Iranian Journal of Electrical and Electronic Engineering

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We give a general overview of the state-of-the-art in subspace system identification methods. We have restricted ourselves to the most important ideas and developments since the methods appeared in the late eighties. First, the basis of linear subspace identification are summarized. Different algorithms one finds in literature (Such as N4SID, MOESP, CVA) are discussed and put into a unifying framework. Further, a comparison between subspace identification and prediction error methods is made on the basis of computational complexity and precision of methods by applying them to a glass tube manufacturing process.

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Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of its uses is for the assessment of diagnostic ECG signal processing devices. So the model should have the capability of producing a wide range of ECG signals, with all the nuances that reflect the sickness to which humans are prone, and this would necessarily include variations in heart rate variability (HRV). In this paper we present a comprehensive model for generating such artificial ECG signals. We incorporate into our model the effects of respiratory sinus arrhythmia, Mayer waves and the important very low frequency component in the power spectrum of HRV. We use the new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced using a radial basis function neural network.

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Automatic signal type identification (ASTI) is an important topic for both the civilian and military domains. Most of the proposed identifiers can only recognize a few types of digital signal and usually need high levels of SNRs. This paper presents a new high efficient technique that includes a variety of digital signal types. In this technique, a combination of higher order moments and higher order cumulants (up to eighth) are proposed as the effective features. A hierarchical support vector machine based structure is proposed as the classifier. In order to improve the performance of identifier, a genetic algorithm is used for parameters selection of the classifier. Simulation results show that the proposed technique is able to identify the different types of digital signal (e.g. QAM128, ASK8, and V29) with high accuracy even at low SNRs.

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Because of temperature independence, high resolution and noiseless outputs, brushless resolvers are widely used in high precision control systems. In this paper, at first dynamic performance characteristics of brushless resolver, considering parameters identification are presented. Then a mathematical model based on d-q axis theory is given. This model can be used for studying the dynamic behavior of the resolver and steady state model is obtained by using dynamic model. The main object of this paper is to present an approach to identify electrical and mechanical parameters of a brushless resolver based on DC charge excitation and weight, pulley and belt method, respectively. Finally, the model of resolver based on the obtained parameters is simulated. Experimental results approve the validity of proposed method.

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Dealing with uncertainty is one of the most critical problems in complicated

pattern recognition subjects. In this paper, we modify the structure of a useful Unsupervised

Fuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types of

fuzzy neurons and its associated self organizing supervised learning algorithm. This

improved five-layer feed forward Supervised Fuzzy Neural Network (SFNN) is used for

classification and identification of shifted and distorted training patterns. It is generally

useful for those flexible patterns which are not certainly identifiable upon their features. To

show the identification capability of our proposed network, we used fingerprint, as the most

flexible and varied pattern. After feature extraction of different shapes of fingerprints, the

pattern of these features, “feature-map”, is applied to the network. The network first

fuzzifies 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 its

specific class as a non fuzzy output. To test our FNN, we applied the standard (NIST

database) and our databases (with 176×224 dimensions). The feature-maps of these

fingerprints contain two types of minutiae and three types of singular points, each of them

is represented by 22×28 pixels, which is less than real size and suitable for real time

applications. The feature maps are applied to the FNN as training patterns. Upon its setting

parameters, the network discriminates 3 to 7 subclasses for each main classes assigned to

one of the subjects.

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In this paper, we propose a new chaos-based communication scheme using the observers. The novelty lies in the masking procedure that is employed to hide the confidential information using the chaotic oscillator. We use a combination of the addition and inclusion methods to mask the information. The performance of two observers, the proportional observer (P-observer) and the proportional integral observer (PI-observer) is compared that are employed as receivers for the proposed communication scheme. We show that the P-observer is not suitable scheme since it imposes unpractical constraints on the messages to be transmitted. On the other hand, we show that the PI-observer is the better solution because it allows greater flexibility in choosing the gains of the observer and does not impose any unpractical restrictions on the message.

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During the past few years, a lot of work has been done on behavioral models and simulation tools. But a need for modeling strategy still remains. The VHDL-AMS language supports the description of analog electronic circuits using Ordinary Differential Algebraic Equations (ODAEs), in addition to its support for describing discrete-event systems. For VHDL-AMS to be useful to the analog design community, efficient semiconductor device models must be available. In this paper, potential merits of the new IEEE VHDL-AMS standard in the field of modeling semiconductor devices are discussed. The device models for diodes and the principles, techniques, and methodology used to achieve the design of an analytical third generation Spice transistor MOS model named EKV are presented. This is done by taking into account the thermoelectrical effect in VHDL-AMS, and with relevant parameters set to match a deep submicron technology developed in VHDL-AMS. The models were validated using System Vision from Mentor Graphics.

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Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in applications dealing with images, it is still in its infancy in speech processing. Age classification, on the other hand, is also concerned as a useful tool in different applications, like issuing different permission levels for different aging groups. This paper concentrates on a comparative study of gender and age classification algorithms applied to speech signal. Experimental results are reported for the Danish Emotional Speech database (DES) and English Language Speech Database for Speaker Recognition (ELSDSR). The Bayes classifier using sequential floating forward selection (SFFS) for feature selection, probabilistic Neural Networks (PNNs), support vector machines (SVMs), the K nearest neighbor (K-NN) and Gaussian mixture model (GMM), as different classifiers, are empirically compared in order to determine the best classifier for gender and age classification when speech signal is processed. It is proven that gender classification can be performed with an accuracy of 95% approximately using speech signal either from both genders or male and female separately. The accuracy for age classification is about 88%.

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In this study, a mathematical model is developed based on algebraic equations which is capable of generating artificially normal events of electrocardiogram (ECG) signals such as P-wave, QRS complex, and T-wave. This model can also be implemented for the simulation of abnormal phenomena of electrocardiographic signals such as ST-segment episodes (i.e. depression, elevation, and sloped ascending or descending) and repolarization abnormalities such as T-Wave Alternans (TWA). Event parameters such as amplitude, duration, and incidence time in the conventional ECG leads can be a good reflective of heart electrical activity in specific directions. The presented model can also be used for the simulation of ECG signals on torso plane or limb leads. To meet this end, the amplitude of events in each of the 15-lead ECG waveforms of 80 normal subjects at MIT-BIH Database (www.physionet.org) are derived and recorded. Various statistical analyses such as amplitude mean value, variance and confidence intervals calculations, Anderson-Darling normality test, and Bayesian estimation of events amplitude are then conducted. Heart Rate Variability (HRV) model has also been incorporated to this model with HF/LF and VLF/LF waves power ratios. Eventually, in order to demonstrate the suitable flexibility of the presented model in simulation of ECG signals, fascicular ventricular tachycardia (left septal ventricular tachycardia), rate dependent conduction block (Aberration), and acute Q-wave infarctions of inferior and anterior-lateral walls are finally simulated. The open-source simulation code of above abnormalities will be freely available.

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Artificial neural network (ANN) has application in communication engineering in diverse areas such as channel equalization, channel modeling, error control code because of its capability of nonlinear processing, adaptability, and parallel processing. On the other hand, wavelet transform (WT) with both the time and the frequency resolution provides the exact representation of signal in both domains. Applying these signal processing tools for channel compensation and noise reduction can provide an enhanced performance compared to the traditional tools. In this paper, the slot error rate (SER) performance of digital pulse interval modulation (DPIM) in diffuse indoor optical wireless (OW) links subjected to the artificial light interference (ALI) is reported with new receiver structure based on the discrete WT (DWT) and ANN. Simulation results show that the DWT-ANN based receiver is very effective in reducing the effect of multipath induced inter-symbol interference (ISI) and ALI.

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An adaptive online flux-linkage estimation method for the sensorless control of switched reluctance motor (SRM) drive is presented in this paper. Sensorless operation is achieved through a binary observer based algorithm. In order to avoid using the look up tables of motor characteristics, which makes the system, depends on motor parameters, an adaptive identification algorithm is used to estimate of the nonlinear flux-linkage parameters. This method makes position and speed estimation more accurate and robust towards any model uncertainty, also it is suitable replacement for a priori knowledge of motor characteristics.

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A switched Reluctance motor (SRM) has several desirable features, including simple construction, high reliability and low cost. However, it suffers from large torque ripple, highly non-uniform torque output and magnetization characteristics and large noise. Several studies have succeeded in torque ripple reduction for SRM using Direct Torque Control (DTC) technique. DTC method has many advantages over conventional voltage control and current chopping mode control such as simple algorithm, less torque ripple and instantaneous response to the torque command. In this paper, DTC method is proposed for a 5-phase 10/8 SRM. The performance of the motor is demonstrated through the computer simulation in Mtalab/Simulink. Then, the obtained results are verified by comparison with the corresponding results of a 3-phase 6/4 motor performance.

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The aim of this study is to address a new feature extraction method in the area of the heart arrhythmia classification based on a metric with simple mathematical calculation called Curve-Length Method (CLM). In the presented method, curve length of the under study excerpted segment of signal is considered as an informative feature in which the effect of important geometric parameters of the original signal can be found. To show merits of the presented method, first the original electrocardiogram (ECG) in lead I is pre-processed by removing its baseline wander then by scaling it in the [-1,1] interval. In the next step, using a trous method, discrete wavelet scales 23 and 24 and smoothing function scale 22 are extracted. Afterwards, segments including samples of the QRS complex, P and T waves are estimated via an approximation criterion and CLM is implemented to extract corresponding features from aforementioned scales, smoothing function and also from each original segment. The resulted feature vector (including 12 components) is used to tune an Adaptive Network Fuzzy Inference System (ANFIS) classifier. The presented strategy is applied to classify four categories found in the MIT-BIH Arrhythmia Database namely as Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC) and average values of Se = 99.81%, P+ = 99.80%, Sp = 99.81% and Acc = 99.72% are obtained for sensitivity, positive predictivity, specifity and accuracy respectively showing marginal improvement of the heart arrhythmia classification performance.

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The paper addresses a new QRS complex geometrical feature extraction technique as well as its application for electrocardiogram (ECG) supervised hybrid (fusion) beat-type classification. To this end, after detection and delineation of the major events of ECG signal via a robust algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of three Multi Layer Perceptron-Back Propagation (MLP-BP) neural networks with different topologies and one Adaptive Network Fuzzy Inference System (ANFIS) were designed and implemented. To show the merit of the new proposed algorithm, it was applied to all MIT-BIH Arrhythmia Database records and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.27% was obtained. Also, the proposed method was applied to 8 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, VE, PB, VF) belonging to 19 number of the aforementioned database and the average value of Acc=98.08% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer-reviewed studies in this area.

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In this paper, novel hybrid MOSFET(HMOS) structure has been proposed to reduce the gate leakage current drastically. This novel hybrid MOSFET (HMOS) uses source/drain-to-gate non-overlap region in combination with high-K layer/interfacial oxide as gate stack. The extended S/D in the non-overlap region is induced by fringing gate electric field through the high-k dielectric spacer. The gate leakage behaviour of HMOS has been investigated with the help of compact analytical model and Sentaurus Simulation. The results so obtained show good agreement between model and simulation data. It is found that HMOS structure has reduced the gate leakage current to great extent as compared to conventional overlapped MOSFET structure. Further, the proposed structure had demonstrated improved on current, off current, subthreshold slope and DIBL characteristic.

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Multicamera vehicle tracking is a necessary part of any video-based intelligent transportation system for extracting different traffic parameters such as link travel times and origin/destination counts. In many applications, it is needed to locate traffic cameras disjoint from each other to cover a wide area. This paper presents a method for tracking moving vehicles in such camera networks. The proposed method introduces a new method for handling inter-object occlusions as the most challenging part of the single camera tracking phase. This approach is based on coding the silhouette of moving objects before and after occlusion and separating occluded vehicles by computing the longest common substring of the related chain codes. In addition, to improve the accuracy of the tracking method in the multicamera phase, a new feature based on the relationships among surrounding vehicles is formulated. The proposed feature can efficiently improve the efficiency of the appearance (or space-time) features when they cannot discriminate between correspondent and non-correspondent vehicles due to noise or dynamic condition of traffic scenes. A graph-based approach is then used to track vehicles in the camera network. Experimental results show the efficiency of the proposed methods.

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The effect of optical illumination on DC and dynamic performance of Si1-xGex based double drift region (DDR) (p+pnn+) IMPATT diode operating at W-Band is investigated and compared with its Silicon counterpart. Top Mounted (TM) and Flip Chip (FC) structures are chosen and the composition of photocurrent is altered by shining light on the p+ side and n+ side of the device through optical windows. A double iterative computer simulation method based on drift-diffusion model has been used to study the small signal performance and subsequent modification of the small signal parameters owing to optical illumination. The role of leakage current in controlling the dynamic properties is studied by varying the current multiplication factors for electrons (Mn) and for holes (Mp). It is observed that both the DC and small signal parameters of both the diodes are affected significantly due to optical illumination. Under optical illumination of the device, the frequency shift is observed to be more upwards upon lowering of Mn than lowering of Mp for both the diodes. The frequency chirping in both Si1-xGex and Si IMPATTs are found to be of the order of few GHz, thereby indicating their high photo-sensitiveness at W-Band. But the results significantly indicates that photo-sensitiveness of Si1-xGex IMPATT is much greater than the Si IMPATT which is one of the major findings of this work.

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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: Using robot manipulators for high accuracy applications require precise value of the kinematics parameters. Since measurement of kinematics parameters are usually associated with errors and accurate measurement of them is an expensive task, automatic calibration of robot link parameters makes the task of kinematics parameters determination much easier. In this paper a simple and easy to use algorithm is introduced for correction and calibration of robot kinematics parameters. Actually at several end-effecter positions, the joint variables are measured simultaneously. This information is then used in two different algorithms least square (LS) and Genetic algorithm (GA) for automatic calibration and correction of the kinematics parameters. This process was also tested experimentally via a three degree of freedom manipulator which is actually used as a coordinate measuring machine (CMM). The experimental Results prove that the Genetic algorithms are better for both parameter identification and calibration of link parameters.

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In recent years chaotic secure communication and chaos synchronization have received ever increasing attention. Unfortunately, despite the advantages of chaotic systems, Such as, noise-like correlation, easy hardware implementation, multitude of chaotic modes, flexible control of their dynamics, chaotic self-synchronization phenomena and potential communication confidence due to the very dynamic properties of chaotic nonlinear systems, the performance of most of such designs is not studied and so is not still suitable for wireless channels. To overcome this problem, in this paper a novel wide-band chaos-based communication scheme in multipath fading channels is presented, where the chaotic synchronization is implemented by particle filter observer. To illustrate the effectiveness of the proposed scheme, numerical simulations based on particle filter are presented in different channel conditions and the results are compared with two other EKF and UKF based communication scheme. Simulation results show the Remarkable BER performance of the proposed particle filter-based system in both AWGN and multipath fading channels condition, causes this idea act as a good candidate for asynchronous wide band communication.