Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry

Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR by use of the baseline diversity. Single pass MB acquisition with the advantages of high coherence and simple phase components has a more practical capability in 3D reconstruction than conventional repeat-pass MB acquisition. Using an asymptotic 3D phase unwrapping (PU), it is possible to get a reliable 3D reconstruction using very sparse acquisitions but the interferograms should follow the optimal baseline design. However, current spaceborne SAR system doesn't satisfy this principle, inducing more difficulties in practical application. In this article, a new concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for single-pass reliable 3D surface mapping using the asymptotic 3D PU is proposed. Its optimal MB acquisition is analyzed to achieve both good relative height precision and flexible baseline design. Two indicators, i.e., expected relative height precision and successful phase unwrapping rate, are selected to optimize the system parameters and evaluate the performance of various baseline configurations. Additionally, simulation-based demonstrations are conducted to evaluate the performance in typical scenarios and investigate the impact of various error sources. The results indicate that the proposed TDA-InSAR is able to get the specified MB acquisition for the asymptotic 3D PU, which offers a feasible solution for single-pass 3D SAR imaging.Comment: 16 pages, 20 figure

Data-Driven Modeling of Landau Damping by Physics-Informed Neural Networks

Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems. One of the long-standing problems in plasma physics is the integration of kinetic physics into fluid models, which is often achieved through sophisticated analytical closure terms. In this study, we successfully construct a multi-moment fluid model with an implicit fluid closure included in the neural network using machine learning. The multi-moment fluid model is trained with a small fraction of sparsely sampled data from kinetic simulations of Landau damping, using the physics-informed neural network (PINN) and the gradient-enhanced physics-informed neural network (gPINN). The multi-moment fluid model constructed using either PINN or gPINN reproduces the time evolution of the electric field energy, including its damping rate, and the plasma dynamics from the kinetic simulations. For the first time, we introduce a new variant of the gPINN architecture, namely, gPINN$p$ to capture the Landau damping process. Instead of including the gradients of all the equation residuals, gPINN$p$ only adds the gradient of the pressure equation residual as one additional constraint. Among the three approaches, the gPINN$p$-constructed multi-moment fluid model offers the most accurate results. This work sheds new light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.Comment: 11 pages, 7 figure

A Novel Synthetic Analog of 5, 8-Disubstituted Quinazolines Blocks Mitosis and Induces Apoptosis of Tumor Cells by Inhibiting Microtubule Polymerization

Many mitosis inhibitors are powerful anticancer drugs. Tremendous efforts have been made to identify new anti-mitosis compounds for developing more effective and less toxic anti-cancer drugs. We have identified LJK-11, a synthetic analog of 5, 8-disubstituted quinazolines, as a novel mitotic blocker. LJK-11 inhibited growth and induced apoptosis of many different types of tumor cells. It prevented mitotic spindle formation and arrested cells at early phase of mitosis. Detailed in vitro analysis demonstrated that LJK-11 inhibited microtubule polymerization. In addition, LJK-11 had synergistic effect with another microtubule inhibitor colchicine on blocking mitosis, but not with vinblastine or nocodazole. Therefore, LJK-11 represents a novel anti-microtubule structure. Understanding the function and mechanism of LJK-11 will help us to better understand the action of anti-microtubule agents and to design better anti-cancer drugs

Electromagnetic Wave Theory and Applications

Contains reports on eleven research projects.Joint Services Electronics Program (Contract DAAG29-83-K-0003)Joint Services Electronics Program (Contract DAAL03-86-K-0002)National Science Foundation (Grant ECS82-03390)National Science Foundation (Grant ECS85-04381)Schlumberger-Doll Research CenterNational Aeronautics and Space Administration (Contract NAG 5-141)National Aeronautics and Space Administration (Contract NAS 5-26861)National Aeronautics and Space Administration (Contract NAG 5-270)U.S. Navy - Office of Naval Research (Contract N00014-83-K-0258)National Aeronautics and Space Administration (Contract NAG 5-725)International Business Machines, Inc.Lincoln Laborator

Electromagnetic Wave Theory and Remote Sensing

Contains reports on eight research projects.Joint Services Electronics Program (Contract DAAG29-83-K-0003)National Science Foundation (Grant ECS82-03390)Schlumberger-Doll Research CenterNational Aeronautics and Space Administration (Contract NAG5-141)National Aeronautics and Space Administration (Contract NAS5-26861)National Aeronautics and Space Administration (Contract NAG5-270)U.S. Navy - Office of Naval Research (Contract N00014-83-K-0258)International Business Machines, Inc

Deep Learning as Applied in SAR Target Recognition and Terrain Classification

Deep learning such as deep neural networks has revolutionized the computer vision area. Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin. This paper reviews our works in the application of deep convolutional neural networks to target recognition and terrain classification using the SAR image. A convolutional neural network is employed to automatically extract a hierarchic feature representation from the data, based on which the target recognition and terrain classification can be conducted. Experimental results on the MSTAR benchmark dataset reveal that deep convolutional network could achieve a state-of-the-art classification accuracy of 99% for the 10-class task. For a polarimetric SAR image classification, we propose complex-valued convolutional neural networks for complex SAR images. This algorithm achieved a state-of-the-art accuracy of 95% for the 15-class task on the Flevoland benchmark dataset

The complete mitochondrial genome of Pseudocrossocheilus tridentris (Cypriniformes: Cyprinidae: Labeoninae) and phylogenomic analyses for subfamily Labeoninae

Pseudocrossocheilus tridentris is an endemic fish species in the upper Pearl River with limited published genetic information. In the current study, the complete mitochondrial genome of P. tridentris was determined using next generation sequencing, which was 16,606 base pairs (bp) in length and 91.65% identical to its closest relative with a genome of P. bamaensis. Phylogenetic analysis showed that P. tridentris had a close relationship with its congener P. bamaensis