Iranian Journal of Science and Technology-Transactions of Electrical Engineering最新文献

A novel 13-transistor, low-power true single-phase clocked (TSPC) flip-flop design is proposed which improves clock loading, power consumption, and performance. The power reduction is achieved by applying the stackly arranged low power-on transistor technique to the last stage of the proposed TSPC flip-flop. The circuit is separately implemented using carbon nanotube field effect transistors (CNTFETs), and graphene nanoribbons field effect transistors (GNRFETs). Prior to the final simulations, the optimum parameters of the CNTFETs, and GNRFETs were determined by sweeping their respective design parameters such as oxide thicknesses, nanotube diameters, and the number of nanoribbons. The proposed circuit is then simulated using the optimum transistors. Results demonstrate power consumptions as low as 0.0303 μW and 0.0263 μW, for the CNTFET and GNRFET transistor implementations, respectively, which are at least 72.26% and 94.9% lower than the previously reported flip-flops. Furthermore, it is shown that the proposed flip-flop exhibits better power-delay products (1.830 aJ and 1.519 aJ for CNTFET, and GNRFET implementations, respectively), which are 56.22% and 97.83% lower than those of existing carbon-based or silicon-based designs. This suggests our carbon-based designs as a promising CMOS substitution for low-power, high-performance applications. Both implementations were also investigated for robustness against the variations of supply voltage and operating temperature, and the effects of physical parameters on CNTFET-based implementation were investigated using Monte Carlo analysis. It was shown that although the GNRFET implementation has slightly better performance, by having better power, speed, and PDP, the CNTFET remains completely robust over the simulated ranges of parameters.

The world is marching towards net zero carbon emissions, as a result the use of solar photo voltaic (PV) applications are widely increased. In order to increase the efficiency of solar PV inverters, the multilevel inverters are being employed. The conventional multilevel inverters use more number of switches which may reduce the reliability of the system. To mitigate these issues, reduced switch multilevel inverters are used. In this paper, a novel five level five switch symmetrical multilevel inverter (MLI) suitable for applications of renewable energy is proposed with reduced harmonics, reduced losses and improved efficiency. The proposed topology is designed to generate output voltage with five levels using two voltage sources and 5 switches (4 unidirectional, 1 bi-directional). By varying inputs of the system and processing the data with artificial neural network, the optimum values are obtained to generate gate signals for the switch. The loss analysis model of the proposed multilevel inverter using sinusoidal pulse width modulation technique is designed and simulated in piecewise linear electrical circuit simulation (PLECS). The proposed multilevel inverter is employed for standalone solar PV system. A hardware prototype of the proposed symmetrical five level multilevel inverter for standalone solar PV system is developed to verify the simulation results using DSPACE-1202 controller are presented.

This work proposes a novel tapped transformer multilevel inverter (TTMLI) to address the issues of MLIs that require multiple DC sources or use cascaded transformers. The proposed topology replaces multiple cascaded transformers with a single transformer having taps at the primary winding. The taps of the primary winding are energized by a single DC source through semiconductor switches to produce a stair-case sinusoidal voltage output at the secondary winding. The proposed idea is explained with the help of switching waveforms followed by detailed calculations of the transformer design and voltage/current ratings of the semiconductor switches. The proposed topology is initially tested and verified through simulations, and then validated through a 1 kVA hardware setup with a 12 V input and a 220 V output. The simulation and hardware results of the proposed topology demonstrate huge potential for future research and development in this area. Unlike several other MLI topologies that require multiple DC sources or multiple transformers, the proposed topology uses a single DC source and a single transformer, and hence it is compact. Only a pair of switches conducts at any given time instant resulting in low conduction loss. Energizing a particular tap of the primary winding results in a specific voltage step at the secondary winding. For (N-)level MLI N taps on the primary winding and the same number of switches are required. Hence, the proposed topology is generic to realize (N-)level MLI with (N>1). The hardware complexity increases linearly with an increase in N. Moreover, the proposed topology is equally suited for the implementation of MLIs with symmetric and asymmetric step-sized outputs by adjusting the turn ratios.

Cognitive Radio (CR) is an adaptable communication device driven by a Cognitive Engine (CE). A suitable machine-learning strategy can increase the learning potential of CE. This work proposes an underlay Resource Allocation (RA) framework for maximizing cognitive user Quality of Experience (QoE) while simultaneously protecting the primary network. A non-convex optimization problem is resolved using a combination of two algorithms: Meta Actor-Critic (MAC) loss and Deep Deterministic Policy Gradient (DDPG). The incorporation of the MAC neural network into RA improves the actor’s performance, which in turn boosts the entire system’s learning speed. The proposed framework is validated through a comparative analysis with four contemporary RA models: Q learning, hybrid, dueling DQN, and MAML. The simulation findings exemplify that the proposed method achieves convergence rapidly and improves reward value in a shorter amount of time than the other four extant RA models. Further, the impact of increasing system size on the distortion versus scalability trade-off is also highlighted.

This research presents a novel design for a textile antenna with a nested orbicular shape, featuring a centered hexagonal slot and a depleted ground structure (DGS). The proposed antenna is specifically tailored to cater to the demanding requirements of Industrial, Scientific, and Medical (ISM) bands, as well as applications in Wi-Fi, Wireless Local Area Network (WLAN), and Bluetooth technologies. The nested orbicular configuration is chosen for its compactness and geometric versatility, allowing for efficient integration into wearable and textile-based communication systems. The incorporation of a centered hexagonal slot within the antenna structure serves to enhance bandwidth and improve overall performance. This slot is strategically positioned to influence the electromagnetic characteristics of the antenna, resulting in increased bandwidth and improved impedance matching across multiple frequency bands. Additionally, the depleted ground structure further contributes to enhanced performance by reducing unwanted radiation and minimizing the impact of surrounding environmental factors on the antenna’s efficiency. The proposed antenna design is characterized and analyzed using advanced simulation tools and measurement techniques to validate its performance across the target frequency bands. The textile nature of the antenna ensures flexibility and comfort, making it an ideal candidate for integration into wearable devices for seamless connectivity in diverse communication scenarios. The proposed antenna has impedance bandwidth of 70.7% covering the band from 1.495 to 3.13 GHz. The proposed antenna has maximum gain of 3.25 dB.

This paper aims to present vision-based navigation structures for a wheeled mobile robot using optical flow techniques. The two algorithms of the differential approach are examined and investigated for visual motion in unknown static and dynamic indoor environments. Horn-Schunck (HS) and Lucas-Kanade (LK) algorithms of the optical flow (OF) technique are employed to extract information about the environment surrounding the controlled robot by an installed color camera on the robot platform. Obstacles and objects are identified and detected based on image processing and video acquisition steps for the different tasks of mobile robots: navigation of one robot with static obstacle avoidance, navigation with dynamic obstacle avoidance, and multi-robot navigation with a static obstacle. The proposed control structures are based on motion estimation and decision mechanisms that use the necessary measured variables calculated by optical flow algorithms to carry out the appropriate steering actions to guide autonomously the robot in its workspace. The efficiency of the proposed control structures is tested in 2D and 3D environments using the Virtual Reality Modeling Language (VRML) Toolbox of Matlab. The obtained simulation results are discussed and investigated, and they will be compared to demonstrate the autonomous navigation of mobile robots without any collision with obstacles for these visual-based navigation systems.

Attackers perform malicious activities by sending URLs to victims via e-mail, SMS, social network messages, and other means. Recently, intruders have been generating malicious URLs algorithmically. They also use shortening or obfuscation services to bypass firewalls and other security barriers. Some machine learning methods have been presented in order to identify malicious URLs from normal ones, all of which are subject to classification errors. On the other hand, it is impractical to have a complete and up-to-date blacklist due to large number of daily generated malicious URLs. Therefore, calculating the URLs security risk would be more helpful than URLs classification. In this way a user can correctly decide whether to use an unfamiliar URL if they know its associated security risk. In this study, the problem of URLs security risk computation is introduced and two effective novel criteria for this problem are proposed. Based on these criteria, a security risk score can be estimated for each incoming URL. In the first criterion, based on previous malicious and non-malicious URL instances, the extracted features of a URL are divided into two categories, those increase the risk and those reduce the security risk. In the second criterion, security risk score of an unknown URL is estimated based on its distances to nearest known malicious and also safe URLs. For both criterion, corresponding formulations and algorithms are also designed and are described. Extensive empirical evaluations on various real datasets show the effectiveness of the proposed criteria in terms of malicious URL detection rate. Moreover, our experiments show that the proposed metrics significantly outperforms previously proposed risk score criteria.

In this paper, we present an innovative strategy for nonlinear model predictive control by employing a discrete-time NARX-Laguerre model. This latter model is crafted through the expansion of discrete-time NARX model parameters using a set of five independent Laguerre bases. A notable benefit of this approach is a substantial reduction in the number of parameters compared to the classical NARX model. However, the realization of this reduction depends on the careful selection of optimal Laguerre poles that define these bases. The parameters of the NARX-Laguerre model are determined through a recursive methodology. This resulting model is subsequently applied in the implementation of nonlinear model predictive control. To formulate the optimization problem, we incorporate a performance criterion that takes into account both process input and output constraints. We assess the effectiveness of this novel approach to nonlinear model predictive control through experimentation on the Twin Rotor System.

This work studies the effect of array dimensions on the tracking performance of a single line-of-sight (LoS) path channel in a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communications system utilizing adaptive filters. We evaluate the performance of the least mean squares filter and compare it with a reference extended Kalman filter in full-dimensional (FD) MIMO channels. Two-dimensional (2D) arrays are deployed to control the elevation and azimuth planes in different tracking scenarios. This paper assumes pedestrian communication between a person in a hall and a station. The state vector in our model comprises the angular channel parameters (the angles of arrival and departure) and the channel path gain. We use the mean squared error (MSE) to evaluate our results. The tracking results of the FD channel parameters are also compared to those of the 2D channel parameters to emphasize the role of the 2D array deployments compared to one-dimensional (1D) arrays to track in an mmWave communications system. Our results confirm that the array configuration is more important than the array size in beam tracking at the mmWave band.

This paper addresses the performance of a Fractional Order based Tilt-Integral-Derivative controller employed in a power system to manage the load frequency control (LFC) problem when connected to a power system tuned by a recently reported Search and Rescue (SAR) metaheuristic algorithm. Simulation studies have shown that the proposed optimization technique has resulted in successfully adapting the controller parameters. However, analysis has also been carried out to investigate the behavior of the SAR algorithm when certain intrinsic factors involved with it have been varied. To validate the present contribution, a few power system models comprising multi–area and multi-source generating units have been considered, along with incorporating some non-linear characteristics, namely governor dead band and generation rate constraint, with an intention to create a realistic scenario. A single-objective minimization function using the performance index ITAE is used to facilitate the algorithm in tuning the parameters of the FOTID controller. A comparison has been made to show the potentiality of the SAR-tuned FOTID controller with the conventional Tilt-Integral-Derivative as well as Proportional-Integral-Derivative controllers. Assessment of the dynamic behaviors of the system models by embedding the FOTID controller has been made by comparing them with the conventional controllers using simulation studies. The stubbornness of the SAR:FOTID controller by varying internal parameters associated and the external disturbances has been validated by carrying out the sensitivity and robust analyses, respectively. Also, the efficiency of the SAR algorithm based tuning technique has also been experimented with some well-known algorithms like the Whale Optimization, Flower Pollination and the Particle Swarm Optimization algorithms in the present study. Conclusions are drawn after an exhaustive analysis, which justifies that the SAR-optimized FOTID controller can also be a competitor as it provides quite satisfactory control action to minimize issues related to the LFC concern. Furthermore, the limitations of the method proposed have also been discussed, and the techniques to overcome them have been recommended. Moreover, the future plan of action for the current work has also been presented in this article.