The Dual-Polarized Staggered Stacked Patches Antenna

A new dual-polarized staggered and stacked patches antenna with wide impedance band-width and high isolation is proposed.The antenna consists of two groups of radiation patches,in 7 layers,and uses the orthogonal adjacent coupling structure on staggered layer to excite a pair of linear polarization modes.Thanks to the staggered feeder mode,it has increased the isolation performance be-tween ports and compressed the transverse size of the antenna.As a result of the combination of staggered stack-up between the patches and the stepped gradient shape of the main radiating patches,it has effectively expanded the impedance bandwidth of the antenna.The proposed antenna is simulated,fabricated and measured.The staggered feeding structure effectively reduces the cross-sectional area of the antenna,and greatly improves the isolation between feeding ports.The measurement results show that the impedance bandwidths for vertical and horizontal polarization modes are 40.2%(638-960 MHz)and 40.0%(645-968 MHz)respectively when the return loss is lower than-10 dB,and the isolation between feeding ports is better than-30 dB.Meanwhile,the antenna has a stable and symmetrical radiation pattern across the working band,therefore making it suitable to be used as antenna and antenna array element of mobile wireless communication base stations.

Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network

Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.

Analysis and applications of a frequency selective surface via a random distribution method

A novel frequency selective surface （FSS） for reducing radar cross section （RCS） is proposed in this paper. This FSS is based on the random distribution method, so it can be called random surface. In this paper, the stacked patches serving as periodic elements are employed for RCS reduction. Previous work has demonstrated the efficiency by utilizing the microstrip patches, especially for the reflectarray. First, the relevant theory of the method is described. Then a sample of a three-layer variable-sized stacked patch random surface with a dimension of 260 mm x 260 mm is simulated, fabricated, and measured in order to demonstrate the validity of the proposed design. For the normal incidence, the 8-dB RCS reduction can be achieved both by the simulation and the measurement in 8 GHz-13 GHz. The oblique incidence of 30° is also investigated, in which the 7-dB RCS reduction can be obtained in a frequency range of 8 GHz-14 GHz.