Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 4 3 2008 10 1 Steganalysis Method for LSB Replacement Based on Local Gradient of Image Histogram 59 70 EN M. Mahdavi N Sh. Samavi Y N. Zaker N M. Modarres-Hashemi N In this paper we present a new accurate steganalysis method for the LSBreplacement steganography. The suggested method is based on the changes that occur in thehistogram of an image after the embedding of data. Every pair of neighboring bins of ahistogram are either inter-related or unrelated depending on whether embedding of a bit ofdata in the image could affect both bins or not. We show that the overall behavior of allinter-related bins, when compared with that of the unrelated ones, could give an accuratemeasure for the amount of the embedded data. Both analytical analysis and simulationresults show the accuracy of the proposed method. The suggested method has beenimplemented and tested for over 2000 samples and compared with the RS Steganalysismethod. Mean and variance of error were 0.0025 and 0.0037 for the suggested methodwhere these quantities were 0.0070 and 0.0182 for the RS Steganalysis. Using 4800samples, we showed that the performance of the suggested method is comparable withthose of the RS steganalysis for JPEG filtered images. The new approach is applicable forthe detection of both random and sequential LSB embedding. LSB Replacement,Steganalysis,Steganography, http://ijeee.iust.ac.ir/article-1-73-en.html http://ijeee.iust.ac.ir/article-1-73-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 4 3 2008 10 1 An Improved Fuzzy Neural Network for Solving Uncertainty in Pattern Classification and Identification 71 78 EN M. Hariri Y S. B. Shokouhi N N. Mozayani N Dealing with uncertainty is one of the most critical problems in complicatedpattern recognition subjects. In this paper, we modify the structure of a useful UnsupervisedFuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types offuzzy neurons and its associated self organizing supervised learning algorithm. Thisimproved five-layer feed forward Supervised Fuzzy Neural Network (SFNN) is used forclassification and identification of shifted and distorted training patterns. It is generallyuseful for those flexible patterns which are not certainly identifiable upon their features. Toshow the identification capability of our proposed network, we used fingerprint, as the mostflexible and varied pattern. After feature extraction of different shapes of fingerprints, thepattern of these features, “feature-map”, is applied to the network. The network firstfuzzifies the pattern and then computes its similarities to all of the learned pattern classes.The network eventually selects the learned pattern of highest similarity and returns itsspecific class as a non fuzzy output. To test our FNN, we applied the standard (NISTdatabase) and our databases (with 176×224 dimensions). The feature-maps of thesefingerprints contain two types of minutiae and three types of singular points, each of themis represented by 22×28 pixels, which is less than real size and suitable for real timeapplications. The feature maps are applied to the FNN as training patterns. Upon its settingparameters, the network discriminates 3 to 7 subclasses for each main classes assigned toone of the subjects. Classification,Fingerprint,Fuzzy Neural Network,Fuzzy Neurons,Identification,Supervised Learning Algorithm, http://ijeee.iust.ac.ir/article-1-69-en.html http://ijeee.iust.ac.ir/article-1-69-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 4 3 2008 10 1 One Terminal Digital Algorithm for Adaptive Single Pole Auto-Reclosing Based on Zero Sequence Voltage 71 78 EN S. Jamali Y A. Parham N This paper presents an algorithm for adaptive determination of the dead timeduring transient arcing faults and blocking automatic reclosing during permanent faults onoverhead transmission lines. The discrimination between transient and permanent faults ismade by the zero sequence voltage measured at the relay point. If the fault is recognised asan arcing one, then the third harmonic of the zero sequence voltage is used to evaluate theextinction time of the secondary arc and to initiate reclosing signal. The significantadvantage of this algorithm is that it uses an adaptive threshold level and therefore itsperformance is independent of fault location, line parameters and the system operatingconditions. The proposed algorithm has been successfully tested under a variety of faultlocations and load angles on a 400KV overhead line using Electro-Magnetic TransientProgram (EMTP). The test results validate the algorithm ability in determining thesecondary arc extinction time during transient faults as well as blocking unsuccessfulautomatic reclosing during permanent faults. Adaptive Auto-Reclosing,Secondary Arc,Transmission Line Protection,Zero Sequence Voltage, http://ijeee.iust.ac.ir/article-1-68-en.html http://ijeee.iust.ac.ir/article-1-68-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 4 3 2008 10 1 Dynamic Performance Prediction of Brushless Resolver 94 103 EN D. Arab-Khaburi N F. Tootoonchian Y Z. Nasiri-Gheidari N A mathematical model based on d-q axis theory and dynamic performance characteristic of brushless resolvers is discussed in this paper. The impact of rotor eccentricity on the accuracy of position in precise applications is investigated. In particular, the model takes the stator currents of brushless resolver into account. The proposed model is used to compute the dynamic and steady state equivalent circuit of resolvers. Finally, simulation results are presented. The validity and usefulness of the proposed method are thoroughly verified with experiments. Brushless Resolver,Dynamic Performance,Simulation,Steady State Behavior, http://ijeee.iust.ac.ir/article-1-70-en.html http://ijeee.iust.ac.ir/article-1-70-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 4 3 2008 10 1 Static Security Constrained Generation Scheduling Using Sensitivity Characteristics of Neural Network 104 114 EN M. R. Aghamohammadi Y This paper proposes a novel approach for generation scheduling using sensitivitycharacteristic of a Security Analyzer Neural Network (SANN) for improving static securityof power system. In this paper, the potential overloading at the post contingency steadystateassociated with each line outage is proposed as a security index which is used forevaluation and enhancement of system static security. A multilayer feed forward neuralnetwork is trained as SANN for both evaluation and enhancement of system security. Theinput of SANN is load/generation pattern. By using sensitivity characteristic of SANN,sensitivity of security indices with respect to generation pattern is used as a guide line forgeneration rescheduling aimed to enhance security. Economic characteristic of generationpattern is also considered in the process of rescheduling to find an optimum generationpattern satisfying both security and economic aspects of power system. One interestingfeature of the proposed approach is its ability for flexible handling of system security intogeneration rescheduling and compromising with the economic feature with any degree ofcoordination. By using SANN, several generation patterns with different level of securityand cost could be evaluated which constitute the Pareto solution of the multi-objectiveproblem. A compromised generation pattern could be found from Pareto solution with anydegree of coordination between security and cost. The effectiveness of the proposedapproach is studied on the IEEE 30 bus system with promising results. Generation Scheduling,Neural Network,Overloading,Sensitivity,Static Security, http://ijeee.iust.ac.ir/article-1-71-en.html http://ijeee.iust.ac.ir/article-1-71-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 4 3 2008 10 1 A Modified Proportional Navigation Guidance for Range Estimation 115 126 EN A. Moharampour Y J. Poshtan N A. Khaki-Sedigh N In this paper, after defining pure proportional navigation guidance in the 3-dimensional state from a new point of view, range estimation for passive homing missiles isexplained. Modeling has been performed by using line of sight coordinates with a particulardefinition. To obtain convergent estimates of those state variables involved particularly inrange channel and unavailable from IR trackers, nonlinear filters such as sequential U-Dextended Kalman filter and Unscented Kalman filter in modified spherical coordinatecombined with a modified proportional navigation guidance law are proposed. Simulationresults indicate that the proposed tracking filters in conjunction with the dual guidance laware able to provide the convergence of the range estimate for both maneuvering and nonmaneuveringtargets. IR Homing Missile,Pure Proportional Navigation Guidance,Range, http://ijeee.iust.ac.ir/article-1-72-en.html http://ijeee.iust.ac.ir/article-1-72-en.pdf