Scientia Iranica
Volume:24 Issue: 6, Nove-Dec 2017

This paper proposes and compares three anomaly correction methods in embedded systems: 1) Markov-based, 2) Stide (Sequence Time-Delay Embedding)-based, and 3) Cluster-based correction methods. All these methods work online on data streams coming from sensors of embedded systems. In these methods, detection is first obtained using training on normal data, and next in runtime, the correction mechanisms can be applied. Markov-based method is based on probabilities between states, Stide-based method is based on storing common events and Cluster-based is based on clustering similar members. In detection phase, these methods check normal behavior of input data based on what is learned at train phase. Evaluation are performed using a total of 7000 data sets. The window size and the number of injected anomalies varied between 3 and 5, 1 and 7, respectively. Correction coverage for Markov-based, Stide-based, and Cluster-based methods are on average 77.66%, 60.9%, and 70.36%, respectively. Therefore, Markov-based method is the best in terms of correction coverage. Moreover, area overheads of these methods are 249.64, 63.35 and 2.08 μm2, respectively. As a trade-off, Cluster-based method shows best correction coverage compared to area, power and delay overheads.

Online Social Networks (OSNs) are very popular and users share various information in these networks. To protect these resources from unauthorized accesses, these frameworks must support flexible access control mechanisms. Semantic technology provides new opportunities for this purpose. This paper proposes a Prioritized Ontology. Based Access Control (POBAC) model for protecting user's information in OSNs.......

Cognitive Radio (CR) technology enables Dynamic Spectrum Access (DSA) to ameliorate the eciency of under-utilized licensed bands and overcrowded unlicensed bands. However, providing an acceptable service for cognitive users requires more sophisticated approaches due to the existence of Primary Users (PU) with high priority over licensed bands. Additionally, reducing interference with PUs so that they can communicate without interruption is of paramount importance. In order to meet requirements of users as much as possible and reduce interference with PUs, a new Class-based Multi-path On-demand Routing protocol for Cognitive radio networks (CMORC) is proposed. CMORC is a multipath routing protocol which de nes two classes of routing to make delay or interference optimum. To the best of our knowledge, it is the rst multipath protocol that takes into consideration both route discovery and rate allocation. CMORC fi nds optimum sending rates for each path by solving an optimization problem for each routing class. Since it is shown to be easy to solve these optimization problems, CMORC is an ecient and practical solution for radio equipment with limited hardware. The simulation results reveal that CMORC outperforms the other recent multipath routing protocol, D2CARP, in terms of interference, delay and packet delivery ratio depending on the class type.

This paper proposes a new method for automatic generation of test cases using model based testing. As test model, class and state diagrams are used, and constraints are expressed using OCL. First, the state machine is converted into a mathematical representation in AMPL. Then, using a search algorithm and based upon coverage criteria, the abstract paths are selected from state machine. Second, using symbolic execution, the generated abstract path along with the constraints on this path is converted into the data of generated mathematical model. Third, the generated mathematical problem is solved with solvers that have interface with AMPL, and the test data is produced for each abstract test case. Finally, the generated test data and abstract paths are transformed into executable test cases. All-Transitions and All-States coverage criteria are used for conduct search algorithm, as well as the criteria for evaluate the quality of generated test cases. To validate the work, by utilizing various solvers, the test cases are generated for various problems.
The proposed technique is implemented as a tool, named MoBaTeG. The tool shows good result in terms of test case generation execution time, test goals satisfaction rate, source code instructions coverage, and also boundary values generation.

The optimal design of grid-connected Hybrid Renewable Energy Systems (HRESs) is studied by using multi-objective evolutionary algorithm in this paper. With the total system cost and fuel emissions to be minimized, a two-objective optimization model of the hybrid system is established. Then, a modifi ed preference-inspired co-evolutionary algorithm is, for the first time, applied to fi nd the optimal con guration of a grid-connected hybrid system. As an example, a grid-connected hybrid system, including PV panels, wind turbines, and diesel generators, has been designed and good results are obtained which show that the proposed method is effective.

The main purpose of this paper is the assessment of emotions using Electroencephalogram (EEG) and peripheral physiological signals and improve recognition accuracy of emotional states using combination mechanism. At the first step, according to the type of signals, effective features were extracted in the time and frequency domains; then, by using the Fisher’s Linear Discriminant (FLD) method, the most effective features were selected. Based on these features, six classifiers were used: Support Vector Machine (SVM), Nearest Mean (NM), K-Nearest Neighborhood (K-NN), 1-Nearest Neighborhood (1-NN), FLD and Linear Discriminant Analysis (LDA). They classified emotions in two classes (low and high) through arousal, valence and liking dimensions. The leave-one-out cross-validation (LOOCV) method has been implemented to evaluate the performance of classifiers. To enhance the accuracy of classification, combination at feature and classifier levels were performed. Via the concatenation method, combination at feature level was done. Then, by Majority voting, Fixed and Stacking algorithms, combination at classifier level were implemented. Results showed that these classifiers were selected properly and comparing with previous works, good improvements were achieved by them. Finally, by using combination methods, obtained recognition accuracy was much more reliable and combination at classifier level resulted in significant improvement

The Portable Neuromodulation Stimulator (PoNS) is a compact, self-contained device that delivers a fixed sequence of dc-balanced voltage pulses to the anterior-dorsal tongue through a matrix of 143 gold-plated electrodes. This form of stimulation is being investigated as a possible aid to rehabilitation of motor, cognitive, and emotional symptoms resulting from a range of neurological disorders of traumatic, degenerative, or developmental origin. This article provides a technical overview of the PoNS device as well as a summary of applications research to date.

In this paper, we propose a new high accuracy method based on non-polynomial spline for the numerical solution of two-dimensional elliptic partial di erential equations. Using a non-polynomial spline approximation in the xdirection and central di erence in the y- direction, we obtain a new nine-point compact finite di erence formulation. A four point group explicit (GE) iterative scheme with an acceleration tool is then applied to the obtained system. The formulation procedure is presented in detail. The eciency of the proposed method is then illustrated by some test problems. The numerical results are found to be in good agreement with the exact solutions

An active inter-phase reactor（AIPR）is often employed to inject triangle current to improve the input current quality of double star diode rectifier. Due to the existed leakage inductance of transformer, the input current harmonics suppression ability of the injected triangle current would be weaken. In this paper, the current commutation process of the double star diode rectifier with AIPR is analyzed initially. Then, according to the relation between the input current and output current of the double star transformer, the relation between the leakage inductance and the input line current of double star diode rectifier with AIPR is established. Such factors like the input current THD, the input current lag angle and power factor of the double star diode rectifier with AIPR, their relations with the leakage inductance are also obtained. The theoretical analysis demonstrate that the leakage inductance increase the input current THD and lag angle. It indicates that the leakage inductance decrease the displacement factor and power factor. To ensure the input current THD is less than 5% and the power factor is more than 0.998, the leakage inductance factor KLs should be less than 0.22. Simulation results verify the theoretical analysis.

In this study the potential of wind energy in Osmaniye Region has been analysed statistically in the basis of information that has been measured hourly between the years of 2009-2013. The two parameter Weibull Distribution Function is generally applied to evaluate potential of wind energy at any region. This study introduced the evaluation of Weibull Distribution Function parameters which are obtained by different kinds of numerical methods, namely Graphical Method (GM), Moment Method (MM), Energy Pattern Method (EPM), Energy Trend Method (ETM) and Maximum Likelihood Method (MLM). The Relative Percentage Error (RPE) statistical test is used to compare the efficiency of all used methods. The calculated power density of all used numerical methods is a major key issue for suitability use of wind energy. The evaluation of Weibull parameters and wind power distribution play a crucial role in producing electricity from wind power. The results of the used methods are compared and the obtained pre-research results show that the wind energy potential in Osmaniye Region is statistically suitable for electricity production.

The introduction of the unique technologies of development of solar energy (SE) actual energy, economic and environmental problems. The main obstacles are the instability and distraction SE, low technical and economic indicators of the known systems and installations for SE. Existing combined systems partially eliminate these drawbacks. For their wide distribution requires a fundamental modernization, taking into account the peculiarities of technological processes. It is proposed to develop a new energy-saving technology and an integrated system for livestock farms. The new system performs these functions - it recycles heat organize their movement and accumulation, smooth’s out the uneven SE. The main components of the system are: solar power plant (SPP), milk cooler, climate unit, heat pump (HP), the battery heat, automatic control system and device heating and hot water. The main goal - lower cost of the energy produced and the elimination of the uneven SE, compared to known SPP, is achieved through the flow of energy from the sources mentioned above.

In this study the effect of parametric variatıon of the pole-arc offset distance on the performance of a permanent-magnet synchronous generator (PMSG) used in wind energy applications has been investigated. Fourier analyses of the voltage wave-shapes that are obtained by the parametric variation of the pole-arc offset value have been made. The THD is determined for each voltage wave-shape. As a result of optimization study the efficiency and the output power have been kept approximately constant. The THD of the output voltage has been decreased

This paper presents a new analytical solution method for Supply Function Equilibrium (SFE)-based bidding strategy in electricity markets. It is assumed that every generation company (GenCo) has some generation units and bids a linear supply function (LSF) for each of its units to the independent system operator (ISO). The problem is modeled as a bi-level optimization problem; in inner level, ISO clears the market to maximize social welfare, and in outer level, each GenCo tries to maximize its individual welfare. The proposed method is used to solve the outer level problem using an iterative algorithm, in which LSF coefficients are parameterized. The formulation is developed for both elastic and non-elastic demands for single and multi-generator cases and also for integrated energy and spinning reserve (SR) markets with three models of competitions: a-, b- and k-parameterization. Three sample networks are used to evaluate the proposed method. The results show that the proposed method is effective, and accurate for GenCos’ strategic bidding in electricity markets compared with other optimization algorithms.

Edge detection in medical imaging is a significant task for object recognition of human organs and it is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an efficient approach based on generalized Hill entropy to find a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: firstly, a global threshold is calculated by means of generalized Hill entropy and used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The final edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared against Canny and Tsallis entropy by using sets of medical images corrupted with various types of noise. We used Pratt’s Figure of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicate that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four different types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images.

Asymmetry and symmetry coexist in natural and human processes and the interaction of asymmetric action (recursion) and symmetric opposition (sinusoidal waves) are instrumental in generating creative features. Autism Spectrum Disorder (ASD) is a disorder in which asymmetry and functionality of brain hemispheres are affected. In this study, difference in brain asymmetry in ASD and normal children and the effect of voice on asymmetry are being investigated. Because of Abnormal cortical voice processing in ASD data recording is done in two situations: animation with audio (V–A) for 5 minutes, and watching the animation with muted audio band (VwA). Two Indexes Divergence (D) and number of poincaré section points further from threshold (HD) as new biomarkers are being extracted. Hemispheric asymmetry in ASD children does not follow norm patterns and in all statistical tests H and HD indexes confirm a disorder in hemisphere’s functionality which can be globally unveiled with poincaré section and extracted information. Two remarkable features of presented method are: data recording protocol specialized for ASD children and new practical time series analysis for detecting episodic patterns (complexes) as hallmark of ASD dynamic and arrangement as an empirical measure of nonrandom complexity. Presented method could detect this complexes.

The Generalized Cross Correlation (GCC) framework is one of the most widely used methods for Time Difference of Arrival (TDOA) estimation and Sound Source Localization (SSL). TDOA estimation using cross correlation without any pre-filtering of the received signals has a large amount of errors in real environments. Thus, several filters (weighting functions) have been proposed in the literature to improve the performance of TDOA estimation. These functions aim to mitigate TDOA estimation error in noisy and reverberant environments. Most of these methods consider the noise or reverberation and as one of them increases, TDOA estimation error increases. In this paper, we proposed a new weighting function. This function is a combined and modified version of Maximum Likelihood (ML) and PHAT-rg functions. We named our proposed function as Modified Maximum Likelihood with Coherence (MMLC). This function has merits of both the ML and PHAT-rg functions and can work properly in both noisy and reverberant environments. We evaluate our proposed weighting function using real and synthesized datasets. Simulation results shows that our proposed filter has better performance in terms of TDOA estimation error and anomalous estimations

In this paper, a voltage-mode full-wave rectifier circuit is proposed. The proposed full-wave rectifier circuit consists of only a single negative-type second-generation current conveyor, two diodes and two matched resistors. The proposed circuit without requiring any external bias voltages and currents has a simple structure using a minimum number of active and passive components. It is implemented with AMS 0.35 µm technology operating with ± 1.65 V. Computer simulation and experimental results are included to verify the theory.