Iranian Journal of Electrical and Electronic Engineering
Volume:8 Issue: 2, Jun 2012

In this paper we introduce two innovative image and video watermarking algorithms. The paper’s main emphasis is on the use of chaotic maps to boost the algorithms’ security and resistance against attacks. By encrypting the watermark information in a one dimensional chaotic map, we make the extraction of watermark for potential attackers very hard. In another approach, we select embedding positions by a two dimensional chaotic map which enables us to satisfactorily distribute watermark information throughout the host signal. This prevents concentration of watermark data in a corner of the host signal which effectively saves it from being a target for attacks that include cropping of the signal. The simulation results demonstrate that the proposed schemes are quite resistant to many kinds of attacks which commonly threaten watermarking algorithms.

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some kinds of weights for reducing the effect of noises in clustering. Experimental results using, two artificial datasets, five real datasets, viz., Iris, Cancer, Wine, Glass and a speech corpus used in a GMM-based speaker identification task show that compared to three well-known clustering algorithms, namely, the Fuzzy Possibilistic C-Means, Credibilistic Fuzzy C-Means and Density Weighted Fuzzy C-Means, our approach is less sensitive to outliers and noises and has an acceptable computational complexity.

In this paper, a novel design of all-solid photonic bandgap fiber with ultra-low confinement loss is proposed. The confinement loss is reduced remarkably by managing the number of rods rings, up-doping level, pitch value, and rods diameters. Moreover, the designed PCF shows ultra-flattened dispersion in L- and U-band. Furthermore, a new design, based on introducing of an extra ring of air holes on the outside of the all-solid bandgap structure, is then proposed and characterized. We demonstrate that it significantly reduces the fiber diameter to achieve negligible confinement loss. The validation of the proposed design is carried out by employing a two dimensional finite difference frequency domain with perfectly matched layers.

One of the most important features of the Active Inductors (AIs) is their input equivalent resistance, namely series-loss resistance, which should be low enough to have a high Quality Factor (QF). Most of the previous methods by this goal did not yield a high enough QF. This paper presents a new method, namely applying an RC feedback, to cancel series-loss resistance entirely. As the RC feedback cancels series-loss resistance, it enhances the Self-Resonant Frequency (SRF) as well. The SRF of the AI has a range as high as 0.25-12.5 GHz. Compared to the previous reports, the QF has been improved by applying the RC feedback. The structure is such that the QF can be adjusted independent of the SRF. For example, a very high quality factor of 13159 at the frequency of 6.6 GHz with a 2.2 nH inductance is obtained, while noise voltage and power dissipation are less than 4.6 nV Hz and 4 mW, respectively. The AI is designed and simulated using 90 nm CMOS process and 1.2 V power supply. To the best of authors’ knowledge, this is the first time an RC feedback has been implemented to cancel series-loss resistance.

This paper presents a comparative study on the Predictive Direct Torque Control method and the Indirect Space Vector Modulation Direct Torque Control method for a Doubly-Fed Induction Machine (DFIM) which its rotor is fed by an Indirect Matrix Converter (IMC). In Conventional DTC technique, good transient and steady-state performances are achieved but it presents a non constant switching frequency behavior and non desirable torque ripples. However, in this paper by using the proposed methods, a fixed switching frequency is obtained. In this model Doubly-Fed Induction Machine is connected to the grid by the stator and the rotor is fed by an Indirect Matrix Converter. Functionally this converter is very similar to the Direct Matrix Converter, but it has separate line and load bridges. In the inverter stage, the Predictive method and ISVM method are employed. In the rectifier stage, in order to reduce losses caused by snubber circuits, the rectifier fourstep commutation method is employed. A comparative study between the Predictive DTC and ISVM-DTC is performed by simulating these control systems in MATLAB/SIMULINK software environments and the obtained results are presented and verified.

The switched reluctance motor is a singly excited, doubly salient machine which can be used in generation mode by selecting the proper firing angles of the phases. Due to its robustness, it has the potential and the ability to become one the generators to be used in harsh environment. This paper presents an energy conversion by a Switched Reluctance Generator (SRG) when bifilar converter circuit and discrete position sensors are employed. As the generator’s speed increases by a prime mover the shape of current waveform changes in such a way that limits the production of generating voltage. At high speeds, it is possible for the phase current never reaches the desired value to produce enough back-emf for sufficient voltage generation, therefore, the output power falls off. In order to remedy this problem, the phase turn on angle is advanced in a way that the phase commutation begins sooner. Since one of the advantages of this type of generator is its variable speed then, the amount of advancing for the turn on angle should be accomplished automatically to obtain the desired output voltage according to the speed of the generator, meaning, as the generator speed increases so should the turn on angle and vice versa. In this respect, this paper introduces an electronic circuit in conjunction with the position sensors and the drive converter to achieve this task for a desired output voltage when a SRG feeding a resistive load. To evaluate the generator performance, two types of analysis, namely numerical technique and experimental studies have been utilized on a 6 by 4, 30 V, SRG. In the numerical analysis, due to highly non-linear nature of the motor, a three dimensional finite element analysis is employed, whereas in the experimental study, a proto-type generator and its circuitries have been built and tested using bifilar converter. A linear analysis of the current waveform for the generator under different advancements of the turn on angle has been performed numerically and experimentally and the results are presented.

This paper presents a decision making approach for mid-term scheduling of large industrial consumers based on the recently introduced class of Stochastic Dominance (SD)- constrained stochastic programming. In this study, the electricity price in the pool as well as the rate of availability (unavailability) of the generating unit (forced outage rate) is considered as uncertain parameters. The self-scheduling problem is formulated as a stochastic programming problem with SSD constraints by generating appropriate scenarios for pool price and self-generation unit's forced outage rate. Furthermore, while most approaches optimize the cost subject to an assumed demand profile, our method enforces the electricity consumption to follow an optimum profile for mid-term time scheduling, i.e. three months (12 weeks), so that the total production will remain constant.

This paper proposes a method for determining the price bidding strategies of market participants consisting of Generation Companies (GENCOs) and Distribution Companies (DISCOs) in a day-ahead electricity market, while taking into consideration the load forecast uncertainty and demand response programs. The proposed algorithm tries to find a Pareto optimal point for a risk neutral participant in the market. Because of the complexity of the problem a stochastic method is used. In the proposed method, two approaches are used simultaneously. First approach is Fuzzy Genetic Algorithm for finding the best bidding strategies of market players, and another one is Mont-Carlo Method that models the uncertainty of load in price determining algorithm. It is demonstrated that with considering transmission flow constraints in the problem, load uncertainty can considerably influences the profits of companies and so using the second part of the proposed algorithm will be useful in such situation. It is also illustrated when there are no transmission flow constraints, the effect of load uncertainty can be modeled without using a stochastic model. The algorithm is finally tested on an 8 bus system.

This paper proposes a Trust-Region Based Augmented Method (TRALM) to solve a combined Environmental and Economic Power Dispatch (EEPD) problem. The EEPD problem is a multi-objective problem with competing and non-commensurable objectives. The TRALM produces a set of non-dominated Pareto optimal solutions for the problem. Fuzzy set theory is employed to extract a compromise non-dominated solution. The proposed algorithm is applied to the standard IEEE 30 bus six-generator test system. Comparison of TRALM results with the various algorithms, reported in the literature shows that the solutions of the proposed algorithm are very accurate for the EEPD problem.

Induction motor is the most popular load in the industry, it is very important to study about the effects of voltage quality on induction motor performance. One of the most important voltage quality problems in power system is voltage unbalance. This paper evaluates and compares two methods including finite element method (FEM) and equivalent electrical circuit simulation for investigation of the effects of voltage unbalance conditions on the performance of a three- phase induction motor. For this purpose, a threephase squirrel cage induction motor is simulated using Finite Element Method and equivalent electrical circuit parameters of the FEM model is estimated by genetic algorithm. Then, some unbalanced voltages are applied on the FEM model of the Motor and the resulted power and losses are compared with calculated values using equivalent electrical circuit simulation in same voltage conditions.