Radar Intelligent Processing Technology and Application for Weak Target

Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing

Detection and Classification of Maritime Target with Micro-motion Based on CNNs

In this paper, Convolutional Neural Networks (CNN) are used to detect and classify micro-Doppler effects of maritime targets by using generalized learning ability for high-dimensional features. Based on the micro-motion model of maritime targets, two-dimensional time-frequency maps of four types of micro-motion signals are constructed in the measured sea clutter background. These maps were used as training and test datasets. Furthermore, three types of CNN models, i.e., LeNet, AlexNet, and GoogleNet, are used in binary detection and multiple micro-motion classifications. The effects of signal-to-noise ratio on detection and classification performance are also studied. Compared with the traditional support vector machine method, the proposed method can learn the micro-motion features intelligently, and has performed better in detection and classification. Thus, this study can provide a new technical approach for radar target detection and recognition under a cluttered background

TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph

Multi-hop logical reasoning over knowledge graph (KG) plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding (CQE) methods for reasoning focus on static KGs, while temporal knowledge graphs (TKGs) have not been fully explored. Reasoning over TKGs has two challenges: 1. The query should answer entities or timestamps; 2. The operators should consider both set logic on entity set and temporal logic on timestamp set. To bridge this gap, we define the multi-hop logical reasoning problem on TKGs. With generated three datasets, we propose the first temporal CQE named Temporal Feature-Logic Embedding framework (TFLEX) to answer the temporal complex queries. We utilize vector logic to compute the logic part of Temporal Feature-Logic embeddings, thus naturally modeling all First-Order Logic (FOL) operations on entity set. In addition, our framework extends vector logic on timestamp set to cope with three extra temporal operators (After, Before and Between). Experiments on numerous query patterns demonstrate the effectiveness of our method

Hypothermia-Mediated Apoptosis and Inflammation Contribute to Antioxidant and Immune Adaption in Freshwater Drum, <i>Aplodinotus grunniens</i>

Hypothermia-exposure-induced oxidative stress dysregulates cell fate and perturbs cellular homeostasis and function, thereby disturbing fish health. To evaluate the impact of hypothermia on the freshwater drum (Aplodinotus grunniens), an 8-day experiment was conducted at 25 °C (control group, Con), 18 °C (LT18), and 10 °C (LT10) for 0 h, 8 h, 1 d, 2 d, and 8 d. Antioxidant and non-specific immune parameters reveal hypothermia induced oxidative stress and immunosuppression. Liver ultrastructure alterations indicate hypothermia induced mitochondrial enlargement, nucleoli aggregation, and lipid droplet accumulation under hypothermia exposure. With the analysis of the transcriptome, differentially expressed genes (DEGs) induced by hypothermia were mainly involved in metabolism, immunity and inflammation, programmed cell death, and disease. Furthermore, the inflammatory response and apoptosis were evoked by hypothermia exposure in different immune organs. Interactively, apoptosis and inflammation in immune organs were correlated with antioxidation and immunity suppression induced by hypothermia exposure. In conclusion, these results suggest hypothermia-induced inflammation and apoptosis, which might be the adaptive mechanism of antioxidation and immunity in the freshwater drum. These findings contribute to helping us better understand how freshwater drum adjust to hypothermia stress