The popularization of unmanned aerial vehicles (UAVs) has raised critical concerns about public security and personal privacy, necessitating drone recognition technology. Radio frequency-based monitoring methods are widely used because of their advantage of non-line-of-sight. However, most existing radio frequency-based recognition methods have closed-set assumption vulnerabilities. Although several open-set methods have been proposed, they still face robustness deficiencies when video transmission signal attenuation conditions are severe or when interference signals are present. To address this challenge, we propose a lightweight open-set drone recognition (LOSDR) method. First, IQ data are transformed into time-frequency spectra via a short-time Fourier transform. Second, regional block details and global frequency-hopping patterns are jointly extracted via a flight control signal block extractor and pattern extractors. Then, a semantic alignment module, which integrates text embeddings derived from the contrastive language-image pretraining model, and a class decorrelation module with orthogonal constraints are designed to increase the degree of interclass separation and reduce the ambiguity of classification. Additionally, an adaptive dynamic entropy threshold mechanism is used to balance known/unknown decisions under varying noise and interference levels. We construct datasets for three different electromagnetic environments. Extensive experiments conducted on these datasets demonstrate that LOSDR outperforms the state-of-the-art methods in terms of generalizability and accuracy.

Automatic modulation recognition (AMR) plays a crucial role in non-cooperative communication environment for identifying modulation types of received radio signals. Recently, the achievements of deep learning (DL) have sparked significant interest in applying DL to the field of AMR. However, existing DL-based AMR methods only use image modal or sequence modal as input, which cannot leverage sufficient information of the signal in complicated electromagnetic environment characterized by scarce labeled sample and multipath fading. To overcome this limitation, we explore different modal representations of the signal to fully exploit their complementary information and propose a hybrid modal contrast and fusion method for automatic modulation recognition (HMCF-AMR). It consists of two stages: 1) modal-level feature contrast for self-supervised pre-training and 2) modal-level feature fusion for supervised fine-tuning. In modal-level feature contrast, sequence encoder and image encoder are designed to extract multi-scale features of the modulated signal from the image modal and sequence modal. Meanwhile, a multi-task collaborative pre-training method combining generative and contrastive learning is achieved to enhance and align different modal representations. In modal-level feature fusion, an attentional feature fusion mechanism integrates the features learned from different modal to further improve modulation recognition performance and online learning is implemented by fine-tuning to handle different scenarios. Simulation results show that our proposed HMCF-AMR outperforms other baseline models in both adequate-sample and few-shot scenarios and demonstrates greater robustness in complicated multipath fading channels.

MOTIVATION: RNA editing generates post-transcriptional sequence alterations. Detection of RNA editing sites (RESs) typically requires the filtering of SNVs called from RNA-seq data using an SNP database, an obstacle that is difficult to overcome for most organisms. RESULTS: Here, we present a novel method named SPRINT that identifies RESs without the need to filter out SNPs. SPRINT also integrates the detection of hyper RESs from remapped reads, and has been fully automated to any RNA-seq data with reference genome sequence available. We have rigorously validated SPRINT's effectiveness in detecting RESs using RNA-seq data of samples in which genes encoding RNA editing enzymes are knock down or over-expressed, and have also demonstrated its superiority over current methods. We have applied SPRINT to investigate RNA editing across tissues and species, and also in the development of mouse embryonic central nervous system. A web resource (http://sprint.tianlab.cn) of RESs identified by SPRINT has been constructed. AVAILABILITY AND IMPLEMENTATION: The software and related data are available at http://sprint.tianlab.cn. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.