Aiming at the time-consuming problem of the full-wave (FW) simulation of the scattering characteristics of the traditional graphene reconfigurable reflectarray antenna, a fast prediction method of electromagnetic (EM) response based on deep learning is proposed. The convolutional neural network (CNN) method in deep learning is effectively used in the research of this paper. This method first discretizes the input vector (patch geometry, chemical potential, frequency, incident angle, etc.) of the graphene reflectarray antenna, and then preprocesses the data into a two-dimensional image suitable for CNN training, and finally uses CNN to train the model instead of extensive FW simulation calculations, the EM response of the reflectarray antenna is calculated. The training results of three algorithms of support vector regression (SVR), radial basis function network (RBFN) and CNN are comprehensively compared. The experimental results show that CNN method has good performance and accuracy in the EM response prediction of the graphene reconfigurable reflectarray antenna, with an accuracy of over 99%, and can also save at least 99% of time.INDEX TERMS Convolutional neural network (CNN), electromagnetic (EM) response, graphene, reconfigurable reflectarray antenna.
Natural renewable glucose or lactose upon reaction with dodecylamine has been converted into N-dodecylglucosylamine or N-dodecyllactosylamine, which upon acid−alkali reaction with dicarboxylic acid (HOOC(CH 2 ) n-2 COOH, n = 3,4,5,6,8) gives a series of sugar-based pseudogemini surfactants (G-n, L-n, respectively). Some physicochemical properties such as the critical micelle concentration (CMC), equilibrium surface tension at the CMC (γ CMC ), effectiveness (π CMC ), efficiency (pC 20 ), maximum surface excess (Γ max ), minimum surface area (A min ), counterion binding of micelles (β), and the changes of standard Gibbs free energy (ΔG m 0 ), enthalpy (ΔH m 0 ) and entropy (ΔS m 0 ) for processes of micellization in the range 298.15 K to 328.15 K have been evaluated by surface tension and electroconductometry methods in aqueous solutions of these pseudogemini surfactants. The results revealed that most of the above properties depend on dicarboxylic acid linker length and headgroup saccharide size. These findings help with understanding the structure-properties relationships of surfactants so as to construct new pseudogemini surfactants.
The Direction-of-Arrival (DoA) and bandwidth (BW) estimation strategy impinging on a linear array using multiple snapshots data is addressed within the multitask Bayesian Compressive Sensing (MT-BCS). The DoA estimation is used as the reconstruction of sparse signal constrained by the Laplace prior through multitask Bayesian Compressive Sensing. Receiving wideband signal data through linear array, the space is divided into I parts according to the equal interval. The data of interest are assumed to be represented as I-dimensional vector, and the wideband signal can be reconstructed accurately using only a small number M. The receiving antenna operates in the frequency range fmin,fmax. Starting from the voltages measured at the output of the array elements at a multiple time instants at fp=fmin+Δf,p=1,…,P, the retrieval of the DoAs is addressed by means of a customized strategy based on MT-BCS in order to correlate the solutions obtained over different frequency samples. The bandwidth of the signals is obtained as a byproduct by identifying at which frequencies the MT-BCS estimations include a signal along the ith (i = 1,…, I) sampling direction. From the outputs of different frequencies, we can know the DoA and BW of signals. A preliminary numerical result is reported to show the behavior of the proposed approach in multiple snapshots data.
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