Abstract: A power measurement algorithm of the input mix components of the noise signal and narrowband interference is considered using functional transformations of the input mix in the postdetection processing channel. The algorithm efficiency analysis has been carried out for different interference-to-signal ratio. Algorithm performance features have been explored by numerical experiment results.
Abstract: Understanding road features such as lanes, the color
of lanes, and sidewalks in a live video captured from a moving
vehicle is essential to build video-based navigation systems. In this
paper, we present a novel idea to understand the road features using
support vector machines. Various feature vectors including color
components of road markings and the difference between two
regions, i.e., chosen AOIs, and so on are fed into SVM, deciding
colors of lanes and sidewalks robustly. Experimental results are
provided to show the robustness of the proposed idea.
Abstract: In this paper, we give an overview of an online elearning
tool which has been developed for kids aged from nine to
eleven years old in Mauritius for the self-study of Mathematics in
order to prepare them for the CPE examination. The software does
not intend to render obsolete the existing pedagogical approaches.
Nowadays, the teaching-learning process is mainly focused towards
the class-room model. Moreover, most of the e-learning platforms
that exist are simply static ways of delivering resources using the
internet. There is nearly no interaction between the learner and the
tool. Our application will enable students to practice exercises online
and also work out sample examination papers. Another interesting
feature is that the kid will not have to wait for someone to correct the
work as the correction will be done online and on the spot. Additional
feedback is also provided for some exercises.
Abstract: Helical milling operations are used to generate or
enlarge boreholes by means of a milling tool. The bore diameter can be
adjusted through the diameter of the helical path. The kinematics of
helical milling on a three axis machine tool is analysed firstly. The
relationships between processing parameters, cutting tool geometry
characters with machined hole feature are formulated. The feed motion
of the cutting tool has been decomposed to plane circular feed and
axial linear motion. In this paper, the time varying cutting forces acted
on the side cutting edges and end cutting edges of the flat end cylinder
miller is analysed using a discrete method separately. These two
components then are combined to produce the cutting force model
considering the complicated interaction between the cutters and
workpiece. The time varying cutting force model describes the
instantaneous cutting force during processing. This model could be
used to predict cutting force, calculate statics deflection of cutter and
workpiece, and also could be the foundation of dynamics model and
predicting chatter limitation of the helical milling operations.
Abstract: In the current age, retrieval of relevant information
from massive amount of data is a challenging job. Over the years,
precise and relevant retrieval of information has attained high
significance. There is a growing need in the market to build systems,
which can retrieve multimedia information that precisely meets the
user's current needs. In this paper, we have introduced a framework
for refining query results before showing it to the user, using ambient
intelligence, user profile, group profile, user location, time, day, user
device type and extracted features. A prototypic tool was also
developed to demonstrate the efficiency of the proposed approach.
Abstract: The daily increase of organic waste materials resulting
from different activities in the country is one of the main factors for
the pollution of environment. Today, with regard to the low level of
the output of using traditional methods, the high cost of disposal
waste materials and environmental pollutions, the use of modern
methods such as anaerobic digestion for the production of biogas has
been prevailing. The collected biogas from the process of anaerobic
digestion, as a renewable energy source similar to natural gas but
with a less methane and heating value is usable. Today, with the help
of technologies of filtration and proper preparation, access to biogas
with features fully similar to natural gas has become possible. At
present biogas is one of the main sources of supplying electrical and
thermal energy and also an appropriate option to be used in four
stroke engine, diesel engine, sterling engine, gas turbine, gas micro
turbine and fuel cell to produce electricity. The use of biogas for
different reasons which returns to socio-economic and environmental
advantages has been noticed in CHP for the production of energy in
the world. The production of biogas from the technology of anaerobic
digestion and its application in CHP power plants in Iran can not only
supply part of the energy demands in the country, but it can
materialize moving in line with the sustainable development. In this
article, the necessity of the development of CHP plants with biogas
fuels in the country will be dealt based on studies performed from the
economic, environmental and social aspects. Also to prove the
importance of the establishment of these kinds of power plants from
the economic point of view, necessary calculations has been done as
a case study for a CHP power plant with a biogas fuel.
Abstract: In this paper, we propose a texture feature-based
language identification using wavelet-domain BDIP (block difference
of inverse probabilities) and BVLC (block variance of local
correlation coefficients) features and FFT (fast Fourier transform)
feature. In the proposed method, wavelet subbands are first obtained
by wavelet transform from a test image and denoised by Donoho-s
soft-thresholding. BDIP and BVLC operators are next applied to the
wavelet subbands. FFT blocks are also obtained by 2D (twodimensional)
FFT from the blocks into which the test image is
partitioned. Some significant FFT coefficients in each block are
selected and magnitude operator is applied to them. Moments for each
subband of BDIP and BVLC and for each magnitude of significant
FFT coefficients are then computed and fused into a feature vector. In
classification, a stabilized Bayesian classifier, which adopts variance
thresholding, searches the training feature vector most similar to the
test feature vector. Experimental results show that the proposed
method with the three operations yields excellent language
identification even with rather low feature dimension.
Abstract: Nanomaterials have attracted considerable attention
during the last two decades, due to their unusual electrical, mechanical
and other physical properties as compared with their bulky
counterparts. The mechanical properties of nanostructured materials
show strong size dependency, which has been explained within the
framework of continuum mechanics by including the effects of surface
stress. The size-dependent deformations of two-dimensional
nanosized structures with surface effects are investigated in the paper
by the finite element method. Truss element is used to evaluate the
contribution of surface stress to the total potential energy and the
Gurtin and Murdoch surface stress model is implemented with
ANSYS through its user programmable features. The proposed
approach is used to investigate size-dependent stress concentration
around a nanosized circular hole and the size-dependent effective
moduli of nanoporous materials. Numerical results are compared with
available analytical results to validate the proposed modeling
approach.
Abstract: This study analyzes the effect of discretization on
classification of datasets including continuous valued features. Six
datasets from UCI which containing continuous valued features are
discretized with entropy-based discretization method. The
performance improvement between the dataset with original features
and the dataset with discretized features is compared with k-nearest
neighbors, Naive Bayes, C4.5 and CN2 data mining classification
algorithms. As the result the classification accuracies of the six
datasets are improved averagely by 1.71% to 12.31%.
Abstract: This paper focuses on the use of project work as a
pretext for applying the conventions of writing, or the correctness of
mechanics, usage, and sentence formation, in a content-based class in
a Rajabhat University. Its aim was to explore to what extent the
student teachers’ academic achievement of the basic writing features
against the 70% attainment target after the use of project is. The
organization of work around an agreed theme in which the students
reproduce language provided by texts and instructors is expected to
enhance students’ correct writing conventions. The sample of the
study comprised of 38 fourth-year English major students. The data
was collected by means of achievement test and student writing
works. The scores in the summative achievement test were analyzed
by mean score, standard deviation, and percentage. It was found that
the student teachers do more achieve of practicing mechanics and
usage, and less in sentence formation. The students benefited from
the exposure to texts during conducting the project; however, their
automaticity of how and when to form phrases and clauses into
simple/complex sentences had room for improvement.
Abstract: On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
Abstract: Electronic commerce is growing rapidly with on-line
sales already heading for hundreds of billion dollars per year. Due to
the huge amount of money transferred everyday, an increased
security level is required. In this work we present the architecture of
an intelligent speaker verification system, which is able to accurately
verify the registered users of an e-commerce service using only their
voices as an input. According to the proposed architecture, a
transaction-based e-commerce application should be complemented
by a biometric server where customer-s unique set of speech models
(voiceprint) is stored. The verification procedure requests from the
user to pronounce a personalized sequence of digits and after
capturing speech and extracting voice features at the client side are
sent back to the biometric server. The biometric server uses pattern
recognition to decide whether the received features match the stored
voiceprint of the customer who claims to be, and accordingly grants
verification. The proposed architecture can provide e-commerce
applications with a higher degree of certainty regarding the identity
of a customer, and prevent impostors to execute fraudulent
transactions.
Abstract: The paper presents a multimodal approach for biometric authentication, based on multiple classifiers. The proposed solution uses a post-classification biometric fusion method in which the biometric data classifiers outputs are combined in order to improve the overall biometric system performance by decreasing the classification error rates. The paper shows also the biometric recognition task improvement by means of a carefully feature selection, as much as not all of the feature vectors components support the accuracy improvement.
Abstract: Needs of an efficient information retrieval in recent
years in increased more then ever because of the frequent use of
digital information in our life. We see a lot of work in the area of
textual information but in multimedia information, we cannot find
much progress. In text based information, new technology of data
mining and data marts are now in working that were started from the
basic concept of database some where in 1960.
In image search and especially in image identification,
computerized system at very initial stages. Even in the area of image
search we cannot see much progress as in the case of text based
search techniques. One main reason for this is the wide spread roots
of image search where many area like artificial intelligence,
statistics, image processing, pattern recognition play their role. Even
human psychology and perception and cultural diversity also have
their share for the design of a good and efficient image recognition
and retrieval system.
A new object based search technique is presented in this paper
where object in the image are identified on the basis of their
geometrical shapes and other features like color and texture where
object-co-relation augments this search process.
To be more focused on objects identification, simple images are
selected for the work to reduce the role of segmentation in overall
process however same technique can also be applied for other
images.
Abstract: The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for crossdomain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.
Abstract: In textile industry, besides the conventional textile
products, technical textile goods, that have been brought external
functional properties into, are being developed for technical textile
industry. Especially these products produced with weaving
technology are widely preferred in areas such as sports, geology,
medical, automotive, construction and marine sectors. These textile
products are exposed to various stresses and large deformations under
typical conditions of use. At this point, sufficient and reliable data
could not be obtained with uniaxial tensile tests for determination of
the mechanical properties of such products due to mainly biaxial
stress state. Therefore, the most preferred method is a biaxial tensile
test method and analysis. These tests and analysis is applied to fabrics
with different functional features in order to establish the textile
material with several characteristics and mechanical properties of the
product. Planar biaxial tensile test, cylindrical inflation and bulge
tests are generally required to apply for textile products that are used
in automotive, sailing and sports areas and construction industry to
minimize accidents as long as their service life. Airbags, seat belts
and car tires in the automotive sector are also subject to the same
biaxial stress states, and can be characterized by same types of
experiments. In this study, in accordance with the research literature
related to the various biaxial test methods are compared. Results with
discussions are elaborated mainly focusing on the design of a biaxial
test apparatus to obtain applicable experimental data for developing a
finite element model. Sample experimental results on a prototype
system are expressed.
Abstract: There have been many games developing simulation
of soccer games. Many of these games have been designed with
highly realistic features to attract more users. Many have also
incorporated better artificial intelligent (AI) similar to that in a real
soccer game. One of the challenging issues in a soccer game is the
cooperation, coordination and negotiation among distributed agents
in a multi-agent system. This paper focuses on the incorporation of
multi-agent technique in a soccer game domain. The better the
cooperation of a multi-agent team, the more intelligent the game will
be. Thus, past studies were done on the robotic soccer game because
of the better multi-agent system implementation. From this study, a
better approach and technique of multi-agent behavior could be
select to improve the author-s 2D online soccer game.
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: The current speech interfaces in many military
applications may be adequate for native speakers. However,
the recognition rate drops quite a lot for non-native speakers
(people with foreign accents). This is mainly because the nonnative
speakers have large temporal and intra-phoneme
variations when they pronounce the same words. This
problem is also complicated by the presence of large
environmental noise such as tank noise, helicopter noise, etc.
In this paper, we proposed a novel continuous acoustic feature
adaptation algorithm for on-line accent and environmental
adaptation. Implemented by incremental singular value
decomposition (SVD), the algorithm captures local acoustic
variation and runs in real-time. This feature-based adaptation
method is then integrated with conventional model-based
maximum likelihood linear regression (MLLR) algorithm.
Extensive experiments have been performed on the NATO
non-native speech corpus with baseline acoustic model trained
on native American English. The proposed feature-based
adaptation algorithm improved the average recognition
accuracy by 15%, while the MLLR model based adaptation
achieved 11% improvement. The corresponding word error
rate (WER) reduction was 25.8% and 2.73%, as compared to
that without adaptation. The combined adaptation achieved
overall recognition accuracy improvement of 29.5%, and
WER reduction of 31.8%, as compared to that without
adaptation.
Abstract: Clustering is one of an interesting data mining topics
that can be applied in many fields. Recently, the problem of cluster
analysis is formulated as a problem of nonsmooth, nonconvex optimization,
and an algorithm for solving the cluster analysis problem
based on nonsmooth optimization techniques is developed. This
optimization problem has a number of characteristics that make it
challenging: it has many local minimum, the optimization variables
can be either continuous or categorical, and there are no exact
analytical derivatives. In this study we show how to apply a particular
class of optimization methods known as pattern search methods
to address these challenges. These methods do not explicitly use
derivatives, an important feature that has not been addressed in
previous studies. Results of numerical experiments are presented
which demonstrate the effectiveness of the proposed method.