Abstract: Freeways are originally designed to provide high
mobility to road users. However, the increase in population and
vehicle numbers has led to increasing congestions around the world.
Daily recurrent congestion substantially reduces the freeway capacity
when it is most needed. Building new highways and expanding the
existing ones is an expensive solution and impractical in many
situations. Intelligent and vision-based techniques can, however, be
efficient tools in monitoring highways and increasing the capacity of
the existing infrastructures. The crucial step for highway monitoring
is vehicle detection. In this paper, we propose one of such
techniques. The approach is based on artificial neural networks
(ANN) for vehicles detection and counting. The detection process
uses the freeway video images and starts by automatically extracting
the image background from the successive video frames. Once the
background is identified, subsequent frames are used to detect
moving objects through image subtraction. The result is segmented
using Sobel operator for edge detection. The ANN is, then, used in
the detection and counting phase. Applying this technique to the
busiest freeway in Riyadh (King Fahd Road) achieved higher than
98% detection accuracy despite the light intensity changes, the
occlusion situations, and shadows.
Abstract: This article outlines conceptualization and
implementation of an intelligent system capable of extracting
knowledge from databases. Use of hybridized features of both the
Rough and Fuzzy Set theory render the developed system flexibility
in dealing with discreet as well as continuous datasets. A raw data set
provided to the system, is initially transformed in a computer legible
format followed by pruning of the data set. The refined data set is
then processed through various Rough Set operators which enable
discovery of parameter relationships and interdependencies. The
discovered knowledge is automatically transformed into a rule base
expressed in Fuzzy terms. Two exemplary cancer repository datasets
(for Breast and Lung Cancer) have been used to test and implement
the proposed framework.
Abstract: Camera calibration is an indispensable step for augmented
reality or image guided applications where quantitative information
should be derived from the images. Usually, a camera
calibration is obtained by taking images of a special calibration object
and extracting the image coordinates of projected calibration marks
enabling the calculation of the projection from the 3d world coordinates
to the 2d image coordinates. Thus such a procedure exhibits
typical steps, including feature point localization in the acquired
images, camera model fitting, correction of distortion introduced by
the optics and finally an optimization of the model-s parameters. In
this paper we propose to extend this list by further step concerning
the identification of the optimal subset of images yielding the smallest
overall calibration error. For this, we present a Monte Carlo based
algorithm along with a deterministic extension that automatically
determines the images yielding an optimal calibration. Finally, we
present results proving that the calibration can be significantly
improved by automated image selection.
Abstract: In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.
Abstract: Environmental factors affect agriculture production
productivity and efficiency resulted in changing of profit efficiency.
This paper attempts to estimate the impacts of environmental factors
to profitability of rice farmers in the Red River Delta of Vietnam. The
dataset was extracted from 349 rice farmers using personal
interviews. Both OLS and MLE trans-log profit functions were used
in this study. Five production inputs and four environmental factors
were included in these functions. The estimation of the stochastic
profit frontier with a two-stage approach was used to measure
profitability. The results showed that the profit efficiency was about
75% on the average and environmental factors change profit
efficiency significantly beside farm specific characteristics. Plant
disease, soil fertility, irrigation apply and water pollution were the
four environmental factors cause profit loss in rice production. The
result indicated that farmers should reduce household size, farm
plots, apply row seeding technique and improve environmental
factors to obtain high profit efficiency with special consideration is
given for irrigation water quality improvement.
Abstract: We present a new method to reconstruct a temporally
coherent 3D animation from single or multi-view RGB-D video data
using unbiased feature point sampling. Given RGB-D video data, in
form of a 3D point cloud sequence, our method first extracts feature
points using both color and depth information. In the subsequent
steps, these feature points are used to match two 3D point clouds in
consecutive frames independent of their resolution. Our new motion
vectors based dynamic alignement method then fully reconstruct
a spatio-temporally coherent 3D animation. We perform extensive
quantitative validation using novel error functions to analyze the
results. We show that despite the limiting factors of temporal and
spatial noise associated to RGB-D data, it is possible to extract
temporal coherence to faithfully reconstruct a temporally coherent
3D animation from RGB-D video data.
Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Abstract: In this work, we improve a previously developed
segmentation scheme aimed at extracting edge information from
speckled images using a maximum likelihood edge detector. The
scheme was based on finding a threshold for the probability density
function of a new kernel defined as the arithmetic mean-to-geometric
mean ratio field over a circular neighborhood set and, in a general
context, is founded on a likelihood random field model (LRFM). The
segmentation algorithm was applied to discriminated speckle areas
obtained using simple elliptic discriminant functions based on
measures of the signal-to-noise ratio with fractional order moments.
A rigorous stochastic analysis was used to derive an exact expression
for the cumulative density function of the probability density
function of the random field. Based on this, an accurate probability
of error was derived and the performance of the scheme was
analysed. The improved segmentation scheme performed well for
both simulated and real images and showed superior results to those
previously obtained using the original LRFM scheme and standard
edge detection methods. In particular, the false alarm probability was
markedly lower than that of the original LRFM method with
oversegmentation artifacts virtually eliminated. The importance of
this work lies in the development of a stochastic-based segmentation,
allowing an accurate quantification of the probability of false
detection. Non visual quantification and misclassification in medical
ultrasound speckled images is relatively new and is of interest to
clinicians.
Abstract: This paper proposes a technique to block adult images displayed in websites. The filter is designed so as to perform even in exceptional cases such as, where face detection is not possible or improper face visibility. This is achieved by using an alternative phase to extract the MFC (Most Frequent Color) from the Human Body regions estimated using a biometric of anthropometric distances between fixed rigidly connected body locations. The logical results generated can be protected from overriding by a firewall or intrusion, by encrypting the result in a SSH data packet.
Abstract: Speech corpus is one of the major components in a
Speech Processing System where one of the primary requirements
is to recognize an input sample. The quality and details captured
in speech corpus directly affects the precision of recognition. The
current work proposes a platform for speech corpus generation using
an adaptive LMS filter and LPC cepstrum, as a part of an ANN
based Speech Recognition System which is exclusively designed to
recognize isolated numerals of Assamese language- a major language
in the North Eastern part of India. The work focuses on designing an
optimal feature extraction block and a few ANN based cooperative
architectures so that the performance of the Speech Recognition
System can be improved.
Abstract: The paper presents a technique suitable in robot
vision applications where it is not possible to establish the object position from one view. Usually, one view pose calculation methods
are based on the correspondence of image features established at a
training step and exactly the same image features extracted at the
execution step, for a different object pose. When such a
correspondence is not feasible because of the lack of specific features
a new method is proposed. In the first step the method computes
from two views the 3D pose of feature points. Subsequently, using a
registration algorithm, the set of 3D feature points extracted at the execution phase is aligned with the set of 3D feature points extracted
at the training phase. The result is a Euclidean transform which have
to be used by robot head for reorientation at execution step.
Abstract: Nowadays, OCR systems have got several
applications and are increasingly employed in daily life. Much
research has been done regarding the identification of Latin,
Japanese, and Chinese characters. However, very little investigation
has been performed regarding Farsi/Arabic characters recognition.
Probably the reason is difficulty and complexity of those characters
identification compared to the others and limitation of IT activities in
Farsi and Arabic speaking countries. In this paper, a technique has
been employed to identify isolated Farsi/Arabic characters. A chain
code based algorithm along with other significant peculiarities such
as number and location of dots and auxiliary parts, and the number of
holes existing in the isolated character has been used in this study to
identify Farsi/Arabic characters. Experimental results show the
relatively high accuracy of the method developed when it is tested on
several standard Farsi fonts.
Abstract: We report on the development of a model to
understand why the range of experience with respect to HIV
infection is so diverse, especially with respect to the latency period.
To investigate this, an agent-based approach is used to extract highlevel
behaviour which cannot be described analytically from the set
of interaction rules at the cellular level. A network of independent
matrices mimics the chain of lymph nodes. Dealing with massively
multi-agent systems requires major computational effort. However,
parallelisation methods are a natural consequence and advantage of
the multi-agent approach and, using the MPI library, are here
implemented, tested and optimized. Our current focus is on the
various implementations of the data transfer across the network.
Three communications strategies are proposed and tested, showing
that the most efficient approach is communication based on the
natural lymph-network connectivity.
Abstract: Due to the non-linear characteristics of photovoltaic
(PV) array, PV systems typically are equipped with the capability of
maximum power point tracking (MPPT) feature. Moreover, in the
case of PV array under partially shaded conditions, hotspot problem
will occur which could damage the PV cells. Partial shading causes
multiple peaks in the P-V characteristic curves. This paper presents a
hybrid algorithm of Particle Swarm Optimization (PSO) and
Artificial Neural Network (ANN) MPPT algorithm for the detection
of global peak among the multiple peaks in order to extract the true
maximum energy from PV panel. The PV system consists of PV
array, dc-dc boost converter controlled by the proposed MPPT
algorithm and a resistive load. The system was simulated using
MATLAB/Simulink package. The simulation results show that the
proposed algorithm performs well to detect the true global peak
power. The results of the simulations are analyzed and discussed.
Abstract: In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.
Abstract: The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.
Abstract: The paper proposes a way of parallel processing of
SURF and Optical Flow for moving object recognition and tracking.
The object recognition and tracking is one of the most important task
in computer vision, however disadvantage are many operations cause
processing speed slower so that it can-t do real-time object recognition
and tracking. The proposed method uses a typical way of feature
extraction SURF and moving object Optical Flow for reduce
disadvantage and real-time moving object recognition and tracking,
and parallel processing techniques for speed improvement. First
analyse that an image from DB and acquired through the camera using
SURF for compared to the same object recognition then set ROI
(Region of Interest) for tracking movement of feature points using
Optical Flow. Secondly, using Multi-Thread is for improved
processing speed and recognition by parallel processing. Finally,
performance is evaluated and verified efficiency of algorithm
throughout the experiment.
Abstract: This work evaluated the effect of temperature on headspace solid-phase microextraction of volatile and furanic compounds in coated fish sticks. The major goal was the analysis of the samples as consumed, to reproduce volatile compounds people feel when consuming those products. Extraction at 37 ºC (the human body temperature) throughout the HS-SPME analysis of volatile and furanic compounds in coated fish was compared with higher extraction temperatures, which are frequently used for this kind of determinations. The profile of volatile compounds found in deepfried (F) and non-fried (NF) coated fish at 37 and 50 ºC was different from that obtained at 80 ºC. Concerning furan and its derivatives, an extra formation of these compounds was observed at higher extraction temperatures. The analysis of volatile and furanic compounds in fish coated sticks simulating the cooking and eating conditions can be reliably carried out setting the headspace absorption temperature at 37 ºC.
Abstract: Vehicle detection is the critical step for highway monitoring. In this paper we propose background subtraction and edge detection technique for vehicle detection. This technique uses the advantages of both approaches. The practical applications approved the effectiveness of this method. This method consists of two procedures: First, automatic background extraction procedure, in which the background is extracted automatically from the successive frames; Second vehicles detection procedure, which depend on edge detection and background subtraction. Experimental results show the effective application of this algorithm. Vehicles detection rate was higher than 91%.
Abstract: The assessment of surface waters in Enugu metropolis
for fecal coliform bacteria was undertaken. Enugu urban was divided
into three areas (A1, A2 and A3), and fecal coliform bacteria
analysed in the surface waters found in these areas for four years
(2005-2008). The plate count method was used for the analyses. Data
generated were subjected to statistical tests involving; Normality test,
Homogeneity of variance test, correlation test, and tolerance limit
test. The influence of seasonality and pollution trends were
investigated using time series plots. Results from the tolerance limit
test at 95% coverage with 95% confidence, and with respect to EU
maximum permissible concentration show that the three areas suffer
from fecal coliform pollution. To this end, remediation procedure
involving the use of saw-dust extracts from three woods namely;
Chlorophora-Excelsa (C-Excelsa),Khayan-Senegalensis,(CSenegalensis)
and Erythrophylum-Ivorensis (E-Ivorensis) in
controlling the coliforms was studied. Results show that mixture of
the acetone extracts of the woods show the most effective
antibacterial inhibitory activities (26.00mm zone of inhibition)
against E-coli. Methanol extract mixture of the three woods gave best
inhibitory activity (26.00mm zone of inhibition) against S-areus, and
25.00mm zones of inhibition against E-Aerogenes. The aqueous
extracts mixture gave acceptable zones of inhibitions against the
three bacteria organisms.