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Tomographic synthetic aperture radar is an advanced multi‐channel interferometric technique for retrieving 3‐D spatial information. It can be regarded as an inherently sparse reconstruction problem and can be solved using compressive sensing algorithms. However, the performances are limited by the number of acquisitions and suffer from computational burdens in practice. This paper proposes a novel method based on deep learning, which is carried out and optimized in an end‐to‐end manner by the generative adversarial neural networks. The proposed method applies the cascaded U‐Net architectures to achieve the reconstruction of full‐channel synthetic aperture radar images and the refinement of obtained tomographic results, respectively. The proposed network is trained using simulated data and validate the technique on simulated and real data. The tests show promising results with the limited number of acquisitions while reducing the computation time. [ABSTRACT FROM AUTHOR]

Hippocampal CA1 neurons show intense firing at specific spatial locations, modulated by isolated landmarks. However, the impact of real‐world scene transitions on neuronal activity remains unclear. Moreover, long‐term neural recording during movement challenges device stability. Conventional rigid‐based electrodes cause inflammatory responses, restricting recording durations. Inspired by the jellyfish tentacles, the multi‐conductive layer ultra‐flexible microelectrode arrays (MEAs) are developed. The tentacle MEAs ensure stable recordings during movement, thereby enabling the discovery of soft boundary neurons. The soft boundary neurons demonstrate high‐frequency firing that aligns with the boundaries of scene transitions. Furthermore, the localization ability of soft boundary neurons improves with more scene transition boundaries, and their activity decreases when these boundaries are removed. The innovation of ultra‐flexible, high‐biocompatible tentacle MEAs improves the understanding of neural encoding in spatial cognition. They offer the potential for long‐term in vivo recording of neural information, facilitating breakthroughs in the understanding and application of brain spatial navigation mehanisms. [ABSTRACT FROM AUTHOR]

Over time, Synthetic Aperture Radar (SAR) imaging techniques have been developed from twodimensional SAR, two-and-a-half-dimensional SAR (InSAR), and three-dimensional SAR to multi-dimensional SAR. This has led to great technological achievements. This paper briefly summarizes the development of SAR and its imaging technology, presents the concept of holographic SAR and clearly defines it for the first time, and highlights the differences and connection between the holographic SAR definition and existing concepts such as holographic radar, circular tomographic SAR, and multi-dimensional SAR. On this basis, under the framework of the existing multi-dimensional SAR techniques, the imaging system and signal model of holographic SAR are established, and preliminary imaging ideas are proposed, which provides a preliminary theoretical and technical framework for the development of holographic SAR technology.

Sparse signal processing-based Synthetic Aperture Radar (SAR) imaging, also known as sparse SAR imaging, is the main research direction of sparse microwave imaging theory. Compared with a conventional SAR system, sparse SAR imaging radar has significant potential to improve imaging performance. However, because it requires heavy computations, the application of sparse SAR imaging in large-scene recovery has become difficult, which restricts its further applications. Additionally, complex SAR images, rather than raw data, are usually used for data archiving due to a number of reasons such as data copyright and system confidentiality. Therefore, it is worthwhile to study how sparse imaging can be achieved using only Matched Filtering (MF) recovered complex images with less computational cost. GaoFen-3 is China’s first 1-m resolution multi-polarization C-band satellite. It has a high-resolution, wide swath imaging ability and hence plays an important role in disaster monitoring and ocean surveillance applications. In this paper, we introduce a complex image-based sparse SAR imaging method to process GaoFen-3 complex image data and improve image performance. Experimental results show that the sparse imaging results have lower sidelobes, higher signal-toclutter and noise ratio, and better target distinguishing ability compared with inputted images. Additionally, sparse imaging can effectively preserve the statistical distribution and phase information of images that makes the recovered GaoFen-3 sparse image-based applications such as interferometric synthetic aperture radar and constant false alarm ratio detection possible.

BackgroundMotivated by the challenges in assessing physician-level cancer screening performance and the negative impact of misclassification, we propose a method (using mammography as an example) that enables confident assertion of adequate or inadequate performance or alternatively recognizes when more data is required.MethodsUsing established metrics for mammography screening performance-cancer detection rate (CDR) and recall rate (RR)-and observed benchmarks from the Breast Cancer Surveillance Consortium (BCSC), we calculate the minimum volume required to be 95% confident that a physician is performing at or above benchmark thresholds. We graphically display the minimum observed CDR and RR values required to confidently assert adequate performance over a range of interpretive volumes. We use a prospectively collected database of consecutive mammograms from a clinical screening program outside the BCSC to illustrate how this method classifies individual physician performance as volume accrues.ResultsOur analysis reveals that an annual interpretive volume of 2770 screening mammograms, above the United States' (US) mandatory (480) and average (1777) annual volumes but below England's mandatory (5000) annual volume is necessary to confidently assert that a physician performed adequately. In our analyzed US practice, a single year of data uniformly allowed confident assertion of adequate performance in terms of RR but not CDR, which required aggregation of data across more than one year.ConclusionFor individual physician quality assessment in cancer screening programs that target low incidence populations, considering imprecision in observed performance metrics due to small numbers of patients with cancer is important.

In the design of flapping-wing micro-size air vehicles capable of hovering, wings serve as the primary source of hovering power, making the analysis of aerodynamics and aerodynamic efficiency crucial. Traditional quasi-steady models treat the wings as single rigid plane, neglecting the deformable characteristics of flexible wings. This paper proposes a multi-plane method that, in conjunction with various design parameters of flexible wings in a two-dimensional plane, analyzes their deformation characteristics under the assumption of multiple planes in three-dimensional space, and describes the deformation of wings during flapping. By combining the quasi-steady aerodynamic model, aerodynamic analysis of the deformed wings can be conducted. The relationship between the slack angle, wing flapping position, and wing deformation are analyzed, along with their effects on aerodynamics and aerodynamic efficiency. Experiments validate the deformation patterns of wings during flapping and compare the simulated aerodynamic forces with measured ones. The results indicate that wing deformation can be accurately described by adjusting the parameters in the multi-plane method and that the aerodynamic analysis using this method closely approximates the average lift results. Additionally, the multi-plane method establishes a connection between wing morphology and aerodynamic forces and efficiency, providing valuable insights for aerodynamic analysis. [ABSTRACT FROM AUTHOR]

High-resolution wide-swath (HRWS) imaging is the research focus of the modern spaceborne synthetic-aperture radar (SAR) imaging field, with significant relevance and vast application potential. Staggered SAR, as an innovative imaging system, mitigates blind areas across the entire swath by periodically altering the radar pulse repetition interval (PRI), thereby extending the swath width to multiples of that achievable by conventional systems. However, the staggered mode introduces inherent challenges, such as nonuniform azimuth sampling and echo data loss, leading to azimuth ambiguities and substantially impacting image quality. This paper proposes a sparse SAR imaging method for the low-oversampled staggered mode via compound regularization. The proposed method not only effectively suppresses azimuth ambiguities arising from nonuniform sampling without necessitating the restoration of missing echo data, but also incorporates total variation (TV) regularization into the sparse reconstruction model. This enhances the accurate reconstruction of distributed targets within the scene. The efficacy of the proposed method is substantiated through simulations and real data experiments from spaceborne missions. [ABSTRACT FROM AUTHOR]

Conventional Synthetic Aperture Radar (SAR) moves along a straight line and forms a linear synthetic apertures. It can only obtain the two-dimensional (2-D) image of illuminated scene that is the projection of the three-dimensional (3-D) real scene onto a slant plane. The slant plane 2-D SAR image, however, suffers from layover and foreshortening effects. 3-D SAR imaging enables 3-D resolving capability by extending the acquisition of frequency information from 2-D to 3-D. It can obtain the 3-D distribution of scattering centers; therefore, it solves the geometric deformation problems of layover and foreshortening. 3-D SAR imaging has become a trending topic in research on SAR techniques worldwide. In this paper, we first introduced the concept of 3-D SAR imaging and several typical 3-D SAR imaging modes. Furthermore, it provides a discussion on research progress at home and abroad, particularly focusing on the progress of our research team. Finally, future research prospects are presented.

The integrated Synthetic Aperture Radar (SAR) and Communication system can improve the networking capabilities of SAR, realize the real-time transmission of detection data, and improve the overall performance of the system. In the working process of the integrated platform, Doppler shift and multipath effects will be introduced, which make the orthogonality of the widely studied Orthogonal Frequency Division Multiplexing (OFDM) integrated waveforms unable to be maintained, and the imaging and communication performance is impaired. In this paper, the Filter Bank Multicarrier (FBMC) waveform used for the integrated SAR and communication systems is proposed. On the one hand, FBMC waveforms require low orthogonality between subcarriers, which can counter Doppler and multipath effects. On the other hand, FBMC waveforms do not use Cyclic Prefix (CP), so the false targets can be avoided and spectral efficiency can be improved. This paper analyzes the integrated performance of FBMC waveforms, studies the effects of multipath and Doppler shift on FBMC waveforms in the integrated system, and proposes a Doppler compensation algorithm for integrated FBMC waveform with large frequency shift. According to the above analysis, the FBMC waveform has better performance in the wide swath SAR and communication integration system. Simulation results verify the conclusion.

Array InSAR can generate 3D point clouds with the use of SAR images of the observed scene, which are obtained using multiple channels in a single flight. Its resolution power in elevation enables one to solve the layover problem. However, due to the limited number of arrays and the short baseline length, the resolution power in elevation is restricted. Together with the layover phenomenon of the urban buildings, the result of 3D reconstruction suffers from poor accuracy in positioning, and it is difficult to extract the effective characteristics of the buildings. In view of this situation, this paper proposed a 3D reconstruction method of array InSAR based on Gaussian mixture model clustering. First, the 3D point clouds of the observed scene are obtained by an algorithm with super-resolution based on compressive sensing, and then the scatters of buildings are extracted by density estimation; after which the method of Gaussian mixture model clustering is used to classify the 3D point clouds of the buildings. Finally, the inverse SAR images of each region are obtained by using the system parameters, and the 3D reconstruction of the buildings is completed. Based on the actual data of the first domestic 3D imaging experiment by airborne array InSAR, the validity of the algorithm is confirmed and the 3D imaging results of the buildings are obtained.

Combining synthetic-aperture imaging and coherent-light detection technology, the weak signal identification capacity of Synthetic Aperture Ladar (SAL) reaches the photo level, and the image resolution exceeds the diffraction limit of the telescope to obtain high-resolution images irrespective to ranges. This paper introduces SAL, including the development path, technology characteristics, and the restriction of imaging swath. On the basis of this, we propose to integrate the SAL technology for extending its swath. By analyzing the scanning-operation mode and the signal model, the paper explicitly proposes that the former mode will be the developmental trend of the SAL technology. This paper also introduces the flight demonstrations of the SAL and the imaging results of remote targets, showing the potential of the SAL in long-range, high-resolution, and scanning-imaging applications. The technology and the theory of the scanning mode of SAL compensates for the defects related to the swath and operation efficiency of the current SAL. It provides scientific foundation for the SAL system applied in wide swath, high resolution earth observation, and the ISAL system applied in space-targets imaging.