The poor quality images with scarce target characteristics and complicated textures make it tough to enhance target detectability and background suppressibility simultaneously in infrared small target detection. Representation learning has gradually shown superiority, but two key issues arise. The first is constructing an appropriate dictionary. The second is a comprehensive and accurate representation learning model. This paper introduces a low-rank and sparse spatial-temporal tensor representation learning model based on local binary contrast measure (STRL-LBCM), which transforms the target detection into an optimization task for representation and decomposition of background, target and noise components from spatial-temporal aspects. First, we construct an IR spatial-temporal tensor from the original sequence, followed by improving GoDec plus to obtain a sparse part and a background dictionary, which lays the foundation for representation learning model. Then, a rank surrogate named fully connected tensor nuclear norm (FCTNN) is proposed for low-rank coefficient, aiming at low-rank background estimation. Meanwhile, with the sparse part, we design a saliency prior called local binary contrast measure (LBCM), which extracts more effective prior information for guiding target detection. Furthermore, not only Frobenius norm but also a 3D weight total variation (3DWTV) regularization are utilized to preserve background details and remove noise. Finally, an efficient solving framework is designed for the proposed model. Comprehensive experiments reveal that STRL-LBCM provides superior TD, BS and overall performance compared with sixteen target detection methods.

As extensive renewable energy-based distributed generators (DGs) are integrated into the microgrids (MGs), system inertia drops sharply, posing enormous challenges to power quality. Inspired by the conventional synchronous generator(SG), a novel control structure called virtual synchronous generator (VSG) is proposed to provide ancillary rotor regulation. Moreover, existing distributed control algorithms highly rely on explicit state sharing, which exposes MGs to potential privacy leakage threats. To this end, a distributed self-triggered privacy-preserving secondary control of VSG-based AC MGs is proposed to achieve control objectives while protecting the initial and real-time values. First, VSG is modeled by mimicking the operation of SG to provide inertial and damping support for MGs. Considering the nonlinear dynamic characteristics of VSGs, a novel VSG-integrated distributed secondary control strategy with inertial-support is established to achieve control objectives. Moreover, we present a more comprehensive definition of privacy preservation. Next, a Paillier encryption-based privacy-preserving algorithm is innovatively developed to tackle the potential data disclosure problems that need to be faced when performing continuous information interaction. Further, to attenuate the effect of extra encryption costs, we propose a self-triggered mechanism to significantly reduce 86% communication resources. Finally, the superior performance of the proposed strategy is testified by numerical case studies.

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent.

As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperation.