Abstract: This paper presents a new heuristic algorithm useful
for long-term planning of survivable WDM networks. A multi-period
model is formulated that combines network topology design and
capacity expansion. The ability to determine network expansion
schedules of this type becomes increasingly important to the
telecommunications industry and to its customers. The solution
technique consists of a Genetic Algorithm that allows generating
several network alternatives for each time period simultaneously and
shortest-path techniques to deduce from these alternatives a least-cost
network expansion plan over all time periods. The multi-period
planning approach is illustrated on a realistic network example.
Extensive simulations on a wide range of problem instances are
carried out to assess the cost savings that can be expected by
choosing a multi-period planning approach instead of an iterative
network expansion design method.
Abstract: Quantitative measurements of tumor in general and tumor volume in particular, become more realistic with the use of Magnetic Resonance imaging, especially when the tumor morphological changes become irregular and difficult to assess by clinical examination. However, tumor volume estimation strongly depends on the image segmentation, which is fuzzy by nature. In this paper a fuzzy approach is presented for tumor volume segmentation based on the fuzzy connectedness algorithm. The fuzzy affinity matrix resulting from segmentation is then used to estimate a fuzzy volume based on a certainty parameter, an Alpha Cut, defined by the user. The proposed method was shown to highly affect treatment decisions. A statistical analysis was performed in this study to validate the results based on a manual method for volume estimation and the importance of using the Alpha Cut is further explained.
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: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.
Abstract: In this study, a new and fast algorithm for Ascending
Aorta (AscA) and Descending Aorta (DesA) segmentation is
presented using Computed Tomography Angiography images. This
process is quite important especially at the detection of aortic
plaques, aneurysms, calcification or stenosis. The applied method has
been carried out at four steps. At first step, lung segmentation is
achieved. At the second one, Mediastinum Region (MR) is detected
to use in the segmentation. At the third one, images have been
applied optimal threshold and components which are outside of the
MR were removed. Lastly, identifying and segmentation of AscA and
DesA have been carried out. The performance of the applied method
is found quite well for radiologists and it gives enough results to the
surgeries medically.
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: Enhancement of the performance of a reverse osmosis
(RO) unit through periodic control is studied. The periodic control
manipulates the feed pressure and flow rate of the RO unit. To ensure
the periodic behavior of the inputs, the manipulated variables (MV)
are transformed into the form of sinusoidal functions. In this case, the
amplitude and period of the sinusoidal functions become the
surrogate MV and are thus regulated via nonlinear model predictive
control algorithm. The simulation results indicated that the control
system can generate cyclic inputs necessary to enhance the closedloop
performance in the sense of increasing the permeate production
and lowering the salt concentration. The proposed control system can
attain its objective with arbitrary set point for the controlled outputs.
Successful results were also obtained in the presence of modeling
errors.
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: This paper presents a fast and efficient on-line technique for estimating impedance of unbalanced loads in power systems. The proposed technique is an application of a discrete timedynamic filter based on stochastic estimation theory which is suitable for estimating parameters in noisy environment. The algorithm uses sets of digital samples of the distorted voltage and current waveforms of the non-linear load to estimate the harmonic contents of these two signal. The non-linear load impedance is then calculated from these contents. The method is tested using practical data. Results are reported and compared with those obtained using the conventional least error squares technique. In addition to the very accurate results obtained, the method can detect and reject bad measurements. This can be considered as a very important advantage over the conventional static estimation methods such as the least error square method.
Abstract: Nature conducts its action in a very private manner. To
reveal these actions classical science has done a great effort. But
classical science can experiment only with the things that can be seen
with eyes. Beyond the scope of classical science quantum science
works very well. It is based on some postulates like qubit,
superposition of two states, entanglement, measurement and
evolution of states that are briefly described in the present paper.
One of the applications of quantum computing i.e.
implementation of a novel quantum evolutionary algorithm(QEA) to
automate the time tabling problem of Dayalbagh Educational Institute
(Deemed University) is also presented in this paper. Making a good
timetable is a scheduling problem. It is NP-hard, multi-constrained,
complex and a combinatorial optimization problem. The solution of
this problem cannot be obtained in polynomial time. The QEA uses
genetic operators on the Q-bit as well as updating operator of
quantum gate which is introduced as a variation operator to converge
toward better solutions.
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: 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: Design of a fixed parameter robust STATCOM controller for a multi-machine power system through an H-? based loop-shaping procedure is presented. The trial and error part of the graphical loop-shaping procedure has been eliminated by embedding a particle swarm optimization (PSO) technique in the design loop. Robust controllers were designed considering the detailed dynamics of the multi-machine system and results were compared with reduced order models. The robust strategy employing loop-shaping and PSO algorithms was observed to provide very good damping profile for a wide range of operation and for various disturbance conditions.
Abstract: This paper proposes a low-cost reconfigurable
architecture for AES algorithm. The proposed architecture separates
SubBytes and MixColumns into two parallel data path, and supports
different bit-width operation for this two data path. As a result, different number of S-box can be supported in this architecture. The
throughput and power consumption can be adjusted by changing the
number of S-box running in this design. Using the TSMC 0.18μm CMOS standard cell library, a very low-cost implementation of 7K
Gates is obtained under 182MHz frequency. The maximum throughput is 360Mbps while using 4 S-Box simultaneously, and the
minimum throughput is 114Mbps while only using 1 S-Box
Abstract: Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.
Abstract: Motion estimation is the most computationally
intensive part in video processing. Many fast motion estimation
algorithms have been proposed to decrease the computational
complexity by reducing the number of candidate motion vectors.
However, these studies are for fast search algorithms themselves while
almost image and video compressions are operated with software
based. Therefore, the timing constraints for running these motion
estimation algorithms not only challenge for the video codec but also
overwhelm for some of processors. In this paper, the performance of
motion estimation is enhanced by using Intel's Streaming SIMD
Extension 2 (SSE2) technology with Intel Pentium 4 processor.
Abstract: Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound (TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a novel method for automatic prostate segmentation in TRUS images is presented. This method involves preprocessing (edge preserving noise reduction and smoothing) and prostate segmentation. The speckle reduction has been achieved by using stick filter and top-hat transform has been implemented for smoothing. A feed forward neural network and local binary pattern together have been use to find a point inside prostate object. Finally the boundary of prostate is extracted by the inside point and an active contour algorithm. A numbers of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with MSE less than 4.6% relative to boundary provided manually by physicians.
Abstract: This paper presents a novel control strategy of a threephase
four-wire Unified Power Quality (UPQC) for an improvement
in power quality. The UPQC is realized by integration of series and
shunt active power filters (APFs) sharing a common dc bus capacitor.
The shunt APF is realized using a thee-phase, four leg voltage source
inverter (VSI) and the series APF is realized using a three-phase,
three leg VSI. A control technique based on unit vector template
technique (UTT) is used to get the reference signals for series APF,
while instantaneous sequence component theory (ISCT) is used for
the control of Shunt APF. The performance of the implemented
control algorithm is evaluated in terms of power-factor correction,
load balancing, neutral source current mitigation and mitigation of
voltage and current harmonics, voltage sag and swell in a three-phase
four-wire distribution system for different combination of linear and
non-linear loads. In this proposed control scheme of UPQC, the
current/voltage control is applied over the fundamental supply
currents/voltages instead of fast changing APFs currents/voltages,
there by reducing the computational delay and the required sensors.
MATLAB/Simulink based simulations are obtained, which support
the functionality of the UPQC. MATLAB/Simulink based
simulations are obtained, which support the functionality of the
UPQC.