Surface plasmon resonance (SPR) with two-dimensional (2D) materials is proposed to enhance the sensitivity of sensors. A novel Goos–Hänchen (GH) shift sensing scheme based on blue phosphorene (BlueP)/transition metal dichalogenides (TMDCs) and graphene structure is proposed. The significantly enhanced GH shift is obtained by optimizing the layers of BlueP/TMDCs and graphene. The maximum GH shift of the hybrid structure of Ag-Indium tin oxide (ITO)-BlueP/WS₂–graphene is −2361λ with BlueP/WS₂ four layers and a graphene monolayer. Furthermore, the GH shift can be positive or negative depending on the layer number of BlueP/TMDCs and graphene. For sensing performance, the highest sensitivity of 2.767 × 10⁷λ/RIU is realized, which is 5152.7 times higher than the traditional Ag-SPR structure, 2470.5 times of Ag-ITO, 2159.2 times of Ag-ITO-BlueP/WS₂, and 688.9 times of Ag-ITO–graphene. Therefore, such configuration with GH shift can be used in various chemical, biomedical and optical sensing fields.

Automatic Modulation Classification (AMC) is a crucial task in the field of wireless communication, allowing for the identification of the modulation scheme of a received radio signal without prior knowledge of the communication system. Recently, AMC approaches based on Deep Learning (DL) have achieved outstanding results. However, the majority of current DL-based AMC methods face challenges in achieving high recognition accuracy while remaining computationally efficient. Some researchers have designed autoencoder-based models to generate low-dimensional temporal feature embeddings of the radio signal, thereby reducing the number of model parameters while maintaining high performance in recognizing modulation formats. However, when further improving AMC performance via learning low-dimensional spatial-temporal feature representations, traditional autoencoder models require both a convolutional decoder and an LSTM decoder to reconstruct temporal and spatial features separately, which unavoidably raises the model parameters. In this paper, we propose a spatiotemporal feature sharing reconstructing network (STARNet) to simultaneously extract low-dimensional spatial and temporal feature representations of radio signals using a single autoencoder structure, thereby reducing the number of model parameters and improving AMC performance. Additionally, we construct a Hybrid Attentive Ghost (HA-Ghost) to automatically extract discriminative radio signal spatial information according to signal reconstruction performance. Extensive experiments on benchmark datasets demonstrate that the proposed STARNet achieves an average modulation classification accuracy of 63.64%, outperforming previous state-of-the-art models. Despite extracting more types of features, STARNet has only 14,860 parameters, which is smaller than existing spatiotemporal autoencoder-based methods.

Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable denoising preprocess. In this paper, a denoising-guided disentangled network based on an inception structure is proposed to simultaneously complete the denoising and recognition of radar signals in an end-to-end manner. The pure radar signal representation (PSR) is disentangled from the noise signal representation (NSR) through a feature disentangler and used to learn a radar signal modulation recognizer under low-SNR environments. Signal noise mutual information loss is proposed to enlarge the gap between the PSR and the NSR. Experimental results demonstrate that our method can obtain a recognition accuracy of 98.75% in the −8 dB SNR and 89.25% in the −10 dB environment of 12 modulation formats.

Aquatic debris monitoring is of great importance to human health, aquatic habitats and water transport. In this paper, we first introduce the prototype of an aquatic sensor node equipped with an embedded camera sensor. Based on this sensing platform, we propose a fast and accurate debris detection algorithm. Our method is specifically designed based on compressive sensing theory to give full consideration to the unique challenges in aquatic environments, such as waves, swaying reflections, and tight energy budget. To upload debris images, we use an efficient sparse recovery algorithm in which only a few linear measurements need to be transmitted for image reconstruction. Besides, we implement the host software and test the debris detection algorithm on realistically deployed aquatic sensor nodes. The experimental results demonstrate that our approach is reliable and feasible for debris detection using camera sensors in aquatic environments.

Anchor-free detection methods identify different objects by perceiving bounding box keypoints without predefined anchor boxes, which have attracted much attention due to their straightforward design and comparable performance. Currently, most anchor-free methods detect bounding box corners to regress object locations. In clutter environments, the bounding box corners may lie in background regions, which have limited relation with the object itself. In addition, the relationships between object keypoints are always neglected, potentially affecting the perceptibility of the detector for high-precision object detection. In this paper, we propose the Keypoint Relational Regression Network (KRRNet) to detect object keypoints with semantic relations instead of bounding box corners. The relational regression head is designed to enhance the keypoint relationship exploration capability and reason accuracy object locations. Moreover, the random background sampling strategy is proposed to sample negative background points around foreground object regions and form point pairs with object keypoints. Then, KRRNet can explicitly learn discriminative feature embedding from contrastive learning to pull close the positive pairs and push apart the negative pairs, resisting the influence of surrounding complex environments. KRRNet can be trained on one Nvidia RTX 3090 GPU and achieves a single-scale test AP of 48.9% and multi-scale test AP of 50.6% on the MS-COCO test-dev with the backbone of Hourglass-104, surpassing state-of-the-art bottom-up anchor-free detector using the same backbone.