Automatic modulation recognition (AMR) is a crucial step in wireless communication systems, which identifies the modulation scheme from detected signals to provide key information for further processing. However, previous work has mainly focused on the identification of a single signal, overlooking the phenomenon of multiple signal superposition in practical channels and the signal detection procedures that must be conducted beforehand. Considering the susceptibility of radio frequency (RF) signals to noise interference and significant spectral variations, we propose a novel Hierarchical Feature Integration (HIFI)-YOLO framework for multi-signal joint detection and modulation recognition. Our HIFI-YOLO framework, with its unique design of hierarchical feature integration, effectively enhances the representation capability of features in different modules, thereby improving detection performance. We construct a large-scale AMR dataset specifically tailored for scenarios of the coexistence or overlapping of multiple signals transmitted through channels with realistic propagation conditions, consisting of diverse digital and analog modulation schemes. Extensive experiments on our dataset demonstrate the excellent performance of HIFI-YOLO in multi-signal detection and modulation recognition as a joint approach.

MOTIVATION: scATAC-seq enables high-resolution mapping of cis-regulatory elements. It has been widely applied to uncover cell-type-specific regulatory networks and complement scRNA-seq analysis in numerous studies. However, a large number of datasets generated by scATAC-seq remain underutilized due to limited exploration of super-enhancers/typical enhancers and gene markers. A comprehensive resource enabling cell-type-specific annotation of cis-regulatory elements and their dynamic enhancer-gene linkages remains an urgent unmet need for scATAC-seq. RESULTS: We present dbscATAC, a specialized single-cell database for annotating super-enhancers, gene markers, and enhancer-gene interactions derived from scATAC-seq data. Using improved machine learning algorithms, we identified 213 835 super-enhancers across 520 tissue/cell types from three species, as well as 347 484 gene markers, 13 470 526 enhancers, and 10 402 346 enhancer-gene interactions derived from 1 668 076 single cells spanning 1028 tissue/cell types in 13 species. An easy-to-use online platform with multiple analytic modules and hierarchical query options was developed for searching, browsing and visualizing single-cell super-enhancers, enhancers, and gene markers. dbscATAC provides a comprehensive resource to facilitate the exploration of enhancer landscapes, gene regulation, and cell-type-specific characteristics in single-cell epigenomics. AVAILABILITY AND IMPLEMENTATION: The database with all the super-enhancer/enhancer annotation data is available at http://singlecelldb.com/dbscATAC/index.php. And the source code of dbscATAC for prediction of SEs, enhancers, and gene markers are available at https://github.com/EvansGao/dbscATAC. The source code, tissue/cell type description, and data summary can be downloaded at DOI: 10.6084/m9.figshare.28706414.scATAC-seq, Database, Super-enhancers/enhancers, Gene markers.

Radio frequency fingerprint identification (RFFI) leverages signal distortions caused by hardware impairments to identify transmitters, thereby enhancing IoT security. However, current radio frequency fingerprints (RFFs) modelings typically focus on partial hardware impairments, risking incomplete RFF extraction and limited RFF understanding. This study aims to refine the modeling of RFFs and guide the development of robust and accurate RFFI approaches based on this model. Specifically, we propose a comprehensive time-domain signal distortion model based on hardware impairments in wireless transmission circuit components, revealing that RFFs can be categorized into two types: 1) fine-grained RFF and 2) coarse-grained RFF. The former encompass localized distortions, such as mismatches, intersymbol interference, and nonlinear distortions; the latter relate to global features, including frequency spurs, phase noise, and crystal oscillator frequency offset. Subsequently, we analyze the impact of interference on the RFF model and propose necessary methods to mitigate the interference. Combining the comprehensive analysis of the RFF model and interference, we summarize three primary characteristics of RFFs: 1) multiscale; 2) fixedness; and 3) ubiquity. These characteristics indicate that convolutional neural networks (CNNs) from the visual domain cannot be directly transferred or simply adapted in terms of input data shape for application in RFFI. Therefore, we propose an enhanced CNN architecture with grouped convolutions and channel fusion modules for effective RFF extraction. To demonstrate the generalizability of our approach, we conduct extensive experiments using three public IoT signal datasets. Experimental results demonstrate that our method exhibits excellent identification performance and robustness against interference across various environments.

This study investigates global ionosphere responses and driving mechanisms during the May 2024 super geomagnetic storm, using data from 6268 GNSS stations, multi-source observations from ground- and satellite -based platforms, and empirical-physical models. It focuses on particle composition and temperature variation, ionospheric storm, large-scale traveling ionospheric disturbance (LSTID), scintillation, and vertical disturbance. Results show that enhanced polar energy input during the storm increased electron density (Ne) and electron/ion temperatures in the nighttime F2 layer, with electron heating concentrated above 300 km and reaching up to 4000 K, while electron precipitation occurred into the E layer, and broader ion heating across the F2–E layers, reaching up to 2000 K. These energy inputs further drove Ne gradient variations and turbulence, synchronizing high-latitude LSTIDs, scintillations, and ionospheric storms, with stronger and longer disturbances in the Northern Hemisphere (NH). Multiple global disturbances exhibited spatial variability and coupling structures reflected in intensity, meridional propagation, and multi-scale co-evolution. The strongest responses were observed in the high-latitude F2 layer, with evident time delays during equatorward propagation, following a decrease–then–increase pattern with decreasing latitude. High-latitude enhancements were linked to Joule heating, particle precipitation, and storm-induced Ne variations, weakened during propagation to mid-latitudes due to ion drag and viscosity, but reintensified at lower latitudes due to penetration electric field (PEF) and diurnal solar radiation coupling. LSTIDs driven by atmospheric gravity waves responded more rapidly and intensely to energy input than scintillations. During the main phase, over North America below 60°N and Southern Hemisphere (SH) at magnetic latitudes above 20°S, the eastward PEF, an ΣO/N₂ increase to 1.4, and O⁺ enhancement jointly elevated Ne by 63.8%, causing the strongest positive storm. The strongest disturbances occurred in the Americas, featuring LSTIDs with meridional velocities (Vm) of 840–910 m/s and amplitudes (Amp) over 25 TECU, along with banded scintillations (Amp>1.3 TECU/min), and vertical disturbances propagating upward at 22–27 m/s, lifting the entire ionosphere. Eurasia experienced notable negative storms (Ne reduced by 53.2%), due to ΣO/N₂ depletion to below 0.2, NO⁺ enhancement, and westward PEF. European LSTIDs were short-period, densely overlapping (Vm>1250 m/s, Amp<13 TECU, period<40 minutes). Over Eurasia, ionosondes recorded a marked Ne decrease by up to 8.2 × 10⁵ el/cm³, nearly destroyed ionospheric layering, and observed downward-propagating disturbances at 21–23 m/s. Regarding the vertical structure, global peak Ne variations correlated closely with ionospheric storms and ΣO/N₂ changes, while peak heights exhibited global uplift driven by plasma drifts and thermospheric expansion. Differences in LSTIDs propagation characteristics driven by auroral electrojets and electric fields, AGWs induced by Joule heating and particle precipitation, hemispheric asymmetries in energy deposition and the Coriolis force, intensified summer-to-winter seasonal circulation and winds, and ion-drag differences modulated by local solar radiation were further analyzed. In the recovery phase, global negative storms emerged due to ΣO/N₂ depletion, with no notable LSTIDs or scintillations observed after 14 UT on May 11 as storm energy input had weakened. However, in the Asia-Pacific, increased ΣO/N₂ and solar radiation caused a re-intensification of disturbances that persisted until 21:30 UT.

Tracking the kinetic state of a non-cooperative target in time-varying non-line-of-sight (NLOS) environment is a challenging problem in many applications. The distance estimation (DE) of a target in such tracking system, produced by local anchors, can be influenced by a positive bias caused by refraction and reflection in the physical channel within a NLOS environment. Specifically, in a DE-based positioning network, such as a Time Difference of Arrival (TDOA) localization system with several listening anchors blocked, the NLOS error can cause an overall deviation in positioning, which in turn affects the accuracy of trajectory estimations. In this paper, we develop a regression-based error transition model to associate the NLOS errors with the target states by its previous trajectory. Then, by employing a joint variational Bayesian (VB) approximation, we decouple this association iteratively through algorithm. Thereby facilitating an accurate estimation of the posterior distribution for target trajectories and the ranging error. Experiments involving an actual Unmanned Aerial Vehicle (UAV) and TDOA localization system validate the robustness and performance of our algorithm compared to existing approaches.

Radio Frequency Fingerprinting (RFF)-based identification methods have the potential to enhance the security of the Internet of Things (IoT). However, conventional fingerprinting techniques based on standard sample rates face limitations related to noise and device scale. The utilization of high sample rate receivers offers a promising solution to mitigate these constraints. Nonetheless, the challenge lies in extracting RFFs from the collected ultra-long signals. Image-based methods, which accumulate signals in the time domain, reduce the difficulty of extracting RFFs from long signals but overlook the fine-grained RFFs in the time domain. To solve this problem, this paper proposes RFF modeling for long signals, emphasizing the importance of obtaining short-term time-varying RFFs and time-invariant RFFs. Combining an analysis of the inductive biases of convolutional neural networks, we propose a backbone network named GResNet, which is capable to effectively extract these two types of RFFs. An information fusion module is added to improve identification performance. Extensive experiments are conducted with 100 LoRa devices, demonstrating that our method outperforms existing RFFI techniques based on standard sample rate or high sample rate signals. Furthermore, our approach maintains robust performance within a wide range of SNRs.

The rapid expansion of the Internet of Things (IoT) has heightened the necessity for device identity authentication to ensure security. Radio frequency fingerprint identification (RFFI) has emerged as a promising solution for this purpose, which leverage unique signal distortions caused by hardware impairments to authenticate device identities. However, most RFFI methods operate under a closed-set assumption and usually mistakenly identify unknown devices from the open set as known devices. In this paper, we propose a Synthetic Feature Constrain for open-set Recognition (SFCR) method to maintain classification performance on known devices and identify unknowns. Specifically, we modify the nonlinear characteristics of known devices based on the power amplifier nonlinearity model of radio frequency fingerprints (RFF) to synthesize signals for unknown devices. Furthermore, we propose a synthetic feature constraint to calibrate the position of synthetic devices in the feature space, such that they lie between the feature centers of the collective synthetic and originating known devices. As synthetic devices represent only a subset of the real unknown devices, we also introduce a calibration method for the prediction results. Experiments on a publicly available LoRa device dataset have validated the effectiveness of our approach. The code is released on github.com/wzyxwqx/SFCR.

Time-sparse frequency-hopping (FH) signals detection and sequences identification present a significant challenge in spectrum tracking of multiple targets. The non-continuous observations that arise from their temporal sparsity complicates identification efforts in low signal-to-noise ratio (SNR) environments. In this letter, a dynamic temporal perception probability hypothesis density (DTP-PHD) filter for spectrum tracking of multiple targets with potential periodicity was proposed by leveraging the periodicity alignment likelihood ratio (PALR). The PALR enables the estimation of the time transition function of targets' FH signals, which also facilitates the extraction of the spectrum track by identifying each target using a joint posterior intensity. Moreover, a closed-form solution of DTP-PHD was derived under linear Gaussian assumptions. The validity of the periodicity estimation was established by implementing a particle version of the proposed algorithm, which demonstrated robust tracking performance in noisy environments.

Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery.

Summary: Analysis pipelines are an essential part of bioinformatics research, and ad hoc pipelines are frequently created by researchers for prototyping and proof-of-concept purposes. However, most existing pipeline management system or workflow engines are too complex for rapid prototyping or learning the pipeline concept. A lightweight, user-friendly and flexible solution is thus desirable. In this study, we developed a new pipeline construction and maintenance tool, PipelineDog. This is a web-based integrated development environment with a modern web graphical user interface. It offers cross-platform compatibility, project management capabilities, code formatting and error checking functions and an online repository. It uses an easy-to-read/write script system that encourages code reuse. With the online repository, it also encourages sharing of pipelines, which enhances analysis reproducibility and accountability. For most users, PipelineDog requires no software installation. Overall, this web application provides a way to rapidly create and easily manage pipelines. Availability and implementation: PipelineDog web app is freely available at http://web.pipeline.dog. The command line version is available at http://www.npmjs.com/package/pipelinedog and online repository at http://repo.pipeline.dog. Contact: [email protected] or [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: A proper target or marker is essential in any diagnosis (e.g. an infection or cancer). An ideal diagnostic target should be both conserved in and unique to the pathogen. Currently, these targets can only be identified manually, which is time-consuming and usually error-prone. Because of the increasingly frequent occurrences of emerging epidemics and multidrug-resistant 'superbugs', a rapid diagnostic target identification process is needed. RESULTS: A new method that can identify uniquely conserved regions (UCRs) as candidate diagnostic targets for a selected group of organisms solely from their genomic sequences has been developed and successfully tested. Using a sequence-indexing algorithm to identify UCRs and a k-mer integer-mapping model for computational efficiency, this method has successfully identified UCRs within the bacteria domain for 15 test groups, including pathogenic, probiotic, commensal and extremophilic bacterial species or strains. Based on the identified UCRs, new diagnostic primer sets were designed, and their specificity and efficiency were tested by polymerase chain reaction amplifications from both pure isolates and samples containing mixed cultures. AVAILABILITY AND IMPLEMENTATION: The UCRs identified for the 15 bacterial species are now freely available at http://ucr.synblex.com. The source code of the programs used in this study is accessible at http://ucr.synblex.com/bacterialIdSourceCode.d.zip CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.