نشریه مهندسی برق
سال چهل و ششم شماره 3 (پیاپی 77، پاییز 1395)

Moving target tracking is a process in which an object is tracked and its location is determine in each frame. The goal of this process is facilitating the subsequent processing to analyze the behavior or detect moving objects. In this paper a new approach for aerial moving targets tracking based on feature matching algorithms have been proposed. The main challenge is classification of extracted features from background and foreground regions. To solve this problem, key points and their corresponding on the patterns extracted from consecutive frames, is calculated by the KLT algorithm. Then for each of this points, six attributes such as color, mean, variance and range of variation are calculated. Using these attributes and Bayesian discriminant function, extracted key points classified. In addition to resisting the proposed algorithm to scale change of target the object history scale in the 10 previous frames is used. Proposed algorithm was performed on an AIRCRAFT TRACKING standard database. Experimental results show the effectiveness of the proposed method against KLT and SURF tracking algorithms in term of accuracy.

Question classification is one of the vital components of information retrieval and question answering systems. The goal of question classification is to accurately identify the expected answer type of question and assign a label to it based on the class that the question placed in. So far, several studies have been done in this area using rule-based methods and machine learning techniques. The objective of this study is to combine results of these two approaches in order to enhance the accuracy of classification. The main innovation in this research is the enrichment of the bag of words feature vector of categorization rules. The importance of the integrated method presented in this paper is the use of rule repository with different categorization structure than the existing structure for question classification. Experimental results of the proposed method on UIUC dataset show its effectiveness on improving the question classification accuracy.

Nowadays using meta-heuristic algorithms besides T-way testing strategy is increasing to generate minimum test suites automatically. Genetic Algorithm, Ant Colony, Simulated Annealing, and Tabu Search play an important role in this regard. However, most of these algorithms cannot generate test suits efficiently for T>3 due to their complex structure and complicated computations. In this paper, we propose a hybrid approach using hill climbing and bat search algorithms to minimum test suit generation. Our proposed solution uses T-way strategy to test suit generation for different configuration of the system. The proposed solution can generate test suits up to T=10. Since one of the most important criterions for the evaluation of test suits is the array size, hence we compare our results with other existing approaches in terms of this criterion. Our results show that our proposed solution outperforms other approaches.

Over recent years, z-source inverters have been introduced as single stage buck-boost converters, although they may have limited boosting capability compared to multi stage dc-dc converters, yet z-source inverters are more suitable since the added new stages could lose the efficiency. Therefore, many researches have given their focus to develop z-source inverters to achieve higher voltage boost factor. In this paper, novel inverter based on the capacitor assisted extended boost z-source inverter with combination of the coupled transformer is presented. In proposed topology, discontinuous current asymmetrical Г-z-source inverter is used as the basic structure and the voltage gain is increased by lowering the transformer turns ratio.Therefore, unlike the second extension of the capacitor assisted extended boost z-source inverter, it is not required to add passive components to increase the output voltage. The performance of the proposed inverter is verified with simulation results in MATLAB/SIMULINK and the simple boost control method is used for modulation.

This paper presents the design of a high resolution- low power 12-Bit Time-to-Digital Converter (TDC) based on Multi Path Gated Ring Oscillator (M-path GRO) in 130nm CMOS-TSMC technology. Using 2 M path GRO with different resolutions, coarse and fine resolutions, in Vernier structure enhances the effective resolution of the TDC. The designed M-path GRO TDC realized in 130nm CMOS-TSMC technology at circuit level. Simulation results show that the coarse, fine and effective resolutions of this TDC are 10PS, 8PS, 2PS respectively, and it has 8nS dynamic range. In addition, the DNL and INL of this TDC are 0.6LSB and 2LSB. The power consumption of designed TDC is 4.5mw with 1.2V power supply at 5 MSPS.

In this paper, we investigate the problem of waveform design to estimate the delay and Doppler parameters of a target based on the Cramer-Rao Bound (CRB). After obtaining the CRBs, the OFDM signal model is used for the waveform design and by minimizing the CRBs two waveforms are obtained. The performance of waveforms is inspected using the Cross Ambiguity Function (CAF) and the Merit Factor (MF) is used for more evaluations. The increase of the MF is equivalent to the increase of the Probability of detection (Pd) of the target. Simulation results show that using the proposed waveforms, the amount of MF increases which is equivalent to decreasing the side lobes in the CAF and increasing the Pd of targets.

In this paper, a single switch transformerless high step up dc-dc converter with low voltage stress on the active switch is proposed. In this converter only one active switch is used which makes the control scheme simple as well as reducing the switching power loss. The voltage gain of the proposed converter is higher than the conventional boost converter and the proposed converter expands the continues conduction mode (CCM) operation region. The proposed converter has low voltage stress on the active switch which makes reducing the switching power loss. The proposed converter can be operated in the continuous conduction mode (CCM) and the discontinuous conduction mode (DCM). In this paper, different operation modes of the proposed converter, calculation of the voltage gain, the currents that flow through the components, efficiency and capacitors voltage ripple are presented. To verify the operation of the proposed converter, simulation results via PSCAD software and experimental results are provided.

In this paper a low-power, low-noise logarithmic amplifier is presented in order to use in the front-end of bio-potential recording microsystems. To avoid increasing the temperature and so destruction of the surrounding tissue, low-power performance is critical. The logarithmic characteristic is obtained by using the piece-wise linear approximation and the parallel summation topology. This structure is capable to generate both current and voltage outputs. The offset cancellation mechanism in the feedback loop has been employed to remove the DC offset. The amplifier has been simulated in a 0.18 µm CMOS process. Implementation of the circuit is done by drawing the layout. The post simulation results demonstrate a CMRR of 130.2 dB at 50/60 Hz and an input referred noise of 3.22 µVrms in a band width of 0.1-10kHz. The power consumption is 3.96 µW from a 1.2 V power supply.

In this paper guidance and control of UAVs has been discussed using autonomous path planning system to operate different missions under various conditions and with minimum human intervention. An autonomous system can be divided into path planning system and path following system. The path planning system is responsible for producing a flight path while the path following system generates proper commands to control and guide plane through the path. In this paper some waypoints has been considered in a hypothetical environment with obstacles and no fly zone regions and the optimal path is generated using genetic algorithm. First, the path will be refined to prevent from collusion, then in order to be a flyable path, we will smooth the path considering UAV’s dynamic constraints and finally the generated path is used as an input for UAV. Using BTT guidance method with nonlinear controllers have caused UAV to follow the reference path faster with lower deviation error from the path and all simulation results are shown.

In the cloud environment, load balancing is done by choosing the appropriate VM (Virtual Machine) between existing VMs to execute the given task. Choosing the appropriate VM to do any task, is function of various parameters. In this paper, we choose the most appropriate VM to execute the task by considering each of criteria. Therefore, we consider this selection as a Multi Attribute Decision Making (MADM) problem. At first, we model the problem in terms of effective parameters in performance by considering the basic goals of the load balancing. Then, we solve the model by using the non-determenestic taxonomy method, which is one of the most widely used method for MADM problems. In this method which is called TLB, desirability of each VM is calculated according to the weights assigned to the criteria and the level of importance of each criterion for the user which is determined based on entropy method.Finally, the best VM is choosen based on its assigned value. To study the performance of the proposed approach, extensive simulations is carried out in CloudSim simulator which show that the proposed method has better performance compared to FIFO, DLB and WRR method.

Designing market operator (MO) combined with distributed network operator (DNO) for systems constituting a network of Microgrids (MG) is the main goal of this paper. Energy storage (ES) devices and demand response resources from one MG can also participate as consumer and/or generator (known as Prosumer) with other MGs within the proposed structure. When the Prosumers are functioning as generators, they try to maximize their profit. However, the Prosumers try to minimize market clearing price (MCP) and to participate in the game structure for reaching Nash equilibrium when they are participating as consumer. The outcome of the proposed algorithm is the optimal power share for different players in the market which satisfies the aforementioned goals for each player. Additionally, the amount of obtained profit by each player is determined. The proposed controller is a hierarchical bi-level controller and its ability has been evaluated in the optimum control of a network of MGs consisting of distributed energy resources. The simulation results show the effectiveness of the proposed algorithm to encourage the market participation and to improve the profit for participants.

Many countries have paid attention to the electric vehicles during the past decade. This kind of vehicle is equipped with an energy-saving system which is usually composed of a set of batteries. This system also suffers from several problems including excessive warming, low energy-saving capacity and low efficiency; hence, development of electric vehicles has been influenced by costs caused by such problems. A possible and proper way to solve some of these problems and to improve function of the energy- saving system is installing a supercapacitor as an accessory energy reservoir. Aiming at reducing both size and wastes of energy-saving system and also integrating the system's output current, this paper has not only used a three port bidirectional converter, as an interface among battery, supercapacitor and electric motor, but also has considered the way by which power of both reservoirs (i.e. battery and supercapacitor) are controlled directly though turning this convertor on. It has been implemented through two controlling layers including a fuzzy controller and an adjusting block. To analyze the result, an electric vehicle equipped with the suggested saving- energy system, as a sample, was manufactured by the authors and then the practical results were studied. Moreover, the suggested energy-saving system, in comparison to the common energy-saving systems, was modeled in MATLAB/SIMULINK. Modeling and practical results demonstrate that the efficiency of the suggested strategy is higher than that in other strategies.

The least mean square (LMS) adaptive algorithm is widely used in acoustic noise cancellation (ANC) scenario. In this scenario, speech signals usually have high amplitude and sudden variations that are modeled by impulsive disturbances and it is wellknown that the acoustic channels usually have been sparse impulse response. Impulsive noise and sparsity of the acoustic channel are two important challenges in the ANC scenario that have paid special attention, recently. This paper presents a novel adaptive noise cancellation algorithm, to address the poor performance of the LMS algorithm in presence of impulsive noise along with a sparse impulse response. In order to eliminate impulsive noise from speech signal, the information theoretic criterion is used in the proposed cost function andthe zero norm is also employed to deal with the sparsity feature of the acoustic channel impulse response. Simulation results indicates the superiority of the proposed algorithm in presence of impulsive noise along with sparsity condition of acoustic channel.

Reactive power management is a vital part of the power system operation to reduce power losses and to release available capacity of transmission lines. This further motivated the reactive power market development for distributed generation (DG) units at distribution networks. In this study, a mechanism is proposed for reactive power pricing of various DG technologies considering different cost functions of DG units. For this purpose, the reactive power production cost curve of the DG units is linearized using a piecewise linear approximation into 10 predetermined regions. In this approach, at first, DGs offer their prices for each of 10 regions and then an integrated price is determined for each region based on the price of reactive power purchase from transmission network. The market clearing price of reactive power is calculated by a multi-objective optimization algorithm (NSGAII).

Concerning radio resource management in different generations of cellular networks, the focus upon the function of mobile phones in idle mode as well as the mechanism of cell selection and re-selection procedures should assume great importance. In this paper, based upon the last 3GPP standards together with considering applicable requirements of telecommunication industry in Iran, effective parameters related to cell selection and reselection in GERAN, UTRAN and EUTRAN networks are examined. Then the operation of mobile phones in the borders between home PLMN and roaming PLMN is investigated and Equivalent PLMN feature affection in these areas is discussed. Ultimately, two existing problems related to MCI PLMN are analyzed and some suitable solutions presented. The first issue is related to the problem of cell re-selection in the boarder of MCI PLMN and East Azerbaijan province WLL PLMN. The second issue considers the problem of inter system (GERAN and UTRAN) cell re-selection on the MCI network compared with MTNIrancell Network. In this article, some approaches in order to solve the problems are presented and consequences of practical measures expressed.

Packet classification is a fundamental process in network processors. In this process, input packets are classified into distinct set of flows via matching against a set of filters. Software implementation of packet classification algorithms, though having lower cost and more scalability as compared with hardware implementations, are slower. In this paper, we use parallel processing capabilities of the graphical processors to accelerate Hierarchical-Trie packet classification algorithm and propose different scenarios based on the architecture of their global and shared memories. Results of implementing these scenarios, conforming computed time and memory complexities, show that the performance of the scenarios that divide the filter set into sub-trees, equal to/ smaller than the shared memory and copy them to it, is lower than that of a scenario which keeps the total data structure in the global memory. The performance of these scenarios increases by decreasing the number of sub-trees and duplicated filters. Moreover, a scenario that can keep hierarchical tree and corresponding filters in shared memory, without any partitioning, is the best scenario. The experimental results show that, on a same GPU, this scenario attains a throughput of approximately 2.1 times compared to the existing methods.

Energy management systems (EMSs) are essential tools for efficient, reliable, and cost effective operation of a Microgrid (MG) with different energy resources. They optimally schedule MG’s operation considering electrical and thermal loads simultaneously. In this paper, an EMS is proposed to minimize operational cost of an industrial MG. Cost function of the MG operation is formulated using short-term generation scheduling for DGs –generally CHP type- and demand response (DR). To effectively address the existing uncertainty in forecasted load demand and day-ahead energy market prices, the proposed approach is formulated in the form of a scenario-based stochastic energy management problem. The DR program is modelled based on various load shifting contracts, from MG operator perspective. Additionally, both islanded and grid-tied operation of the MG is handled within the same framework. To demonstrate the effectiveness of the proposed method, modified IEEE 18-bus test system including 12 DGs is adapted. Simulation results prove the effectiveness of the proposed method in optimal cost-effective management of the MG at hand.

In this paper, we designed and fabricated a microfluidic chip for in vitro oocyte maturation. All processes have been simulated using COMSOL multiphysics software prior to fabrication of the microchip, to achieve an optimized geometry. Finally, using soft lithography, we will discuss the different stages of optimized manufacturing process for the designed microchip in detail. The fabricated microchip has been implemented using the commercial elastomer, Poly dimethyl siloxane (PDMS) in the microfabrication laboratory and have been clinically evaluated over mice oocytes in different stages using infertility laboratory facilities and its issues has been identified and resolved. The maturation process has been compared to the standard IVM of oocytes and significant improvement has been observed.

The IEEE 802.15.4e standard was proposed to improve the reliability of data transmission at the MAC layer for low power and lossy networks such as wireless sensor networks. The possibility of using multiple non-overlapping channels decreases the probability of collision among cometiting nodes, which, in turn, results in improving the quality of service as well as reducing energy consumption. The TSCH mechanism permits scheduling each data transmission on a pair of channel/timeslot using frequency hopping. However, the standard does not specify how to allocate channel/timeslot for scheduling data transmissions .In this paper, we propose a channel/slot scheduling based on 802.15.4e-TSCH mechanism for wirless sensor networks. In our mechanism, each node determines its requirements in terms of channel/slots by receiving scheduling matrices from its neighbors and according to the traffic volume passing through it. In this mechanism, we allocate dedicated cells for the nodes in the vicinity of the sink and also for the congested nodes. This mitigates the funneling effect. The simulation results over OpenWSN emulator confirm the efficiency of our mechanism.

In this paper, a new method for nonlinear teleoperation system with uncertainty in dynamical parameters and gravitational acceleration is introduced. This system can be used in space teleoperation where the slave robot works on uncertain height with unknown gravitational acceleration. Also the proposed method can be used when the master and slave robot work on different latitude. In the adaptive controller which is used in this paper, the gradient descent method is used to update parameters. In the proposed method, the bound of unknown parameters is not used in the controller. The proposed method cannot estimate the parameters of the master and slave robots and the unknown gravitational acceleration but can cause a proper functioning of the system. To verify the efficiency of the proposed method, it is applied on 2-DOF planar robots with constant delays in the transmission channel.

In this paper, a robust fuzzy PID controller which is called online fuzzy PID (OFPID), for frequency control of an islanded microgrid is proposed. The proposed controller strategy is such that the fuzzy PID coefficients are automatically adjusted by fuzzy set theory at each time. To improve the performance of the proposed controller, the proposed improved differential evolution (IDE) algorithm which has suitable convergence speed in nonlinear functions optimization is used to optimal tune of its parameters including coefficients, membership functions, fuzzy rules weight and rules in three stages. To robust controller design in different operation points, the OFPID controller parameters is optimized by considering the uncertainties on some microgrid component parameters. The motivation for the proposed this control strategy is combination of fuzzy theory feature and IDE optimization algorithm to reduce control efforts and find better fuzzy system to achieve robust performance of microgrid frequency control. The test microgrid, is composed of distributed generation units such as diesel generators, photovoltaic, fuel cells with electrolyzer and wind turbine, as well as energy storage units such as flywheel and battery. In order to get closer to the frequency response of real microgrid, nonlinear factors on the distributed generation and energy storage sources have been considered. Simulation results by applying different disturbances show the good performance of the proposed OFPID controller than the optimized PID and classical fuzzy PID controllers.

The presence of Distributed Generation (DG) resources in power system, versus economic benefits, can be cause a miss- coordination in operation of overcurrent relay. In this paper, a new index is proposed to determine the impact of DG installation on coordination constraints of overcurrent relay, independent of relay settings. In the presence of DG in network, the fault current passing through the backup and primary relays change in compare to the no presence of DG. According to this, the proposed index is defined as the difference between the rate of change of fault current passing through backup relay and the rate of change of fault current passing through primary relay, for each relay pairs. In the presence of DG, mathematically proven that the sign of the proposed index for each coordination constraints, indicates the increase or decrease of coordination time interval and the measure of proposed index indicates the reduction of coordination time interval. The proposed index for the coordination constraints of overcurrent relay of an 8-bus test system is determined at each DG location. The results have shown the correctness of proposed index to evaluate the impact of location and capacity of DG on the coordination constraints. Also, the locations and capacities of DG have been ranked base on proposed index.

The bandwidth allocation in IEEE 802.11 is defined as assigning two-dimensional blocks in time and frequency domain (which are called Bursts) to users. Resource allocation is vital for system efficiency; however, the detail is not defined in the standard and left open to the implementer. Many algorithms have been proposed for this purpose in OFDMA-based networks. In this paper, a genetic algorithm for burst construction in WiMAX networks is proposed which adheres to rectangular structure of downlink frames in IEEE 802.16 standard. In the proposed genetic algorithm, user requests with specific area are mapped to chromosomes. After passing the evolution stages, an optimized burst allocation is obtained for the requests in terms of shape and position. The goal of the proposed genetic algorithm is to locate maximum number of bursts in the frame such that, wasted space is minimized. Simulation results confirm that, our proposed algorithm can produce a near-to-optimal solution by means of an Iterative search.

In this paper, analysis and control of tension leg platforms (TLP) has been discussed, which is one of the most common offshore floating wind turbine systems. The most important subject about installation of these turbines is how to control and float them on the water. In order to design the controller for the system, first, a non-linear function will be defined as a Lyapunov candidate, then we will prove that TLP systems with multiple time-varying delays, in the presence of foreign force is stable and by linear matrix inequalities (LMI) tool, the controller will be designed. Finally, by simulation in Matlab-Simulink, we show that all of the state variables of the system are asymptotically stable and regulatory converge to zero.

New dual-channel perceptually motivated subspace-based approaches are proposed for enhancement of speech corrupted by noise. The proposed methods take the frequency masking properties of the human auditory system into account and reduce perceptual effects of the residual noise. The perceptually constrained quotient singular value decomposition (PCQSVD) algorithm uses the particle swarm optimization (PSO) technique to estimate the additive noise. Very carefully performed objective evaluations and subjective tests show that the proposed approaches here can offer improved speech quality, as compared to previous methods, in the case of stationary and nonstationary noises, especially when the additive noise is nonwhite.

Most of the problems in the real world are of dyanamic optimization ones. It means that their optima may change over time, thus algorithms to solve these problems must be well adapt to their conditions in such a way that they should be able to track the optima during evolution. This article proposes a chaotic genetic algorithm based on clustering and memory for solving dynamic optimization problems. A chaotic system has more precise prediction of the future in comparison with random system and increases the speed of convergounce in the algorithm. The utilization of some information from the past allows quickly adapting right after a change. Thus the underlining idea of the paper is the use of memory in this field, which is a good strategy to store the useful information and retrieves them for reuse in the future. The clustering method maintains diversity in the memory and the population during running of the algorithm by exchanging information between corresponding clusters (clusters with similar tag) of the memory and the population. This algorithm uses a k-means clustering method to maintain diversity and improve local search. In this paper used tow the main innovation: clustering method and memory update. To test the effectiveness of the proposed method we choose the Moving Peaks Benchmark that has similar behaviors to real world dynamic proplems. The experimental results show the efficiency of the proposed algorithm for solving optimization problems in comparison with other methods.

The shrinking of transistor dimensions and migration to nanometer region have increased the data storage errors in new generations of flash memories. Therefore, the reliability of data storage is an important challenge in the structure of these memories. In order to confront with this challenge, BCH error correction codes are utilized in the controller of these memories. There are two important points in the optimization process of a BCH decoder: speeding up the computation and reducing the hardware complexity. To speed up the decoding process, a parallel architecture is utilized for various building blocks. A Pipeline scheme is also adopted in BCH decoder to increase the throughput. To implement this parallel BCH decoder in an area-efficient manner, an iterative matching scheme is proposed to reduce the Chien search hardware complexity by reducing the number of XOR gates through removing the duplicate gates and sharing the remaining ones. The proposed decoder along with BCH encoder have been implemented in VHDL hardware definition language and synthesized in Xilinx ISE. The proposed decoder has been implemented in VHDL hardware definition language and synthesized in Xilinx ISE. The simulation results show that the proposed algorithm could reduce the decoding time and hardware complexity .

In this paper, a novel hybrid algorithm is represented named SA-COAMF by using cuckoo optimization algorithm, simulated annealing and expected maximization for motif finding problem. SA-COAMF is very efficient in global optimal convergence. In the algorithm, two models of motif representation, consensus and probability matrix representations, are applied to take the advantage of them. SA-COAMF is run on experimental datasets (SCPD database). The results are compared with some well-known algorithms (GA- DPAF, PSO and MEME) to show that our algorithm is efficient.

Consumers seek an economic way to procure electrical energy in the competitive electricity market. These ways includes: bilateral contract, participating in the pool market and generating the energy by the self-generating units. Electrical energy should be bought in appropriate time with the best price. In the other hand, effects of increasing the renewable energies have been investigated in the recent years. Considering the uncertainty of these sources increases the complexity of the decision making process as well as un-favorite decision making situations. In this paper, decision making to procure the electrical energy has been investigated in the presence of wind turbine uncertainty. The electricity energy prices and the electricity load demand have also uncertainty. Considering three sources of uncertainty increases the decision making risk. In this regard, an appropriate risk index has been selected to decrease the risk in the system. A simulation is also conducted to demonstrate the wind generation effects. Efficiency of the proposed method has been verified with various simulations.

Multi-carrier systems have a number of physical nodes which are called energy hub, providing energy for consumers. Energy hub converts and stores energy as well. Recently, Model Predictive Control has been widely used to efficiently control Hubs. MPCs show no high performance facing to system uncertainties. In this paper to address this weakness, uncertainties of forecasted electrical loads has been added as disturbance to the state-space model of the system. Then LMI based Robust Model Predictive Controller studied on the EH consisting of transformer, boiler, CHP, electrical and heat storages considering demand side management. As simulation results show, Robust predictive controller carry outs more precise planning for the system than predictive controller, whilst uncertainty exists.