نشریه مهندسی برق
سال پنجاه و یکم شماره 2 (پیاپی 96، Summer 2021)

In this article, we consider a secure cognitive radio network (CRN) deploying non-orthogonal multiple access (NOMA). A mixed delay constrained multicast and unicast traffic is received by the intended CRN receivers, while keeping the traffic secret from the eavesdroppers. Physical layer security (PLS) is deployed to secure the confidential messages of both primary user (PU) and secondary user (SU). A queue management policy (QMP) is proposed to enhance the quality of service (QoS) of CRN. The proposed QMP adaptively uses all or some of the SU's resources towards the transmission of the PU's packet, this decision being based on the packet's delay experienced in the PU queue. Exact delay distribution of PU traffic is derived via a novel multi-regime Markov fluid queue (MRMFQ) model. Thanks to the closed form expressions, the proposed QMP is optimized to provide the highest attainable throughput for SU, while satisfying PU's QoS constraints. It is shown via numerical examples that NOMA based CRN consistently outperforms orthogonal multiple access (OMA) based counterpart. We also show that the performance improvements gained by the proposed QMP depends on the intensity of PU traffic as well as the channel conditions. Moreover, a heuristic suboptimal parameter selection procedure with significantly lesser computational complexity is proposed for less capable devices.

This paper proposes an optimized adaptive combined hierarchical sliding mode controller (ACHSMC) for a class of under-actuated time-varying systems in presence of uncertainties and noise. For this purpose, the un-modeled dynamics and friction force are modeled as additive and multiplicative uncertainties, respectively. A combined hierarchical sliding mode controller (CHSMC) is designed using two layers of sliding manifolds. Then, the controller is adapted by considering a time-varying coefficient of the second layer sliding manifold of CHSMC system. The stability of this controller is approved by Lyapunov theorem. Finally, this method is performed on an under-actuated crane model that has two subsystems: trolley and payload can be controlled by a single input signal and the first layer sliding manifold parameter of ACHSMC is optimized by genetic algorithm (GA) to save energy of input signal. The simulation results show the stability and robust performance of the proposed controller against input noise and additive and multiplicative uncertainties and time varying parameters of the system compared to CHSMC method.

Source modelling is a gateway to the fascinating world of source coding. Many real-world sources are sparse or have a sparse representation. According to this fact, this work has focused on providing a new model to represent real-world non-strictly sparse (compressible) sources. To this aim, a novel model has been evolved from a simple sparse binary source to reflect the characteristics of compressible sources. The model is capable to represents real-world compressible sources by classifying samples into different classes based on their magnitudes. The model parameters are estimated using an innovative approach, a combination of a clustering technique and the binary genetic algorithm. The ability of the new approach has been assessed in modeling DCT coefficients of still images and video sequences. The proposed model also inspires an efficient coding approach to compress a wide range of sources including compressible sources. Comparison with classical well-known distributions including Laplace, Cauchy, and generalized Gaussian distribution and also with the most recent Noisy BG model reveals the capabilities of the proposed model in describing the characteristics of sparse sources. The numerical results based on the “chi-square goodness of fit” show that the proposed model provides a better fit to reflect the statistical characteristics of compressible sources.

To achieve high-quality software, different tasks such as testing should be performed. Testing is known as a complex and time-consuming task. Efficient test suite generation (TSG) methods are required to suggest the best data for test designers to obtain better coverage in terms of testing criteria. In recent years, researchers to generate test data in time-efficient ways have presented different types of methods. Evolutionary and swarm-based methods are among them. This work is aimed to study the applicability of swarm-based methods for efficient test data generation in EvoSuite. The Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) are used here. These methods are added to the EvoSuite. The methods are adapted to work in a discrete search space of test data generation problem. Also, a movement pattern is presented for generating new solutions. The performances of the presented methods are compared over 103 java classes with two built-in genetic-based methods in EvoSuite. The results show that swarm-based methods are successful in solving this problem and competitive results are obtained in comparison with the evolutionary methods.

With regards to wireless receiver systems, the effects of noise from all the following phases can be reduced using the gain of the low-noise amplifier (LNA). Therefore, boosting the specified signal power without adding a lot of noise and distortion will be necessary for the signal to be retrievable in the later phases or stages of the system. The proposed method in this study for noise reduction is based on the combination of two techniques: reversed- phase noise signal and non-reversed phase signal. The theoretical model for noise cancellation is presented along with the equations for the overall noise value, which are derived based on a two-port model. The circuit design is implemented using the  technology on a Cadence Spectre RF tool. The current study also implemented a CMOS UWB LNA configuration with inter-stage matching as well as shunt-series inductive peaking. This design uses inductive source degeneration cascode technology along with an inter-stage matching network. Moreover, to boost impedance matching and power gain, a Chebyshev band pass filter is placed at the input while a shunt-series inductive peaking is placed at the output.

In this paper, pyramidal shaped GaN-based quantum dots (QDs) with different sizes in each layer, surrounded by  is proposed for infrared photodetector mainly to enhance the detector performance. In this model, we are considering the QDs sizes’ distribution to calculate all parameters instead of using Poisson distribution to express the inhomogeneous broadening just in the absorption coefficient. To model the performance of the devices, the Schrödinger equation has been solved using the effective mass approximation; then, the absorption coefficient, the gain, the responsivity, the electron mobility, the dark current, and the detectivity as a function of temperature for different biases are obtained. Significant improvements in the optical behavior are seen in the modeled results at T = 220 K.

Density functional theory (DFT) and thermal DFT (thDFT) calculations were used to evaluate the energy band structure, bandgap, and the total energy of various graphene quantum dots (GQDs). The DFT calculations were performed using local density approximation for the exchange-correlation functional and norm-conserving pseudopotentials. We consider the triangular and hexagonal GQDs with zigzag and armchair edges and 1-3 nm dimensions with many hundred atoms. The simulation results show that all of these GQDs are direct bandgap semiconductors with a flat band structure, and they are suitable for electronics and optoelectronics applications. Analysis of GQDs in which the A and B sublattice symmetries were broken showed degenerate zero-energy shells. Using the thDFT calculations carried out at temperatures up to 1400 K, we evaluated the temperature dependence of the GQDs bandgaps and total energies via entropy-term and electron’s kinetic energy. The obtained results indicate that the ground-state DFT calculations are valid for determining the electronic properties of GQDs up to room temperature. Moreover, we tune semi-empirical parameters of the tight-binding model by the DFT results in small GQDs to reduce the computational cost of electronic structure calculations for large GQDs, which contained up to thousands of atoms.

Balancing between demand and supply of grids is the most important task of the power systems operators and control systems. Otherwise, the possibility of frequency instability and severe damages to equipment are present. Primary frequency control (PFC) is the first and main control action in the grid in front of the active power imbalance disturbances. In this paper, the effects of the spinning reserve characteristics and the grid dynamic parameters, on PFC performance and maximum frequency decline (frequency nadir), are investigated. Then, a comprehensive equation is presented to determine the maximum frequency deviation after a large power imbalance in the grid. This equation considers all effective factors such as volume and speed of the primary frequency reserve (PFR), grid inertia constant, grid load level, and the frequency-dependent loads. The correctness of the presented equation is verified through different simulations. Finally, a comprehensive scheme is proposed for the primary frequency control reserve allocation in the grid, in the form of a few equations and instructions.

Because of low losses and voltage drop, fast control of power, the limitless connection distance and isolation issues, using the High Voltage Direct Current (HVDC) transmission system based on Voltage Source Converters (VSC) is recommended to the power transfer in the electrical power networks included the offshore wind power plants (OWPP). The OWPPs are expected to meet the grid code necessities when requested to maintain stability. Utilization of the VSC HVDC along with the OWPP, can improve the control of power flow and the power system dynamic stability. In this paper, the impact of control of VSC HVDC based OWPP, on the dynamic stability of power systems is evaluated.  In this way, the dynamic modeling of power system equipped by the VSC HVDC and OWPP are proposed. In the proposed model, using the concepts of controllability and observability of electromechanical modes of power systems, a new approach to the design a supplementary damping controller in VSC HVDC based OWPP is presented. The damping controller is designed based on the nonlinear adaptive neural networks concepts and trained by a proposed online method. The simulation results which are done in MATLAB, show the effectiveness of the proposed control strategy.

Interference Alignment (IA) with Alamouti coding is an innovative method to increase the multiplexing gain and the diversity gain, simultaneously, in wireless Multiple-Input Multiple-Output (MIMO) systems. The main limitation of this method is requiring Perfect Channel State Information (CSI) to perform the beamforming on transmitters and receivers signals. Contrary to the acceptable performance of Alamouti coding in perfect CSI conditions, its efficiency is severely reduced in imperfect CSI conditions. In order to overcome this shortcoming, this paper discusses about the effect of imperfect CSI on a two user MIMO X-channel which uses interference alignment together with Alamouti coding. Also, a closed form solution is extracted for the received signal in an imperfect CSI scenario. Our analysis shows that when the variance of the CSI error is increased, the Bit Error Rate (BER) of system increases linearly. Also, we propose a joint Power Allocation (PA) and constellation rotation algorithm to improve the performance of system in an imperfect CSI scenario. Computer simulations show a desirable improvement in BER by using PA, constellation rotation and joint of them.

This paper presents enhancement of power system dynamic stability by using unified power flow controller with assuming several real conditions and practical power system constraints. In control method design, we used all four UPFC basic controllers simultaneously with reducing conflict of them by compromising between its control variables. Optimization problem have been used with regarding some constraints of the system and unified power flow controller. Particle swarm optimization algorithm has used to optimize power oscillation damping based on unified power flow controller with an objective function. Simulation results in several multi-machine test power systems demonstrate the capability of applied control system with regarding many constraints and limits of power system and UPFC in different scenarios.

An iteration computation was carried out to investigate electron transport properties in Hg1-xCdxTe. We employed the modified iterative procedure which allows us to increase the computational accuracy in several structures. We considered deformation potential, polar optical phonon, piezoelectric, and ionized impurity scattering. Electron drift mobility is calculated for different temperature and doping dependencies. It was found that the electron drift mobility decreases with the temperature increases from 100K to 300K. Competitions among several temperature-dependent scattering mechanisms create temperature-dependent of MCT mobility. Furthermore, it was concluded that the x-dependence of the Hg1-xCdxTe mobility results primarily from the x-dependence of bandgap, and secondarily the x-dependence of effective masses. In the case of low temperatures, the electron mobility quickly decreases with the increase of doping concentration, while this happens at a slower speed in the case of high temperatures.

This paper presents a 2-bit programmable digital metasurface for real-time beam steering applications at X-band. Tunability of the metasurface is provided by employing a varactor in the unit cell structure to control each unit cell independently. This ability leads to achieve beam steering capability in both elevation and azimuth directions. The structure is designed so that the biasing circuit has no electrical connection to the MS ground. The equivalent circuit model of the unit cell is also presented to better investigate its physical behaviour. Furthermore, the effects of number of unit cells and states of reflection phase on far-field pattern are investigated. Finally, the numerical results are compared to analytical ones, where a good agreement between them is observed.

Feature selection (FS) is served in almost all data mining applications along with some benefits such as reducing the computation and storage cost. Most of the current feature selection algorithms just work in a centralized manner. However, this process does not apply to high dimensional datasets, effectively. In this paper, we propose a distributed version of Minimum Redundancy Maximum Relevance (mRMR) algorithm. The proposed algorithm acts in six steps to solve the problem. It distributes datasets horizontally into subsets, selects and eliminates redundant features, and finally merges the subsets into a single set. We evaluate the performance of the proposed method using different datasets. The results prove that the suggested method can improve classification accuracy and reduce the runtime

A gait energy image (GEI) is a spatial template that collapses regions of motion into a single image in which more moving pixels are brighter than others. The discrete wavelet transform template (DWT-TEMP) is a temporal template that represents the time changes of motion. The static and dynamic information of every video is compressed utilizing these templates. In the proposed method, every video is parted into N groups of successive frames, and the GEI and DWT-TEMP are made for every group, resulting spatial and temporal templates. Transfer learning method has been utilized for classifying. It gives the recognition accuracies of 92.40%, 95.30% and 87.06% for UCF Sport, UCF-11 and Olympic Sport action datasets, respectively.