TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Year:2020 | Volume:50 | Issue:2 (92)|Papers Count:41

In this paper, fault-tolerant controller design problem for Lipschitz nonlinear fractional-order systems in presence of sensor fault is considered. By using the descriptor system theory, a correct estimation of the state vector is achieved and then by using it, a stabilizing state feedback controller is designed. By employing some appropriate techniques, parameters design of the observer and controller are stated in terms of linear matrix inequalities, which there exist powerful toolbox for solving them. The proposed observer can estimate the correct value of the sensor fault vector, thus, it can be utilized for the fault detection, isolation, and identification unit. Besides, the structure of the proposed method is such that the observer and controller design can be performed independently, which facilitate the design process. The effectiveness of the proposed method is shown with numerical simulation results.

The present study aimed at scheming a novel method of detecting target and non-target signals through selection of appropriate and timely chronic intervals of VEP signal leading to increasing the accuracy of data classification and decreasing the number of features. The suggested method was employed on the P300-Speller databases of the BCI2005 competitions and the data recorded by Hoffman et al. using effective and specified channels and SWLDA classifier. The methods available for determining the P300 signals are within a specified range of about 1 second after each stimulation. To this end, we first outlined the time range of the various components of visual Evoked potential including N20, P50, N100, N170, P300, N400 based on the results obtained from the physiologically-based articles. Then, the time intervals were scored by F-Score and the percentages of correct classifications. The most important and effective components of the VEP were selected by SFS Algorithm using the SWLDA classifier and the functions of the optimal combinations were compared with the total length of the signal utilizing two other classifiers namely Bayesand K_NN in order to confirm the functionality of the method. The findings, based on the results obtained from ten subjects, indicated that the most important components for detecting target and non-target signals include P300, N100, and N400 respectively. The method suggested here proved to improve the accuracy of output detection by 3. 95%.

This paper deals with a new strategy for design of analog multiplier which operates in four quadrant. The proposed circuit employs the dual translinear principle with composition of NMOS and PMOS transistors, simultaneously. The important achievements of the circuit are its wide bandwidth, low power consumption as well as the body effect free performance. In order to demonstrate efficiency of the circuit, harmonic distortion analysis due to the mismatch in input signals (IX, IY), transconductance parameters (K) and threshold voltage (Vth) are discussed in detail, and the obtained relations verify the simulation results and indicate the robustness of the circuit in the presence of mismatch. Also, to validate the circuit performance, it is used in two useful applications of amplitude modulator and frequency doubler, and the simulation results of them are presented. Additionally, the Monte Carlo analysis for transistor parameters as well as the temperature variation results demonstrate stable performance of the circuit. This circuit is designed and simulated using HSPICE software with TSMC and level 49 parameters (BSIM3v3) in 180nm technology. The simulation results demonstrate a linearity error of 0. 69%, a THD of 1. 05% in 1MHz, a-3dB bandwidth of 1. 17GHz and a maximum power consumption of 93μ W.

In this paper, an algorithm for bearings-only target tracking (BOT) problem is proposed using the resetting of the covariance matrix of the filter. Poor observability in BOT problem often leads to a bias error or even divergence of the filter. Therefore, due to non-uniqueness of the problem, the covariance value of the filter is not a sufficient condition to indicate the estimation error. In this paper, it is shown that the mutual effects between the covariance of the target range estimation and the Cramer-Rao lower error bound is similar to the stability of a Lyapunov function for detecting the divergence of the recursive filter. Then, this function is used as a correction coefficient of the estimating covariance matching. The multi-model/particle filters are also adopted for estimating the initial range of the target and calculating the Cramer-Rao lower error bound. Using the Monte Carlo simulation method, the proposed algorithm is compared with other conventional filters based on the criteria reported in the literature. Improvement of the results of the target range estimation is also shown, especially in case of observability reduction. All studies conducted in this paper are limited to the problem of surface floats tracking with passive sonar for a typical submarine.

In this paper, a K band microwave sensor is simulated to monitoring of turbine blade and is optimized in CST software and And is embedded in the turbine shell and if any change in the tip of the blade or displacement at the tip clearance, the scattering parameters of this sensor is changed and the scattering parameter obtained from the sensor mounted on the crust is defined as the turbine blade fingerprint. In this paper, the measurements indices based on scattering parameters of the near field of microwave sensor as a blade tip failure detector system as well as k-NN classification algorithm for interpreting measurable scattering parameters to determine the failure amount as a new method for monitoring of turbine blade is presented. The advantage of this method is online monitoring of turbine blades with fully extracting the measuring indices due to the scattering parameters of a sample blade and It has been shown that the k-NN classification method has an acceptable accuracy in identifying and determining the amount of tip clearance and the deformation of the blade because the error rate can be reached below 1. 8% in this way.

In order to monitor the particulates by optical sensor, the amount of absorbed light by particles should be measured simply and cheaply. In this research, an automated, nondestructive system is designed and fabricated using multi-wavelength method that can measure absorption spectrum of solid particles. Calibration of proposed system was also carried out by reference spectrometer and the correction coefficients were obtained. Due to scarcity of data in resulted spectrograph, Hermit interpolation was applied to improve graph resolution. Moreover, instead of utilizing different light sources with different spectrum which is currently common in this field, a wide range of light source was used with various optical filters. The proposed system can measure absorption spectrum of materials in the range of 250nm<𝜆 <1000nm with the accuracy of 1 nm. The absorption spectrum of three different samples were measured that the results show the similarity with the reference system. Comparing this high accurate optical sensing method with other similar methods reveals that the proposed system is cost-effective and can be utilized as a portable absorption analyzer.

In this paper, a digital pseudo-differential Schmitt trigger with very low supply voltage is proposed. The proposed Schmitt trigger is design by exploiting the design procedure of an analog Schmitt trigger. It is implemented only using digital CMOS inverters which is composed of a pseudo-differential comparator consisting of two CMOS inverters and a cross-coupled inverter pair as positive feedback. The proposed circuit is the only digital Schmitt trigger which operates in fully-differential mode that has simultaneously two outputs of non-inverting and inverting and its hysteresis center can be changed by the input voltage. The proposed Schmitt trigger is simulated in 0. 18 um CMOS process with 0. 4 V supply voltage. Due to the very low number of transistors, the proposed circuit occupies 10×9. 8 μ m2 chip area and consumes only 6. 64 nW power.

Star based navigation could be a good alternative for GPS. However there are some limitations. The most important problem is stars detection in daytime. In the near infrared region, reduction of solar radiation is noticeable, hence, radiance of sky (which is sunlight scattering in the sky) noticeably decreases in this spectral window. This region was covered by visible CCD so feasibility study of stars detection in daytime in near infrared can significantly reduce costs in comparison common shortwave infrared systems. . In this paper, sky transmittance is calculated for different wavelengths and sun zenith angles. According to a given model, the intensity of stars light is simulated in near-infrared and evaluated the components of the optical system until a model is presented about stars detection in daytime for the first time. Magnitude of some stars such as Betelgeuse, Vega and Mirach are simulated by the presented model. Also observed magnitude through three optical systems simulated and Ability to detect of the mentioned stars are confirmed. Finally, experimental data are measured by using appropriate optical equipment and servo motor (for star tracking in sky) that these data confirm result of presented model.

High power density of switched-capacitor converters (SCCs) is due to absent of inductive components. But, this subject leads to output voltage regulation problem and hard switching characteristics due to high current spikes, which increase switching losses and noise and limit the power rating. Although, performances of some SCCs have been improved recently by including some small inductors in their topologies, but, only few of them can regulate output voltage and have soft switching characteristics. Here, by combining an active clamp and a boost SCC, both output voltage regulation and soft switching characteristics are achieved, even when wide input voltage and load variations are applied. So, switching frequency can be increased to reduce inductors and capacitors values, due to low switching losses and noise. It reduces the converter volume and also improves its performance, because small inductors and capacitors have better high-frequency characteristics, practically. Therefore, the modified converter is practically much better than the conventional configuration, especially at high power ratings. Here, the modified converter has been analyzed, mathematically, and to verify the given analyses, it has been simulated and implemented at 200kHz for regulating a 400V output voltage, even when wide input voltage (50-100V) and load (400Ω-8kΩ ) variations are applied.

Recommender systems has shown as effective tools that are proposed for helping users to select their interested items. Collaborative filtering is a well-known and frequently used recommender system applied successfully in many e-commerce websites. However, these systems have poor performance while facing cold-start users (items). To address such issues, in this paper, a social regularization method is proposed which combines the social network information of users in a nonnegative matrix factorization framework. The proposed method integrates multiple information sources such as user-item ratings and trust statements to reduce the cold-start and data sparsity issues. Moreover, the alternating direction method is used to improve the convergence speed and reduce the computational cost. We use two well-known datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation methods for recommendation in social networks.

This paper presents performance evaluations for two spectrum-sharing cognitive-radio systems employing cooperative amplify-and-forward relaying in which the best relay is selected for the transmission. In these systems, a secondary user (SU, unlicensed user) shares the spectrum of a primary user (PU, licensed user), in such a way that the interference to the PU is always kept below a predefined threshold. To this end, a power control for the SU is used. In these systems, the SU employs an amplify-and-forward relaying with several relays, where the best relay who has the best channel condition is selected for the transmission. The considered systems are different in the employed relay selection schemes, such that: 1) In the first system, partial relay selection is employed, in which the partial channel state information (CSI) is used to select the best relay. 2) In the second system opportunistic relay selection is employed, in which the full CSI is used to select the best relay. To analyze the system performances, at first the cumulative distribution function (CDF) of the received signal-to-noise-ratio (SNR) for each considered system is derived. Then, using the obtained CDF expressions, closed-form expressions are obtained for three performance metrics: outage probability, the average bit-error-rate (BER), and the average channel capacity. Finally, using the numerical results obtained from the mathematical formulas derived in this paper, we evaluate and compare the systems performances in different cases under different conditions. In addition, to verify the accuracy of the performed analysis, Monte-Carlo simulation results are also provided.

ALU is one of the most sensitive units of a processor, which most of the instructions of a processor are executed by this section. In most of the Architecture methods for mitigation of fault such as temporal redundancy, it is necessary to detect the errors first. Hardware overhead of test circuits is one of the most important disadvantages for testing small circuits which makes designers to use unusual methods in the design of small circuits. In this Article, a new implementation method for Berger code has been used to detect the error and compared with the previous method that is based on the Berger code. In the proposed method, the current mode circuits are employed to reduce the cost of the Berger code implementation. This circuit has higher speed and less hardware complexity than conventional implementations of Berger code. According to the result of this article, the Power of current mode Berger has been reduced at a rate of 51%, and the area of current mode Berger has been reduced at a rate of 74. 3% and also the total cost of the Berger circuit (Power*Delay*Area) has been reduced at a rate of 91%.

Authenticated encryption schemes provide both confidentiality and integrity services, simultaneously. The CAESAR competition is being held with the aim of designing this cipher. An important criterion for selecting the final portfolio, besides security, is the hardware performance of the candidate in the environments with limited resource. In this paper, for the first time for COLM authenticated ciphers from the final round of the CAESAR, an 8-bit lightweight architecture have been presented, which is compatible with API v2. Since COLM scheme uses AES cipher as a primitive, lightweight architecture of Atomic-AES has been selected and adopted according to the API rules. Furthermore, to reduce the area in the hardware implementation, several techniques are used, including implementing one AES core in the datapath, sharing of registers and implementation doubling on the GF (2128) with 8-bit architecture for constructing the higher-order multipliers. Proposed architecture of COLM is implemented on ASIC and FPGA platforms. This architecture is similar in both platforms, but different technology mapping optimization techniques are used for each platform. Comparing the results with 128-bit implementations shows that the area on FPGA and ASIC is reduced by 62% and 74%, respectively. Also, the customized API v2 for 8-bit data width reduced the API area by 8% and 6% on the FPGA and ASIC platforms, respectively.

The heart’ s acoustic signal produced by its mechanical activity can provide useful information on the condition of heart valves. The heart sound auscultation, i. e. listening to the heart sounds with a stethoscope, is therefore a primary method for evaluating the cardiovascular function. This method has advantages of being fast, inexpensive, easy to use and noninvasive. On the other hand, due to the transient and non-stationary nature of PCG signals and auscultatory limitations, the correct medical diagnosis based on the heart sound through a stethoscope requires a lot of expertise and needs referral of the patient to a cardiologist. This is not only time-consuming but also imposes a financial burden on the medical system. Thus, automated detection and analysis of the recorded heart sound auscultation has received a lot of attentions in recent years. Even, this was put to the challenge by the PhysioNet/CinC in 2016. This research is based on the non-stationary nature of PCG signals and proposes a new method based on time– frequency analysis of such signals with the aim to classify heart sounds into normal and abnormal sounds. The proposed methodology uses time-frequency features and two classifiers; AdaBoost and CNN. The publicly available 2016 PhysioNet/CinC 2016 Challenge database was used to evaluate the performance of the proposed method using a leave-one-out cross validation. The experimental results show that the proposed method performs very well and has 1. 5% higher sensitivity compared to the best existing method.

A biosensor for the detection of E. coli O157: H7 bacterium has been designed by using the mean of an organic field effect transistor, and the behavior of the sensor was studied and analyzed under a simulated environment. The design of biosensor includes two essential parts: scheming the structure and choosing the material. In the structure part, an OCMFET configuration, previously used for DNA hybridization, for the first time has been employed for the detection of E. coli with some modifications. In the modified structure, a microfluidic pathway has been added to let the analyte in and out, which makes it possible to detect low volumes of the bacterium in the stream of analyte efficiently and quickly. For the material part, Pentacene, as an organic semiconductor, has been used due to its valuable parameters such as environmental compatibility, cheapness, and easy manufacturing processes. The simulation results are shown 2 minutes after applying the analyte, containing 100 CFU/mL E. coli, 35 bacteria adhere to the active area of the device, leading to a 72 mV shift in the I-V curve. This shift reaches about 174 mV for 86 contained bacteria within 5 minutes which indicates high sensitivity of the sensor. The fast response time, notable sensitivity, biocompatibility, fabricability with current technology, and affordability are among the other benefits of this structure.

Although reducing the size of transistors increased their capabilities considerably, but also resulted in more susceptibility to transient errors. Thus, detecting the sensitive nodes of digital circuits is essential for designing digital circuits at few nanometers. On the other hand, increasing the number of logic gates, transient error types and several error propagation mechanisms in digital circuits, results in considerable complexity in reliability evaluation. Thus, scalability, computational complexity, runtime, accuracy and memory consumption should be taken into account for such evaluation. In this paper, using the concept of the probabilistic signal flow graphs, a new approach is proposed to evaluate the reliability of digital circuits which demonstrates good accuracy, runtime and high level of scalability. Probabilistic evaluation of re-convergent paths, using fixed point theorem and using the sparsity of matrices are features of the proposed approach. Using the proposed approach, the reliability of circuits with feedback and sequential circuits is also evaluated. Simulation results show that the computational complexity of the proposed approach for a combinational and sequential circuit with N nodes is O(N0. 85) and O(N0. 99) respectively.

Triplet-based Spike Timing Dependent Plasticity (TSTDP) is an advanced synaptic plasticity rule that results in improved learning capability compared to the conventional pair-based STDP (PSTDP). The TSTDP rule can reproduce the results of many electrophysiological experiments, where the PSTDP fails, and can be implemented using new nano-scale technologies. Nanocrystalline-silicon thin film transistors (TFT) and memristors are of these nano-scale devices which can be integrated into three-dimensions using low-temperature processing. This paper proposes a new hybrid TFT-memristive circuit that implements the TSTDP. The proposed circuit is designed using current/charge driven memristors and nanoparticle memory-TFTs, as the synapse. Our simulation results demonstrate that the proposed hybrid TFT-memristor circuit induces synaptic weight changes that arise due to the timing differences among pairs and triplets of spikes with a close match to realistic biological measurements. The presented memristive design is an initial step towards developing asynchronous TSTDP learning architectures using memristive devices. These architectures may facilitate the implementation of advanced large-scale neuromorphic systems with applications in real world engineering tasks such as pattern classification. be prepared in one or two paragraphs with less than 200 word and figures, tables, equations, and references should be avoided.

In many practical applications such as inverse synthetic aperture radar (ISAR), signals have block structure. That is the nonzero entries appear in a number of block (cluster). In these signals, the prior information about the number, size, and location of nonzero blocks is not available. According to continuity pattern of ISAR signal, a neighborhood of the coefficients can be considered for dominant scattering. In this paper, we propose a method to reconstruct ISAR sparse signal based on Bayesian compressive sensing according to intra-cluster correlation between neighboring of dominant scattering points. The sparsity of dominant scattering point not only depended on its hyperparameter, but also on neighboring hyperparameters. The proposed method does not depend on prior knowledge of the block structure. In order to model intra-cluster correlations between neighboring scattering points, a Gaussian prior distribution is used and finally variational Bayesian (VB) inference has been used to learn the hyperparameters and sparse signal estimation. The simulation results show the superiority of the proposed method compared with other methods in terms of the correlation, recovery error, perfect recovery percentage, entropy and image contrast.

In this paper, a new method for pricing of Distributed Generations (DG) based on contribution of each zone of distribution network to reliability enhancement is proposed. In the proposed method, some flaws such as zero reliability price allocation to DGs and non-zero merchandising surplus are eliminated. In order to determine the share and price of each zone, cooperation game theory is utilized. For reliability evaluation of distribution network, Markov chain in used in which the operation of DGs in islanded mode is considered as well.

Todays, Chaff as one of the most effective techniques of electronic warfare, has a particular importance. It is difficult to study the behavior of chaff, because of the impossibility of chaff tests in an unechoic chamber. Therefore, the simulation would be a suitable alternative and have importance role for study of its behavior. In this paper, By considering conventional radar, the modeling and simulation of various features of sea chaff for search and tracking modes are performed. For radar search mode, six-dimensional spaces (6Ds) including: The RCS (Radar Cross Section) fluctuation, doppler width, symmetry and sparsity in range axis, polarization effect and standard deviation in range axis have been considered. But for radar tracking mode, modeling and simulation of the process noise in the 1D dimensional space, along with the Kalman prediction covariance matrix has been accomplished. The suggestion to use of process noise as a distinctive feature of the target from chaff is the innovation of this paper. In addition, considering the radar search mode, modeling and simulating its features in various spaces are other achievements of this article.

Quality of service and trustworthiness of data is of high importance for decision making in Internet-of-Things (IoT) applications. Malicious nodes and devices may compromise the quality of service and experience for other nodes through providing invalid data and evaluations. Hence, a trust management system to assess the trust level of users and gathered data is deemed to be essential to every IoT system. The current approach in the literature for computing the trust level is entity-centric trust in which the trust level of end users are estimated. However, the trustworthiness of data is equally important in many applications. In this paper, we propose, Trusty, a hybrid trust computation approach, aiming at trust assessment for both entities as well as data. In our proposed approach, a Bayesian learning method is used for computing the entity trust, while Dempster-Shafer theory is exploited to data fusion and data trustworthiness assessment. We implement Trusty in a smart parking system scenario to investigate the performance of our model in the different settings for misbehavior nodes and faulty sensors. As shown by the extensive simulation experiments, Trusty outperforms the competing approaches in terms of convergence for both data trustworthiness and entity trust.

In this paper, a secure unknown input observer is designed in a networked control system environment. For this propose, the Paillier encryption, which is a semi-homomorphic encryption method, is employed. The algebraic calculations required for the estimation can be performed over the encrypted data and the encrypted states estimation are generated. The boundedness condition of estimation error is obtained by considering the digital processor. This method increases the security of system state estimates and effectively eliminates attacks that disrupt the confidentiality of the data. Simulation on the TE-PCS will be performed to illustrate the results.

In this paper we introduced two types of common household demand, time limited and controlling demands. In this model based on the idea of mutual interaction between these two demands, we propose a new model for home load management. After assigning an innovative concave utility-function model for each demand, we change the common method of summing up the individual utility-functions with multiplication. The introduced model is developed as an MINLP problem that can be successfully handled by available commercial software. The analysis of the numerical examples indicates that they are acceptable in terms of optimality and compliance with actual conditions in human experience.

This paper presents an automated method for calibrating road cameras in order to estimate the speed and dimensions of the vehicles. In this method, in the initial frames, according to the direction of vehicles movement, vanishing points and the hypothetical road surface is obtained. Then, by identifying the foreground using IGMM and removing the shadow, the exact boundary of each vehicle is determined and a 3D bounding box is constructed. To determine the metric coefficients, several vehicles from common classes are identified using the deep VGG neural network in the first few frames. Further, according to the actual dimensions of the vehicles identified in meters and their equivalent dimensions on the road surface in pixels, the metric coefficients are calculated and the calibration parameters are completed. Ultimately, passing cars are projected on the hypothetical page, and by tracking each car, its speed and dimension are calculated. A database of vehicle images was collected to identify common cars. To evaluate our method, a series of videos with ground truth was provided, by simultaneous capture of road vehicles by RGB and laser camera. The mean error of the proposed method for speed estimating is 1. 15 km /h and for dimension estimation is equal to 2. 3%, which shows good performance of the method.

Co-planning of multi-energy networks is of utmost importance for improving the overall productivity of energy infrastructure. In recent years, the use of natural gas distributed generation (GDG) has increased in the electricity industry. In this paper, an AC optimal power flow model for electricity and gas distribution networks is developed in order to meet the multiple objectives of planning. Unlike heuristic methods and separate power flow, the proposed strategy deals with a simultaneous OPF and non-iterative. The problem model is a nonlinear programming model and nonconvex. Due to the formulation complexity and non-linear and non-convex nature of the problem, it is time-consuming, difficult, and sometimes impossible to achieve an optimum response. Therefore, the power flow is reformulated as a linear programming model. This model can be implemented using existing mathematical optimization methods and a desirable convergence to solve the planning problem. The proposed model is implemented on a test system and the numerical results show the efficiency of the proposed model.

Term mismatch is the most important challege in web information retrieval. The term mismatch problem is defined as differences between user queries and contents of documents while referring to the same topic. Query expansion methods deal with term mismatch by reformulating the queries to increase their term-overlap with relevant documents. In this paper, we proposed a query expansion framework based on a deep Siamese LSTM neural network. In addition, we defined the relevant relatedness for the first time and used this concept to label pairs made from user query and candidate query. Weakly-supervised labeled pairs are utilized in training of the deep Siamese network. The trained Siamese network provides labels for testset pairs in addition to contrastive loss values. The contrastive loss value reflects the cost of pulling together similar pairs. Pairs with minimum contrastive loss values are selected and merged together to form one expanded query. Results of our tests showed that the proposed framework outperforms similar word embedding based query expansion methods.

A new clock distribution network using CMOS single-photon avalanche diodes (SPAD) in combination with free space optics is proposed. The system can work without special implementation of optical waveguides. It consists of an optical source with 660 MHz operation, which in the receiver end is composed of three parallel SPADs biased in quench, hold-off and recharge phases. With the pipeline analogues implementation of SPADs and a logic cell output a 660 MHz clock source can be achieved. The proposed system is post layout simulated in 180nm CMOS technology. The power consumption of 6. 74 mW for each SPAD cell, electrical jitter of 237 fs and skew of 43 ps is resulted.

Programmers differ in how they generate source codes. The study of these differences, programming habits, helps us to select and train programmers. This has been studied before and specially among student programmers. To the extent we know, such a study has not been carried out in Iran. To fill this gap and gather local knowledge, in the present research, we distributed an individual difference questionnaire among 52 programmers in various Iranian software companies. Using the collected data, we studied the influence of age, gender, experience and personality on four problem solving strategies: top-down, bottom-up, breadth-first, and depthfirst. We analyzed the data statistically and found that Iranian programmers have a preference towards specific programming strategies as mentioned before in other studies. Besides, age, gender, experience, and personality traits affect their preferences.

In this paper, a decentralized control strategy based on the separation of the energy generation control unit is presented, which leads to stable operation and improve the DC micro-grid (MG) dynamic behavior. By applying the proposed control strategy, the fuel cell (FC) and supercapacitor (SC) units, which form a hybrid distributed generation (DG) source, are simultaneously controlled with independent controllers suggesting independent dynamic control performance. Moreover, in this control strategy, the SC unit, is supplied with a state-of-charge (SOC) regulator block to keep its proper performance. Accordingly, the dynamic separation of energy units affects the dynamic behavior of the overall DCMG and improves the system damping factor. By using the location analysis method of system eigenvalues, the proposed control strategy performance is evaluated. Also, the simulation studies in the PSCAD/IEMTDC software environment confirm the feasibility and proper performance of the proposed scheme.

This paper is dedicated to investigating haplotype estimation based on DNA sequences. The proposed algorithm named as (HapLas) is introduced using relaxation of polynomial optimization based on the Lasserre technique. This algorithm is based on the discrete structure of optimization problem of haplotype estimation which is mapped to a continuous space using the measure theory. Then, relaxation is performed via the properties of the moment matrix. Simulation results show that the proposed algorithm improves the reconstruction rate of the haplotype about five percent in comparison to the SDhaP and RefHap. This is achieved at the cost of increasing the running time and computational complexity which can practically be ignored in medicine.

In this paper, a frequency selective surface-based band pass filter is proposed in terahertz regime. The proposed filter provides a great performance in pass band and out-of-band regions. A fractional bandwidth of 60% around the central frequency of 0. 4 THz is achieved whereas a high transmission magnitude with an acceptable flatness is obtained in comparison to previous reports. Additionally, an equivalent circuit model is extracted for the proposed filter. A comparison between simulation results and those obtained using proposed circuit model shows a good agreement. Moreover, the sensitivity of the proposed filter with respect to the incident wave angle is investigated in the range of 0 to 60̊ . It is demonstrated that the performance of the designed filter is acceptable for various incident angles in the mentioned range. Also, the effects of altering different geometrical parameters are investigated and justified based on the equivalent circuit model.

In this paper, the improvement of small signal stability of islanded inverter-based microgrids by selective dynamic gains of droop are discussed. An acceptable improvement must be reached by using the possible minimum number of voltage source inverters in order to adding the proper dynamic gains to their droop characteristic. First, using the linearized equations of inverter-based islanded microgrids, a state-space equation of islanded microgrids is presented. Then, a microgrid test system is considered and a sensitivity analysis is applyied to its low-frequency eigenvalues wih a low damping factor. Using the sensitivity analysis, the more effective voltage source inverters are identifiyed such that the addition of proper dynamic gains only to their droop characteristic can increase the damping of low-frequency power oscillations of microgrid. A simulation study is carried out in order to verify the effectiveness of selective dynamic gains of droop in PLECS software.

In recent years, electrical networks have been subjected to harmonic pollution due to the expansion of nonlinear loads such as power electronic devices, electric arc furnaces and etc. In this paper, a new method is presented for identifying and determining the harmonic contribution of each feeder connected to a bus in the radial networks. In these networks, using this method, in the buses that has been subjected to the voltage harmonic distortion, it is possible; firstly, the responsible feeder to voltage harmonic could be identified from all feeders connected to the desired bus. Secondly, the harmonic contribution of each feeder could be quantified. In this method, the harmonic contribution of each feeder connected to a bus is separately determined in a voltage harmonic distortion for each harmonic order; in addition, the harmonic contribution of each feeder on total harmonic distortion of the bus is also determined. This method is based on the comparison of each feeder with the ideal feeder corresponding to them. Also, in this paper, a new method for harmonic modeling of each feeder connected to a bus (from a radial network) is also presented, which is based on linear regression between voltage and current samples and minimization of the sum of least mean squares (LMS) errors.

Robustness in response to unexpected events is always ideal for real-world networks. In order to improve the robustness of any network system, it is important to analyze the vulnerability to external interference such as accidental fault or attacks that are introduced into the network elements. In this paper, a novel study investigates the robustness of complex network using the clustering coefficient of networks against loss of elements. In particular, we can identify vertexes that can damage the network due to the weakening of its clustering, which is evaluated by means of the average of clustering coefficient. This is important because any tangible change that leads to a clustering is caused by defects that can reduce network functionality, such as the ability to spread information on a social network. We present this risk analysis as an optimization method and demonstrate the completeness and uncertainty of our approach to identify main vertices for clustering. Finally, we perform comprehensive experiments in the combined social networks that are generated by different models. The experimental results show that the average clustering coefficient is very efficient in analyzing the fault of network node failure. Also, the results confirm that the technique of removing the important nodes, especially in terms of the closeness centrality, is very effective in the analysis of clustering vulnerability.

Many problems in the real world due to the local and global optimization change over time are a matter of dynamic optimization. Therefore, optimization algorithms despite global optimization and tracking of environments changed over time are needed in these environments. We are faced with two problems in designing the particle swarm optimization algorithm for dynamic environments to find the best solution in a short time and follow the solution after the environmental changes: Outdated memory and loss of population diversity in search area. The problem of loss of population diversity is one of the biggest challenges in dynamic environments because diversifying a convergent population to find dynamic optimization and then turning it into a new optimization greatly reduces the efficiency of the algorithm. Given the challenges presented in this paper, a hybrid particle swarm optimization algorithm based on a Gaussian mixture model is proposed. In the proposed method, the change of each particle is based on the result of the best particles in its cluster. The results of the Moving Peak Benchmark experiments show a better performance of the proposed algorithm than other algorithms.

Electrooculogram is a bio-potential signal that can play an important role in Brain Computer Interface systems. EOG, which is the result of moving human eye bulb, has the advantage of relatively easy recording due to its higher amplitude and signal-to-noise ratio compared to other modalities (e. g. EEG). Real-time processing, classification, and feature extraction are another important factors in applicable BCI systems. In the present study, a real-time processing and cost effective BCI system have been designed and developed based on EOG signals. It contains an updated eyeglass for fixing EOG electrodes on the subject’ s face and a 3D accelerometer for detecting subject’ s movement. EOG signals were acquired by subject’ s eye movement toward the four middle part of screen edges, which was placed in front of him/her. Expected results of the system was real-time generating of four different digital commands (Quadcopter navigation) based on the eye movement of a subject in four different directions (up, down, left and right). At the end system have been tested on five different Subjects (five trials for each subject), and 94. 4% of system accuracy in detecting eye movements have been achieved.

In this paper an analytical method for electromotive force (EMF) calculation in an axial flux PM less machine is presented. The analytical method uses the scalar magnetic potential to calculate the magnetic flux density at air gap and winding regions. Laplace equation is solved in several regions of machine and the scalar magnetic potential is calculated, then the magnetic flux density is computed using scalar magnetic potential. Finally the EMF in the stator windings is calculated. This analytical method requires less computational time than conventional finite element methods and is therefore suitable for designing and optimization purposes. The proposed analytical model validated by comparing its results to corresponding 3D finite element analysis as well as experimental results.

Due to shrinking semiconductor device and integrated circuit dimensions into nanometer regime, semiconductor industry is facing challenging problems. Transistors based on carbon nanotubes have attracted attention among logic circuit and digital system designers due to their low dimensions, high speed, low power consumption and similarity of their performance with CMOS transistors. Using Multiple Valued Logic (MVL) causes reduction in both chip area and power consumption in comparison with binary logic due to less mathematical functions. In this paper, we proposed a novel design for a multiplexer with ternaty logic using Carbon Nanotube Field Effect Transistors (CNTFETs). Eventually a comparison has been made between power and performance of this CNTFET based ternary multiplexer and its ternary counterpart in CMOS which is designed in this paper. In continue, the simulation results are presented in 32 nm technology node using HSPICE. The obtained results show between 60% to 65% improvement in latency, between 96. 4% to 98% improvement in power consumption and 99% improvement in enery consumption of ternary multiplexer based on CNTFET in respect to its counterpart in CMOS. Moreover, Power Delay Product (PDP) is improved by 99%.

Several features like channels, bots, supergroups, end-to-end encryption, and personalization available in Telegram, has made the instant messenger popular among rivals. The possibility of creating public channels and supergroups has changed Telegram application to a social network rather than an instant messenger. Although Telegram has more than 200 million active worldwide users and 40 million active Iranian users, there is little research based on Telegram. In this paper we have studied Iranian supergroups. More than 300, 000 Persian supergroups have been analyzed in our study. The results will give a deep insight of Telegram environment to the reader. We have defined some measures to detect high-quality supergroups, too. In our view, a high-quality supergroup is a supergroup whose members are more engaged with its content.

Vertical handover is the essential feature of the next generation heterogeneous wireless networks. Network selection among accessible candidates is the most critical step in vertical handover procedure. Although various algorithms for network selection have been proposed, due to different approaches and diversity of parameters corresponding to this complicated problem, no unified benchmark or global indicator exists to evaluate the performances. In this paper, we define a global performance indicator (GPI) which comprehensively considers all subscriber satisfaction factors to assess users’ quality of experience (QoE). Our method is inspired by “ Analytic Hierarchical Process” (AHP) and systematically utilizes both customer and network side parameters. This idea was led to propose a novel “ automatic context-aware network selection” (ACANS) algorithm. ACANS is a “ network-assisted” , “ user centric” approach which dynamically and automatically affects customer context in the decision of the best network. A practical simulation platform evaluates and compares ACANS to well-known network selection algorithms such as SAW (Simple Additive Weighting), MEW (Multiplicative Exponential Weighting), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and TRUST (TRigger-based aUtomatic Subjective weighTing). Simulation results show that the proposed algorithm is compatible to customer preference and has superior performance in “ No. of handovers” , “ user’ s volume of download” , “ consumed energy” and “ service cost” .