Amirkabir International Journal of Electrical & Electronics Engineering
Volume:54 Issue: 1, Winter-Spring 2022

In this paper, an Unmanned Aerial Vehicle (UAV) assisted cooperative communication system is studied, wherein a source transmits information to the destination through an energy harvesting decode-and-forward UAV. It is assumed that the UAV can freely move in between the source-destination pair to set up line of sight communications with both nodes. Since the battery of the UAV may be limited, it can harvest energy from the received signal by power splitting technique to be able to perfectly transmit data to the destination. Therefore, we study throughput maximization problem for multiple time slots data transmission through the cooperative energy harvesting UAV.  To maximize the throughput, optimal power allocation at the source and the UAV and power splitting ratio at the UAV are studied over each time slot in presence of energy-causality constraints at the UAV. Finally, numerical results are presented to analyze the spectral and energy efficiency of the proposed system, and effects of optimal power allocations and power splitting ratio.  The results indicate that by utilizing optimal resource allocations at the source and the UAV, and utilizing Simultaneous Wireless Information and Power Transfer (SWIPT), significant throughput improvement is achieved compared to those without optimal resource allocation or SWIPT. All of static UAV scenarios (i.e., the maximum throughput between the source and the destination) increases, while there is no need to increase the battery capacity of the UAV.

This paper introduces a new symmetric single-phase 13-Level Flying Capacitor Inverter (13-LFCI) structure, using a two dc sources and three capacitors. The proposed single-phase 13-level inverter has the ability to increase the output voltage with fewer semiconductor components compared to the state-of-the-art structures. The Phase Disposition Sinusoidal Pulse Width Modulation (PDSPWM) method is utilized to produce switching pulses. Using this modulation scheme facilitate the 13-LFCI with self-balancing capacitors' voltage capability. The optimal capacitors are designed for minimum voltage drop in the different loads. The 13-LFCI is also capable of transferring reactive power through R-L loads without any limitations. Furthermore, the inverter can properly generate 5-level, 7-Level, 9-level, 11-level, and 13-level output voltage for different applications with change of modulation index. Moreover, a comparison with state-of-the-art 13-level inverters is provided in terms of the number of active and passive components, boosting ability, reactive power support and voltage conversion ratio in order to show the advantages of the proposed structure. In addition, the theoretically losses are calculated to show the efficiency of the proposed topology in various powers. The performance of the converter is illustrated through different operating conditions simulations of resistive and resistive-inductive loads. Eventually, to confirm different aspects and implementation of the proposed structure, the proposed 13-LFCI has been approved in MATLAB/SIMULINK software, and the achieved simulation results are considered utilizing a laboratory prototype.

Many satellites use closed-loop attitude control to carry out their missions. They use several sensors such as sun, magnetometer, and star tracker to close the control loop. Sun sensors are not operational during the eclipse and therefore, one of the observed vectors is lost.  For this reason, attitude determination in eclipse can be a challenging issue for control engineers. This paper presents a novel idea for producing a new generation of sensors that can measure the induced electric field vector not only in eclipse, but also in all orbit. This electric field comes from a high velocity of the spacecraft in the magnetic field of the Earth. This vector is always perpendicular to the magnetic field; thus, it is never aligned with the magnetic vector and never causes singularity and accuracy decrease. The induced electric field is measured by three RC circuits[1] that are actuated by sinusoidal voltage. The dielectrics of the capacitors are made of ferroelectric materials; therefore, the induced voltage affects the permittivity and voltage of the capacitor. By measuring and calibrating this effect in three perpendicular axes, we can measure the three components of the electric field vector. The theory of the proposed sensor has been developed, and simulation studies validate the results.

In this paper, a new active lossless snubber circuit is proposed, which provides a soft-switching condition for the traditional Pulse Width Modulation (PWM) fly-back converter. This active lossless snubber circuit creates Zero Voltage Switching (ZVS) condition for the main switch, while Zero Current Switching (ZCS) condition is achieved for the auxiliary switch. Moreover, based on the soft-switching condition, diode reverse recovery problem is omitted and leads to reduction of switching losses and increased efficiency. Additionally, the voltage stress of the auxiliary switch is clamped to the input voltage level which leads to its low capacitive turn ON loss. Furthermore, the presented active lossless snubber circuit provides soft-switching condition independent from load condition. The main and auxiliary switches do not need an isolated gate driver, since their source pins are connected to the input ground. In this manuscript, the different operating modes are explained in detail, and a comprehensive design procedure is presented. Furthermore, loss breakdown for converter elements is offered at full load. The simulation results of the proposed converter using PSPICE software are shown for 155V input, 24 V, 120 W output, and 100 kHz switching frequency to justify the theoretical analysis. The proposed converter has high efficiency of 94.08% at full load.

This paper proposes a new non-isolated single-switch DC/DC converter with an ultra-high voltage gain, low input current ripple, low voltage stress, soft-switching operation, and modular scalability for renewable sources applications. With the help of a Three-Winding Coupled-Inductor (TWCI) and Voltage Multipliers circuits, ultra-high voltage gains can be achieved without needing a large duty cycle. A regenerative clamp capacitor recycles the energy stored in the leakage inductor; thus, the maximum voltage across the single power switch is restricted. Moreover, at the turn-on instant of the power switch, a Zero Current Switching (ZCS) condition is achieved. By designing a resonant tank, the switched current value of the main switch at the turn-off instant is reduced significantly. Additionally, the leakage inductor of the TWCI helps all converter diodes to operate under the ZCS condition. Due to full soft-switching performance, the introduced topology can provide a wide output voltage range under a high conversion efficiency. The steady-state analysis and comprehensive comparisons are provided in this paper. A 160 W prototype with 24 V input and 250 V output voltage is developed to validate the theoretical analysis. Due to ZCS operation and low voltage stress (VDS ≈ 40 V), the power loss portion of the MOSFET is low. Moreover, the maximum voltage stresses of diodes are measured as 40 V, 60 V, 90 V, and 110 V, that are well below the output voltage. Furthermore, at the full-load condition, the input current ripple is about 20 % and the measured efficiency is about 96.3%.

The automatic modulation recognition of the received signal is very attractive in both military and civilian applications. In the recent years, deep learning techniques have received much attention due to their excellent performance in signal, audio, image and video processing. This paper examines the feasibility of using deep learning algorithms on automatic recognition of the received radio signals' modulation schemes. Modulation recognition has been performed on eight digital modulation types with a Signal-to-Noise Ratio (SNR) from -20dB to 20dB. Primarily, a Vanilla Neural Network is used to classify the type of modulation. Afterwards, convolutional Neural Network (CNN) and Recurrent Neural Network are applied for modulation recognition. These neural networks are widely used in image and signal processing applications. This is followed by designing the other architectures, including Densely Connected Neural Network (DenseNet), inception network, Recurrent Neural Network (RNN), Long-Short Term Memory network (LSTM), and Convolutional Long-Short Term Memory Deep Neural Network (CLDNN) for modulation recognition problem, and their results are presented. During this investigation, a basic model is initially considered for each architecture, and the network performance is studied afterwards by adjusting its parameters. The simulation results show that the proposed modified CLDNN model can provide an accuracy of 98% in high SNRs.

Coronavirus disease 2019 (COVID-19), is a rapidly spreading disease that has infected millions of people worldwide. One of the essential steps to prevent spreading COVID-19 is an effective screening of infected individuals. In addition to clinical tests like Reverse Transcription-Polymerase Chain Reaction (RT-PCR), medical imaging techniques such as Computed Tomography (CT) can be used as a rapid technique to detect and evaluate patients infected by COVID-19. Conventionally, CT-based COVID-19 detection is performed by an expert radiologist. In this paper, we will completely and utterly discuss COVID-19. We present a deep learning Convolutional Neural Network (CNN) model that we have developed to detect chest CT images with COVID-19 lesions. Afterwards, based on the fact that in an infected individual, more than one slice is involved, we determine and apply the best threshold to detect COVID-19 positive patients. We collected 5,225 CT images from 130 COVID-19 positive patients and 4,955 CT images from 130 healthy subjects. We used 3,684 CT images with COVID-19 lesions and their corresponding slices from healthy control subjects to build our model. We used 5-fold-cross-validation to evaluate the model, in which each fold contains 26 patients and 26 healthy subjects. We obtained a sensitivity of 91.5%±6.8%, a specificity of 94.6%±3.4%, an accuracy of 93.0%±3.9%, a precision of 94.5%±3.5%, and an F1-Score of 0.93±0.04.

A Compact Ultra-Wideband (UWB) single-layer power divider with the out-of-phase feature is proposed. UWB out-of-phase performance is obtained, using wideband microstrip-slot line transitions. All three ports are printed on a single dielectric substrate. The simulation and experimental results of the enhanced UWB power divider indicate stable phase characteristics, high impedance matching responses, and low insertion losses performances at the operating range of 2-12 GHz.

This study has introduced a three-axis capacitive accelerometer, in which the part of the capacitor that calculates acceleration is installed in the z direction in the spring to improve the sensitivity in the said direction. In addition to having the advantages of previous accelerometers, the suggested accelerometer has compensated for previous shortcomings by increasing both sensitivity and pull-in voltage. Moreover, this accelerometer is able to decrease spring torsion and spring nonlinear behavior and provide a more straightforward rigidity computation. Therefore, without increasing the total occupancy level of the sensor, this accelerometer can increase the capacitive planes’ surface area to measure acceleration in z direction, resulting in an increase in sensitivity, while all the advantages of previous accelerometers are kept. In designing this accelerometer, factors such as rise time, overshoot, settling time, and peak time were considered. The proposed properties of the accelerometer were also derived from the perspective of a second-order system. Our designed accelerometer showed an operating frequency up to 20 kHz and a dynamic range up to 1000 g. The sensitivity of the accelerometer was 4fF/g in the z axis direction. Moreover, the sensitivity of the accelerometer in x and y directions was 9fF/g.

One of the critical components in most electromechanical systems are the bearing system. Therefore, a proper condition monitoring method that can classify the type and the severity of electrical machine faults in different load levels is crucial to avoid unwanted downtime and loss of operation. Non-invasive condition monitoring methods based on electrical signatures of machine in an electromechanical system, are considered as simple and cost-effective approaches for the fault detection process. In this paper, a deep learning approach based on a combination of temporal convolutions and Long Short Term Memory (LSTM) network is used for fault diagnosis. The two architectures are both shown to be effective for time-series classification and sequence modeling. Temporal convolutions are shown to be competent in feature extraction for time-series classification; however, they are rarely studied in bearing fault detection and classification in an electromechanical system. The presented method does not need any preprocessing or predetermined signal transformation, and uses the raw time-series sensor data. In this regard, three different faults, as inner race, outer race, and balls are considered for validity of the proposed method. The results show that healthy cases can be separated from faulty cases in different load levels with high accuracy (95.8%).

Model Predictive Control (MPC) has attracted wide attention recently, especially in electrical power converters. MPC advantages include straightforward implementation, fast dynamic response, simple system design, and easy handling of multiple objectives. In conventional MPC, the optimal value of the cost function is obtained after calculating all switching states, which makes this method impossible to implement. In this paper, a Simplified Model Predictive Control (S-MPC) is presented to control the circulating and output currents in a Modular Multilevel Converter (MMC). Using a discrete mathematical model of MMC and the neighboring index values with respect to their previously applied values, the calculation burden can be reduced rapidly, and even the number of Sub-Modules (SMs) increases. The conventional MPC is expressed for comparison with the proposed method. In addition, a bilinear mathematical model of the MMC is derived and discretized to predict the states of the MMC for one step ahead. A sorting algorithm is used to retain the balancing capacitor voltage in each SM, while the cost function guarantees the regulation of the output current, and MMC circulating current. In the simulation section, the proposed method is implemented in a three-phase MMC with four SMs in each arm. The accuracy and performance of the proposed method are evaluated with simulation and experimental results.

First, the impedance source idea is used to alleviate the problems of Voltage Source Inverters (VSI), and Current Source Inverters (CSI), which leads to the introduction of the Z-Source Inverter (ZSI). Afterwards, not only did the Z-source cells were used in the inverter topologies, but they were also added to other power electronic converters, such as dc-dc choppers, ac-dc rectifiers, and ac-ac converters. Moreover, these power electronic converters, which have Z-source cells, are widely used in different applications. In particular, the existing single-phase ac-ac Z-source converters are reviewed in this paper, and their different features are studied in detail. In the past decades, the single-phase Z-source ac-ac converters have attracted significant attention due to their unique features, such as single-stage power conversion, boost in-phase, and buck out-of-phase features. Therefore, several modified or improved structures along with different control methods have been introduced to solve their problems and limitations. Based on the four features, these structures can be categorized into four groups: without magnetic coupling, with magnetic coupling, able to change the frequency, and inherent commutation. In this review, a general diagram is depicted, which many of the existing topologies can be obtained from it. Additionally, new topologies can be obtained by having this general diagram. After studying these structures, a comparative study is made to obtain the advantages and disadvantages of each. In the end, some advice and recommendations are given to develop and obtain new structures.