Iranian Journal of Electrical and Electronic Engineering

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In an environment such as underwater channel where placing test equipments are difficult to handle, it is much practical to have hardware simulators to examine suitably designed transceivers (transmitter/receiver). The simulators of this kind will then allow researchers to observe their intentions and carry out repetitive tests to find suitable digital coding/decoding algorithms. In this paper, a simplified shallow water digital data transmission system is first introduced. The transmission channel considered here is a stochastic DSP hardware model in which signal degradations leads to a severe distortion in phase and amplitude (fades) across the bandwidth of the received signal. A computer base-band channel model with frequency non-selective feature is derived by the authors [10-11]. This system was based on fullraised cosine channel modelling and proved to be the most suitable for vertical and shortrange underwater communication csdfher), with a reflected path (specula component, when the acoustic hydrophone receives reflected signals from surface and bottom of the sea) and a random path (diffused component, when the acoustic hydrophone receives scattered signals from the volume of the sea). The model assumed perfect transmitter-receiver synchronization but utilized realistic channel time delays, and demonstrated the timevarying characteristics of an underwater acoustic channel observed in practice. In this paper, they are used to provide a full system simulation in order to design an adaptive receiver employing the most advanced digital signal processing techniques in hardware to predict realizable error performances.

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A robust skin detector is the primary need of many fields of computer vision, including face detection, gesture recognition, and pornography filtering. Less than 10 years ago, the first paper on automatic pornography filtering was published. Since then, different researchers claim different color spaces to be the best choice for skin detection in pornography filtering. Unfortunately, no comprehensive work is performed on evaluating different color spaces and their performance for detecting naked persons. As such, researchers usualy refer to the results of skin detection based on the work doen for face detection, which underlies different imaging conditions. In this paper, we examine 21 color spaces in all their possible representations for pixel-based skin detection in pornographic images. Consequently, this paper holds a large investigation in the field of skin detection, and a specific run on the pornographic images.

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This paper presents a parametric low differential order model, suitable for mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman filter is proposed for recursively estimating the states and parameters of continuous–time model with discrete measurements for fault detection ends. Typical motor faults as interturn short circuit and increased winding resistance are taken into account. The models are validated against winding function induction motor modeling which is well known in machine modeling field. The validation shows very good agreement between proposed method simulations and winding function method, for short-turn stator fault detection.

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A detector for the case of a radar target with known Doppler and unknown complex amplitude in complex Gaussian noise with unknown parameters has been derived. The detector assumes that the noise is an Auto-Regressive (AR) process with Gaussian autocorrelation function which is a suitable model for ground clutter in most scenarios involving airborne radars. The detector estimates the unknown parameters by Maximum Likelihood (ML) estimation for the use in the Generalized Likelihood Ratio Test (GLRT). By computer simulations, it has been shown that for large data records, this detector is Constant False Alarm Rate (CFAR) with respect to AR model driving noise variance. Also, measurements show the detector excellent performance in a practical setting. The detector’s performance in various simulated and actual conditions and the result of comparison with Kelly’s GLR and AR-GLR detectors are also presented.

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In order to detect targets upon sea surface or near it, marine radars should be capable of distinguishing signals of target reflections from the sea clutter. Our proposed method in this paper relates to detection of dissimilar marine targets in an inhomogeneous environment with clutter and non-stationary noises, and is based on adaptive thresholding determination methods. The variance and the mean values of the noise level have been estimated in this paper, based on non-stationary, statistical methods and thresholding has been carried out using the suggested two-pole recursive filter. Making the rate of false alarm constant, the concerned threshold resolves the hypothesis of existence or absence of the target signal. Performance of the mentioned algorithm has been compared with the well-known conventional method as CA-CFAR in terms of decreasing the losses and increasing calculation speed. The algorithm provided for detection of signal has been implemented as a part of signal-processing algorithms of some practical marine radar. The results obtained from the algorithm performance in a real environment indicate appropriate workability of this method in heterogeneous environment and non-stationary interference.

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In this study, a new long-duration holter electrocardiogram (ECG) major events detection-delineation algorithm is described which operates based on the false-alarm error bounded segmentation of a decision statistic with simple mathematical origin. To meet this end, first three-lead holter data is pre-processed by implementation of an appropriate bandpass finite-duration impulse response (FIR) filter and also by calculation of the Euclidean norm between corresponding samples of three leads. Then, a trous discrete wavelet transform (DWT) is applied to the resulted norm and an unscented synthetic measure is calculated between some obtained dyadic scales to magnify the effects of low-power waves such as P or T-waves during occurrence of arrhythmia(s). Afterwards, a uniform length window is slid sample to sample on the synthetic scale and in each slid, six features namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second order differentiation, curve length, area and variance of the excerpted segment are calculated. Then all feature trends are normalized and superimposed to yield the newly defined multiple-order derivative wavelet based measure (MDWM) for the detection and delineation of ECG events. In the next step, a α-level Neyman-Pearson classifier (which is a false-alarm probability-FAP controlled tester) is implemented to detect and delineate QRS complexes. To show advantages of the presented method, it is applied to MIT-BIH Arrhythmia Database, QT Database, and T-Wave Alternans Database and as a result, the average values of sensitivity and positive predictivity Se = 99.96% and P+ = 99.96% are obtained for the detection of QRS complexes, with the average maximum delineation error of 5.7 msec, 3.8 msec and 6.1 msec for P-wave, QRS complex and T-wave, respectively showing marginal improvement of detection-delineation performance. In the next step, the proposed method is applied to DAY hospital high resolution holter data (more than 1,500,000 beats including Bundle Branch Blocks-BBB, Premature Ventricular Complex-PVC and Premature Atrial Complex-PAC) and average values of Se=99.98% and P+=99.97% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process in a widespread values of signal to noise ratio (SNR), reliable robustness against strong noise, artifacts and probable severe arrhythmia(s) of high resolution holter data and the processing speed 163,000 samples/sec can be mentioned as important merits and capabilities of the proposed algorithm.

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Chaos based communications have drawn increasing attention over the past years. Chaotic signals are derived from non-linear dynamic systems. They are aperiodic, broadband and deterministic signals that appear random in the time domain. Because of these properties, chaotic signals have been proposed to generate spreading sequences for wide-band secure communication recently. Like conventional DS-CDMA systems, chaos-based CDMA systems suffer from multi-user interference (MUI) due to other users transmitting in the cell. In this paper, we propose a novel method based on radial basis function (RBF) for both blind and non-blind multiuser detection in chaos-based DS-CDMA systems. We also propose a new method for optimizing generation of binary chaotic sequences using Genetic Algorithm. Simulation results show that our proposed nonlinear receiver with optimized chaotic sequences outperforms in comparison to other conventional detectors such as a single-user detector, decorrelating detector and minimum mean square error detector, particularly for under-loaded CDMA condition, which the number of active users is less than processing gain.

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Abstract: This paper proposes a novel scheme for multi-static passive radar processing, based on soft-input soft-output processing and Bayesian sparse estimation. In this scheme, each receiver estimates the probability of target presence based on its received signal and the prior information received from a central processor. The resulting posterior target probabilities are transmitted to the central processor, where they are combined, to be sent back to the receiver nodes or used for decision making. The performance of this iterative Bayesian algorithm comes close to the optimal multi-input multi-output (MIMO) radar joint processing, although its complexity and throughput are much less than MIMO radar. Also, this architecture provides a tradeoff between bandwidth and performance of the system. The Bayesian target detection algorithm utilized in the receivers is an iterative sparse estimation algorithm named Approximate Message Passing (AMP), adapted to SISO processing for passive radar. This algorithm is similar to the state of the art greedy sparse estimation algorithms, but its performance is asymptotically equivalent to the more complex l1-optimization. AMP is rewritten in this paper in a new form, which could be used with MMSE initial filtering with reduced computational complexity. Simulations show that if the proposed architecture and algorithm are used in conjunction with LMMSE initial estimation, results comparable to jointly processed basis pursuit denoising are achieved. Moreover, unlike CoSaMP, this algorithm does not rely on an initial estimate of the number of targets.

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Islanded operation of distributed generators is a problem that can take place when they are connected to a distribution system. In this paper an islanding detection method is presented for inverter based distributed generation (DG) using under/over voltage relay. The method is an adaptive one and is based on the change of DG active power reference (Pref) in inverter control interface. The active power reference has a fixed value in normal condition, whereas, if the point of common coupling (PCC) voltage changes, Pref has determined as a linear function of voltage. The slope of Pref is dependent to the load active power (Pload) and should be changed if Pload changes. The non-detection zone (NDZ) of the proposed method is dependent on the accuracy of the voltage measurement equipment if changing of the PCC voltage is sensed, then, islanding will be detected if it is occurred. Also it does not have any negative effects on the distribution system in normal conditions. Moreover, the proposed technique can be applied when two-DG is in the island. The proposed method is evaluated according to the requirements of the IEEE 1547 and UL 1741 standards, using PSCAD/EMTDC software.

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Identification of intentional and unintentional islanding situations of dispersed generators (DGs) is one of the most important protection concerns in power systems. Considering safety and reliability problems of distribution networks, an exact diagnosis index is required to discriminate the loss of the main network from the existing parallel operation. Hence, this paper introduces a new islanding detection method for synchronous machine–based DGs. This method uses the average value of the generator frequency to calculate a new detection index. The proposed method is an effective supplement of the over/under frequency protection (OFP/UFP) system. The analytical equations and simulation results are used to assess the performance of the proposed method under various scenarios such as different types of faults, load changes and capacitor bank switching. To show the effectiveness of the proposed method, it is compared with the performance of both ROCOF and ROCOFOP methods.

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This paper proposes an index for nodal market power detection in power market under locational marginal pricing (LMP). This index is an ex-ante technique to detect the market power. More precisely, this criterion detects the potential of exercising market power regardless of detecting the actual market power. Also it is obvious that pricing and market clearing method affect the potential of exercising market power. Different potential of market power exists in different pricing methods. This index has been analyzed under LMP method which seems to be a desirable environment to exercise market power. In LMP method by load growth, in some determined load levels which is called Critical Load Levels (CLLs), locational marginal prices have step change. This step change in locational marginal prices causes step change in revenue and benefit of Gencos. So it is significant to detect the behavior of Gencos in the CLLs. The proposed criterion has been tested on constant system load and CLLs of system.

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Integration of distributed generations (DGs) in power grids is expected to play an essential role in the infrastructure and market of electrical power systems. Microgrids are small energy systems, capable of balancing captive supply and requesting resources to retain stable service within a specific boundary. Microgrids can operate in grid-connected or islanding modes. Effective islanding detection methods are essential for realizing the optimal operation of microgrids. In this paper, a new passive islanding detection method is presented according to the change rate of DG’s voltage over active power index. This technique has been applied on inverter-based and synchronous-based microgrids. The efficiency of the proposed method is verified through a comprehensive set of simulation studies carried out in Matlab/Simulink.

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The doubly fed induction generator (DFIG) is one of the most popular technologies used in wind power systems. With the growing use of DFIGs and increasing power system dependence on them in recent years, protecting of these generators against internal faults is more considered. Loss of excitation (LOE) event is among the most frequent failures in electric generators. However, LOE detection studies heretofore were usually confined to synchronous generators. Common LOE detection methods are based on impedance trajectory which makes the system slow and also prone to interpret a stable power swing (SPS) as a LOE fault. This paper suggests a new method to detect the LOE based on the measured variables from the DFIG terminal. In this combined method for LOE detection, the rate of change of both the terminal voltage and the output reactive power are utilized and for SPS detection, the fast Fourier transform (FFT) analysis of the output instantaneous active power has been used. The performance of the proposed method was evaluated using Matlab/Simulink interface for various power capacities and operating conditions. The results proved the method's quickness, simplicity and security.

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