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Many of the world's less developed regions may not be able to improve the well-being of rural residents through agricultural revitalization because of their remoteness from agricultural markets. Using the county-level data set of China's underdeveloped old revolutionary base areas from 2010 to 2021, this paper takes the policy planning of rural e-commerce as event intervention to investigate the driving role of the digital product market on agricultural economic development. Empirical results show that rural e-commerce planning policy has significantly promoted the agricultural added value of the pilot counties, and the digital market is the key driving factor of the agricultural economic growth in these underdeveloped areas. Both food production and livestock output have increased significantly as a result of e-commerce policies. Considering the potential bias of the bidirectional fixed effect estimators of staggered differences-in-differences (DID), this study uses heterogeneous robust estimators to verify the growth effect of the agricultural economy. Specifically, digital agricultural markets have significantly promoted agricultural mechanization and significantly improved agricultural total factor productivity. Moreover, empirical evidence does not support transmission mechanisms for off-farm employment and agricultural entrepreneurship. The findings can help less developed countries and regions develop policies to expand the agricultural markets with digital dividends, thereby promoting the development of the agricultural economy. [ABSTRACT FROM AUTHOR]

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Plant nitrogen (N) uptake preference is a key factor affecting plant nutrient acquisition, vegetation composition and ecosystem function. However, few studies have investigated the contribution of different N sources to plant N strategies, especially during the process of primary succession of a glacial retreat area. By measuring the natural abundance of N isotopes (δ15N) of dominant plants and soil, we estimated the relative contribution of different N forms (ammonium-NH4+, nitrate-NO3− and soluble organic N-DON) and absorption preferences of nine dominant plants of three stages (12, 40 and 120 years old) of the Hailuogou glacier retreat area. Along with the chronosequence of primary succession, dominant plants preferred to absorb NO3− in the early (73.5%) and middle (46.5%) stages. At the late stage, soil NH4+ contributed more than 60.0%, In addition, the contribution of DON to the total N uptake of plants was nearly 19.4%. Thus, the dominant plants' preference for NO3− in the first two stages changes to NH4+ in the late stages during primary succession. The contribution of DON to the N source of dominant plants should not be ignored. It suggests that the shift of N uptake preference of dominant plants may reflect the adjustment of their N acquisition strategy, in response to the changes in their physiological traits and soil nutrient conditions. Better knowledge of plant preferences for different N forms could significantly improve our understanding on the potential feedbacks of plant N acquisition strategies to environmental changes, and provide valuable suggestions for the sustainable management of plantations during different successional stages. [ABSTRACT FROM AUTHOR]

In recent years, sparse direction-of-arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar has attracted extensive attention and been extensively studied, especially the method based on the classic least absolute shrinkage and selection operator (LASSO) estimator. The alternating-direction method of multipliers (ADMM) is an effective method for solving this problem at the cost of introducing an additional user parameter. To avoid introducing an additional user parameter, this paper adopts an equivalent transformation in the form of the generalized SParse Iterative Covariance-based Estimation (qSPICE) cost function to obtain a mean squared minimized form of the cost function. Then, the problem is transformed into a sparse optimization problem in the form of a weighted LASSO. Next, this unconstrained optimization problem is decomposed into three subproblems, which are solved separately to reduce the dimension of each problem and thus reduce the overall computational complexity based on ADMM. Simulation results and measured data indicate that the proposed method significantly outperforms the traditional super-resolution DOA estimation method and ADMM-LASSO method and slightly outperforms qSPICE in terms of resolution and sidelobe suppression capability. In addition, the proposed method has a much lower computational complexity and substantially fewer iterations than qSPICE. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimal 3-DoF micro-conveyor based on electromagnetic digital actuators array is proposed. The micro-conveyor consists of four electromagnetic digital actuators. Two specific control strategies have been built to realize the 3-DoF planner conveyance task. A static analytical model and a dynamic semi-analytical model based on the principle have been built for the optimal design, analysis, and necessary calculation of a prototype. The prototype was manufactured by micro-fabrication technology and several experiments were carried out. The experimental results are in good agreement with the modeling results. Benefited from the optimal design and high fabrication precision, the proposed micro-conveyor is proved to be better in magnetic homogeneity of elementary actuators, output stability, long range conveyance linearity, and have one more DoF (planar rotation) compared to the previous work. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00% when 10 training samples each class. [ABSTRACT FROM AUTHOR]

Simple Summary: Low-fat meat is increasingly desired by the public due to the growing popularity of healthy diets, and the excessive accumulation of abdominal fat increases costs in the broiler breeding industry, all of which have encouraged breeding changes in the broiler industry. Investigating fat accumulation at a cellular level from a genetic perspective will help us understand gene-mediated abdominal fat accumulation in chickens. This study aimed to explore the role of the PROS1 gene in adipose cells and its application prospect in broiler breeding. Based on our findings, we found that the PROS1 gene can contribute to adipose cell proliferation and can reduce fat deposits at the cellular level, and its mutations are highly correlated with chicken fat traits. (1) Background: Excessive abdominal fat deposition in broilers not only causes feed waste but also leads to a series of metabolic diseases. It has gradually become a new breeding goal of the broiler industry to improve growth rates and to reduce abdominal fat rates. In a previous study, PROS1 was highly expressed in low-abdominal fat broilers, suggesting a potential role in broilers adipogenesis. However, the function of PROS1 in preadipocytes and its association with abdominal fat traits need to be characterized. (2) Methods: qRT-PCR and Western Blot were used to quantify gene expression at the RNA and protein levels; flow cytometry and EdU were carried out to detect cell proliferation; and a GLM analysis was used to determine the association between PROS1 SNPs and carcass traits. (3) Results: PROS1 was downregulated in high-abdominal fat chicken; PROS1 contributed preadipocyte proliferation but suppressed preadipocyte differentiation; and the SNPs in the PROS1 5′ flank were significantly associated with the abdominal fat weight rate. (4) Conclusions: Chicken PROS1 is able to suppress adipogenesis, and its polymorphisms are associated with the abdominal fat weight rate, which can be considered the molecular markers for chicken breeding, indicating that PROS1 is an effective potential gene in regulating abdominal fat deposition. [ABSTRACT FROM AUTHOR]

The immune cell inflammation response is closely related to the occurrence of disease, and much evidence has shown that circular RNAs (circRNAs) play vital roles in the occurrence of disease. However, the biological function and regulatory mechanisms of circRNAs in the immune cell inflammation response remain poorly understood. In this study, we constructed an inflammatory model using lipopolysaccharide (LPS)-stimulated chicken macrophage lines (also known as HD11) to verify the function and mechanism of the novel circDCLRE1C (ID: gga_circ_0001674), which was significantly upregulated in spleen tissues infected by coccidia and the macrophage cells exposed to LPS. The results showed that circDCLRE1C aggravated LPS-induced inflammation and apoptosis in HD11 cells. Systemically, circDCLRE1C acted as a sponge for miR-214b-3p binding sites thereby regulating the expression of STAT3. The overexpression of miR-214b-3p rescued the pro-inflammatory effect of circDCLRE1C in HD11 cells stimulated with LPS, and rescued the high expression of STAT3. In conclusion, our study showed that circDCLRE1C could aggravate LPS-induced inflammation and apoptosis through competitive adsorption of miR-214b-3p, thereby increasing the expression of STAT3. [ABSTRACT FROM AUTHOR]

The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple–output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high–computational complexity when dealing with massive TDM–MIMO observations, due to high–dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from O K 3 to O K 2 . Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Micro conveyance devices are important elements in high precision positioning systems, which play vital roles in many scientific and industrial fields and are widely needed in many applications. This work presents the modeling and experimental validation of a pre-designed and fabricated micro conveyance device. This micro conveyance device consists of eight elementary electromagnetic digital actuators and can realize a conveyance application based on a stick-slip driving principle. A dynamic model based on the prototype device was established for stick-slip displacement simulation. Four sub-models of the dynamic model regarding the calculation of actuation force and friction force within the device were built and integrated. Experiments focusing on the conveyance application were carried out to test the performance of the prototype device. It has been observed that the simulated results obtained using the dynamic model were in good agreement with the experiment results. The proposed device suggests an alternative to the micro conveyance system. [ABSTRACT FROM AUTHOR]

Deconvolution provides an efficient technology to implement angular super-resolution for scanning radar forward-looking imaging. However, deconvolution is an ill-posed problem, of which the solution is not only sensitive to noise, but also would be easily deteriorate by the noise amplification when excessive iterations are conducted. In this paper, a penalized maximum likelihood angular super-resolution method is proposed to tackle these problems. Firstly, a new likelihood function is deduced by separately considering the noise in I and Q channels to enhance the accuracy of the noise modeling for radar imaging system. Afterwards, to conquer the noise amplification and maintain the resolving ability of the proposed method, a joint square-Laplace penalty is particularly formulated by making use of the outlier sensitivity property of square constraint as well as the sparse expression ability of Laplace distribution. Finally, in order to facilitate the engineering application of the proposed method, an accelerated iterative solution strategy is adopted to solve the obtained convex optimal problem. Experiments based on both synthetic data and real data demonstrate the effectiveness and superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, which is known as flickering characteristics. This phenomenon causes a severe decline in the detection performance of traditional detection methods. A biological memory model-based dynamic programming multi-target joint detection method was proposed to address this issue in this paper. Firstly, a global detection operator is used to discretize the multi-target state into multiple single-target states, achieving the discretization of numerous targets. Meanwhile, updating the formula of the memory weight merit function can strengthen the joint frame correlation of the flickering characteristics target. The progressive loop integral is utilized to update the target states to optimize the candidate target set. Finally, a two-stage threshold criterion is utilized to detect the target at different amplitude levels accurately. Simulation and experimental results are given to validate the assertion that the detection performance of the proposed method is greatly improved under a low SCR of 3-8 dB for multiple flickering target detection. [ABSTRACT FROM AUTHOR]

Many plants show significant biological activity against pests due to their unique chemical constituents. It is important to identify effective constituents for their development and utilization as botanical pesticides. Our previous study showed that Artemisia lavandulaefolia essential oil had biological activity against Plutella xylostella. Here, we isolated and identified the constituents of essential oil from A. lavandulaefolia by silica gel column chromatography. The main constituents identified were eucalyptol and caryophyllene oxide, and they were confirmed by gas chromatography–mass spectrometry (GC–MS). Eucalyptol and caryophyllene oxide showed strong contact toxicity against P. xylostella larvae after 24 h of application (Median lethal dose, LD50 = 76.97 μL/mL and 20.71 mg/mL. Furthermore, the two active constituents against P. xylostella adults showed significant fumigant activity (Mmedian lethal concentration, LC50 = 3.25 μL/L and 1.06 mg/L, respectively. Finally, we measured the detoxification enzymes and acetylcholinesterase of the larvae treated with active constituents. The eucalyptol-treated larvae displayed enhanced carboxylesterase (CarE) and glutathione-S-transferase (GST) activities in an in vivo experiment, but it was lower for acetylcholinesterase (AchE) activity. The activities of the CarE and GST significantly decreased when exposed to caryophyllene oxide. In general, the two active constituents, eucalyptol and caryophyllene oxide, showed high insecticidal activity, which demonstrates their potential to be used as natural insecticides. [ABSTRACT FROM AUTHOR]

Post-harvest diseases of mango reduce fruit quality and cause severe yield losses with completely unmarketable fruits. The most common diseases of mangos are anthracnose (Colletotrichum gloeosporioides). In this study, the antibacterial activities of essential oils from Artemisia scoparia, Artemisia lavandulaefolia, and Artemisia annua against C. gloeosporioides were tested. The results showed that the essential oil of A. scoparia was more effective by the agar diffusion method; the EC50 value was 9.32 µL/mL. The inhibition rate was 100%, at a concentration of 10 μL/mL, through the spore germination method. The morphological changes of the mycelium were observed by scanning electron microscopy (SEM), the mycelia treated with essential oils showed shrinking, deformity, fracture, and dryness through SEM. A. scoparia essential oil was inoculated in vivo and subjected to paroxysm testing under natural conditions. A. scoparia had significantly inhibitory activity, and the inhibition rate was 66.23% in vivo inoculation tests after 10 days. The inhibition rate was 92.06% in the paroxysm test under natural conditions after 15 days. Finally, A. acoparia essential oil was analyzed by gas chromatography-mass spectrometry. The main compounds were 2-ethenyl-Naphthalene (23.5%), 2,4-pentadiynyl-Benzene (11.8%), 1,2-dimethoxy-4-(2-propenyl)-Benzene (10.0%), β-Pinene (8.0%), and 1-methyl-4-(1-methylethyl)-1,4-Cyclohexadiene (6.3%). The results have revealed the potential use of A. scoparia essential oil against post-harvest fungal pathogens C. gloeosporioides. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset. [ABSTRACT FROM AUTHOR]

The maintenance of adaptability to the exposure to agroecological extreme environments is generally a feature after the long-term domestication of crops. Auxin influences plant growth in all environments. At present, the research on the auxin response factors (ARFs) has been very in-depth. However, there is still a large gap in the research on the origin, evolution, and regulatory networks of the Auxin-responsive protein (AUX/IAA) family. Here, we identified 495 AUX/IAAs from 19 representative species covering aquatic algae to angiosperms and found that they originated from early bryophytes and mainly expanded by polyploidy in angiosperms. In the domesticated crop quinoa, the evolutionary model of the IAA family is relatively independent and forms a robust regulatory network with auxin signals and energy metabolism pathways. In the nitrogen-deficient environment, CqIAAs (Chenopodium quinoa AUX/IAAs), auxin signals, and TCA pathway genes induced expression in young roots to promote root elongation and could regulate the balance of carbon and nitrogen metabolism to maintain the adaptation of early seedlings in poor environments. Furthermore, a rapidly evolving CqIAA (AUR62011942) not only has different expression levels in two quinoa seeds but also has a significant stress response when seedlings face nitrogen deficiency stress, which may be a key factor in the adaptive regulation of the barren environment. Our research provides valuable clues for understanding the origin, evolution, and functional innovation of auxin signaling and also provides a reference for future agricultural breeding in the context of global environmental changes. [ABSTRACT FROM AUTHOR]

Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution. However, a narrower beam means a larger antenna aperture. In practical applications, due to platform limitations, the antenna aperture is limited, resulting in a low azimuth resolution. The conventional sparse super-resolution method (SSM) has been proposed for improving the azimuth resolution of scanning radar imaging and achieving superior performance. This method uses the L 1 norm to represent the sparse prior of the target and solves the L 1 regularization problem to achieve super-resolution imaging under the regularization framework. The resolution of strong-point targets is improved efficiently. However, for some targets with typical shapes, the strong sparsity of the L 1 norm treats them as strong-point targets, resulting in the loss of shape characteristics. Thus, we can only see the strong points in its processing results. However, in some applications that need to identify targets in detail, SSM can lead to false judgments. In this paper, a sparse denoising-based super-resolution method (SDBSM) is proposed to compensate for the deficiency of traditional SSM. The proposed SDBSM uses a sparse minimization scheme for denoising, which helps to reduce the influence of noise. Then, the super-resolution imaging is achieved by alternating iterative denoising and deconvolution. As the proposed SDBSM uses the L 1 norm for denoising rather than deconvolution, the strong sparsity constraint of the L 1 norm is reduced. Therefore, it can effectively preserve the shape of the target while improving the azimuth resolution. The performance of the proposed SDBSM was demonstrated via simulation and real data processing results. [ABSTRACT FROM AUTHOR]

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions. [ABSTRACT FROM AUTHOR]

Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O (N 3) . In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O (N 3) to O (N 2) , benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform. [ABSTRACT FROM AUTHOR]

The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L 1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an L 1 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable L 1 regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg-Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from O (N 3) to O (N 2) . It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice. [ABSTRACT FROM AUTHOR]

With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the target category, the SAR ATR system could benefit from the simultaneous extraction of multifarious target attributes. In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously. By introducing deep learning theory into multi-task learning, we first propose a novel multi-task deep learning framework with two main structures: encoder and decoder. The encoder is constructed to extract sufficient image features in different scales for the decoder, while the decoder is a tasks-specific structure which employs these extracted features adaptively and optimally to meet the different feature demands of the recognition and segmentation. Therefore, the proposed framework has the ability to achieve superior recognition and segmentation performance. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, experimental results show the superiority of the proposed framework in terms of recognition and segmentation. [ABSTRACT FROM AUTHOR]

The imagery of airborne highly squinted synthetic aperture radar (SAR) with curved trajectory is a challenging task due to the translational-variant range cell migration (RCM) and azimuth modulation. However, in most cases of practical application, the curved trajectory cannot be accurately known, which brings greater difficulties to the imaging problem. To accommodate these issues, we propose a novel motion modelling and optimisation based imaging algorithm for the highly squinted SAR with unknown curved trajectory. First, to correct the translational-variant RCM, a coarse-to-fine RCM correction scheme as well as a range perturbation approach is applied. Afterwards, an optimisation model of motion information under the criterion of minimum entropy is built during the azimuth processing by nonlinear chirp scaling (NLCS). Correspondingly, a differential evolution (DE) optimisation strategy is proposed to estimate the motion information in an iterative manner. We empirically compare the proposed algorithms with several state-of-the-art highly squinted curved SAR imaging algorithms. Numerical results show the effectiveness of the proposed method in the case without any prior information of the curved trajectory. [ABSTRACT FROM AUTHOR]

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum L p norm problem by solving a sequence of minimum weighted L 2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation. [ABSTRACT FROM AUTHOR]

A random Nyström (R-Nyström) scheme for clutter subspace estimation is proposed in the context of polarimetric space-time adaptive processing (pSTAP). Unlike the standard Nyström scheme making use of only partial columns of the clutter plus noise covariance matrix (CNCM), R-Nyström exploits full CNCM information with a properly designed selection procedure under the newly developed random ridge cross leverage score (RRCLS) criterion. With R-Nyström, sup-ported by the complete CNCM columns, upgraded clutter subspace estimation can be achieved at the expense of an insignificant increase in computational complexity, in contrast to the standard Nyström. The R-Nyström-based pSTAP, termed pR-Nyström, is shown to be superior over the current eigendecomposition-free subspace pSTAP in the signal to clutter plus noise loss and computational complexity. The efficacy of R-Nyström/pR-Nyström is validated by the simulation results. [ABSTRACT FROM AUTHOR]

Deconvolution methods can be used to improve the azimuth resolution in airborne radar imaging. Due to the sparsity of targets in airborne radar imaging, an L 1 regularization problem usually needs to be solved. Recently, the Split Bregman algorithm (SBA) has been widely used to solve L 1 regularization problems. However, due to the high computational complexity of matrix inversion, the efficiency of the traditional SBA is low, which seriously restricts its real-time performance in airborne radar imaging. To overcome this disadvantage, a fast split Bregman algorithm (FSBA) is proposed in this paper to achieve real-time imaging with an airborne radar. Firstly, under the regularization framework, the problem of azimuth resolution improvement can be converted into an L 1 regularization problem. Then, the L 1 regularization problem can be solved with the proposed FSBA. By utilizing the low displacement rank features of Toeplitz matrix, the proposed FSBA is able to realize fast matrix inversion by using a Gohberg–Semencul (GS) representation. Through simulated and real data processing experiments, we prove that the proposed FSBA significantly improves the resolution, compared with the Wiener filtering (WF), truncated singular value decomposition (TSVD), Tikhonov regularization (REGU), Richardson–Lucy (RL), iterative adaptive approach (IAA) algorithms. The computational advantage of FSBA increases with the increase of echo dimension. Its computational efficiency is 51 times and 77 times of the traditional SBA, respectively, for echoes with dimensions of 218 × 400 and 400 × 400 , optimizing both the image quality and computing time. In addition, for a specific hardware platform, the proposed FSBA can process echo of greater dimensions than traditional SBA. Furthermore, the proposed FSBA causes little performance degradation, when compared with the traditional SBA. [ABSTRACT FROM AUTHOR]

Multi-polarized antenna arrays have the ability to provide both the direction and polarization information of the incident signals, which is important in radar, sonar, wireless communication, remote sensing, and so on. In this paper, a diversely polarized linear array of sparsely located but identically oriented tri-polarized vector antennas (VAs) is designed for estimating the direction-of-arrival (DOA) and polarization parameters of the incident signals in the presence of antenna mutual coupling (MC). In order to reduce the inter-VA MC, a new type of sparse array geometry is proposed, wherein the minimum inter-VA spacing is constrained to be no less than one signal wavelength. Considering the intra-VA MC effect, a full-wave electromagnetic simulation is introduced to fit the manifold vector of an isolated VA. Based on the sparse VA array, a polarimetric subspace scheme is proposed for DOA and polarization estimation. When the knowledge about the intra-VA MC is a priori unavailable, an algebraic polarimetric blind scheme is also provided for DOA estimation. Computer simulations and real-world experiments (using an S-band 24-channel tri-polarized array system) validate the efficacy of the designed array geometry along with the parameter estimation methods. [ABSTRACT FROM AUTHOR]

The use of self-consolidating concrete and advanced pumping system enables efficient construction of super high-rise buildings; however, risks such as clogging or even bursting of pipeline still exist. To better understand the fresh concrete pumping mechanisms in detail, the discrete element method is employed in this paper for the numerical simulation of local pumping problems. By modeling the coarse aggregates as rigid clumps and appropriately defining the contact models, the concrete flow in representative pipeline units is well revealed. Important factors related to the pipe geometry, aggregate geometry and pumping condition were considered during a series of parametric studies. Based on the simulation results, their impact on the local pumping performance is summarized. The present work demonstrates that the discrete element simulation offers a useful way to evaluate the influence of various parameters on the pumpability of fresh concrete. [ABSTRACT FROM AUTHOR]

Maritime moving target detection and tracking through particle filter based track-before-detect (PF-TBD) has significant practical value for airborne forward-looking scanning radar. However, villainous weather and surging of ocean waves make it extremely difficult to accurately obtain a statistical model for sea clutter. As the likelihood ratio calculation in PF-TBD is dependent on the distribution of the clutter, the performance of traditional distribution-based PF-TBD seriously declines. To resolve these difficulties, this paper proposes a new target detection and tracking method, named spectral-residual-binary-entropy-based PF-TBD (SRBE-PF-TBD), which is independent from the prior knowledge of sea clutter. In the proposed method, the likelihood ratio calculation is implemented by first extracting the spectral residual of the input image to obtain the saliency map, and then constructing likelihood ratio through a binarization processing and information entropy calculation. Simulation results show that the proposed method had superior performance of maritime moving target detection and tracking. [ABSTRACT FROM AUTHOR]

Non-planar sun sensors can determine solar orientation by existing photodiodes or by reusing solar panels, without increasing the size and mass of spacecraft. However, a limiting factor for the improvement of the accuracy of orientation lies in the lack of a detailed performance assessment on interference suppression. In this paper, a new method that determines solar orientation in the frequency domain is developed for regular pyramid sun sensors, which are formed by regular pyramid arrays. Furthermore, two formulations are established to evaluate the errors of the solar azimuth and elevation angle in solar orientation determination based on the newly proposed frequency-domain method. With these formulations of performance evaluation, we discover the mathematical relationship between the interference spectrum, array geometry, solar irradiance, solar azimuth or elevation angle, and the error in solar orientation determination for the first time. This reveals that the internal interference from the detection system can be completely suppressed in solar orientation determination, and the constant interference can be eliminated in the estimation of solar azimuth angle. Simulation and field experiments validated the effectiveness of the new orientation method, error formulations and performance of each interference source. [ABSTRACT FROM AUTHOR]

In bistatic forward-looking synthetic aperture radar (BFSAR) ground moving target detection (GMTD), the suppression of the strong and heterogeneous ground clutter is one of the most crucial and challenging issues. Due to the bistatic forward-looking mode and long observation time, Doppler ambiguity, range and Doppler cells migration and non-stationary characteristics will exist in clutter receives, which leads to severe performance degradation of the traditional method. Hence, this paper proposes a GMTD method based on joint clutter cancellation in echo-image domain for BFSAR to achieve effective GMTD in heterogeneous BFSAR clutter. First, the pre-filtering and keystone transform are applied to suppress Doppler ambiguity and correct range cell migration, respectively. Then, time-division space-time adaptive clutter cancellation is adopted to suppress clutter at the first time in the echo domain, which can eliminate the effect of the migration of Doppler cells. However, its performance will be severely degraded due to the strong non-stationary characteristic of BFSAR clutter. Finally, adaptive displaced phase center antenna is exploited to suppress the residual non-stationary BFSAR clutter in image domain. Experimental results have shown that the strong non-stationary clutter of BFSAR has been sufficiently suppressed by the proposed method and the SCNR provided is enough to detect a moving target well. [ABSTRACT FROM AUTHOR]

Finding out interested targets from synthetic aperture radar (SAR) imagery is an attractive but challenging problem in SAR application. Traditional target detection is independent on SAR imaging process, which is purposeless and unnecessary. Hence, a new SAR processing approach for simultaneous target detection and image formation is proposed in this paper. This approach is based on SAR imagery formation in time domain and human visual saliency detection. First, a series of sub-aperture SAR images with resolutions from low to high are generated by the time domain SAR imaging method. Then, those multiresolution SAR images are detected by the visual saliency processing, and the corresponding intermediate saliency maps are obtained. The saliency maps are accumulated until the result with a sufficient confidence level. After some screening operations, the target regions on the imaging scene are located, and only these regions are focused with full aperture integration. Finally, we can get the SAR imagery with high-resolution detected target regions but low-resolution clutter background. Experimental results have shown the superiority of the proposed approach for simultaneous target detection and image formation. [ABSTRACT FROM AUTHOR]

Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, due to the varying ocean waves and the strong echo of the sea surface, it is very difficult to detect ships from heterogeneous and strong clutter backgrounds. In this paper, an innovative ship detection method is proposed to effectively distinguish the vessels from complex backgrounds from a SAR image. First, the input SAR image is pre-screened by the maximally-stable extremal region (MSER) method, which can obtain the ship candidate regions with low computational complexity. Then, the proposed local contrast variance weighted information entropy (LCVWIE) is adopted to evaluate the complexity of those candidate regions and the dissimilarity between the candidate regions with their neighborhoods. Finally, the LCVWIE values of the candidate regions are compared with an adaptive threshold to obtain the final detection result. Experimental results based on measured ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods., Competing Interests: The authors declare no conflict of interest.

This paper presents a sparse superresolution approach for high cross-range resolution imaging of forward-looking scanning radar based on the Bayesian criterion. First, a novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more accurate than the conventional convolution model. Then, based on the Bayesian criterion, the widely-used sparse regularization is considered as the penalty term to recover the target distribution. The derivation of the cost function is described, and finally, an iterative expression for minimizing this function is presented. Alternatively, this paper discusses how to estimate the single parameter of Gaussian noise. With the advantage of a more accurate model, the proposed sparse Bayesian approach enjoys a lower model error. Meanwhile, when compared with the conventional superresolution methods, the proposed approach shows high cross-range resolution and small location error. The superresolution results for the simulated point target, scene data, and real measured data are presented to demonstrate the superior performance of the proposed approach.

Bistatic forward-looking SAR (BFSAR) is a kind of bistatic synthetic aperture radar (SAR) system that can image forward-looking terrain in the flight direction of an aircraft. Until now, BFSAR imaging theories and methods for a stationary scene have been researched thoroughly. However, for moving-target imaging with BFSAR, the non-cooperative movement of the moving target induces some new issues: (I) large and unknown range cell migration (RCM) (including range walk and high-order RCM); (II) the spatial-variances of the Doppler parameters (including the Doppler centroid and high-order Doppler) are not only unknown, but also nonlinear for different point-scatterers. In this paper, we put forward an adaptive moving-target imaging method for BFSAR. First, the large and unknown range walk is corrected by applying keystone transform over the whole received echo, and then, the relationships among the unknown high-order RCM, the nonlinear spatial-variances of the Doppler parameters, and the speed of the mover, are established. After that, using an optimization nonlinear chirp scaling (NLCS) technique, not only can the unknown high-order RCM be accurately corrected, but also the nonlinear spatial-variances of the Doppler parameters can be balanced. At last, a high-order polynomial filter is applied to compress the whole azimuth data of the moving target. Numerical simulations verify the effectiveness of the proposed method., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Free of the constraints of orbit mechanisms, weather conditions and minimum antenna area, synthetic aperture radar (SAR) equipped on near-space platform is more suitable for sustained large-scene imaging compared with the spaceborne and airborne counterparts. Terrain observation by progressive scans (TOPS), which is a novel wide-swath imaging mode and allows the beam of SAR to scan along the azimuth, can reduce the time of echo acquisition for large scene. Thus, near-space TOPS-mode SAR (NS-TOPSAR) provides a new opportunity for sustained large-scene imaging. An efficient full-aperture imaging scheme for NS-TOPSAR is proposed in this paper. In this scheme, firstly, two-step processing (TSP) is adopted to eliminate the Doppler aliasing of the echo. Then, the data is focused in two-dimensional frequency domain (FD) based on Stolt interpolation. Finally, a modified TSP (MTSP) is performed to remove the azimuth aliasing. Simulations are presented to demonstrate the validity of the proposed imaging scheme for near-space large-scene imaging application.

Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in nonhomogeneous clutter environments, this paper proposes a new automatic censored cell averaging CFAR detector. The proposed CFAR detector does not require any prior information about the background environment and uses the hypothesis test of the first-order difference (FOD) result of ordered data to reject the unwanted samples in the reference window. After censoring the unwanted ranked cells, the remaining samples are combined to form an estimate of the background power level, thus getting better radar signal detection performance. The simulation results show that the FOD-CFAR detector provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments. Furthermore, the measured results of a low-flying helicopter validate the basic performance of the proposed method.

Follicular fluid (FF) accumulates in the antrum of the ovarian follicle and provides the microenvironment for oocyte development. FF plays an important role in follicle growth and oocyte maturation. The FF provides a unique window to investigate the processes occurring during buffalo follicular development. The observed low quality of buffalo oocytes may arise from the poor follicular microenvironment. Investigating proteins found in buffalo FF (BFF) should provide insight into follicular development processes and provide further understanding of intra-follicular maturation and oocytes quality. Here, a proteomic-based approach was used to analyze the proteome of BFF. SDS-PAGE separation combined with mass spectrometry was used to generate the proteomic dataset. In total, 363 proteins were identified and classified by Gene Ontology terms. The proteins were assigned to 153 pathways, including signaling pathways. To evaluate difference in proteins expressed between BFF with different follicle size (small, <4 mm; and large, >8 mm), a quantitative proteomic analysis based on multi-dimensional liquid chromatography pre-fractionation tandem Orbitrap mass spectrometry identification was performed. Eleven differentially expressed proteins (six downregulated and five upregulated in large BFF) were identified and assigned to a variety of functional processes, including serine protease inhibition, oxidation protection and the complement cascade system. Three differentially expressed proteins, Vimentin, Peroxiredoxin-1 and SERPIND1, were verified by Western blotting, consistent with the quantitative proteomics results. Our datasets offers new information about proteins present in BFF and should facilitate the development of new biomarkers. These differentially expressed proteins illuminate the size-dependent protein changes in follicle microenvironment.

The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry. Deconvolution method can realize the forward looking imaging but it often leads to the noise amplification in the radar image. In this paper, a forward looking radar imaging based on deconvolution method is presented for adverse weather aircraft landing. We first present the theoretical background of forward looking radar imaging task and its application for aircraft landing. Then, we convert the forward looking radar imaging task into a corresponding deconvolution problem, which is solved in the framework of algebraic theory using truncated singular decomposition method. The key issue regarding the selecting of the truncated parameter is addressed using generalized cross validation approach. Simulation and experimental results demonstrate that the proposed method is effective in achieving angular resolution enhancement with suppressing the noise amplification in forward looking radar imaging.

Scanning radar is of notable importance for ground surveillance, terrain mapping and disaster rescue. However, the angular resolution of a scanning radar image is poor compared to the achievable range resolution. This paper presents a deconvolution algorithm for angular super-resolution in scanning radar based on Bayesian theory, which states that the angular super-resolution can be realized by solving the corresponding deconvolution problem with the maximum a posteriori (MAP) criterion. The algorithm considers that the noise is composed of two mutually independent parts, i.e., a Gaussian signal-independent component and a Poisson signal-dependent component. In addition, the Laplace distribution is used to represent the prior information about the targets under the assumption that the radar image of interest can be represented by the dominant scatters in the scene. Experimental results demonstrate that the proposed deconvolution algorithm has higher precision for angular super-resolution compared with the conventional algorithms, such as the Tikhonov regularization algorithm, the Wiener filter and the Richardson-Lucy algorithm.