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The fuzzy cognitive map (FCM) is a powerful model for system state prediction and interpretable knowledge representation. Recent years have witnessed the tremendous efforts devoted to enhancing the basic FCM, such as introducing temporal factors, uncertainty or fuzzy rules to improve interpretation, and introducing fuzzy neural networks or wavelets to improve time series prediction. But how to achieve high-precision yet interpretable prediction in cross-domain real-life applications remains a great challenge. In this article, we propose a novel FCM extension called deep FCM (DFCM) for multivariate time series forecasting, in order to take both the advantage of FCM in interpretation and the advantage of deep neural networks in prediction. Specifically, to improve the predictive power, DFCM leverages a fully connected neural network to model connections (relationships) among concepts in a system, and a recurrent neural network to model unknown exogenous factors that have influences on system dynamics. Moreover, to foster model interpretability encumbered by the embedded deep structures, a partial derivative-based approach is proposed to measure the connection strengths between concepts in DFCM. An alternate function gradient descent algorithm is then proposed for parameter inference. The effectiveness of DFCM is validated over four publicly available datasets with the presence of seven baselines. DFCM indeed provides an important clue to building interpretable predictors for real-life applications.

Recent years have witnessed the emergence of worldwide megalopolises and the accompanying public safety events, making urban safety a top priority in modern urban management. Among various threats, dangerous goods such as gas and hazardous chemicals transported through cities have bred repeated tragedies and become the deadly “bomb” we sleep with every day. While tremendous research efforts have been devoted to dealing with dangerous goods transportation (DGT) issues, further study is still in great need to quantify this problem and explore its intrinsic dynamics from a big data perspective. In this article, we present a novel system called DGeye , to feature a fusion between DGT trajectory data and residential population data for dangers perception and prediction. Specifically, DGeye first develops a probabilistic graphical model-based approach to mine spatio-temporally adjacent risk patterns from population-aware risk trajectories. Then, DGeye builds the novel causality network among risk patterns for risk pain-point identification, risk source attribution, and online risky state prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye in real-life DGT risk management. As a case in point, our report powered by DGeye successfully drove the government to lay down gas pipelines for the famous Guijie food street in Beijing.

A polar format algorithm (PFA) as a high-efficient imaging method is widely used in bistatic spotlight synthetic aperture radar (SAR). In cases where motion error causes great defocusing in PFA images, phase gradient autofocus (PGA) is suitable for motion error compensation due to its robustness. However, a significant coupling of range-azimuth directions in bistatic SAR (BiSAR) makes conventional PGA unapplicable in PFA images. For this limitation, this letter gives the solution to the adaptability of PGA in BiSAR based on the azimuth migration correction in the wavenumber domain. The bistatic motion error is modeled, and the caused phase error is derived and analyzed. Through the wavenumber-domain analysis, the 2-D coupling is indicated still existing after polar format imaging, unlike other algorithms that utilize range cell migration correction (RCMC). Then, a phase compensation for the migration of azimuth direction is proposed to remove the 2-D coupling. Based on this, the new autofocus arithmetic flow for bistatic PFA is designed. The effectiveness of the proposed method is verified by numerical simulations.

To compare the rock breaking performances caused by the arc and wedge hob with two blades, a rock cutting model with two hob blades is established and a series of rock cutting simulations are performed based on the particle flow code. Meanwhile, the rock breaking process, crack extension pattern, rock cutting force and rock cutting efficiency etc. are investigated in this study. The results present that the rock breaking area and the lateral crack length induced by the arc blades is much greater than that induced by the wedge blades. The optimal cutting spacing of the arc and wedge blades is 60 mm and 30 mm respectively. It confirms that the wedge blades can significantly reduce the rock cutting force and raise the rock cutting efficiency compared with the arc blades, which can provide a reference for the hard rock tunneling by the tunnel boring machine.

Exploring efficient, stable, and earth-abundant electrocatalysts for oxygen evolution reaction (OER) is of great significance for clean and renewable energy conversion technologies. In this work, in situ uniform Ni-doped tungsten carbide (Ni/WCX) nanoparticles (~3 nm) on carbon nanofibers (Ni/WCX-CNFs) that were to function as efficient OER catalysts were developed. Both the composition and electronic state of tungsten carbide (WCX: W-WC-W2C) could be regulated through varied Ni coupling. Owing to the synergistic effect between Ni and WCX, the reaction kinetics were facilitated, resulting in improved OER activity with low overpotentials of η10 = 350 mV (modified glassy carbon electrode) and η10 = 335 mV (self-supporting electrode). This work opens a facile territory for the development of cost-effective and highly promising OER electrocatalysts for use in real life applications.

Bistatic forward-looking spotlight synthetic aperture radar with stationary transmitter and maneuvering receiver (STMR-BFSSAR) is a promising sensor for various applications, such as the automatic navigation and landing of maneuvering vehicles. Because of the bistatic forward-looking configuration and the receiver’s maneuvers, conventional image formation algorithms suffer from high computational complexity or small size of a well-focused scene if applied to STMR-BFSSAR. In this article, we propose a wavenumber-domain algorithm for STMR-BFSSAR image formation, which is termed the bistatic-range-Doppler-aperture wavenumber algorithm (BDWA). First, a novel range model in bistatic-range and Doppler-aperture coordinate space instead of conventional Cartesian coordinate space is established by employing the elliptic polar coordinate system and the method of series reversion. The novel range model not only makes the echo’s samples to be regular along the direction of the bistatic-range wavenumber axis but also constructs a curved wavefront close to the true wavefront. Second, an operation termed wavenumber-domain gridding is conceived to regularize the echo’s samples along the Doppler-aperture wavenumber axis, which can be implemented by 1-D interpolation. The proposed algorithm significantly outperforms the conventional algorithms in terms of computational complexity and scene size limits. Both point and distributed targets are simulated for two STMR-BFSSAR systems with different parameters. The simulation results verify the validity and superiority of the proposed BDWA.

The transient electromagnetic (TEM) responses measured in areas with highly polarizable media commonly show induced polarization (IP) effects, which poses difficulties to the TEM data interpretation and inversion with conventional methods. When present, the IP effects break the monotony in TEM decay curves and can even cause sign reversals, and lead to the singularity and non-monotony of inversion method. It is therefore important to take the IP effects into account when processing those TEM data. In this paper, a new inversion method is developed to invert TEM data with IP effects. The Cole-Cole model was introduced to the forward modeling solver so that the IP effects can be accurately calculated. The regularization terms were adapted to the objective function and a global optimization method, the particle swarm optimization algorithm, was used to solve the optimization problem, which weakened the singularity and non-monotony of the inversion greatly. Field data were collected in Wulong gold mine where IP effects were detected. The data were inverted using the method developed in this paper, and the inversion results were able to identify the depth extension of NO. 163 ore-bearing quartz veins and their associated fine-grained diorite veins. The global inversion method in this paper primarily solved the inversion problem of IP-affected TEM responses, and recovered the polarization parameters, which can be used as an aid to the geological interpretation.

Exploration of deep mineralization, particularly where the mineralization of interest is covered by a conductive overburden, is still a challenge for the conventional transient electromagnetic (TEM) method, which measures TEM response using induction coils as the sensor. However, sensors such as fluxgate and superconductive quantum interfere device (SQUID) magnetometers can measure the B-field directly, which can provide more reliable deep information for mineralization exploration. In this paper, we report on the research and development of our newly developed high-temperature superconductor (HTS) SQUID magnetometer, which is cooled by liquid nitrogen at 77 K, and its application in TEM measurement for deep exploration in a gold deposit in China. This improved SQUID magnetometer version has a good performance with noise (60 fT/ $$\sqrt {{\rm{Hz}}} $$ ), slew rate (0.8 mT/S), dynamic range (100 dB), sensitivity (6.25 mV/nT), and bandwidth (DC-20 kHz). To find deep and peripheral ore in the Baiyun gold deposit located in Liaoning Province, NE China, both the SQUID magnetometer and induction coil were used for TEM data acquisition. Results show that TEM can detect the distribution of local strata and the faults contained within them. Results also indicate that the SQUID magnetometer has superior response performance for response over geological targets with slower decay time when compared to the induction coil signals. The SQUID magnetometer is more sensitive at observing the induced-polarization effect which is closely related to the ore-controlling faults.

Geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) can achieve high-resolution Earth observation with superior system flexibility and efficiency, which offers huge potential for advanced SAR applications. In this article, the echo characteristics of GEO-SA-BiSAR are analyzed in detail, including range history, the Doppler parameters, and spatial variance. The distinct features of GEO-SAR and airborne receiver result in the failure of the traditional bistatic SAR range model and imaging methods. In order to deal with these problems and achieve high-precision data focusing on GEO-SA-BiSAR, this article first proposes a novel range model based on one-stationary equivalence (RMOSE) to accommodate the distinctiveness of the GEO-SA-BiSAR echo, which changes with orbit positions of GEO transmitter. Then, a 2-D frequency-domain imaging algorithm is put forward based on RMOSE, which solves the problem of the 2-D spatial variance of GEO-SA-BiSAR. Finally, simulations are presented to demonstrate the effectiveness of the proposed range model and algorithm.

The recent pandemic has painfully reminded us the need for IS design to be informed by possible futures and conscious of undesirable futures – Within months, many of the nice-to-have IS functionali...

Due to its ability of forming continuous images for a ground scene of interest, the video synthetic aperture radar (SAR) has been studied in recent years. However, as video SAR needs to reconstruct many frames, the data are of enormous amount and the imaging process is of large computational cost, which limits its applications. In this article, we exploit the redundancy property of multiframe video SAR data, which can be modeled as low-rank tensor, and formulate the video SAR imaging process as a low-rank tensor recovery problem, which is solved by an efficient alternating minimization method. We empirically compare the proposed method with several state-of-the-art video SAR imaging algorithms, including the fast back-projection (FBP) method and the compressed sensing (CS)-based method. Experiments on both simulated and real data show that the proposed low-rank tensor-based method requires significantly less amount of data samples while achieving similar or better imaging performance.

Low-earth-orbit (LEO) synthetic aperture radar (SAR) can achieve advanced remote sensing applications benefiting from the large beam coverage and long duration time of interested area provided by a geosynchronous (GEO) SAR illuminator. In addition, the receiving LEO SAR system is also cost-effective because the transmitting module can be omitted. In this article, an imaging method for GEO-LEO bistatic SAR (BiSAR) is proposed. First, the propagation delay characteristics of GEO-LEO BiSAR are studied. It is found that the traditional “stop-and-go” propagation delay assumption is not appropriate due to the long transmitting path and high speed of the LEO SAR receiver. Then, an improved propagation delay model and the corresponding range model for GEO-LEO BiSAR are established to lay the foundation of accurate imaging. After analyzing the sampling characteristics of GEO-LEO BiSAR, it is found that only 12.5% sampling data can be acquired in the azimuth direction. To handle the serious sub-Nyquist sampling problem and achieve good focusing results, an imaging method combined with multireceiving technique and compressed sensing is proposed. The multireceiving observation model is first obtained based on the inverse process of a nonlinear chirp-scaling imaging method, which can handle 2-D space-variant echo. Following that, the imaging problem of GEO-LEO BiSAR is converted to an $L_{1}$ regularization problem. Finally, an effective recovery method named complex approximate message passing (CAMP) is applied to obtain the final nonambiguous image. Simulation results show that the proposed method can suppress eight times Doppler ambiguity and obtain the well-focused image with three receiving channels. With the proposed method, the number of required receiving channels can be greatly reduced.

The main challenge of cross-domain text classification is to train a classifier in a source domain while applying it to a different target domain. Many transfer learning-based algorithms, for example, dual transfer learning, triplex transfer learning, etc., have been proposed for cross-domain classification, by detecting a shared low-dimensional feature representation for both source and target domains. These methods, however, often assume that the word clusters matrix or the clusters association matrix as knowledge transferring bridges are exactly the same across different domains, which is actually unrealistic in real-world applications and, therefore, could degrade classification performance. In light of this, in this paper, we propose a softly associative transfer learning algorithm for cross-domain text classification. Specifically, we integrate two non-negative matrix tri-factorizations into a joint optimization framework, with approximate constraints on both word clusters matrices and clusters association matrices so as to allow proper diversity in knowledge transfer, and with another approximate constraint on class labels in source domains in order to handle noisy labels. An iterative algorithm is then proposed to solve the above problem, with its convergence verified theoretically and empirically. Extensive experimental results on various text datasets demonstrate the effectiveness of our algorithm, even with the presence of abundant state-of-the-art competitors.

Electropulsing treatment (EPT) is a promising postprocessing method to optimize microstructure. In the present work, the EPT was applied to treat cemented carbide (WC-15Co). The electropulse-induced microstructural evolution in WC-15Co was investigated. Compared with the untreated sample, the transverse rupture strength and electrical resistance of the treated sample were reduced. However, there was no significant change in the scanning electron microscopy (SEM) microstructure. Therefore, electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) were carried out to detail the microstructural evolution. This process revealed that electropulsing could promote the recrystallization and the transition of e-Co to α-Co, as well as the evolution of microstructure. In addition, the dislocations in the treated sample were significantly fewer than those in the untreated sample.

Fraud detection from massive user behaviors is often regarded as trying to find a needle in a haystack. In this paper, we suggest abnormal behavioral patterns can be better revealed if both sequential and interaction behaviors of users can be modeled simultaneously, which however has rarely been addressed in prior work. Along this line, we propose a COllective Sequence and INteraction (COSIN) model, in which the behavioral sequences and interactions between source and target users in a dynamic interaction network are modeled uniformly in a probabilistic graphical model. More specifically, the sequential schema is modeled with a hierarchical Hidden Markov Model, and meanwhile it is shifted to the interaction schema to generate the interaction counts through Poisson factorization. A hybrid Gibbs-Variational algorithm is then proposed for efficient parameter estimation of the COSIN model. We conduct extensive experiments on both synthetic and real-world telecom datasets in different scales, and the results show that the proposed model outperforms some competitive baseline methods and is scalable. A case is further presented to show the precious explainability of the model.

Echoes of bistatic forward-looking synthetic aperture radar (BFSAR) disperse to multiple range cells and exist spatial frequency extension as well as Doppler spectrum extension (i.e., namely space–time extension). Furthermore, the characteristics of BFSAR clutter are strongly nonstationary and spatial variants. Because of the abovementioned issues, clutter and moving targets are fully overlapped in the initial 3-D space–time–range raw data domain, and the clutter cannot be suppressed effectively. To solve this problem, a BFSAR multipulse displaced phase center antenna (MP-DPCA) method is proposed in this article. First, keystone transform without Doppler ambiguity is applied to remove the coupling between the space–time and range domains. Hence, the overall complex 3-D processing in the space–time–range domain is reduced to independent 2-D processing in each space–time domain. Subsequently, a spatial-dechirp processing is applied in the space–time domain to eliminate the spatial frequency extension. Meanwhile, Doppler parameters of clutter point scatterers are equalized by nonlinear chirp scaling processing in the frequency and time domains. Accordingly, the Doppler spectrum extension of point scatterers can be eliminated by a uniform azimuth dechirp processing. After aforesaid three steps, the clutter and moving targets are separated in the space–time domain. Finally, based on the linear phase difference of clutter between the channels, a multipulse canceller can be designed to suppress the clutter. Compared with the existing space–time adaptive procession (STAP) and DPCA methods, this method not only overcomes the nonstationary problem in BFSAR but also conquers the strict application conditions of DPCA. Simulation results are given to verify the effectiveness of the proposed method.

Nowadays flexible high-performance electromagnetic interference (EMI) shielding materials are greatly demanded in military and daily purpose. To yield high shielding effectiveness (SE), the traditional metallic EMI materials always require a large mass of loading in a composite matrix, which is uneconomic and harmful to the mechanical property of the materials. Herein the homogeneous decoration of Ag nanoparticles on a light-weight and robust carbon nanotube sponge (CNTS) based 3D conductive scaffold is introduced. The flexible Ag/CNTS/PDMS composites exhibit an extremely high and tunable EMI shielding performance with improved mechanical strength and high conductivity. The maximum EMI SE is measured to be even greater than 90 dB in the X-band (8–12 GHz) with a low loading of Ag (3%, wt %) and CNTS (4%, wt %). In addition, the 3D inter-connected pore structure enables EM waves to reflect multiple times inside the Ag/CNTS hybrids which results in a strong absorption ability. The total EMI shielding property can be further conveniently controlled by tuning the Ag-deposition parameters as well as the thickness of the sample. The current approach successfully avoids the dispersion problem of the fillers and will be of great importance in development of high-performance EMI shielding materials.

Personality plays a pivotal role at work. Many scholars have investigated the association between personality and language usage habits in the English corpus. Given that the Chinese language has the largest number of native speakers in the world, it is essential to analyze the pattern of personality expression in Chinese, which has garnered less attention. In this study, we used the TextMind system to examine the correlation between word categories and personality traits based on Chinese Weibo content. We also compared the results with previous studies to demonstrate the similarities and differences of personality expression between English and Chinese. Additionally, this paper established a prediction model based on machine learning methods to recognize personality. Results showed that language features were powerful indicators of personality. Finally, we made recommendations for using personality expression in the recruitment and selection.

In recent years, short text topic modeling has drawn considerable attentions from interdisciplinary researchers. Various customized topic models have been proposed to tackle the semantic sparseness nature of short texts. Most (if not all) of them follow the bag-of-words assumption, which, however, is not adequate since word order and phrases are often critical to capturing the meaning of texts. On the other hand, while some existing topic models are sensitive to word order, they do not perform well on short texts due to the severe data sparseness. To address these issues, we propose the Pseudo-document-based Topical N-Grams model (PTNG), which alleviates the data sparsity problem of short texts while is sensitive to word order. Extensive experiments on three real-world data sets with state-of-the-art baselines demonstrate the high quality of topics learned by PTNG according to UCI coherence scores and more discriminative semantic representation of short texts according to classification results.

With the 1 h-averaged data of atmospheric precipitable water vapor (PWV) for 2015–2018 retrieved from 18 ground-based Global Positioning System (GPS) observation stations near Poyang Lake (PL), China, the diurnal variations of the PWV during midsummer (July-August) are studied by the harmonic method. Results show that significant diurnal variations of PWV are found at the 18 GPS stations. The harmonics with 24 h cycle (diurnal cycle) over PL (i.e., Duchang and Poyang) and Nanchang city only have about 50% (or even smaller than 50%) of variance contribution with the amplitude of about 0.2 mm, while above 70% (or even 80%) of variance contribution occurs elsewhere around PL, with the amplitude of about 0.9 mm. The harmonics with diurnal cycles in most stations peak from afternoon to evening (i.e., 1200-2000 LST), but one exception is Duchang site, where the diurnal cycle peaks in the morning (i.e., 1000 LST). Moreover, the harmonics with 12 h cycle (semidiurnal cycle) have the relatively uniform amplitude of about 0.2 mm, but their variance contributions show uneven distribution, with the contributions of about or above 50% in PL and Nanchang city (the semidiurnal cycles peak about 0000 LST or 1200 LST) and below 30% (or even 10%) in other areas. The preliminary diagnosis analysis shows that the diurnal variation of the low-level (below 850 hPa) air temperature (increasing after the sunrise, decreasing after the sunset, and peaking around 1400-1800 LST) may be responsible for the diurnal cycle. Moreover, in PL (Duchang and Poyang) and Nanchang city, the effects (heating or cooling) of lake and urban, the diurnal variation of the 10 m wind over PL, and the acceleration of PL on overlying air also contributed to the diurnal variation of PWV.

It is always deemed crucial to identify the key factors that could have significant impact on the stock market trend. Recently, an interesting phenomenon has emerged that some of President Trump’s posts in Twitter can surge into a dominant role on the stock market for a certain time period, although studies along this line are still in their infancy. Therefore, in this article, we study whether and how this new-rising information can help boost the performance of stock market prediction. Specifically, we have found that the echoing reinforced effect of financial news with Trump’s market-related tweets can influence the market movement—that is, some of Trump’s tweets directly impact the stock market in a short time, and the impact can be further intensified when it echoes with other financial news reports. Along this line, we propose a deep information echoing model to predict the hourly stock market trend, such as the rise and fall of the Dow Jones Industrial Average. In particular, to model the discovered echoing reinforced impact, we design a novel information echoing module with a gating mechanism in a sequential deep learning framework to capture the fused knowledge from both Trump’s tweets and financial news. Extensive experiments have been conducted on the real-world U.S. stock market data to validate the effectiveness of our model and its interpretability in understanding the usability of Trump’s posts. Our proposed deep echoing model outperforms other baselines by achieving the best accuracy of 60.42% and obtains remarkable accumulated profits in a trading simulation, which confirms our assumption that Trump’s tweets contain indicative information for short-term market trends. Furthermore, we find that Trump’s tweets about trade and political events are more likely to be associated with short-term market movement, and it seems interesting that the impact would not degrade as time passes.

Employer Brand Evaluation (EBE) is to understand an employer’s unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers’ financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety of employers’ information from their employees’ online ratings and comments, which enables EBE from an employee’s perspective. To this end, in this article, we propose a method named Company Profiling–based Collaborative Topic Regression (CPCTR) to collaboratively model both textual (i.e., reviews) and numerical information (i.e., salaries and ratings) for learning latent structural patterns of employer brands. With identified patterns, we can effectively conduct both qualitative opinion analysis and quantitative salary benchmarking. Moreover, a Gaussian processes--based extension, GPCTR, is proposed to capture the complex correlation among heterogeneous information. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and generalizability of our methods in real-life applications. The results clearly show that our methods outperform state-of-the-art baselines and enable a comprehensive understanding of EBE.

Solar thermal and coal are two major energy sources in solar aided power generation (SAPG) systems. Reasonably distinguishing the solar shares of the total electricity output is a problem that must be solved. This study modeled the energy balance, exergy balance and solar exergy balance of the key subsystems in an SAPG system. Meanwhile, this study also designed a new solar share evaluation method by tracing solar exergy flows and allocating exergy destruction. How and how much solar exergy was transferred from the sun to electricity could be theoretically estimated by analyzing the distributions of solar share and solar exergy destruction. The proposed method considered energy level differences between solar energy and coal without assuming plant efficiency or solar shares in each energy stream to be constant. The proposed method could not only be used to calculate solar-generated electricity on the point of exergy transfer and destruction, but also be further used for excavating the potential of solar share expansion.

The correlation network, typically formed by computing pairwise correlations between variables, has recently become a competitive paradigm to discover insights in various application domains, such as climate prediction, financial marketing, and bioinformatics. In this study, we adopt this paradigm to detect cheating behavior hidden in business distribution channels, where falsified big deals are often made by collusive partners to obtain lower product prices—a behavior deemed to be extremely harmful to the sale ecosystem. To this end, we assume that abnormal deals are likely to occur between two partners if their purchase-volume sequences have a strong negative correlation. This seemingly intuitive rule, however, imposes several research challenges. First, existing correlation measures are usually symmetric and thus cannot distinguish the different roles of partners in cheating. Second, the tick-to-tick correspondence between two sequences might be violated due to the possible delay of purchase behavior, which should also be captured by correlation measures. Finally, the fact that any pair of sequences could be correlated may result in a number of false-positive cheating pairs, which need to be corrected in a systematic manner. To address these issues, we propose a correlation network analysis framework for cheating detection. In the framework, we adopt an asymmetric correlation measure to distinguish the two roles, namely, cheating seller and cheating buyer , in a cheating alliance. Dynamic Time Warping is employed to address the time offset between two sequences in computing the correlation. We further propose two graph-cut methods to convert the correlation network into a bipartite graph to rank cheating partners, which simultaneously helps to remove false-positive correlation pairs. Based on a 4-year real-world channel dataset from a worldwide IT company, we demonstrate the effectiveness of the proposed method in comparison to competitive baseline methods.

Fraud detection is usually compared to finding a needle in a haystack and remains a challenging task because fraudulent acts are buried in massive amounts of normal behavior and true intentions may be disguised in a single snapshot. Indeed, fraudulent incidents usually take place in consecutive time steps to gain illegal benefits, which provides unique clues for probing fraudulent behavior by considering a complete behavioral sequence rather than detecting fraud from a snapshot of behavior. Additionally, fraudulent behavior may involve different parties, such that the interaction patterns between sources and targets can help distinguish fraudulent acts from normal behavior. Therefore, in this paper, we model the attributed behavioral sequences generated from consecutive behaviors in order to capture the sequential patterns, while those that deviate from the pattern can be detected as fraudulence. Considering the characteristics of the behavioral sequence, we propose a novel model, NHA-LSTM, by augmenting the traditional LSTM with a modified forget gate, where the interval time between consecutive time steps is considered. Furthermore, we design a self-historical attention mechanism to allow for long time dependencies, which can help identify repeated or cyclical appearances. In addition, we propose an enhanced network embedding method, FraudWalk, to construct embeddings for the nodes in the interaction network with regard to higher-order interactions and particular time constraints for revealing potential group fraudulence. The node embeddings, along with the feature vectors, are fed into the model to capture the interactions between sources and targets. To validate the effectiveness of sequential behavior embeddings, we experiment on a real-world telecommunication dataset with prediction and classification tasks based on the learned embeddings. The experimental results show that the learned embeddings can better identify fraudulent behavior. Finally, we visualize the weights of the attention mechanism to provide a rational interpretation of human behavioral patterns.

Bistatic synthetic aperture radar (BiSAR) imaging is faced with two major challenges: large scene imaging and adaptability to unideal platform motion in practice. In order to deal with these two problems, a generalized wavefront-curvature-corrected polar format algorithm (PFA) is proposed in this article. The traditional PFA is little restricted on geometry configuration and platform motion, but its application to large scene imaging is limited by the far-field planar wavefront assumption. To solve this limitation, this article derives the phase error caused by wavefront curvature and analyzes its influence on both geometric distortion and defocusing effect in detail. Based on the analysis, we present a wavefront curvature completely correcting method through space-variant phase compensation using the analytical wavefront curvature phase in wavenumber-domain, which is derived through method of series reversion. What's more, an efficient realization of the space-variant phase compensation based on two-stage image division is given to avoid high overlap rate in the traditional image division method. The proposed method can obtain well focused and geometric undistorted image for BiSAR under complicated flight paths, and it also keeps the logarithmic complexity of traditional PFA. The effectiveness of the proposed method is verified by numerical simulations.

Synthetic aperture radar (SAR) has extensive applications in both civilian and military fields for its ability to create high-resolution images of the ground target without being affected by weather conditions and daytime or nighttime effect. As fueled by the several decades’ advancement of SAR, existing SAR systems exhibit high imaging capabilities, namely, significantly high two-dimensional resolution. However, in accordance with Nyquist's sampling theory, the increase in resolution implies an evident increase in the amount of sampling data, thereby causing numerous limitations to practical application. To solve this problem, several compressive sensing (CS) and matrix sensing (MS) techniques have been applied to SAR imaging, wherein the existing knowledge of sparsity or low-rankness is exploited to reconstruct the SAR image based on an under-sampled SAR raw data. In this study, we take a different approach, wherein redundancy property of the SAR image is further exploited. The SAR image is split into a sparse matrix and a low rank matrix. Thus, the SAR imaging processor is modelled as a problem of joint sparse and low rank matrices recovery. An Orthogonal Sparse and Rank-one Pursuit (OSRanP) algorithm is newly proposed to solve this problem in SAR imaging case, where there isn’t any prior information of the exact sparsity or low-rankness value at hand. As revealed from the results here, the proposed method outmatches the CS and MS methods in sampling efficiency in both simulations and experiments.

In real application scenario of bistatic forward-looking synthetic aperture radar (BFSAR), ground moving target (GMT) is generally smeared severely in SAR image, due to its unknown range cell migration (RCM) and Doppler frequency migration (DFM). When GMT moves along an unknown curve trajectory, its high-order RCM and DFM (including the second- and third-order terms) would further aggravate the difficulty of GMT refocusing. To address this issue, an efficient GMT refocusing method via keystone-based delay-correlation transform and fast searching Fourier transform (KDCT-FSFT) is proposed. First, the KDCT is proposed to correct the first- to third-order RCMs regardless of target's motion state and position information. Meanwhile, the order of GMT's phase response is reduced as well. Then, FSFT is applied to estimate the third-order Doppler parameter of GMT. In the following, a 2-D fast Fourier transform (2D-FFT) can be applied to integrate the target signal coherently in Doppler parameters domain, where the Doppler centroid and Doppler frequency rate of GMT can be estimated. Finally, with the aforesaid estimated Doppler parameters, RCM and DFM can be well corrected and target with unknown curve motion can be finely refocused. Compared with the existing methods, not only the refocusing accuracy of the proposed method is higher, but also its processing is more efficient, since the procedures in the proposed method are performed with respect to all the range cells in the corresponding aperture, i.e., GMT refocusing is achieved by the 2-D data received in one aperture, rather than along every single range cell. Both the simulation and experimental results are given to validate the effectiveness of the proposed method.

Real-time passenger flow prediction plays an important role in subway network design and management. Most of the existing prediction algorithms only consider the sequence of passenger flow volume, however, ignore the influence of other outer factors, for example, the weather conditions, air quality and temperature. In this paper, a systematic framework, MetroEye, is proposed for weather-aware prediction of real-time passenger flow. The framework contains an offline system and an online system. The offline system adopts a conditional random field (CRF) model to establish the relationship between passenger flow volume and weather factors. Experimental results show the superior prediction accuracy of the model, especially in large stations. The online system provides efficient methods to simulate the real-time passenger flow volume. Due to its high practicality, MetroEye has been adopted by Beijing Urban Rail Transit Control Center to monitor the passenger flow status of the Beijing subway system.

Purpose This paper aims to develop an integrated and portable desktop 3D printer using direct extruding technology to expand applied material field. Different from conventional fused deposition modeling (FDM) which uses polymer filaments as feedstock, the developed system can fabricate products directly using polymer pellets. And its printing properties are also investigated. Design/methodology/approach A conical screw-based extrusion deposition (CSBED) system was developed with a large taper conical screw to plasticize and extrude fed materials. The 3D printer was developed with assistance of precision positioning and controlling system. Biocompatible thermoplastic polyurethane (TPU) pellets were selected as raw materials for experiments. The influences of four processing parameters: nozzle temperature, fill vector orientation, layer thickness and infill density on the product’s internal structure and tensile properties were investigated. Findings It is concluded that the customized system has a high manufacturing accuracy with a diminutive global size and is suitable for printing soft materials such as TPU. Theoretical calculation shows the developed conical screw is more effective in plasticizing and extruding compared with conventional screw. Printed samples can achieve applicable tensile properties under harmonious parameter cooperation. Deposited materials are found to have voids among adjacent roads under unbefitting parameters. Originality/value The developed system efficiently improves material limitations compared to commercial FDM systems and exhibits great potential in medical field because soft materials such as biocompatible TPU pellets can be directly used.

3D printing technologies have exhibited their superiorities in the polymer processing field. In this work, thermoplastic polyurethane (TPU)-based soft parts reinforced with in situ polylactic acid (PLA) microfibers were successfully fabricated using a customized fused deposition modeling (FDM) printer. The non-printability of composite filaments was theoretically proved. The morphology of PLA microfibers was carefully characterized using scanning electron microscopy, and microfibers with a large aspect ratio can be observed in extruded strands and printed parts. The thermal and mechanical properties of printed samples were studied and compared with a compression molding sample. It was found that the crystallinity of PLA in compression molding and FDM samples increased from 17.72% to above 24.92%. The tensile strength of FDM samples was about 90% higher than that of the compression molding sample, while elongation at break reduced by half. The results can be attributed to the deposition-induced effects, an...

Real-time dynamic vascular network imaging can provide accurate hemodynamic and anatomical information, facilitating the diagnosis of blood circulatory system-related diseases and achieving precise evaluation of therapeutic effects. In vivo luminescence imaging in the NIR-IIb biological window (1500-1700 nm) has developed into a next generation of optical imaging method with significantly improved temporal-spatial resolution and penetration depth. Unfortunately, an imaging contrast agent capable of emitting NIR-IIb luminescence with sufficient brightness in this region is lacking. Herein, we designed and proposed a type of dye-sensitized rare earth-doped nanoparticle (RENPs@Alk-pi) with obviously boosted NIR-IIb emission and high biocompatibility, which can be used to realize the real-time NIR-IIb luminescence imaging with high temporal-spatial resolution and contrast. The dye sensitization process provides a 40-fold enhanced brightness of the NIR-IIb emission at 1525 nm of Er3+. Consequently, the RENPs@Alk-pi was not only able to depict a vascular network but also applicable in noninvasively monitoring the dynamic vascular processes and changes in the vascular anatomy of two blood circulatory system-related disorders, including hindlimbs ischemia and atherosclerosis. Our research provides a powerful tool for evaluating vascular network-related dysfunction and physiological processes.

Geosynchronous (GEO) spaceborne–airborne bistatic synthetic aperture radar (GEO-BiSAR), where the high-altitude transmitter provides continuous illumination for the receiver, is capable of providing benefits to remote sensing applications. However, obtaining a focused image with high efficiency is a challenging work. The severe 2-D space-variant range cell migration and Doppler modulation introduced by the two moving platforms make the echo hard to be focused. Moreover, the azimuth ambiguity due to the low pulse repetition frequency adopted by the GEO illuminator seriously deteriorates the quality of the final image. In order to simultaneously suppress the azimuth ambiguity and obtain well-focused images, a weighted fast factorized backprojection (FFBP) method is proposed for multichannel GEO BiSAR in this letter. First, the Doppler ambiguity of GEO BiSAR is analyzed and the azimuth multichannel receiving technique is introduced. Then, the multichannel transfer function for GEO BiSAR is derived. Based on the multichannel transfer function, a weighted FFBP method is proposed to achieve accurate focusing and ambiguity suppression. Finally, the simulation results validate the effectiveness of the proposed method.

A bistatic forward-looking synthetic aperture radar (BFSAR) is increasingly in the focus of study, as it breaks through the limitations of imaging on a forward-looking terrain of a moving platform. Fast factorized backprojection (FFBP) is a reliable BFSAR imaging algorithm, with both imaging precision and efficiency being taken into consideration. In FFBP imaging, compensation of the motion errors is important to obtain a well-focused image. To accomplish an accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, because of the accuracy limit of such systems, motion errors are difficult to be compensated correctly, resulting chiefly in resolution decrease in the final images. To deal with such a problem, we propose an FFBP imaging algorithm integrated with motion trajectory estimation for BFSAR. First, a coarse-to-fine residual range cell migration (RCM) correction scheme is applied to ensure the residual RCM within a range resolution. Then, an optimization model with respect to motion trajectory estimation under the criterion of maximum image sharpness is built during FFBP imaging. According to the coarse-to-fine residual RCM correction scheme and the motion trajectory estimation model, a block coordinate descent technique based on steepest descent optimization method integrated with residual RCM correction is proposed to estimate the motion errors in FFBP imaging. We empirically compare the proposed method with several state-of-the-art BFSAR imaging and autofocus algorithms. Simulations on BFSAR data show that the proposed method is more accurate and has similar computational cost.

Polar format algorithm (PFA) as is an efficient algorithm can be well applied to spotlight bistatic synthetic aperture radar (SAR). However, a planar wavefront assumption which leads to image distortion is made. In this study, a new method to compensate for the wavefront curvature of PFA image is presented and it can well correct the defocusing effect and geometry distortion even under arbitrary platforms paths. By projecting the coefficients of Taylor expansion from the azimuth-time domain to wavenumber domain, the new method does not need repeated interpolations to construct the compensation filters for all the errors, which can decrease computational expense in a way. Finally, the effectiveness of this method is verified by numerical simulation.

Back-projection (BP) is a reliable synthetic aperture radar (SAR) imaging algorithm because of its high-resolution and strong adaptability. However, it is hard to implement because of its high computational complexity. Fast factorized BP (FFBP) is a new way to fix this problem. Like traditional BP, FFBP is compatible with arbitrary flight paths if the track deviations are measured within fractions of a wavelength. However, when the motion information is not accurate enough, autofocus become an important way to get well-focused images. In this paper, we present an effective autofocus method for FFBP to solve the imaging problem caused by platform’s motion errors. First, an image quality evaluation function with unknown phase error based on image sharpness for FFBP is established. Then, the phase error computation for autofocus is modeled as an optimization problem. Second, the coordinate descent (CD) and secant processing are introduced to the maximum image sharpness problem. The proposed method keeps the rapid imaging performance of FFBP and solves well the motion error compensation problem. In the end, simulated data and real data were used to verify the effectiveness of the proposed algorithm.

Being capable of providing weather-independent, day-and-night, forward-looking, and high-resolution images, bistatic forward-looking synthetic aperture radar (BFSAR) is a promising sensing technique in applications such as the scene-matching-aided navigation for recently emerging high-speed maneuvering platforms (HMPs). Because of the high speed and the great maneuverability of HMPs and the bistatic forward-looking configuration, conventional image formation algorithms, such as polar format algorithm (PFA), are no longer suitable for HMP-borne BFSAR (HMP-BFSAR). Hence, in this paper, we propose a novel PFA for HMP-BFSAR image formation. In the proposed PFA, a range model, termed as quasi-continuous -move range model, is established by taking the maneuvers of the receiver during pulse propagation into account instead of adopting stop-and-go approximation. Moreover, to take advantage of the collected $k$ -set efficiently, an affine mapping, termed as $k$ -set affine mapping, is conceived to transform the parallelogram-shaped $k$ -set support region to a horizontal and quasi-rectangular one. Furthermore, to compensate for the defocus effect induced by wavefront curvature, a closed-form refocus filter based on the implicit function theorem is derived. Both point target simulation and distributed target simulation are presented in this paper. The simulation results show that the proposed PFA significantly outperforms the conventional PFA in terms of focusing quality and computational efficiency when applied to HMP-BFSAR image formation.

In the era of big data, it is extremely challenging to decide what information to receive and filter out in order to effectively acquire high-quality information, particularly in social media where large-scale User Generated Contents (UGC) is widely and quickly disseminated. Considering that each individual user in social network can take actions to drive the process of information diffusion, it is naturally appealing to aggregate spreading information effectively at the individual level by regarding each user as a social sensor. Along this line, in this paper, we propose a framework for effective information acquisition in social media. To be more specific, we introduce a novel measurement, the preference-based Detection Ability to evaluate the ability of social sensors to detect diffusing events, and the problem of effective information acquisition is then reduced to achieving social sensing maximization through discovering valid social sensors. In pursuit of social sensing maximization, we propose two algorithms to resolve the longstanding problems in traditional greedy methods from the perspectives of efficiency and performance. On the one hand, we propose an efficient algorithm termed LeCELF, which resolves the redundant re-evaluations in the traditional Cost-Effective Lazy Forward (CELF) algorithm. On the other hand, we observe the participation paradox phenomenon in the social sensing network, and proceed to propose a randomized selection-based algorithm called FRIENDOM to choose social sensors to improve the effectiveness of information acquisition. Experiments on a disease spreading network and real-world microblog datasets have validated that LeCELF greatly reduces the running time, whereas FRIENDOM achieves a better detection performance. The proposed framework and corresponding algorithms can be applicable in many other settings in resolving information overload problems.

An echo model plays a pivotal role in the processing of synthetic aperture radar (SAR) data. It has been shown that the widely adopted echo models based on stop-and-go approximation (SAG) are unsuitable for use in many emerging radar systems with high resolution or high-speed platforms. More accurate echo models without SAG have been proposed for some special monostatic scenarios, such as low-Earth-orbit SAR and geosynchronous SAR. However, due to their inherent assumptions, such as linear and constant-speed/orbit-based motion, these conventional echo models cannot be applied to general cases, which involve bistatic configurations and maneuvers. Maneuvers or nonlinear/inconstant-speed motion are very common in cases, such as circular SAR and maneuvering-platform-borne SAR. In this letter, we propose a generalized echo model for bistatic SAR, which accounts for maneuvers of the platforms during pulse propagation. Moreover, we obtain a high-precision closed-form expression of the model by approximating high-order terms of the accurate range history of the receiver. Compared with the conventional models, the proposed echo model can be applied to a much greater number of cases, ranging from slow and constant-speed platforms to fast and maneuvering platforms. Theoretical analysis and backprojection-based image formation simulations confirm the validity of the proposed echo model.

The U.S. housing collapse in 2007 is widely blamed for inducing a financial crisis that spread to the real economy and caused a severe and prolonged downturn. This paper develops a model to investigate the role of gasoline price shocks in triggering the housing market collapse and identifies a new channel through which energy price shocks affect the financial market and the macro economy. Results suggest that unanticipated gasoline price shocks increase the cost of work commutes, lower the value of homes away from the city center, and increase foreclosure rates as homeowners either cannot afford mortgage payments amid elevated gas expenditures or seek to abandon underwater homes. The model predicts that gasoline price shocks disproportionately affect suburban households that face greater exposure due to longer commutes and lower incomes. Empirical evidence from the 2007–08 housing collapse is presented to corroborate this theory.

Nowadays, people are facing more and more threats from solar UV radiation due to global ozone layer depletion and the ozone hole. Therefore, it is highly desirable to develop a fast, simple, and sensitive sensor for solar UV radiation. In this work, recently emerging perovskite quantum dots (PQDs) with excellent optical properties are used for sensing solar UV radiation. PQDs with high quantum yields are protected in solid paraffin to obtain paraffin-PQD composite (P-PQD) film. The hydrophobic and compact paraffin not only can protect PQDs from degradation by water and air for long-term use but also is transparent to UV light and thus maintains the high quantum yield of PQDs. The P-PQD film shows high sensing sensitivity for solar UV radiation based on which a novel visual ratiometric luminescence sensing strategy for UV light has been developed. The visual ratiometric luminescence sensor for solar UV radiation adopts a paraffin-encapsulated red fluorescence PQD (P-r-PQD) film as the sensing unit for UV light and a green luminescence light-emitting diode as reference light. When exposed to different solar UV radiation, the sensing film shows wide color variations from green to red, which can easily observed by the naked eye or recorded by a camera. On the basis of this, a simple and visualized solar UV index-reporting system has been established.

In geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-BiSAR) system, the airborne platform achieves high-resolution imaging by passively receiving the signal from the interested scenario. In this paper, the Doppler characteristics of GEO-BiSAR and the individual contribution of the transmitter and the receiver are first analyzed. The airborne receiver is found to be the dominant contributor for the total Doppler bandwidth, which will lead to Doppler spectrum aliasing regarding the low pulse repetition frequency (PRF) adopted by the GEO-SAR. In order to suppress the Doppler ambiguity without adjusting the PRF of GEO-SAR, azimuth multichannel receiving technique is introduced to the airborne receiver. The multichannel transfer function is derived based on the method of series reversion and the spectrum reconstruction algorithm is then modified for multichannel GEO-BiSAR. Moreover, the reconstruction performance is closely related to the corresponding spacing between each channel (i.e., channel configuration). Therefore, the channel configuration design for GEO-BiSAR aims at optimizing the azimuth ambiguity-to-signal ratio with a satisfactory level of signal-to-noise ratio scaling factor by adjusting the channel configuration. The channel configuration design is modeled as a constrained single objective optimization problem (CSOP). Then, a channel configuration design method based on differential evolution and feasibility rule is proposed to solve the CSOP and obtain the channel configuration for the receiver with the optimal reconstruction performance. Finally, simulations results are presented to verify the effectiveness of the proposed method, and characteristics of channel configuration are analyzed in detail, which can be a practical guide for the implementation of multichannel GEO-BiSAR systems.

The parallel machine scheduling problems exist in many real-life industrial examples. In order to effectively utilize the machines and decline the total waiting time of jobs assigned to eac...

Due to its flexibility and low cost, the bistatic forward-looking synthetic aperture radar (BFSAR) which employs side-looking transmitter and forward-looking receiver has been studied in recent years. Sparsity-based techniques have been applied in the field of BFSAR imaging and show great potential. In sparsity-based BFSAR imaging, compensation of the motion errors is crucial to get a well-focused image. For fields that admit a sparse representation, we propose a sparsity-based imaging approach integrated with motion error estimation and compensation in this paper. First, a novel joint phase-amplitude compensation-based motion error correction scheme is developed to cope with the spatial variance of motion error. Then, an inversion observation model of the range-Doppler algorithm combined with motion error correction is derived, based on which a joint problem of BFSAR imaging and motion error estimation is formulated as a sparse recovery problem and solved in an iterative way, where in each iteration, both image formation and motion error correction are carried out. Experiments on both the simulated and real BFSAR data show that the proposed method can obtain a more accurate estimation result, and generate better focused images compared with the existing methods.

Bistatic range sum (BRS) equation and Doppler equation of bistatic synthetic aperture radar (BiSAR) can be used to achieve target localization in BiSAR images. Because of the double square roots ex...

Despite that the accuracy and efficiency of stereo matching technology have significantly improved in the past decades, the issue of edge-blurring remains a challenge to most of the existing approaches. In this paper, we propose a minimum spanning tree (MST) based stereo matching method by using the image edge and segmentation optimization to preserve the image boundary. We first exploit a fast disparity range estimation method by combining the Surf and Akaze feature points to improve the computational efficiency. Second, we utilize the image edges and brightness information to generate a self-adaptive weight function, which is able to significantly improve the accuracy of MST aggregating in the regions of complex texture and boundaries with similar color distribution. Third, we employ the image segmentation to extract the invalid regions of the estimated disparity map, and propose a post-processing scheme to refine the disparity result. Finally, we run our method on several Middlebury and KITTI datasets. The comparison results between our method and other state-of-the-art approaches demonstrate that the proposed method has high accuracy for disparity computation and is especially robust to the edge-blurring.

Recently, compressive sensing (CS) has been applied in synthetic aperture radar (SAR) imaging, which is increasingly in the focus of study and shows great potential. In CS-based SAR imaging, motion errors of the moving platform introduce inaccuracies in the observation model, which cause various degradations in the final images. To accomplish accurate motion compensation during CS-based SAR imaging, we propose a fast CS-based SAR imaging integrated with motion compensation method. First, CS-based imaging based on the utilization of inverse observation deduced from the inversion of conventional imaging procedures is applied, which is much more computational efficient than the exact observation model. Then, an improved inverse observation model integrated with motion compensation is derived. In the improved model, spatially variant azimuth phase errors are taken into consideration. Joint SAR imaging and motion compensation are formulated as a sparse recovery problem and solved in an iterative way, wherein each iteration both image formation and motion compensation are carried out. The processing of SAR data shows that the proposed method can obtain better focused images compared with the existing SAR imaging and motion compensation methods.

Geosynchronous (GEO) spaceborne–airborne multistatic synthetic aperture radar (GEO MulSAR) is more flexible and accessible in remote sensing applications because of the high-altitude illuminator and the separation of the receivers and transmitter. In addition, the information obtained by the multiple airborne receivers can be fused to enhance the spatial resolution. However, the fused spatial resolution severely depends on the applied multistatic topology. To achieve the optimal fused spatial resolution by properly adjusting the imaging topology, a topology design method is proposed in this letter. First, the spatial resolution model of GEO MulSAR is given, and the dependence of the spatial resolution on the multistatic topology is analyzed in detail. Then, a topology design method is proposed to obtain the best multistatic topology that simultaneously optimizes the resolution cell area and resolution disequilibrium factor. Finally, the simulation results validate the effectiveness of the proposed method, and some insights into designing the multistatic topology are given.

Bistatic synthetic aperture radar (SAR) is able to break through the limitation of monostatic SAR on forward-looking area imaging with appropriate geometry configurations. Thanks to such an ability, bistatic forward-looking SAR (BFSAR) has extensive potential practical applications. For the focusing problem of conventional side-looking SAR, $\omega\hbox{--}k$ algorithm is accepted as the ideal solution. In this paper, the $\omega\hbox{--}k$ algorithm will be discussed in BFSAR geometry. As for the bistatic configuration, spatial domain linearization procedure should be carried out to extract a range variable from the point target reference spectrum (PTRS) in the existing $\omega\hbox{--}k$ algorithms. With respect to the BFSAR geometry, nevertheless, the linearization procedure reduces the accuracy of PTRS seriously. To cope with such a problem, a novel $\omega\hbox{--}k$ algorithm for BFSAR is proposed. In the proposed method, the range variable is modeled as a parameterized polynomial, and the corresponding PTRS with respect to two-dimensional frequencies is established. Then, the parameters are estimated by differential evolution to minimize the PTRS errors for each range coordinate and frequency point. Based on the estimated PTRS, the BFSAR data can be focused well by the proposed $\omega\hbox{--}k$ algorithm. Simulation results verify the effectiveness of the proposed method.

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.