Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: With the advancement of knowledge about the utility
and impact of sustainability, its feasibility has been explored into
different walks of life. Scientists, however; have established their
knowledge in four areas viz environmental, economic, social and
cultural, popularly termed as four pillars of sustainability. Aspects of
environmental and economic sustainability have been rigorously
researched and practiced and huge volume of strong evidence of
effectiveness has been founded for these two sub-areas. For the social
and cultural aspects of sustainability, dependable evidence of
effectiveness is still to be instituted as the researchers and
practitioners are developing and experimenting methods across the
globe. Therefore, the present research aimed to identify globally used
practices of social and cultural sustainability and through evidence
synthesis assess their outcomes to determine the effectiveness of
those practices. A PICO format steered the methodology which
included all populations, popular sustainability practices including
walkability/cycle tracks, social/recreational spaces, privacy, health &
human services and barrier free built environment, comparators
included ‘Before’ and ‘After’, ‘With’ and ‘Without’, ‘More’ and
‘Less’ and outcomes included Social well-being, cultural coexistence,
quality of life, ethics and morality, social capital, sense of
place, education, health, recreation and leisure, and holistic
development. Search of literature included major electronic
databases, search websites, organizational resources, directory of
open access journals and subscribed journals. Grey literature,
however, was not included. Inclusion criteria filtered studies on the
basis of research designs such as total randomization, quasirandomization,
cluster randomization, observational or single studies
and certain types of analysis. Studies with combined outcomes were
considered but studies focusing only on environmental and/or
economic outcomes were rejected. Data extraction, critical appraisal
and evidence synthesis was carried out using customized tabulation,
reference manager and CASP tool. Partial meta-analysis was carried
out and calculation of pooled effects and forest plotting were done.
As many as 13 studies finally included for final synthesis explained
the impact of targeted practices on health, behavioural and social
dimensions. Objectivity in the measurement of health outcomes
facilitated quantitative synthesis of studies which highlighted the
impact of sustainability methods on physical activity, Body Mass
Index, perinatal outcomes and child health. Studies synthesized
qualitatively (and also quantitatively) showed outcomes such as
routines, family relations, citizenship, trust in relationships, social
inclusion, neighbourhood social capital, wellbeing, habitability and
family’s social processes. The synthesized evidence indicates slight
effectiveness and efficacy of social and cultural sustainability on the
targeted outcomes. Further synthesis revealed that such results of this
study are due weak research designs and disintegrated implementations. If architects and other practitioners deliver their
interventions in collaboration with research bodies and policy
makers, a stronger evidence-base in this area could be generated.
Abstract: Despite the wide spread use of synthetic dyes, natural
dyes are still exploited and used to enhance its inherent aesthetic
qualities as a major material for beautification of the body. Centuries
before the discovery of synthetic dyes, natural dyes were the only
source of dye open to mankind. Dyes are extracted from plant -
leaves, roots and barks, insect secretions, and minerals. However,
research findings have made it clear that of all, plants- leaves, roots,
barks or flowers are the most explored and exploited in which henna
(Lawsonia innermis L.) is one of those plants. Experiment has also
shown that henna is used in body painting in conjunction with an
alkaline (Ammonium Sulphate) as a fixing agent. This of course
gives a clue that if colour derived from henna is properly
investigated, it may not only be used for body decoration but
possibly, may have affinity to fiber substrate. This paper investigates
the dyeing potentials – dye ability and fastness qualities of henna dye
extracts on cotton and linen fibers using mordants like ammonium
sulphate and other alkalis (hydrosulphate and caustic soda, potash,
common salt, potassium alum). Hot and cold water and ethanol
solvent were used in the extraction of the dye to investigate the most
effective method, dye ability, and fastness qualities of these extracts
under room temperature. The results of the experiment show that
cotton have a high rate of dye intake than other fiber. On a similar
note, the colours obtained depend most on the solvent used. In
conclusion, hot water extraction appears more effective. While the
colours obtained from ethanol and both cold hot methods of
extraction range from light to dark yellow, light green to army green
and to some extent shades of brown hues.
Abstract: Journal bearings used in IC engines are prone to premature
failures and are likely to fail earlier than the rated life due to
highly impulsive and unstable operating conditions and frequent
starts/stops. Vibration signature extraction and wear debris analysis
techniques are prevalent in industry for condition monitoring of
rotary machinery. However, both techniques involve a great deal of
technical expertise, time, and cost. Limited literature is available on
the application of these techniques for fault detection in reciprocating
machinery, due to the complex nature of impact forces that
confounds the extraction of fault signals for vibration-based analysis
and wear prediction. In present study, a simulation model was developed to investigate
the bearing wear behaviour, resulting because of different operating
conditions, to complement the vibration analysis. In current
simulation, the dynamics of the engine was established first, based on
which the hydrodynamic journal bearing forces were evaluated by
numerical solution of the Reynold’s equation. In addition, the
essential outputs of interest in this study, critical to determine wear
rates are the tangential velocity and oil film thickness between the
journals and bearing sleeve, which if not maintained appropriately,
have a detrimental effect on the bearing performance. Archard’s wear prediction model was used in the simulation to
calculate the wear rate of bearings with specific location information
as all determinative parameters were obtained with reference to crank
rotation. Oil film thickness obtained from the model was used as a
criterion to determine if the lubrication is sufficient to prevent contact
between the journal and bearing thus causing accelerated wear. A
limiting value of 1 μm was used as the minimum oil film thickness
needed to prevent contact. The increased wear rate with growing
severity of operating conditions is analogous and comparable to the
rise in amplitude of the squared envelope of the referenced vibration
signals. Thus on one hand, the developed model demonstrated its
capability to explain wear behaviour and on the other hand it also
helps to establish a co-relation between wear based and vibration
based analysis. Therefore, the model provides a cost effective and
quick approach to predict the impending wear in IC engine bearings
under various operating conditions.
Abstract: This paper introduces an effective method of
segmenting Korean text (place names in Korean) from a Korean road
sign image. A Korean advanced directional road sign is composed of
several types of visual information such as arrows, place names in
Korean and English, and route numbers. Automatic classification of
the visual information and extraction of Korean place names from the
road sign images make it possible to avoid a lot of manual inputs to a
database system for management of road signs nationwide. We
propose a series of problem-specific heuristics that correctly segments
Korean place names, which is the most crucial information, from the
other information by leaving out non-text information effectively. The
experimental results with a dataset of 368 road sign images show 96%
of the detection rate per Korean place name and 84% per road sign
image.
Abstract: High Performance Liquid Chromatography (HPLC)
method was developed and validated for simultaneous estimation of
6-Gingerol(6G) and 6-Shogaol(6S) in joint pain relief gel containing
ginger extract. The chromatographic separation was achieved by
using C18 column, 150 x 4.6mm i.d., 5μ Luna, mobile phase
containing acetonitrile and water (gradient elution). The flow rate
was 1.0 ml/min and the absorbance was monitored at 282 nm. The
proposed method was validated in terms of the analytical parameters
such as specificity, accuracy, precision, linearity, range, limit of
detection (LOD), limit of quantification (LOQ), and determined
based on the International Conference on Harmonization (ICH)
guidelines. The linearity ranges of 6G and 6S were obtained over 20-
60 and 6-18 μg/ml respectively. Good linearity was observed over the
above-mentioned range with linear regression equation Y= 11016x-
23778 for 6G and Y = 19276x-19604 for 6S (x is concentration of
analytes in μg/ml and Y is peak area). The value of correlation
coefficient was found to be 0.9994 for both markers. The limit of
detection (LOD) and limit of quantification (LOQ) for 6G were
0.8567 and 2.8555 μg/ml and for 6S were 0.3672 and 1.2238 μg/ml
respectively. The recovery range for 6G and 6S were found to be
91.57 to 102.36 % and 84.73 to 92.85 % for all three spiked levels.
The RSD values from repeated extractions for 6G and 6S were 3.43
and 3.09% respectively. The validation of developed method on
precision, accuracy, specificity, linearity, and range were also
performed with well-accepted results.
Abstract: Foundation differential settlement and supported
structure tilting are an occasionally occurred engineering problem.
This may be caused by overloading, changes in ground soil properties
or unsupported nearby excavations. Engineering thinking points
directly toward the logic solution for such problem by uplifting the
settled side. This can be achieved with deep foundation elements
such as micro-piles and macro-piles™, jacked piers, and helical piers,
jet grouted mortar columns, compaction grout columns, cement
grouting or with chemical grouting, or traditional pit underpinning
with concrete and mortar. Although, some of these techniques offer
economic, fast and low noise solutions, many of them are quite the
contrary. For tilted structures, with the limited inclination, it may be much
easier to cause a balancing settlement on the less-settlement side
which shall be done carefully in a proper rate. This principal has been
applied in Leaning Tower of Pisa stabilization with soil extraction
from the ground surface. In this research, the authors attempt to
introduce a new solution with a different point of view. So, the
micro-tunneling technique is presented in here as an intended ground
deformation cause. In general, micro-tunneling is expected to induce
limited ground deformations. Thus, the researchers propose to apply
the technique to form small size ground unsupported holes to produce
the target deformations. This shall be done in four phases: 1.
Application of one or more micro-tunnels, regarding the existing
differential settlement value, under the raised side of the tilted
structure. 2. For each individual tunnel, the lining shall be pulled out
from both sides (from jacking and receiving shafts) in the slow rate.
3. If required, according to calculations and site records, an additional
surface load can be applied on the raised foundation side. 4. Finally, a
strengthening soil grouting shall be applied for stabilization after
adjustment. A finite element based numerical model is presented to simulate
the proposed construction phases for different tunneling positions and
tunnels group. For each case, the surface settlements are calculated
and induced plasticity points are checked. These results show the
impact of the suggested procedure on the tilted structure and its
feasibility. Comparing results also show the importance of the
position selection and tunnels group gradual effect. Thus, a new
engineering solution is presented to one of the structural and
geotechnical engineering challenges.
Abstract: Round addition differential fault analysis using
operation skipping for lightweight block ciphers with on-the-fly key
scheduling is presented. For 64-bit KLEIN, it is shown that only a pair
of correct and faulty ciphertexts can be used to derive the secret master
key. For PRESENT, one correct ciphertext and two faulty ciphertexts
are required to reconstruct the secret key. Furthermore, secret key
extraction is demonstrated for the LBlock Feistel-type lightweight
block cipher.
Abstract: Obturator Foramen is a specific structure in Pelvic
bone images and recognition of it is a new concept in medical image
processing. Moreover, segmentation of bone structures such as
Obturator Foramen plays an essential role for clinical research in
orthopedics. In this paper, we present a novel method to analyze the
similarity between the substructures of the imaged region and a hand
drawn template as a preprocessing step for computation of Pelvic
bone rotation on hip radiographs. This method consists of integrated
usage of Marker-controlled Watershed segmentation and Zernike
moment feature descriptor and it is used to detect Obturator Foramen
accurately. Marker-controlled Watershed segmentation is applied to
separate Obturator Foramen from the background effectively. Then,
Zernike moment feature descriptor is used to provide matching
between binary template image and the segmented binary image for
final extraction of Obturator Foramens. Finally, Pelvic bone rotation
rate calculation for each hip radiograph is performed automatically to
select and eliminate hip radiographs for further studies which depend
on Pelvic bone angle measurements. The proposed method is tested
on randomly selected 100 hip radiographs. The experimental results
demonstrated that the proposed method is able to segment Obturator
Foramen with 96% accuracy.
Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
Abstract: A problem of complex mineral resources development is urgent and priority, it is aimed at realization of the processes of their ecologically safe development, one of its components is revealing the influence of the forms of element compounds in raw materials and in the processing products. In view of depletion of the precious metal reserves at the traditional deposits in the XXI century the large-size open cast deposits, localized in black shale strata begin to play the leading role. Carbonaceous (black) shales carry a heightened metallogenic potential. Black shales with high content of carbon are widely distributed within the scope of Bureinsky massif. According to academician Hanchuk`s data black shales of Sutirskaya series contain generally PGEs native form. The presence of high absorptive towards carbonaceous matter gold and PGEs compounds in crude ore results in decrease of valuable components extraction because of their sorption into dissipated carbonaceous matter.
Abstract: Growing human population has placed increased
demands on water supplies and spurred a heightened interest in
desalination infrastructure. Key elements of the economics of
desalination projects are thermal and electrical inputs. With growing
concerns over use of fossil fuels to (indirectly) supply these inputs,
coupling of desalination with nuclear power production represents a
significant opportunity. Individually, nuclear and desalination
technologies have a long history and are relatively mature. For
desalination, Reverse Osmosis (RO) has the lowest energy inputs.
However, the economically driven output quality of the water
produced using RO, which uses only electrical inputs, is lower than the
output water quality from thermal desalination plants. Therefore,
modern desalination projects consider that RO should be coupled with
thermal desalination technologies (MSF, MED, or MED-TVC) with
attendant steam inputs to permit blending to produce various qualities
of water. A large nuclear facility is well positioned to dispatch large
quantities of both electrical and thermal power. This paper considers
the supply of thermal energy to a large desalination facility to examine
heat balance impact on the nuclear steam cycle. The APR1400 nuclear
plant is selected as prototypical from both a capacity and turbine cycle
heat balance perspective to examine steam supply and the impact on
electrical output. Extraction points and quantities of steam are
considered parametrically along with various types of thermal
desalination technologies to form the basis for further evaluations of
economically optimal approaches to the interface of nuclear power
production with desalination projects. In our study, the
thermodynamic evaluation will be executed by DE-TOP, an IAEA
sponsored program. DE-TOP has capabilities to analyze power
generation systems coupled to desalination plants through various
steam extraction positions, taking into consideration the isolation loop
between the nuclear and the thermal desalination facilities (i.e., for
radiological isolation).
Abstract: Tamil handwritten document is taken as a key source
of data to identify the writer. Tamil is a classical language which has
247 characters include compound characters, consonants, vowels and
special character. Most characters of Tamil are multifaceted in
nature. Handwriting is a unique feature of an individual. Writer may
change their handwritings according to their frame of mind and this
place a risky challenge in identifying the writer. A new
discriminative model with pooled features of handwriting is proposed
and implemented using support vector machine. It has been reported
on 100% of prediction accuracy by RBF and polynomial kernel based
classification model.
Abstract: This paper will discuss how we optimize our physical
verification flow in our IC Design Department having various rule
decks from multiple foundries. Our ultimate goal is to achieve faster
time to tape-out and avoid schedule delay. Currently the physical
verification runtimes and memory usage have drastically increased
with the increasing number of design rules, design complexity, and
the size of the chips to be verified. To manage design violations, we
use a number of solutions to reduce the amount of violations needed
to be checked by physical verification engineers. The most important
functions in physical verifications are DRC (design rule check), LVS
(layout vs. schematic), and XRC (extraction). Since we have a
multiple number of foundries for our design tape-outs, we need a
flow that improve the overall turnaround time and ease of use of the
physical verification process. The demand for fast turnaround time is
even more critical since the physical design is the last stage before
sending the layout to the foundries.
Abstract: This study was to explore and utilize the fresh rind of
mangosteen Index Colour 5 as an upcoming raw material for the
production of natural dyes. Rind from the fresh mangosteen Index
Colour 5 was utilized to extract the dyes. The established extracts
were experimented on silk fabrics via three types of mordanting and
dyeing procedures; pre-mordanting, simultaneous mordanting and
post-mordanting. As a result, the applications of the freeze-drying
methodology and mechanizable equipment have helped to produce
excellent range of natural colours. Silk fabric treated simultaneously
with mordanting and dyeing with extract dye Index Colour 5
produced a brilliant shade of the red colour and the colour from this
index is also discovered sensitive to light and washing during the
fastness tests. The preliminary evaluation and instrumentation
analysis allowed us to examine whether the application of different
mordanting and dyeing procedures with the same extract samples and
concentrations affected the colours and shades of the fabric samples.
Abstract: Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.
Abstract: In this paper, we present a new segmentation approach
for focal liver lesions in contrast enhanced ultrasound imaging. This
approach, based on a two-cluster Fuzzy C-Means methodology,
considers type-II fuzzy sets to handle uncertainty due to the image
modality (presence of speckle noise, low contrast, etc.), and to
calculate the optimum inter-cluster threshold. Fine boundaries are
detected by a local recursive merging of ambiguous pixels. The
method has been tested on a representative database. Compared to
both Otsu and type-I Fuzzy C-Means techniques, the proposed
method significantly reduces the segmentation errors.
Abstract: Speaker Identification (SI) is the task of establishing
identity of an individual based on his/her voice characteristics. The SI
task is typically achieved by two-stage signal processing: training and
testing. The training process calculates speaker specific feature
parameters from the speech and generates speaker models
accordingly. In the testing phase, speech samples from unknown
speakers are compared with the models and classified. Even though
performance of speaker identification systems has improved due to
recent advances in speech processing techniques, there is still need of
improvement. In this paper, a Closed-Set Tex-Independent Speaker
Identification System (CISI) based on a Multiple Classifier System
(MCS) is proposed, using Mel Frequency Cepstrum Coefficient
(MFCC) as feature extraction and suitable combination of vector
quantization (VQ) and Gaussian Mixture Model (GMM) together
with Expectation Maximization algorithm (EM) for speaker
modeling. The use of Voice Activity Detector (VAD) with a hybrid
approach based on Short Time Energy (STE) and Statistical
Modeling of Background Noise in the pre-processing step of the
feature extraction yields a better and more robust automatic speaker
identification system. Also investigation of Linde-Buzo-Gray (LBG)
clustering algorithm for initialization of GMM, for estimating the
underlying parameters, in the EM step improved the convergence rate
and systems performance. It also uses relative index as confidence
measures in case of contradiction in identification process by GMM
and VQ as well. Simulation results carried out on voxforge.org
speech database using MATLAB highlight the efficacy of the
proposed method compared to earlier work.