Majlesi Journal of Electrical EngineeringDetection of Acute Atrial-Ventricular Arrhythmias Based on ECG Delineator: Evaluation on MIT/BIH Standard DatabasesVolume 2 (2008)Issue 1, June 200820240228Detection of Acute Atrial-Ventricular Arrhythmias Based on ECG Delineator: Evaluation on MIT/BIH Standard Databases10.1234/mjee.v2i1.3ENJournal Article20240228AbstractIn this paper we use an efficient arrhythmia algorithm based on wavelet transform. In first step, QRS complexes are detected. Then each QRS is delineated by detecting and identifying the peaks of the individual waves, complex onset and end. Then the determination of P and T wave peaks, onset and ends are performed. Finally the Ventricular flutter, ventricular tachycardia, supra ventricular tachycardia, ventricular fibrillation, atrial fibrillation and atrial flutter that are kinds of acute ventricular-atrial arrhythmias are detected. In the proposed algorithm, we used a second order spline as mother wavelet and improved the previous algorithms proposed by other investigators. We evaluated the algorithm on some manually annotated single ECG signals selected from MIT-BIH arrhythmia databases. This algorithm may achieve the mean detection accuracy of about 80% in these arrhythmias.Majlesi Journal of Electrical EngineeringA Study on Structure, Performance, Fabrication and Application of Micro MotorsVolume 2 (2008)Issue 1, June 200820240228A Study on Structure, Performance, Fabrication and Application of Micro Motors10.1234/mjee.v2i1.38ENJournal Article20240228Nowadays, micro motors have widespread applications in the industry. However, not many references exist about these motors. To remove this problem, a comprehensive review on micro motors is presented in this paper. For this purpose, different type of micro motors discussed and their potential and challenges are addressed. Besides, a comparison between electrostatic and electromagnetic micro motors is performed. Regarding their efficiency, power density, force to dimensions dependency and their advantages and drawbacks are investigated. Finally, the methods of fabrication of some of their applications are expressed.Majlesi Journal of Electrical EngineeringCSM Temper Mill System Identification and Modeling of Mobarake Steel ComplexVolume 2 (2008)Issue 1, June 200820240228CSM Temper Mill System Identification and Modeling of Mobarake Steel Complex10.1234/mjee.v2i1.39ENJournal Article20240228System identification is defined as modeling a system, using the input-output data. In this paper, CSM temper mill line was studied and parametric system identification was explained. Using ARX method, the experimental system was modeled and identified. Good agreement was obtained when comparing extracted model outputs with the experimental data.Majlesi Journal of Electrical EngineeringA New Hybrid Watermarking Algorithm for Images in Frequency DomainVolume 2 (2008)Issue 1, June 200820240228A New Hybrid Watermarking Algorithm for Images in Frequency Domain10.1234/mjee.v2i1.40ENJournal Article20240228In recent years, digital watermarking has become a popular technique for digital images by hiding secret information which can protect the copyright. The goal of this paper is to develop a hybrid watermarking algorithm. This algorithm used DCT coefficient and DWT coefficient to embedding watermark, and the extracting procedure is blind. The proposed approach is robust to a variety of signal distortions, such as JPEG, image cropping and scaling.Majlesi Journal of Electrical EngineeringEEG Pattern Recognition to Diagnose Epilepsy Using Wavelet and Chaos TransformationsVolume 2 (2008)Issue 1, June 200820240228EEG Pattern Recognition to Diagnose Epilepsy Using Wavelet and Chaos Transformations10.1234/mjee.v2i1.41ENJournal Article20240228By the time-frequency transformations like wavelet and chaos theory to find the feature from sub-bands, it is possible to diagnose the epilepsy although there are some noises and signals. To decompose the EEG into sub-bands such as delta, theta, alpha, beta and gamma, wavelet analysis is used. Chaos theory is used to compute standard deviation, correlation dimension and Lyapunov exponent from the sub-bands, then the neuron system and other classifiers, standard deviations and averages are used to increase the diagnosis accuracy of epilepsy for all three groups of normal, ictal, and inter ictal.Results show a fuzzy subtractive clustering in a specific distance including 8 parameters (persistence 96.8% and standard deviation 0.7) and by Ensemble averaging including 6 parameters (persistence 97.5% and standard deviation 0) is better than other methods and proper for clustering epilepsy disease. This statistics is considerable while visual consideration by specialized neurologists isn’t more than 80 percent.Majlesi Journal of Electrical EngineeringSynthesis of a Logic-Based Switching H2/H∞ Controller:A Fuzzy Supervisor ApproachVolume 2 (2008)Issue 1, June 200820240228Synthesis of a Logic-Based Switching H2/H∞ Controller:A Fuzzy Supervisor Approach10.1234/mjee.v2i1.42ENJournal Article20240228In this paper, the synthesis of switching H2/H∞ controller is considered which achieves a minimum bound on the H2 performance level, while satisfying the H∞ performance. The proposed hybrid control scheme is based on a fuzzy supervisor which manages the combination of controllers. A convex formulation of the two controllers leads to a structure which benefits from the advantages of both controllers to ensure a good performance in both the transient phase (H2) and the steady phase (H∞). The stability analysis uses the Lyapunov technique, inspired from switching system theory, to prove that the system with the proposed controller remains globally stable despite the configuration changing. The conservatism introduced by means of the proposed methodology is significantly decreased in comparison to the Lyapunov shaping methods which oblige the designer to employ a common Lyapunov matrix for all the performance criteria and design one controller that satisfy all the objectives. The results of the common H2/H∞ methods can be derived from proposed method easily.Majlesi Journal of Electrical EngineeringNetworked Control System Simulation Methods:A Comparative StudyVolume 2 (2008)Issue 1, June 200820240228Networked Control System Simulation Methods:A Comparative Study10.1234/mjee.v2i1.44ENJournal Article20240228In this paper, we examine several frameworks for NCS simulation: a MATLAB-based package called True Time, the Agent/Plant addition to the ns-2 network simulator, and other MATLAB -based frameworks. We analyze the accuracy, speed, and ease of use of two different methods of simulating system dynamics using the Agent/Plant extension to ns-2. We also introduce a hybrid system model, which we simulate in MATLAB, to verify the simulation of system dynamics in ns-2. We then proceed to use the ns-2 framework and Agent/Plant, using an Euler approximation for the continuous system dynamics, to simulate both the inverted pendulum and pitch control systems on the network topology described in Chapter 2. We examine the performance of these systems as the traffic on the network increases, due to both additional NCSs and non-NCS cross-traffic. We also examine the effects of non-random vs. random sample scheduling, and observe the periodicity of packet loss under both scheduling policies.Majlesi Journal of Electrical EngineeringAutomotic Recognition of Sleep Spindles Based on Two-Stage Classifier with Artificial Neural Networks and Support Vector MachinesVolume 2 (2008)Issue 1, June 200820240228Automotic Recognition of Sleep Spindles Based on Two-Stage Classifier with Artificial Neural Networks and Support Vector Machines10.1234/mjee.v2i1.45ENJournal Article20240228Sleep spindles are one of the most important transient waveforms found in the sleep EEG signal. Here, we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles (SS) in a 19-channel electroencephalographic signal. In the first stage, a pre-processing perception is used for enhancing overall detection and also reducing computation time. In the second stage, the selected Sleep spindles (SS), classified with neural network post-classifier. Classifying tools in post-processing procedure were MLP and RBSVM that their operations are compared in the last section of the report. Visual inspection of 19-channel EEG from six subjects by one expert in this theme, showed that RBSVM operation is better than MLP with BP (Back propagation) training, that SVM provided 91.4% average sensitivity and 3.85% average false detection rate.