Amirkabir International Journal of Electrical & Electronics Engineering
Volume:56 Issue: 2, Summer-Autumn 2024

In this paper, a novel low-profile and low cross-polarization metasurface antenna is proposed for 5G mm-wave applications. The proposed antenna consists of two layers, with a slot antenna as the base and a novel metasurface layer on top. The metasurface layer is a 3×3 array of patches. By strategically incorporating slits and stubs within the middle patches, the undesired degenerate mode is separated from the fundamental mode, and higher-order modes are suppressed that typically appear in conventional metasurfaces. Additionally, rectangular slots are added in the middle of the corner patches to shift higher-order modes to frequencies beyond the desired operating bandwidth, mitigating issues such as beam splitting and beam squint in the radiation patterns. Experimental measurements demonstrate that the proposed metasurface antenna operates over a bandwidth of 25.14% (23.3 GHz to 30 GHz), with a return loss better than 10 dB, a peak gain of 8.1 dB, and an XP level lower than -26 dB and -53 dB at  and  planes, respectively. Compared to conventional metasurface antennas, our design reduces the antenna dimension by 62%, resulting in a compact size of 0.72λ0 × 0.72λ0 × 0.08λ0. Furthermore, we validate the performance of the single-element antenna by employing it in a 2×2 Multiple Input-Multiple Output (MIMO) configuration without requiring additional inter-element spacing. The MIMO antenna exhibits promising performance as well. Overall, our proposed low-profile and low cross-polarization metasurface antenna shows great potential for 5G mm-wave applications, offering improved efficiency and reduced size compared to conventional designs.

In this research paper, an innovative approach is employed to design and simulate a Bethe hole coupler utilizing ridge gap waveguide (RGW) technology. The RGW structure includes two parallel metal plates, a ridge, and strategically placed pins. To achieve the coupler structure, we place two waveguides facing each other, and between these two waveguides, there is a perforated metal plate. Initially, four holes are set in the plate between the waveguides to enhance the coupling coefficient's bandwidth. The radius of the holes is initially calculated and designed to achieve a 20 dB coupling. The validity of the design is confirmed using CST software. RGW transition design is usually more difficult than other gap waveguide structures. For this reason, in this paper, a technique called Step Ridge Waveguide Excitation (SRWE) is introduced for efficient excitation of the designed coupler. The proposed methodology is implemented in the Ku band, which is suitable for satellite communication with high bandwidth. The dimensions of our structure are 50mm × 30mm × 17mm. Additionally, a 20±2 dB coupling coefficient is obtained for a fractional bandwidth (FBW) of approximately 29%. It demonstrates a directivity range of 6-16 dB, and the isolation is lower than 23 dB, surpassing the performance of similar previous works.

Interference channel with a cognitive relay (IFC-CR), as one of the complex channels, consists of two transmitter-receiver pairs, where, one relay knowing some information of transmitted messages cooperates with transmitters to improve the achievable rate region and overall communication performance. The cognitive relays hold a pivotal role in the context of cognitive radio networks (CRN), where efficient spectrum utilization is a paramount concern. To study the impact of a cognitive relay in a wireless interference channel it is necessary to compute the rate region of wireless IFC-CR. In this paper, the capacity inner and outer bounds of IFC-CR known for discrete alphabet and memoryless channels are extended to the continuous alphabet wireless version. Due to High computational complexity, the gap between the outer and inner bounds is determined through Numerical Results. Various scenarios about transmitter power levels and noise variance are considered to encompass a diverse range of real-world conditions. The inner and outer bounds provided in this paper become valuable tools for various aspects of practical analysis, for example, the inner bound can be used to investigate the coverage region and the outer bound for the outage probability, thereby, facilitating practical decision-making in wireless communication system design and optimization.

In specialized fields, the accurate answering of visual questions is crucial for practical applications, and this study focuses on improving a visual question-answering model for artistic images by utilizing a dataset with both visual and knowledge-based questions. The approach involves employing a pre-trained BERT model to understand query nature and using the iQAN model with MLB and MUTAN mechanisms for visual queries, along with an XLNet-based model for knowledge-based information. The results demonstrate a 78.92% accuracy for visual questions, 47.71% for knowledge-based questions, and an overall accuracy of 55.88% by combining both branches. Additionally, the research explores the impact of parameters like the number of glances and activation functions on the model's performance.

Tumors refer to abnormal growth of cells in the body. Early diagnosis of tumors plays a crucial role in improving treatment conditions , quality of life and patient survival. Deep learning methods are effective for medical image segmentation, but they struggle with tumors in magnetic resonance images (MRI) due to variations in intensity and appearance. Existing models like U-Net face challenges due to the integration of high-level and low-level features, leading to confusion. Our proposed model addresses the above issues by utilizing two techniques and fewer parameters compared to the existing methods, achieving higher accuracy. In the first technique, dilated convolution (DC) blocks with proportional rates are used to integrate high-level and low-level features. The second technique involves selecting dilated spatial pyramid (DSP) blocks, which increase the receptive field of features while maintaining their resolution, contributing to the network's generalization. The proposed model improves training, network depth, and feature extraction by incorporating a residual block. It outperforms the traditional U-Net model in terms of segmentation accuracy and network stability. We evaluated the model using the BraTS 2018 dataset, obtaining Dice coefficients of 0.906, 0.817, and 0.839 for the whole tumor (WT), the enhancing tumor (ET), and the tumor core (TC), respectively.

Recognizing the emotions from speech signals is very important in different applications of human-computer-interaction (HCI). In this paper, we present a novel model for speech emotion recognition (SER) based on new multi-task parallel convolutional autoencoder (PCAE) and transformer networks. The PCAEs have been proposed to generate high-level informative harmonic sparse features from the input. With the aid of the proposed parallel CAE, we can extract nonlinear sparse features in an ensemble manner improving the accuracy and the generalization of the model. These PCAEs also address the problem of the loss of initial sequential information during convolution operations for SER tasks. We have also proposed using a transformer in parallel with PCAEs to gather long-term dependencies between speech samples and make use of its self-attention mechanism. Finally, we have proposed a multi-task loss function made up of two terms of classification and AE mapper losses. This multi-task loss tries not only to reduce the classification error but also the regression error caused by the PCAEs which also work as mappers between the input and output Mel-frequency-cepstral-coefficients (MFCCs). Thus, we can both focus on finding accurate features with PCAEs and improving the classification results. We have evaluated our proposed method on the RAVDESS SER dataset in different terms of accuracy, precision, recall, and f1-score. The average accuracy of the proposed model on eight emotions outperforms all the recent baselines.

Various wind turbines have been manufactured for converting wind power into electric energy. They are fixed speed concepts with squirrel cage induction generators, limited variable speed concepts with wound rotor induction generators, variable speed concepts with double fed induction generators, direct-drive concepts with electrically excited synchronous generators and gearbox-free concepts with permanent magnet induction technologies. The composed components and the power curve of these technologies are different and to select an appropriate wind turbine for a wind site, in addition to the economic parameter, reliability criterion must be considered. To address this, a reliability model is developed in this paper that considers both component failure and the unpredictable nature of wind speed for different types of wind turbines. The optimal state number of reliability presentations is determined using XB index calculation and fuzzy c-means clustering method to create multi-state presentations for wind turbines. The proposed approach can be used to determine the most reliable wind turbine for a given wind site by assessing the adequacy of the electric network containing various types of wind turbines. The approach's effectiveness is demonstrated through adequacy assessments of the RBTS and IEEE-RTS, which contain various types of wind turbines.

In the electricity market, generation company attempts to maximize their profit in a bidding strategy approach. As the transactions of power and spinning reserve are done in a transmission network, consideration of transmission constraints and spinning reserve uncertainties becomes necessary. In the bidding strategy problem, there are various demand uncertainties. Usually, electricity markets consider a fixed spinning reserve with fixed request probability to ensure that demand is met. However, the actual spinning reserve is stochastic in quantity and requests hours that should be modeled and simulated. Another demand uncertainty is demand response programs include various stochastic types. One of the most famous demand response programs is electric vehicle parking with stochastic charging/discharging amounts and hours. The objection of this study is solving the bidding strategy problem considering transmission constraints, spinning reserve uncertainty, and electric vehicle parking as a demand response program based on a heuristic approach. An actual spinning reserve model using normal distribution is proposed and three case studies are presented. In the first case, improvement in profit of the generation company by 4.15-47.95% and 20.84-31.30% under single and double-sided auctions are reached, respectively. Where transmission constraints and spinning reserve uncertainty are considered, the optimal bidding strategy problem is solved in the energy and spinning reserve market for three-generation companies in the IEEE 6-bus network where transmission constraints are satisfied at all scenarios of spinning reserve requests. When electric vehicle parking is considered, it is shown that demand response programs have direct effects of bidding parameters such as market clearing price, generation companies power awarded and profits.

Power distribution utilities need to have an effective and suitable service restoration (SR) plan to reconnect customers quickly after power outages. This article presents a heuristic bi-stage SR algorithm that first re-energizes some of the loads fast by remote-controlled switches (RCSs) in the first stage and then continues to restore the rest of the network in the second stage with all possible switching actions using RCSs and manual switches (MSs). This study also includes finding the optimal Switching Sequences (SSs) and the estimated energy not supplied  (EENS) as an objective function. Moreover, the proposed method is more attractive and practical because it considers the time of occurrence of the failure and the daily load curve, the location of the load transfer capability, and the traffic conditions of the network. In this method, repair crew (RC) and mobile power sources (MPSs) are also important for the restoration process. The heuristic SR algorithm was tested on a standard IEEE 70-bus system in different scenarios. The results showed a significant difference in the solutions to the problem and the ENS in different scenarios. Lastly, it was concluded that this heuristic method would produce optimal, precise, and feasible solutions for SR in distribution networks.

The operation of protection systems has a considerable impact on power system reliability. The main reason for cascading outages is protection system misoperation. Protection systems affect power system reliability from two perspectives: First, incorrect operation of the protection system due to the failure of any of its components that causes failure to operate or undesired tripping. Second is the incorrect operation of the protection system due to the incorrect setting of relays. In the second case, the protection system is healthy, and incorrect operation is only the result of the erroneous setting of relays. In this paper, an analysis of power system reliability regarding failure and incorrect settings of the protection system is paid. This paper proposes an eight-state Markov model for a transmission line and its protection system incorporating protection system miscoordination distingue from failure to operate and undesired trip. The situation of network lines in the period of simulation time has been determined by the sequential Monte Carlo method, and the reliability indices such as Loss of Load Probability (LOLP), Loss of Load Expectation (LOLE), Expected Energy Not Supplied (EENS), and Expected Frequency of Load Curtailment (EFLC) are calculated. The proposed model is applied to a 6-bus IEEE RBTS network, and the reliability indices are calculated and compared from both perspectives to show the importance of the proposed model.

Voice-Coil Actuators (VCAs) serve as indispensable components in precision motion applications, valued for their linear force performance, smooth operation, and compact size. The optimal design of VCAs not only contributes to cost reduction in manufacturing and assembly but also results in a more compact size and increased bandwidth, enhancing VCA's overall performance. On the other hand, developing a model that captures the nonlinear features of actuator dynamics is essential for advancing high-performance controllers. In addition, such a model can be very useful in simulating the performance of controllers before implementation, as well as studying the behavior of different parts of the actuator during its operation. This paper introduces a novel approach to VCA design through a multi-objective optimization problem solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Once the optimal design is determined, it undergoes validation via finite-element analysis. After validating the optimal design, the actuator is fabricated and assembled through a detailed electromechanical design. To mitigate potential noise issues arising from switching circuits, the study proposes a high-bandwidth analog servo amplifier. This amplifier is designed to effectively mitigate noise problems, ensuring the seamless operation of the VCA in practical applications. Before modeling, the characterization process is undertaken through a combination of simulation and experimental tests. Finally, the effectiveness of the multi-physics-based modeling approach, enhanced by experimentally-driven characteristics, is evaluated against empirical results.

DC microgrids have emerged as a promising solution to provide reliable and efficient power for various applications. However, similar to any power system, DC microgrids are prone to faults that can disrupt their performance. Accordingly, the lack of publication of sufficient standards and guidelines for the protection of DC microgrids makes it necessary to develop protection methods in these networks. Therefore, the purpose of this paper is to create a new fault detection method in islanded DC microgrids. In this method, the current signal samples are entered into a chaotic state, and using the feature of sensitivity to the initial conditions of this method, it accurately identifies the fault. In this case, the signal undergoes a very large chang during the fault, which is easily visible compared to the normal state. It should be noted that, unlike other methods, in the proposed method in this paper, only one measurement unit is used in the DC bus for sampling signals. Therefore, there is no need to use communication links in the proposed method. The proposed method has been implemented using MATLAB/Simulink software on a sample DC microgrid. The results show that the proposed method is capable to detect pole-to-pole and pole-to-ground faults on the microgrids and also faults on the distributed generations and electrical vehicles. Also, results prove that this method is resistant to the operational uncertainty of distributed generations, electrical vehicles, and the destructive effects of noise on the sampled signals.