Abstract: As the development of digital technology is increasing,
Digital cinema is getting more spread.
However, content copy and attack against the digital cinema becomes
a serious problem. To solve the above security problem, we propose
“Additional Watermarking" for digital cinema delivery system. With
this proposed “Additional watermarking" method, we protect content
copyrights at encoder and user side information at decoder. It realizes
the traceability of the watermark embedded at encoder.
The watermark is embedded into the random-selected frames using
Hash function. Using it, the embedding position is distributed by Hash
Function so that third parties do not break off the watermarking
algorithm.
Finally, our experimental results show that proposed method is much
better than the convenient watermarking techniques in terms of
robustness, image quality and its simple but unbreakable algorithm.
Abstract: The H.264/AVC standard uses an intra prediction, 9
directional modes for 4x4 luma blocks and 8x8 luma blocks, 4
directional modes for 16x16 macroblock and 8x8 chroma blocks,
respectively. It means that, for a macroblock, it has to perform 736
different RDO calculation before a best RDO modes is determined.
With this Multiple intra-mode prediction, intra coding of H.264/AVC
offers a considerably higher improvement in coding efficiency
compared to other compression standards, but computational
complexity is increased significantly. This paper presents a fast intra
prediction algorithm for H.264/AVC intra prediction based a
characteristic of homogeneity information. In this study, the gradient
prediction method used to predict the homogeneous area and the
quadratic prediction function used to predict the nonhomogeneous
area. Based on the correlation between the homogeneity and block
size, the smaller block is predicted by gradient prediction and
quadratic prediction, so the bigger block is predicted by gradient
prediction. Experimental results are presented to show that the
proposed method reduce the complexity by up to 76.07%
maintaining the similar PSNR quality with about 1.94%bit rate
increase in average.
Abstract: Segmentation is an important step in medical image
analysis and classification for radiological evaluation or computer
aided diagnosis. The CAD (Computer Aided Diagnosis ) of lung CT
generally first segment the area of interest (lung) and then analyze
the separately obtained area for nodule detection in order to
diagnosis the disease. For normal lung, segmentation can be
performed by making use of excellent contrast between air and
surrounding tissues. However this approach fails when lung is
affected by high density pathology. Dense pathologies are present in
approximately a fifth of clinical scans, and for computer analysis
such as detection and quantification of abnormal areas it is vital that
the entire and perfectly lung part of the image is provided and no
part, as present in the original image be eradicated. In this paper we
have proposed a lung segmentation technique which accurately
segment the lung parenchyma from lung CT Scan images. The
algorithm was tested against the 25 datasets of different patients
received from Ackron Univeristy, USA and AGA Khan Medical
University, Karachi, Pakistan.
Abstract: Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.
Abstract: A self-evolution algorithm for optimizing neural networks using a combination of PSO and JPSO is proposed. The algorithm optimizes both the network topology and parameters simultaneously with the aim of achieving desired accuracy with less complicated networks. The performance of the proposed approach is compared with conventional back-propagation networks using several synthetic functions, with better results in the case of the former. The proposed algorithm is also implemented on slope stability problem to estimate the critical factor of safety. Based on the results obtained, the proposed self evolving network produced a better estimate of critical safety factor in comparison to conventional BPN network.
Abstract: A suspension bridge is the most suitable type of structure for a long-span bridge due to rational use of structural materials. Increased deformability, which is conditioned by appearance of the elastic and kinematic displacements, is the major disadvantage of suspension bridges. The problem of increased kinematic displacements under the action of non-symmetrical load can be solved by prestressing. The prestressed suspension bridge with the span of 200 m was considered as an object of investigations. The cable truss with the cross web was considered as the main load carrying structure of the prestressed suspension bridge. The considered cable truss was optimized by 47 variable factors using Genetic algorithm and FEM program ANSYS. It was stated, that the maximum total displacements are reduced up to 29.9% by using of the cable truss with the rational characteristics instead of the single cable in the case of the worst situated load.
Abstract: The Boundary Representation of a 3D manifold contains
FACES (connected subsets of a parametric surface S : R2 -!
R3). In many science and engineering applications it is cumbersome
and algebraically difficult to deal with the polynomial set and
constraints (LOOPs) representing the FACE. Because of this reason, a
Piecewise Linear (PL) approximation of the FACE is needed, which is
usually represented in terms of triangles (i.e. 2-simplices). Solving the
problem of FACE triangulation requires producing quality triangles
which are: (i) independent of the arguments of S, (ii) sensitive to the
local curvatures, and (iii) compliant with the boundaries of the FACE
and (iv) topologically compatible with the triangles of the neighboring
FACEs. In the existing literature there are no guarantees for the point
(iii). This article contributes to the topic of triangulations conforming
to the boundaries of the FACE by applying the concept of parameterindependent
Gabriel complex, which improves the correctness of the
triangulation regarding aspects (iii) and (iv). In addition, the article
applies the geometric concept of tangent ball to a surface at a point to
address points (i) and (ii). Additional research is needed in algorithms
that (i) take advantage of the concepts presented in the heuristic
algorithm proposed and (ii) can be proved correct.
Abstract: Video watermarking is usually considered as watermarking of a set of still images. In frame-by-frame watermarking approach, each video frame is seen as a single watermarked image, so collusion attack is more critical in video watermarking. If the same or redundant watermark is used for embedding in every frame of video, the watermark can be estimated and then removed by watermark estimate remodolulation (WER) attack. Also if uncorrelated watermarks are used for every frame, these watermarks can be washed out with frame temporal filtering (FTF). Switching watermark system or so-called SS-N system has better performance against WER and FTF attacks. In this system, for each frame, the watermark is randomly picked up from a finite pool of watermark patterns. At first SS-N system will be surveyed and then a new collusion attack for SS-N system will be proposed using a new algorithm for separating video frame based on watermark pattern. So N sets will be built in which every set contains frames carrying the same watermark. After that, using WER attack in every set, N different watermark patterns will be estimated and removed later.
Abstract: Cheating on standardized tests has been a major
concern as it potentially minimizes measurement precision. One
major way to reduce cheating by collusion is to administer multiple
forms of a test. Even with this approach, potential collusion is still
quite large. A Latin-square treatment structure for distributing
multiple forms is proposed to further reduce the colluding potential.
An index to measure the extent of colluding potential is also
proposed. Finally, with a simple algorithm, the various Latin-squares
were explored to find the best structure to keep the colluding
potential to a minimum.
Abstract: This paper presents an optimal design of poly-phase induction motor using Quadratic Interpolation based Particle Swarm Optimization (QI-PSO). The optimization algorithm considers the efficiency, starting torque and temperature rise as objective function (which are considered separately) and ten performance related items including harmonic current as constraints. The QI-PSO algorithm was implemented on a test motor and the results are compared with the Simulated Annealing (SA) technique, Standard Particle Swarm Optimization (SPSO), and normal design. Some benchmark problems are used for validating QI-PSO. From the test results QI-PSO gave better results and more suitable to motor-s design optimization. Cµ code is used for implementing entire algorithms.
Abstract: To solve the quick and accurate level-adjusting
problem in the process of spacecraft precise mating, automatic leveling
and equalizing hoist device for spacecraft is developed. Based on
lifting point adjustment by utilizing XY-workbench, the leveling and
equalizing controller by a self-adaptive control algorithm is proposed.
By simulation analysis and lifting test using engineering prototype,
validity and reliability of the hoist device is verified, which can meet
the precision mating requirements of practical applications for
spacecraft.
Abstract: The necessity of solving multi dimensional
complicated scientific problems beside the necessity of several
objective functions optimization are the most motive reason of born
of artificial intelligence and heuristic methods.
In this paper, we introduce a new method for multiobjective
optimization based on learning automata. In the proposed method,
search space divides into separate hyper-cubes and each cube is
considered as an action. After gathering of all objective functions
with separate weights, the cumulative function is considered as the
fitness function. By the application of all the cubes to the cumulative
function, we calculate the amount of amplification of each action and
the algorithm continues its way to find the best solutions. In this
Method, a lateral memory is used to gather the significant points of
each iteration of the algorithm. Finally, by considering the
domination factor, pareto front is estimated. Results of several
experiments show the effectiveness of this method in comparison
with genetic algorithm based method.
Abstract: In this paper, we argue the security protocols of
ZigBee wireless sensor network in MAC layer. AES 128-bit
encryption algorithm in CCM* mode is secure transferred data;
however, AES-s secret key will be break within nearest future.
Efficient public key algorithm, ECC has been mixed with AES to
rescue the ZigBee wireless sensor from cipher text and replay attack.
Also, the proposed protocol can parallelize the integrity function to
increase system performance.
Abstract: A color image edge detection algorithm is proposed in
this paper using Pseudo-complement and matrix rotation operations.
First, pseudo-complement method is applied on the image for each
channel. Then, matrix operations are applied on the output image of
the first stage. Dominant pixels are obtained by image differencing
between the pseudo-complement image and the matrix operated
image. Median filtering is carried out to smoothen the image thereby
removing the isolated pixels. Finally, the dominant or core pixels
occurring in at least two channels are selected. On plotting the
selected edge pixels, the final edge map of the given color image is
obtained. The algorithm is also tested in HSV and YCbCr color
spaces. Experimental results on both synthetic and real world images
show that the accuracy of the proposed method is comparable to
other color edge detectors. All the proposed procedures can be
applied to any image domain and runs in polynomial time.
Abstract: This paper presents an approach for daily optimal operation of distribution networks considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. A genetic algorithm is used to solve the optimal operation problem. The approach is tested on an IEEE34 buses distribution feeder.
Abstract: This paper mainly studies the analyses of parameters
in the intersection collision avoidance (ICA) system based on the radar
sensors. The parameters include the positioning errors, the repeat
period of the radar sensor, the conditions of potential collisions of two
cross-path vehicles, etc. The analyses of the parameters can provide
the requirements, limitations, or specifications of this ICA system. In
these analyses, the positioning errors will be increased as the measured
vehicle approach the intersection. In addition, it is not necessary to
implement the radar sensor in higher position since the positioning
sensitivities become serious as the height of the radar sensor increases.
A concept of the safety buffer distances for front and rear of the
measured vehicle is also proposed. The conditions for potential
collisions of two cross-path vehicles are also presented to facilitate the
computation algorithm.
Abstract: An economic operation scheduling problem of a
hydro-thermal power generation system has been properly solved by
the proposed multipath adaptive tabu search algorithm (MATS). Four
reservoirs with their own hydro plants and another one thermal plant
are integrated to be a studied system used to formulate the objective
function under complicated constraints, eg water managements,
power balance and thermal generator limits. MATS with four subsearch
units (ATSs) and two stages of discarding mechanism (DM),
has been setting and trying to solve the problem through 25 trials
under function evaluation criterion. It is shown that MATS can
provide superior results with respect to single ATS and other
previous methods, genetic algorithms (GA) and differential evolution
(DE).
Abstract: Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.
Abstract: From a set of shifted, blurred, and decimated image , super-resolution image reconstruction can get a high-resolution image. So it has become an active research branch in the field of image restoration. In general, super-resolution image restoration is an ill-posed problem. Prior knowledge about the image can be combined to make the problem well-posed, which contributes to some regularization methods. In the regularization methods at present, however, regularization parameter was selected by experience in some cases and other techniques have too heavy computation cost for computing the parameter. In this paper, we construct a new super-resolution algorithm by transforming the solving of the System stem Є=An into the solving of the equations X+A*X-1A=I , and propose an inverse iterative method.
Abstract: With the exponential growth of networked system and
application such as eCommerce, the demand for effective internet
security is increasing. Cryptology is the science and study of systems
for secret communication. It consists of two complementary fields of
study: cryptography and cryptanalysis. The application of genetic
algorithms in the cryptanalysis of knapsack ciphers is suggested by
Spillman [7]. In order to improve the efficiency of genetic algorithm
attack on knapsack cipher, the previously published attack was
enhanced and re-implemented with variation of initial assumptions
and results are compared with Spillman results. The experimental
result of research indicates that the efficiency of genetic algorithm
attack on knapsack cipher can be improved with variation of initial
assumption.