This Letter proposes an approach to develop a spoof surface plasmon polariton (SSPP) transmission line (TL) by loading short-circuited (SC) shunt stubs. Lower out-of-band rejections can be flexibly controlled without affecting the upper cutoff frequency by independently modifying the stubs. Dispersion analysis of the SSPP unit is realized by theoretical calculation and circuit simulation to predict the upper cutoff frequency of the proposed SSPP TL simultaneously. Also, parametric sweeping of the SC shunt stubs is performed based on circuit simulation to investigate their impacts on the lower and upper out-of-band rejections of the proposed SSPP TL. In addition, electric field distributions of different types of TLs are simulated and compared to study the transmission characteristics of the proposed SSPP TL. The lower cutoff frequency can be flexibly tuned in a wide range, from 1.2 to 2.1 GHz, in the simulations. The measured 3-dB fractional bandwidth is about 128.1%, covering a range from 1.19 to 5.43 GHz. The numerical and experimental results are compatible, which verifies the feasibility of the proposed approach. This approach can offer more convenience and flexibility for controlling the rejections of the SSPP by introducing up to four tuning parameters. More importantly, the proposed SSPP TL avoids using the substrate integrated waveguide (SIW) technique, which shows the potential to decrease the transverse width ( 0.44 λ g ), especially at lower frequencies (e.g., 1.2 GHz), and to reduce the complexity in designing the high-efficiency transition. This work paves the way for the development of novel SSPP-based microwave devices.

Since adaptive beamformer suffers from output performance degradation in the presence of interference nonstationarity and signal steering vector mismatch, a novel robust null broadening adaptive beamforming is proposed. The proposed method is realized by the combination of projection transform and diagonal loading techniques. First, a new projection matrix with null broadening ability is constructed and then projects the array received data onto the projection matrix. With the diagonal loading technique, a new sample covariance matrix is obtained. The theoretical analysis shows that the projection transform operation can expand the incident direction of the interference and improve orthogonality between the signal-plus-interference and the noise subspaces; thus the proposed beamformer can effectively broaden the jammer null and enhance the null depth. The analytical expressions of the proposed algorithm are also provided, which are efficient and easily solved. Simulation results are presented and demonstrated that the proposed beamformer can provide strong robustness against signal steering vector mismatch and jammer motion.

In this paper, a strategy to develop a compact transition of the spoof surface plasmon polariton (SSPP) transmission line (TL) is proposed. First, an equivalent distributed circuit model is employed for the theoretical analysis and optimization design of the SSPP unit. The mapping relation between the unit performance and the geometric parameters is deduced from the transmission matrix. The calculated results are compared with the numerical ones from the three-dimensional (3D) simulations for validation. Then, a compact transition (only 0.26λ _g) is built with only two matching units and a tapered strip through optimizations. The optimizations are implemented with the circuit simulations based on the equivalent model, which can remarkably save time in comparison with the 3D simulations. The transition principle is also explained by quantitatively extracting the dispersion properties and impedance characteristics. Finally, a prototype of the proposed SSPP TL is fabricated and measured for demonstration. The measured operating band (0–7.7 GHz) is almost up to the cut-off frequency (about 8 GHz), which remains the inherent broadband low-pass transmission characteristics. Meanwhile, the measured in-band return loss is almost higher than 10 dB, which verifies the high-efficiency propagation. This work can pave the way for building up a new SSPP-based framework of microwave circuits.

Utilizing multi-modal data, as opposed to only hyperspectral image (HSI), enhances target identification accuracy in remote sensing. Transformers are applied to multi-modal data classification for their long-range dependency but often overlook intrinsic image structure by directly flattening image blocks into vectors. Moreover, as the encoder deepens, unprofitable information negatively impacts classification performance. Therefore, this paper proposes a learnable transformer with an adaptive gating mechanism (AGMLT). Firstly, a spectral–spatial adaptive gating mechanism (SSAGM) is designed to comprehensively extract the local information from images. It mainly contains point depthwise attention (PDWA) and asymmetric depthwise attention (ADWA). The former is for extracting spectral information of HSI, and the latter is for extracting spatial information of HSI and elevation information of LiDAR-derived rasterized digital surface models (LiDAR-DSM). By omitting linear layers, local continuity is maintained. Then, the layer Scale and learnable transition matrix are introduced to the original transformer encoder and self-attention to form the learnable transformer (L-Former). It improves data dynamics and prevents performance degradation as the encoder deepens. Subsequently, learnable cross-attention (LC-Attention) with the learnable transfer matrix is designed to augment the fusion of multi-modal data by enriching feature information. Finally, poly loss, known for its adaptability with multi-modal data, is employed in training the model. Experiments in the paper are conducted on four famous multi-modal datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and Houston2013 (HU). The results show that AGMLT achieves optimal performance over some existing models.

Transformers are increasingly popular in computer vision, which treat an image as a sequence of image patches and learn robust global features from the sequence. However, pure transformers are not entirely suitable for hyperspectral and LiDAR image classification because image classification requires both robust global features and discriminative local features. Therefore, this paper introduces a novel convolution interaction transformer network (CITNet) for jointly classifying hyperspectral and LiDAR images. The process begins with a carefully designed multi-scale asymmetric depthwise convolution (MADC) module that exploits the local-global correlations of shallow features. On this basis, a novel local-global transformer (LGTM) is equipped with a local-global feed-forward network (LGF) to extract in-depth local-global joint features from the multimodal data. Then, an optimization convolution cross-attention (OCA) module, incorporating a convolution layer, is developed to simulate the spatial relationships of semantic tokens. Finally, extensive experiments are conducted on the well-known Trento (TR), Augsburg (AU), MUUFL (MU), and Houston2013 (HU) datasets. The overall accuracy (OA) reaches 99.76%, 97.40%, 91.06%, and 99.90%, respectively, which are 0.2%-1.66%, 0.32%-7.37%, 1.52%-12.71%, and 0.14%-93.79% higher than the state-of-the-art (SOTA) methods, demonstrating the effectiveness of CITNet in improving the joint classification accuracy of hyperspectral and LiDAR images.

In hyperspectral image classification (HSIC), every pixel of the HSI is assigned to a land cover category. While convolutional neural network (CNN)-based methods for HSIC have significantly enhanced performance, they encounter challenges in learning the relevance of deep semantic features and grappling with escalating computational costs as network depth increases. In contrast, the transformer framework is adept at capturing the relevance of high-level semantic features, presenting an effective solution to address the limitations encountered by CNN-based approaches. This article introduces a novel adaptive learnable spectral–spatial fusion transformer (ALSST) to enhance HSI classification. The model incorporates a dual-branch adaptive spectral–spatial fusion gating mechanism (ASSF), which captures spectral–spatial fusion features effectively from images. The ASSF comprises two key components: the point depthwise attention module (PDWA) for spectral feature extraction and the asymmetric depthwise attention module (ADWA) for spatial feature extraction. The model efficiently obtains spectral–spatial fusion features by multiplying the outputs of these two branches. Furthermore, we integrate the layer scale and DropKey into the traditional transformer encoder and multi-head self-attention (MHSA) to form a new transformer with a layer scale and DropKey (LD-Former). This innovation enhances data dynamics and mitigates performance degradation in deeper encoder layers. The experiments detailed in this article are executed on four renowned datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and the University of Pavia (UP). The findings demonstrate that the ALSST model secures optimal performance, surpassing some existing models. The overall accuracy (OA) is 99.70%, 89.72%, 97.84%, and 99.78% on four famous datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and University of Pavia (UP), respectively.