License plate detection and recognition are still important and challenging tasks in natural scenes. At present, most methods have favorable effect on license plate recognition under restrictive conditions, and most of such license plates are shot under good angle and light conditions. However, for license plates under non-restrictive conditions, such as dark, bright, rotated conditions etc. from the Chinese City Parking Dataset (CCPD), the performance of some methods of license plate recognition will be significantly reduced. In order to improve the accuracy of license plate recognition under unrestricted conditions, a robust license plate recognition model is proposed in this paper, which mainly includes license plate feature extraction, license plate character localization, and feature extraction of characters. First of all, the model can activate the regional features of characters and fully extract the character features of license plates. Then locate each license plate character through Bi-LSTM combined with the context location information of license plates. Finally, 1D-Attention is adopted to enhance useful character features after Bi-LSTM positioning, and reduce useless character features to realize effective acquisition of character features of license plates. A large number of experimental results demonstrate that the proposed algorithm has good performance under unrestricted conditions, which proves the effectiveness and robustness of the model. In CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Tilt, CCPD-Weather, CCPD-Challenge and other sub-datasets, the recognition rates reach 99.3%, 98.5%, 98.6%, 96.4%, 99.3% and 86.6% respectively.

Integrated Sensing and Communication (ISAC) is a key technology for 6G and vehicle networking. With the continuous improvement of communication and sensing performance requirements, high frequency scenarios such as ultra large scale Multiple-Input Multiple-Output (MIMO), Extremely Large-scale Antenna Array (ELAA), and millimeter waves (mmWave) have become research hotspots in ISAC. In scenarios such as mmWave and terahertz (THz), with the increase of frequency and the application of ELAA, the electromagnetic environment shifts from far-field to near-field. Unlike the far-field, the near-field electromagnetic wavefront changes from a plane to a sphere, and the dimension of the antenna steering vector function changes from a single angle dimension to two dimensions: distance and angle. Therefore, suitable beamforming schemes need to be designed for near-field ISAC scenarios. Firstly, this paper models the near-field ISAC system. Then three ISAC beamforming schemes are proposed for near-field scenario: Conjugate Sensing Beamforming (CSBF), Null-space Sensing Beamforming (NSBF), and Joint Communication and Sensing Beamforming (JCSBF). The maximal SINR of NSBF is used as the SINR threshold for JCSBF. The complexities and the suitable application scenarios for the three proposed near-field ISAC beamforming schemes are analyzed also. The closed form solutions for two performance indicators which are communication rate (spectrum efficiency) and sensing rate are derived also. Finally, the performances of the three proposed beamforming schemes and the state-of-the-art near-field ISAC beamforming scheme are compared through simulation. The simulation results show that compared with the state-of-the-art scheme, the proposed three beamforming schemes all have lower complexity, and JCSBF also brings certain performance improvement with good robustness and single/multi-target detection ability.

Remote sensing images have the characteristics of a multi-scale, high resolution, and complex background, making the accuracy of small target detection too low. We propose an improved method based on the “you-only-look-once” v5 network model to make it more suitable for small target detection. First, we design a four-scale detection layer that improves the depth of the network and the accuracy of small target detection. Then, we introduce complete intersection over union-non-maximum suppression (NMS) to replace the commonly used NMS as a suppression of the prediction bounding box, and it also considers the overlap area, center distance, and aspect ratio to improve the accuracy of predicting bounding boxes. Furthermore, the remote sensing image is segmented into several fixed-size smaller images, making its size suitable for the input of the model to avoid target loss caused by directly scaling the size. The experimental results show that, compared to the four available detection methods, the improved algorithm significantly improves the detection accuracy of targets from small aircraft and ships in complex environments. The miss detection rate is also greatly reduced, especially for small targets, which verifies the effectiveness and robustness of the algorithm. Moreover, the detection rate has been greatly improved, which can meet the demand for real-time detection.

Transformers have recently achieved significant breakthroughs in various visual tasks. However, these methods often overlook the optimization of interactions between convolution and transformer blocks. Although the basic attention module strengthens the feature selection ability, it is still weak in generating superior quality output. In order to address this challenge, we propose the integration of sub-pixel space and the application of sparse coding theory in the calculation of self-attention. This approach aims to enhance the network’s generation capability, leading to the development of a sparse-activated sub-pixel transformer network (SSTNet). The experimental results show that compared with several state-of-the-art methods, our proposed network can obtain better generation results, improving the sharpness of object edges and the richness of detail texture information in super-resolution generated images.

In the context of the increasing popularity of Big Data paradigms and deep learning techniques, we introduce a novel large-scale hyperspectral imagery dataset, termed Orbita Hyperspectral Images Dataset-1 (OHID-1). It comprises 10 hyperspectral images sourced from diverse regions of Zhuhai City, China, each boasting 32 spectral bands with a spatial resolution of 10 meters and spanning a spectral range of 400–1000 nanometers. The core objective of this dataset is to elevate the performance of hyperspectral image classification and pose substantial challenges to existing hyperspectral image processing algorithms. When compared to traditional open-source hyperspectral datasets and recently released large-scale hyperspectral datasets, OHID-1 presents more intricate features and a higher degree of classification complexity by providing 7 classes labels in wider area. Furthermore, this study demonstrates the utility of OHID-1 by testing it with selected hyperspectral classification algorithms. This dataset will be useful to advance cutting-edge research in urban sustainable development science, land use analysis. We invite the scientific community to devise novel methodologies for an in-depth analysis of these data.