Abstract: For over a decade, the Pulse Coupled Neural Network
(PCNN) based algorithms have been successfully used in image
interpretation applications including image segmentation. There are
several versions of the PCNN based image segmentation methods,
and the segmentation accuracy of all of them is very sensitive to the
values of the network parameters. Most methods treat PCNN
parameters like linking coefficient and primary firing threshold as
global parameters, and determine them by trial-and-error. The
automatic determination of appropriate values for linking coefficient,
and primary firing threshold is a challenging problem and deserves
further research. This paper presents a method for obtaining global as
well as local values for the linking coefficient and the primary firing
threshold for neurons directly from the image statistics. Extensive
simulation results show that the proposed approach achieves
excellent segmentation accuracy comparable to the best accuracy
obtainable by trial-and-error for a variety of images.
Abstract: In 1990 [1] the subband-DFT (SB-DFT) technique was proposed. This technique used the Hadamard filters in the decomposition step to split the input sequence into low- and highpass sequences. In the next step, either two DFTs are needed on both bands to compute the full-band DFT or one DFT on one of the two bands to compute an approximate DFT. A combination network with correction factors was to be applied after the DFTs. Another approach was proposed in 1997 [2] for using a special discrete wavelet transform (DWT) to compute the discrete Fourier transform (DFT). In the first step of the algorithm, the input sequence is decomposed in a similar manner to the SB-DFT into two sequences using wavelet decomposition with Haar filters. The second step is to perform DFTs on both bands to obtain the full-band DFT or to obtain a fast approximate DFT by implementing pruning at both input and output sides. In this paper, the wavelet-based DFT (W-DFT) with Haar filters is interpreted as SB-DFT with Hadamard filters. The only difference is in a constant factor in the combination network. This result is very important to complete the analysis of the W-DFT, since all the results concerning the accuracy and approximation errors in the SB-DFT are applicable. An application example in spectral analysis is given for both SB-DFT and W-DFT (with different filters). The adaptive capability of the SB-DFT is included in the W-DFT algorithm to select the band of most energy as the band to be computed. Finally, the W-DFT is extended to the two-dimensional case. An application in image transformation is given using two different types of wavelet filters.
Abstract: Flow-shop scheduling problem (FSP) deals with the
scheduling of a set of jobs that visit a set of machines in the same
order. The FSP is NP-hard, which means that an efficient algorithm
for solving the problem to optimality is unavailable. To meet the
requirements on time and to minimize the make-span performance of
large permutation flow-shop scheduling problems in which there are
sequence dependent setup times on each machine, this paper
develops one hybrid genetic algorithms (HGA). Proposed HGA
apply a modified approach to generate population of initial
chromosomes and also use an improved heuristic called the iterated
swap procedure to improve initial solutions. Also the author uses
three genetic operators to make good new offspring. The results are
compared to some recently developed heuristics and computational
experimental results show that the proposed HGA performs very
competitively with respect to accuracy and efficiency of solution.
Abstract: This paper presents a new high speed simulation methodology to solve the long simulation time problem of CMOS image sensor matrix. Generally, for integrating the pixel matrix in SOC and simulating the system performance, designers try to model the pixel in various modeling languages such as VHDL-AMS, SystemC or Matlab. We introduce a new alternative method based on spice model in cadence design platform to achieve accuracy and reduce simulation time. The simulation results indicate that the pixel output voltage maximum error is at 0.7812% and time consumption reduces from 2.2 days to 13 minutes achieving about 240X speed-up for the 256x256 pixel matrix.
Abstract: Research results and optimal parameters investigation
of laser cut and profiling of diamond and quartz substrates by
femtosecond laser pulses are presented. Profiles 10 μm in width, ~25
μm in depth and several millimeters long were made. Investigation of
boundaries quality has been carried out with the use of AFM
«Vecco». Possibility of technological formation of profiles and
micro-holes in diamond and quartz substrates with nanometer-scale
boundaries is shown. Experimental results of multilayer dielectric
cover treatment are also presented. Possibility of precise upper layer
(thickness of 70–140 nm) removal is demonstrated. Processes of thin
metal film (60 nm and 350 nm thick) treatment are considered.
Isolation tracks (conductance ~ 10-11 S) 1.6–2.5 μm in width in
conductive metal layers are formed.
Abstract: In this paper, a neural network technique is applied to
real-time classifying media while a projectile is penetrating through
them. A laboratory-scaled penetrating setup was built for the
experiment. Features used as the network inputs were extracted from
the acceleration of penetrator. 6000 set of features from a single
penetration with known media and status were used to train the neural
network. The trained system was tested on 30 different penetration
experiments. The system produced an accuracy of 100% on the
training data set. And, their precision could be 99% for the test data
from 30 tests.
Abstract: Organization of video databases is becoming difficult
task as the amount of video content increases. Video classification
based on the content of videos can significantly increase the speed of
tasks such as browsing and searching for a particular video in a
database. In this paper, a content-based videos classification system
for the classes indoor and outdoor is presented. The system is
intended to be used on a mobile platform with modest resources. The
algorithm makes use of the temporal redundancy in videos, which
allows using an uncomplicated classification model while still
achieving reasonable accuracy. The training and evaluation was done
on a video database of 443 videos downloaded from a video sharing
service. A total accuracy of 87.36% was achieved.
Abstract: This research aims at development of the Multiple
Intelligences Measurement of Elementary Students. The structural
accuracy test and normality establishment are based on the Multiple
Intelligences Theory of Gardner. This theory consists of eight aspects
namely linguistics, logic and mathematics, visual-spatial relations,
body and movement, music, human relations, self-realization/selfunderstanding
and nature. The sample used in this research consists
of elementary school students (aged between 5-11 years). The size of
the sample group was determined by Yamane Table. The group has
2,504 students. Multistage Sampling was used. Basic statistical
analysis and construct validity testing were done using confirmatory
factor analysis. The research can be summarized as follows; 1.
Multiple Intelligences Measurement consisting of 120 items is
content-accurate. Internal consistent reliability according to the
method of Kuder-Richardson of the whole Multiple Intelligences
Measurement equals .91. The difficulty of the measurement test is
between .39-.83. Discrimination is between .21-.85. 2). The Multiple
Intelligences Measurement has construct validity in a good range,
that is 8 components and all 120 test items have statistical
significance level at .01. Chi-square value equals 4357.7; p=.00 at the
degree of freedom of 244 and Goodness of Fit Index equals 1.00.
Adjusted Goodness of Fit Index equals .92. Comparative Fit Index
(CFI) equals .68. Root Mean Squared Residual (RMR) equals 0.064
and Root Mean Square Error of Approximation equals 0.82. 3). The
normality of the Multiple Intelligences Measurement is categorized
into 3 levels. Those with high intelligence are those with percentiles
of more than 78. Those with moderate/medium intelligence are those
with percentiles between 24 and 77.9. Those with low intelligence
are those with percentiles from 23.9 downwards.
Abstract: The evaluation and measurement of human body
dimensions are achieved by physical anthropometry. This research
was conducted in view of the importance of anthropometric indices
of the face in forensic medicine, surgery, and medical imaging. The
main goal of this research is to optimization of facial feature point by
establishing a mathematical relationship among facial features and
used optimize feature points for age classification. Since selected
facial feature points are located to the area of mouth, nose, eyes and
eyebrow on facial images, all desire facial feature points are extracted
accurately. According this proposes method; sixteen Euclidean
distances are calculated from the eighteen selected facial feature
points vertically as well as horizontally. The mathematical
relationships among horizontal and vertical distances are established.
Moreover, it is also discovered that distances of the facial feature
follows a constant ratio due to age progression. The distances
between the specified features points increase with respect the age
progression of a human from his or her childhood but the ratio of the
distances does not change (d = 1 .618 ) . Finally, according to the
proposed mathematical relationship four independent feature
distances related to eight feature points are selected from sixteen
distances and eighteen feature point-s respectively. These four feature
distances are used for classification of age using Support Vector
Machine (SVM)-Sequential Minimal Optimization (SMO) algorithm
and shown around 96 % accuracy. Experiment result shows the
proposed system is effective and accurate for age classification.
Abstract: Corner detection and optical flow are common techniques for feature-based video stabilization. However, these algorithms are computationally expensive and should be performed at a reasonable rate. This paper presents an algorithm for discarding irrelevant feature points and maintaining them for future use so as to improve the computational cost. The algorithm starts by initializing a maintained set. The feature points in the maintained set are examined against its accuracy for modeling. Corner detection is required only when the feature points are insufficiently accurate for future modeling. Then, optical flows are computed from the maintained feature points toward the consecutive frame. After that, a motion model is estimated based on the simplified affine motion model and least square method, with outliers belonging to moving objects presented. Studentized residuals are used to eliminate such outliers. The model estimation and elimination processes repeat until no more outliers are identified. Finally, the entire algorithm repeats along the video sequence with the points remaining from the previous iteration used as the maintained set. As a practical application, an efficient video stabilization can be achieved by exploiting the computed motion models. Our study shows that the number of times corner detection needs to perform is greatly reduced, thus significantly improving the computational cost. Moreover, optical flow vectors are computed for only the maintained feature points, not for outliers, thus also reducing the computational cost. In addition, the feature points after reduction can sufficiently be used for background objects tracking as demonstrated in the simple video stabilizer based on our proposed algorithm.
Abstract: In this research, heat transfer of a poly Ethylene
fluidized bed reactor without reaction were studied experimentally
and computationally at different superficial gas velocities. A multifluid
Eulerian computational model incorporating the kinetic theory
for solid particles was developed and used to simulate the heat
conducting gas–solid flows in a fluidized bed configuration.
Momentum exchange coefficients were evaluated using the Syamlal–
O-Brien drag functions. Temperature distributions of different phases
in the reactor were also computed. Good agreement was found
between the model predictions and the experimentally obtained data
for the bed expansion ratio as well as the qualitative gas–solid flow
patterns. The simulation and experimental results showed that the gas
temperature decreases as it moves upward in the reactor, while the
solid particle temperature increases. Pressure drop and temperature
distribution predicted by the simulations were in good agreement
with the experimental measurements at superficial gas velocities
higher than the minimum fluidization velocity. Also, the predicted
time-average local voidage profiles were in reasonable agreement
with the experimental results. The study showed that the
computational model was capable of predicting the heat transfer and
the hydrodynamic behavior of gas-solid fluidized bed flows with
reasonable accuracy.
Abstract: This paper presents a new STAKCERT KDD
processes for worm detection. The enhancement introduced in the
data-preprocessing resulted in the formation of a new STAKCERT
model for worm detection. In this paper we explained in detail how
all the processes involved in the STAKCERT KDD processes are
applied within the STAKCERT model for worm detection. Based on
the experiment conducted, the STAKCERT model yielded a 98.13%
accuracy rate for worm detection by integrating the STAKCERT
KDD processes.
Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.
Abstract: Interactions among proteins are the basis of various
life events. So, it is important to recognize and research protein
interaction sites. A control set that contains 149 protein molecules
were used here. Then 10 features were extracted and 4 sample sets
that contained 9 sliding windows were made according to features.
These 4 sample sets were calculated by Radial Basis Functional neutral
networks which were optimized by Particle Swarm Optimization
respectively. Then 4 groups of results were obtained. Finally, these 4
groups of results were integrated by decision fusion (DF) and Genetic
Algorithm based Selected Ensemble (GASEN). A better accuracy was
got by DF and GASEN. So, the integrated methods were proved to
be effective.
Abstract: Fuel cell's system requires regulating circuit for
voltage and current in order to control power in case of connecting to
other generative devices or load. In this paper Fuel cell system and
convertor, which is a multi-variable system, are controlled using
sliding mode method. Use of weighting matrix in design procedure
made it possible to regulate speed of control. Simulation results show
the robustness and accuracy of proposed controller for controlling
desired of outputs.
Abstract: The quantitative determination of several trace
elements (Cr, As, Se, Cd, Hg, Pb) existing as inorganic impurities in
some oriental herb-products such as Lingzhi Mushroom capsules,
Philamin powder, etc using ICP-MS has been studied. Various
instrumental parameters such as power, gas flow rate, sample depth, as
well as the concentration of nitric acid and thick background due to
high concentration of possible interferences on the determination of
these above-mentioned elements was investigated and the optimum
working conditions of the sample measurement on ICP-MS
(Agilent-7500a) were reported. Appropriate isotope internal standards
were also used to improve the accuracy of mercury determination.
Optimal parameters for sampling digestion were also investigated. The
recovery of analytical procedure was examined by using a Certified
Reference Material (IAEA-CRM 359). The recommended procedure
was then applied for the quantitative determination of Cr, As, Se, Cd,
Hg, Pb in Lingzhi Mushroom capsule, and Philamine powder samples.
The reproducibility of sample measurement (average value between
94 and 102%) and the uncertainty of analytical data (less than 20%)
are acceptable.
Abstract: A four element prototype phased array surface probe
has been designed and constructed to improve clinical human
prostate spectroscopic data. The probe consists of two pairs of
adjacent rectangular coils with an optimum overlap to reduce the
mutual inductance. The two pairs are positioned on the anterior and
the posterior pelvic region and two couples of varactors at the input
of each coil undertake the procedures of tuning and matching. The
probe switches off and on automatically during the consecutive
phases of the MR experiment with the use of an analog switch that is
triggered by a microcontroller. Experimental tests that were carried
out resulted in high levels of tuning accuracy. Also, the switching
mechanism functions properly for various applied loads and pulse
sequence characteristics, producing only 10 μs of latency.
Abstract: Analytical procedure was carried out in this paper to
calculate the ultimate load capacity of reinforced concrete corbels
strengthened or repaired externally with CFRP sheets. Strut and tie
method and shear friction method proposed earlier for analyzing
reinforced concrete corbels were modified to incorporate the effect of
external CFRP sheets bonded to the corbel. The points of weakness
of any method that lead to an inaccuracy, especially when
overestimating test results were checked and discussed. Comparison
of prediction with the test data indicates that the ratio of test /
calculated ultimate load is 0.82 and 1.17 using strut and tie method
and shear friction method, respectively. If the limits of maximum
shear stress is followed, the calculated ultimate load capacity using
shear friction method was found to underestimates test data
considerably.
Abstract: In this paper, a numerical solution based on nonpolynomial
cubic spline functions is used for finding the solution of
boundary value problems which arise from the problems of calculus
of variations. This approximation reduce the problems to an explicit
system of algebraic equations. Some numerical examples are also
given to illustrate the accuracy and applicability of the presented
method.
Abstract: In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L1 model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms.