TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Year:2022 | Volume:52 | Issue:3|Papers Count:7

In this paper, a class of low-cost, and single-layer reflectarray antenna is proposed for X-band in which an array of cross bow-tie patches is printed on an inhomogeneous substrate. Such an inhomogeneous substrate is made up periodic air-holes drilled within FR4 dielectric. In analyzing the unit cell of such a structure, two different parameters of cross bow-tie patch (length and angle) are varied and a phase range close to 700o is obtained that is a good choice for designing a broadband reflectarray. In the design procedure, an efficient phase synthesis technique is applied to minimize the adverse effects of frequency dispersion causing by the differential space phase delay at different frequencies. This technique optimizes the metallization arrangement, and helps to design a reflectarray with a good frequency response. To validate the obtained numerical results, a 270×270×2.4mm3 reflectarray with focal length 26.9cm is fabricated and measured. Measurements show a peak gain 28.1dB with a 1.5-dB gain bandwidth of 34% and maximum efficiency 57.5%. It is experimentally shown that the gain and bandwidth of the reflectarray with inhomogeneous dielectric is better than homogeneous one.

This paper proposes a seven-level grid-tied Photovoltaic (PV) inverter based on the switched-capacitor technique with input voltage boosting capability. This topology includes an electrolytic capacitor, eight power switches, and two input dc sources. The output peak magnitude of the proposed inverter is one-half times the input voltage sources. The components count of the proposed inverter is reduced in comparison to recent several-level inverters which are used for grid-tied PV applications. This topology does not require an independent control method for balancing voltage of capacitor and has a self-balancing mechanism for the capacitor voltage. Morover, the Model Predictive Control (MPC) is utilized as current injection technique to produce proper output voltage levels. A capacitor optimal design for resistive load and inductive-resistive load has been analyzed to reduce of volume and size of the proposed inverter.  The theoretical loss calculation of the proposed inverter under different load conditions has been analyzed. The performance of the proposed inverter is simulated by MATLAB/Simulink software and the results are presented under on-grid and off-grid conditions. Finally, a 200W laboratory prototype with 180V maximum output voltage is tested to validate the simulation and the theoretical analysis.

The electrical behavior of neurons can be more complex in the presence of autapse. In the presence of an autaptic connection, the Izhikevich neuron model can show a variety of dynamic behaviors, such as chaotic behavior. This paper presents a novel, high speed and robust pseudo random number generator based on the autaptic Izhikevich neuron oscillator and its FPGA implementation. The autaptic Izhikevich neuron model is simulated and dynamically analyzed. Then, the proposed pseudo-random number generator is modeled and simulated using the Xilinx system generator platform, synthesized using Xilinx Synthesis Tool, and implemented on the XILINX SPARTAN-6 XC6SLX9 FPGA evaluation board. As a post processing operation, the XOR function is used to increase the randomness of the output bit sequences. The FPGA implementation results show that the implementation cost of the proposed pseudo-random number generator is lower than similar works, and the proposed generator achieves a maximum frequency of 63.2 MHz. The NIST test suite is used for testing the quality of the generated bit sequences. The NIST test results indicates the high quality of the generated random bit sequence.

Due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. In CPM, a part of investment cost of each generator is recovered through fixed payments. However, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. Using the system dynamics (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

Intracytoplasmic sperm injection (ICSI) is one of the most successful techniques of Assisted Reproductive Technology (ART) and is mostly in use for the treatment of infertility with male factors. In this method, before injecting sperm into the intracytoplasmic of the oocyte, cumulus cells around the oocyte must be stripped to facilitate the injection process. To achieve this, both enzymatic and mechanical methods are used in embryological laboratories for denudation, which has major deficiencies, including the possibility of damaging the oocyte prior to the injection process. In this research, a microfluidic-based device is introduced for the separation of cumulus cells around the oocyte with minimum manual operations. The results prove high efficiency, and non-destructive denudation of the oocyte with the reduced amount of culture medium leads to the low-cost preparation process of oocytes. The process can also be integrated with ICSI chips under development and will be reported shortly.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.