is one of the most important global environmental
challenges, with implications for food production, water supply,
health, energy, etc. Addressing climate change requires a good
scientific understanding as well as coordinated action at national and
global level. The climate change issue is part of the larger challenge
of sustainable development. As a result, climate policies can be more
effective when consistently embedded within broader strategies
designed to make national and regional development paths more
sustainable. The impact of climate variability and change, climate
policy responses, and associated socio-economic development will
affect the ability of countries to achieve sustainable development
goals.
A very well calibrated Soil and Water Assessment Tool (R2 =
0.9968, NSE = 0.91) was exercised over the Khatra sub basin of the
Kangsabati River watershed in Bankura district of West Bengal,
India, in order to evaluate projected parameters for agricultural
activities. Evapotranspiration, Transmission Losses, Potential
Evapotranspiration and Lateral Flow to reach are evaluated from the
years 2041-2050 in order to generate a picture for sustainable
development of the river basin and its inhabitants.
India has a significant stake in scientific advancement as well as
an international understanding to promote mitigation and adaptation.
This requires improved scientific understanding, capacity building,
networking and broad consultation processes. This paper is a
commitment towards the planning, management and development of
the water resources of the Kangsabati River by presenting detailed
future scenarios of the Kangsabati river basin, Khatra sub basin, over
the mentioned time period.
India-s economy and societal infrastructures are finely tuned to the
remarkable stability of the Indian monsoon, with the consequence
that vulnerability to small changes in monsoon rainfall is very high.
In 2002 the monsoon rains failed during July, causing profound loss
of agricultural production with a drop of over 3% in India-s GDP.
Neither the prolonged break in the monsoon nor the seasonal rainfall
deficit was predicted. While the general features of monsoon
variability and change are fairly well-documented, the causal
mechanisms and the role of regional ecosystems in modulating the
changes are still not clear. Current climate models are very poor at
modelling the Asian monsoon: this is a challenging and critical
region where the ocean, atmosphere, land surface and mountains all
interact. The impact of climate change on regional ecosystems is
likewise unknown. The potential for the monsoon to become more
volatile has major implications for India itself and for economies
worldwide. Knowledge of future variability of the monsoon system,
particularly in the context of global climate change, is of great
concern for regional water and food security.
The major findings of this paper were that of all the chosen
projected parameters, transmission losses, soil water content,
potential evapotranspiration, evapotranspiration and lateral flow to
reach, display an increasing trend over the time period of years 2041-
2050.
Abstract: Most neural network (NN) models of human category learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to under predict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level.
Abstract: This study investigated climatic factors associated
with influenza cases in Southern Thailand. The main aim for use
regression analysis to investigate possible causual relationship of
climatic factors and variability between the border of the Andaman
Sea and the Gulf of Thailand. Southern Thailand had the highest
Influenza incidences among four regions (i.e. north, northeast, central
and southern Thailand). In this study, there were 14 climatic factors:
mean relative humidity, maximum relative humidity, minimum
relative humidity, rainfall, rainy days, daily maximum rainfall,
pressure, maximum wind speed, mean wind speed, sunshine duration,
mean temperature, maximum temperature, minimum temperature,
and temperature difference (i.e. maximum – minimum temperature).
Multiple stepwise regression technique was used to fit the statistical
model. The results indicated that the mean wind speed and the
minimum relative humidity were positively associated with the
number of influenza cases on the Andaman Sea side. The maximum
wind speed was positively associated with the number of influenza
cases on the Gulf of Thailand side.
Abstract: Fourier transform infrared (FT-IR) spectroscopic imaging
is an emerging technique that provides both chemically and
spatially resolved information. The rich chemical content of data
may be utilized for computer-aided determinations of structure and
pathologic state (cancer diagnosis) in histological tissue sections for
prostate cancer. FT-IR spectroscopic imaging of prostate tissue has
shown that tissue type (histological) classification can be performed to
a high degree of accuracy [1] and cancer diagnosis can be performed
with an accuracy of about 80% [2] on a microscopic (≈ 6μm)
length scale. In performing these analyses, it has been observed
that there is large variability (more than 60%) between spectra from
different points on tissue that is expected to consist of the same
essential chemical constituents. Spectra at the edges of tissues are
characteristically and consistently different from chemically similar
tissue in the middle of the same sample. Here, we explain these
differences using a rigorous electromagnetic model for light-sample
interaction. Spectra from FT-IR spectroscopic imaging of chemically
heterogeneous samples are different from bulk spectra of individual
chemical constituents of the sample. This is because spectra not
only depend on chemistry, but also on the shape of the sample.
Using coupled wave analysis, we characterize and quantify the nature
of spectral distortions at the edges of tissues. Furthermore, we
present a method of performing histological classification of tissue
samples. Since the mid-infrared spectrum is typically assumed to
be a quantitative measure of chemical composition, classification
results can vary widely due to spectral distortions. However, we
demonstrate that the selection of localized metrics based on chemical
information can make our data robust to the spectral distortions
caused by scattering at the tissue boundary.