Search Results (53)

Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents a non-iterative real-time Feature Sub-image Based Stripmap Phase Gradient Autofocus (FSI-SPGA) algorithm. The FSI-SPGA algorithm combines 2D Constant False Alarm Rate (CFAR) for coarse point selection and spatial decorrelation for refined point selection. This approach enables the accurate extraction of high-quality scattering points. Using these points, the algorithm constructs a feature sub-image containing comprehensive phase error information and performs a non-iterative phase error estimation based on this sub-image. To address the multifunctional, low-power, and real-time requirements of small UAV SAR, we designed a highly efficient hybrid architecture. This architecture integrates dataflow reconfigurability and dynamic partial reconfiguration and is based on an ARM + FPGA platform. It is specifically tailored to the computational characteristics of the FSI-SPGA algorithm. The proposed scheme was assessed using data from a 6 kg small SAR system equipped with centimeter-level INS/GPS. For SAR images of size 4096 × 12,288, the FSI-SPGA algorithm demonstrated a 6 times improvement in processing efficiency compared to traditional methods while maintaining the same level of precision. The high-efficiency reconfigurable ARM + FPGA architecture processed the algorithm in 6.02 s, achieving 12 times the processing speed and three times the energy efficiency of a single low-power ARM platform. These results confirm the effectiveness of the proposed solution for enabling high-quality real-time SAR imaging under stringent SwaP constraints. Full article

The performance of oil–water coalescence separation elements currently fails to meet the increasing demands of the oily wastewater treatment industry. To address this challenge, a series of fiber coalescing filters were developed through an underwater superoleophobic modification process using a simple impregnation technique. The effect of varying surface wettability on the separation efficiency of oil-in-water (O/W) emulsions stabilized with surfactants was investigated. The results demonstrate that, after undergoing underwater superoleophobic modification, the separation efficiency of the fiber filter material improved by 33.9%, the pressure drop was reduced by 46.1%, and the steady-state quality factor increased by 83.3%. Building upon these findings, an oil-repellent pore size gradient structure was introduced for the coalescence separation of surfactant-stabilized oil-in-water emulsions. This structure exhibited outstanding characteristics, including a low pressure drop and a high-quality factor. Furthermore, when processing emulsions stabilized with surfactants such as OP-10 (nonionic), CTAB (cationic), and SDS (anionic), the structure maintained high separation efficiencies of 93.6%, 96.4%, and 97.2%, respectively, after 10 cycles. Finally, based on experimental data and theoretical analysis, a separation mechanism for oil–water coalescence using superoleophobic pore size gradient filtration materials is proposed. This structure demonstrates significant potential for widespread application in liquid–liquid separation technologies. Full article

High-density planting is crucial for maximizing the genetic potential of soybean cultivars to achieve higher yields. However, increasing the planting density can lead to the risk of plant lodging. Therefore, the identification of gene loci associated with lodging resistance is considered critical for the development of high-yielding, lodging-resistant soybean cultivars. In this study, 338 natural soybean accessions from the similar latitude were used to identify candidate genes associated with lodging resistance. Based on 9,400,987 SNPs, the soybean population was classified into three subpopulations. Genome-wide association analysis revealed that under planting densities of 300,000 and 150,000 plants/ha, a total of 20 significant SNPs were repeatedly detected under both planting densities, distributed across 14 chromosomes of soybeans. A hotspot region was identified on chromosome 19, from which seven candidate genes were detected. Based on haplotype and gene expression analyses, Glyma.19g212800 (SUS3) and Glyma.19g212700 (GH9B13) were found to be associated with significant phenotypic variations and were identified as candidate genes. RNA-seq analysis showed that DEGs were primarily enriched in the starch and sucrose metabolism pathways. The differential expression of Glyma.19g212800 among soybean haplotypes was further validated by qRT-PCR. By participating in sucrose decomposition and polysaccharide metabolism processes, it regulated cellulose content, thereby affecting the soybean plant lodging. This study facilitated the dissection of genetic networks underlying lodging traits in soybean, which benefits the genetic improvement of high-yield soybean with dense planting. Full article

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground-moving target indication (GMTI) radar systems has received great interest. However, because of size, weight, and power (SWaP) limitations, the UAV may not be able to equip a highly accurate inertial navigation system (INS), which leads to reduced accuracy in the moving target relocation. To solve this issue, we propose using an image registration algorithm, which matches a Doppler beam sharpening (DBS) image of detected moving targets to a synthetic aperture radar (SAR) image containing coordinate information. However, when using conventional SAR image registration algorithms such as the SAR scale-invariant feature transform (SIFT) algorithm, additional difficulties arise. To overcome these difficulties, we developed a new image-matching algorithm, which first estimates the errors of the UAV platform to compensate for geometric distortions in the DBS image. In addition, to showcase the relocation improvement achieved with the new algorithm, we compared it with the affine transformation and second-order polynomial algorithms. The findings of simulated and real-world experiments demonstrate that our proposed image transformation method offers better moving target relocation results under low-accuracy INS conditions. Full article

Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, two primary factors degrade radar imaging quality: (1) inherent limitations in antenna radiation efficiency, which lead to low-frequency signal loss and subsequent time-domain ringing artifacts; (2) impedance mismatch at the antenna terminals, causing standing wave reflections that exacerbate the ringing phenomenon. This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. Validation through numerical simulations and experimental measurements demonstrates significant suppression of time-domain ringing and improved target clarity. The proposed approach holds critical importance for advancing impulse UWB SAR systems, particularly in scenarios requiring high-resolution imaging. Full article

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground moving target indication (GMTI) radar systems has received great interest. In GMTI, moving target relocation is an essential requirement, because the positions of the moving targets are usually displaced. For a multichannel radar system, the position of moving targets can be accurately obtained by estimating their interferometric phase. However, the high position accuracy requirements of antennas and the computational resource requirements of algorithms limit the applications of relocation algorithms in UAV-borne GMTI radar systems. In addition, the clutter’s interferometric phase can be severely affected by the undesired phase error in the site. To overcome these issues, we propose an improved knowledge-based (KB) algorithm. In the algorithm, moving targets can be relocated by comparing their interferometric phase with the clutter’s phase. As for the undesired phase error, the algorithm first employs a random sample consensus (RANSAC) algorithm to iteratively filter the outliers. Compared with other classic relocation algorithms, the proposed algorithm shows better relocation accuracy and can be applied in real-time applications. The performance of the proposed improved KB algorithm was evaluated using both simulated and real experimental data. Full article

Synthetic aperture radar (SAR) systems, as wideband radar systems, are inherently susceptible to interference signals within their operational frequency band, which significantly affects SAR signal processing and image interpretation. Recent studies have demonstrated that semiparametric methods (e.g., the RPCA method) exhibit excellent performance in suppressing these interference signals. However, these methods predominantly focus on processing SAR’s raw echo data, which does not satisfy the sparsity requirements and entails extremely high computational complexity, complicating integration with imaging algorithms. This paper introduces an effective method for suppressing interference signals by leveraging the sparsity of the SAR image domain. It utilizes the sparsity of the interference signal in the two-dimensional frequency domain, following focusing processing, rather than relying on low-rank properties. This approach significantly reduces the computational complexity. Ultimately, the effectiveness and efficiency of the proposed algorithm are validated through experiments conducted with simulated and real SAR data. Full article

High-precision, robust, and rapid three-dimensional (3D) passive positioning of the radiation source is critical for modern reconnaissance systems. While synthetic aperture technology has advanced 2D passive positioning performance, existing methods fail to achieve full 3D positioning with sufficient accuracy and computational efficiency. This is because of the inherent limitations of the single-station platform in resolving elevation-angle ambiguity. To address this gap, we propose a Cross-Track Interferometric Synthetic Aperture (CISA) 3D passive positioning algorithm. The algorithm innovatively realizes robust elevation-angle measurement by recursively deriving the long baseline unambiguous phase difference step-by-step from a virtual short baseline. The 3D positioning is achieved by combining passive synthetic aperture and interferometric angle measurement. Furthermore, we establish the incoherence model of synthetic aperture passive positioning for the first time and propose a compensation method based on static acquisition data to improve the practicability of CISA. Simulation and experimental results demonstrate that the proposed CISA algorithm achieves a positioning accuracy of 4.73‰R, improves computational efficiency by 1–2 orders of magnitude compared to conventional methods, and exhibits superior robustness to noise. The research can provide a reference for the method research and engineering realization of synthetic aperture 3D passive positioning. Full article

Unmanned Aerial Vehicle (UAV)-based distributed Synthetic Aperture Radar (SAR) is a current research focus. Phase synchronization is crucial for eliminating the non-coherence of distributed systems. However, as the number of UAVs increases, fast time-varying multipath effects caused by rotors can lead to multipath fading. This degrades the signal-to-noise ratio (SNR) of the synchronization link and distorts the synchronization waveform. It further breaks the reciprocity of the dual one-way synchronization link, ultimately degrading phase synchronization accuracy. We propose a robust method for spike detection and error propagation to improve phase synchronization precision. Using the Hampel filter, we detect pulse peak position jitter and remove observations from anomalous links. We then use data fusion based on minimum variance to recover synchronization errors in these links, leveraging the redundancy in synchronization phase matrices. The effectiveness of the proposed method is confirmed through flight test data from a four-UAV distributed TomoSAR experiment. Compared to the maximum-peak detection method, the phase accuracy is improved from 12.84 deg to 0.61 deg. This method supports the application of distributed SAR. Full article

Multiple-Input Multiple-Output (MIMO) radar enjoys the advantages of a high degree of freedom and relatively large virtual aperture, so it has various forms of applications in several aspects such as remote sensing, autonomous driving and radar imaging. Among all multiplexing schemes, Time-Division Multiplexing (TDM)-MIMO radar gains a wide range of interests, as it has a simple and low-cost hardware system which is easy to implement. However, the time-division nature of TDM-MIMO leads to the dilemma between the lower Pulse Repetition Interval (PRI) and more transmitters, as the PRI of a TDM-MIMO system is proportional to the number of transmitters while the number of transmitters significantly affects the resolution of MIMO radar. Moreover, a high PRI is often needed to obtain unambiguous imaging results for MIMO-SAR 3D imaging applications on a fast-moving platform such as a car or an aircraft. Therefore, it is of vital importance to develop an algorithm which can achieve unambiguous TDM-MIMO-SAR 3D imaging even when the PRI is low. Inspired by the motion compensation problem associated with TDM-MIMO radar imaging, this paper proposes a novel imaging algorithm which can utilize the phase shift induced by the time-division nature of TDM-MIMO radar to achieve unambiguous MIMO-SAR 3D imaging. A 2D-Compressed Sensing (CS)-based method is employed and the proposed method, which is called HPC-2D-FISTA, is verified by simulation data. Finally, a real-world experiment is conducted to show the unambiguous imaging ability of the proposed method compared with the ordinary matched-filter-based method. The effect of velocity error is also analyzed with simulation results. Full article

The rapid and accurate annotation of the complex compounds and metabolites in natural products remains a significant challenge. In this study, we developed an integrated strategy to efficiently and accurately profile both the prototypes and metabolites of natural products in vivo. And this was achieved by establishing a gypenosides constituent database and utilizing R programming language to combine sample selection, virtual metabolite database construction, polygon mass defect filtering, and Kendrick mass defect filtering. In addition, the annotation strategy was successfully applied to identify the prototypes and metabolites of gypenosides in mice serum. As a case study, gypenoside LXXV was used to validate the feasibility of this approach. The results demonstrated 36 prototypes and 108 metabolites were annotated from the serum by the established annotation strategy. The prototype and eight metabolites of gypenoside LXXV were further confirmed, indicating that the proposed strategy is available. This study provides a novel approach for the rapid and accurate identification of prototypes and metabolites of natural products and offers new insights into the metabolic processes of gypenosides in vivo. Full article

Dendrobium is one of the most important orchids with high medicinal value. The diverse pharmacological activities of Dendrobium are attributed to its rich content of secondary metabolites. Due to the high variety and content of sesquiterpenoids in Dendrobium, more studies on their pharmacological activities have been reported. More than 100 sesquiterpenoids have been isolated from the roots and stems of Dendrobium, and these compounds have been shown to play important roles in a variety of diseases. However, there is a lack of systematic summarization of the chemical structures and pharmacological activities of sesquiterpenoids in Dendrobium Sw. Here, we reviewed the chemical structures of the sesquiterpenoids in Dendrobium plants, classified them according to the skeleton types, and summarized their pharmacological activities and mechanisms of action, with a view to laying a foundation for future studies on their pharmacological activity mechanisms and rational clinical applications. Full article

Assessing changes of the mass balance in the Antarctic ice sheet in the context of global warming is a key focus in polar study. This study analyzed the spatiotemporal variation in the Antarctic ice sheet’s mass balance, both as a whole and by individual basins, from 2003 to 2016 and from 2018 to 2022 using GRACE RL06 data published by the Center for Space Research (CSR) and ERA-5 meteorological data. It explored the lagged relationships between mass balance and precipitation, net surface solar radiation, and temperature, and applied the random forest method to examine the relative contributions of these factors to the ice sheet’s mass balance within a nonlinear framework. The results showed that the mass loss rates of the Antarctic ice sheet during the study periods were −123.3 ± 6.2 Gt/a and −24.8 ± 52.1 Gt/a. The region with the greatest mass loss was the Amundsen Sea in West Antarctica (−488.8 ± 5.3 Gt/a and −447.9 ± 14.7 Gt/a), while Queen Maud Land experienced the most significant mass accumulation (44.9 ± 1.0 Gt/a and 30.0 ± 3.2 Gt/a). The main factors contributing to surface ablation of the Antarctic ice sheet are rising temperatures and increased surface net solar radiation, each showing a lag effect of 1 month and 2 months, respectively. Precipitation also affects the loss of the ice sheet to some extent. Over time, the contribution of precipitation to the changes in the ice sheet’s mass balance increases. Full article

Electrochemical water splitting, a sustainable method for hydrogen production, faces the challenge of slow oxygen evolution reaction (OER) kinetics. Iridium oxide (IrO₂) is widely regarded as the most effective catalyst for OER due to its excellent properties. Compared to nanoparticles, IrO₂ thin films exhibit significant advantages in OER, including a uniform and stable catalytic interface and excellent mechanical strength. This paper reviews recent advancements in one-step deposition techniques for the preparation of IrO₂ thin films and their application in OER. Additionally, it analyzes the advantages and disadvantages of various methods and the latest research achievements, and briefly outlines the future trends and applications. Full article

A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart city applications, such as effective urban planning and public transportation system optimization. In this work, we study the problem of detecting fine-grained metro trips from cellular trajectory data. Existing trip-detection approaches designed for GPS trajectories are often not applicable to cellular data due to the issues of location noise and irregular data sampling in cellular data. Moreover, most cellular data-based methods focus on identifying coarse-grained transportation modes, failing to detect fine-grained metro trips accurately. To address the limitations of existing works, we propose a novel and efficient fine-grained metro-trip detection (FGMTD) model in this work. By considering both the local and global spatial–temporal characteristics of a trajectory and the metro network, FGMTD can effectively mitigate the effects of location noise and irregular data sampling, ultimately improving the accuracy and reliability of the detection process. In particular, FGMTD employs a spatial–temporal hidden Markov model with efficient index strategies to capture local spatial–temporal characteristics from individual positions and metro stations, and a weighted trip-route similarity measure to consider global spatial–temporal characteristics from the entire trajectory and metro route. We conduct extensive experiments on two real datasets to evaluate the effectiveness and efficiency of our proposed approaches. The first dataset contains cellular data from 30 volunteers, including their actual trip details, while the second dataset consists of data from 4 million users. The experiments illustrate the significant accuracy of our approach (with a precision of $87.80\%$ and a recall of $84.28\%$). Moreover, we demonstrate that FGMTD is efficient in detecting fine-grained trips from a large amount of cellular data, achieving this task within 90 min of processing a day’s data from 4 million users. Full article