Beyond the scope of conventional metasurface, which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurface design. In this paper, benefiting from Deep Neural Network (DNN), an inverse design procedure of a metasurface in an ultra-wide working frequency band is presented in which the output unit cell structure can be directly computed by a specified design target. To reach the highest working frequency for training the DNN, we consider 8 ring-shaped patterns to generate resonant notches at a wide range of working frequencies from 4 to 45 GHz. We propose two network architectures. In one architecture, we restrict the output of the DNN, so the network can only generate the metasurface structure from the input of 8 ring-shaped patterns. This approach drastically reduces the computational time, while keeping the network’s accuracy above 91%. We show that our model based on DNN can satisfactorily generate the output metasurface structure with an average accuracy of over 90% in both network architectures. Determination of the metasurface structure directly without time-consuming optimization procedures, an ultra-wide working frequency, and high average accuracy equip an inspiring platform for engineering projects without the need for complex electromagnetic theory.
In this paper, we present a novel approach for the design of leaky-wave holographic antennas that generates OAMcarrying electromagnetic waves by combining Flat Optics (FO) and machine learning (ML) techniques. To improve the performance of our system, we use a machine learning technique to discover a mathematical function that can effectively control the entire radiation pattern, i.e., decrease the side lobe level (SLL) while simultaneously increasing the central null depth of the radiation pattern. Precise tuning of the parameters of the impedance equation based on holographic theory is necessary to achieve optimal results in a variety of scenarios. In this research, we applied machine learning to determine the approximate values of the parameters. We can determine the optimal values for each parameter, resulting in the desired radiation pattern, using a total of 77,000 generated datasets. Furthermore, the use of ML not only saves time, but also yields more precise and accurate results than manual parameter tuning and conventional optimization methods.
Monitoring and controlling the state of polarization of electromagnetic waves is of significant interest for various basic and practical applications such as linear position sensing and medical imaging. Here, we propose the first conformal digital metamaterial absorber to detect the polarization state of THz incident waves. The proposed polarimeter is capable of characterizing four independent polarization states of (TE, TM, ±45° linear, and RCP/LCP) by observing the reflectivity of the structure with respect to the x- and y-direction. Besides, the proposed structure displays a strong absorptivity above 90% up to the incidence angle of 50° for oblique incident waves with different polarizations. By mere changing the bias voltage of two orthogonal VO2 microwires via two independent computer-programmed multichannel DC network, distinct conditions for reflected waves occurs under excitations of different polarizations, whereby the polarization state of the incident wave may readily be estimated. We believe that the proposed metasurface-based polarimeter can pave the way for polarization detection applications on curved surfaces.
The purpose of this paper is to present a deep learning model that simultaneously estimates targets and wall parameters in through-the-wall radar (TWR). As a result of the complexity of the environments in which through-the-wall radars operate, TWR faces many challenges. The propagation of radar signals through walls is further delayed and attenuated than in free space. Therefore, the targets are less able to be detected and the images of the targets are distorted and defocused as a consequence. To address the above challenges, two modes are considered in this work: single targets and two targets. In both cases, permittivity and wall thickness are considered, along with the target’s center in two dimensions and the permittivity of targets. Therefore, in the case of a single target, we estimate five values, whereas in the case of two targets, we estimate eight values simultaneously, each representing the mentioned parameters. As a result of using deep neural networks to solve the task of target locating problem in TWR, the model has a better chance of learning and increased accuracy if it involves more parameters (such as wall parameters and permittivity of the wall) in the target location problem. In this way, the accuracy of target locating improved when two wall parameters were considered in problem. A deep neural network model was used to estimate wall permittivity and thickness, as well as two-dimensional coordinates and permittivity of targets with 99% accuracy in single-target and two-target modes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.