Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows superior performance over the state-of-the-art methods on both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table

Research on the Rotary Ultrasonic Facing Milling of Ceramic Matrix Composites

AbstractCeramic matrix composites (CMC) has got increasing importance in many fields of industry, especially in the aerospace. However, due to the special properties, the conventional machining methods are generally very challenging for CMC. The rotary ultrasonic machining (RUM) is a high efficiency processing technology for these advanced materials. This paper carried out research on the rotary ultrasonic facing milling of C/SiC and developed the cutting force simulation software to optimize the cutting parameters. Verification experiments were conducted showing that the efficiency improved by RUM is 5.8 times while the surface quality is improved by 54.4% compared with the conventional milling

Tuning the flat bands by the interlayer interaction, spin-orbital coupling and electric field in twisted homotrilayer MoS2_2

Ultraflat bands have already been detected in twisted bilayer graphene (TBG) and twisted bilayer transition metal dichalcogenides (tb-TMDs), which provide a platform to investigate strong correlations. In this paper, the electronic properties of twisted trilayer molybdenum disulfide (TTM) are investigated via an accurate tight-banding Hamiltonian. We find that the highest valence bands are derived from $\Gamma$-point of the constituent monolayer, exhibiting a graphene-like dispersion or becoming isolated flat bands. The lattice relaxation, local deformation, and electric field can significantly tune the electronic structures of TTM with different starting stacking arrangements. After introducing the spin-orbital coupling (SOC) effect, we find a spin-valley-layer locking effect at the minimum of conduction band at K- and K$^\prime$-point of the Brillouin zone, which may provide a platform to study optical properties and magnetoelectric effects

TBPLaS: a Tight-Binding Package for Large-scale Simulation

TBPLaS is an open-source software package for the accurate simulation of physical systems with arbitrary geometry and dimensionality utilizing the tight-binding (TB) theory. It has an intuitive object-oriented Python application interface (API) and Cython/Fortran extensions for the performance critical parts, ensuring both flexibility and efficiency. Under the hood, numerical calculations are mainly performed by both exact diagonalizatin and the tight-binding propagation method (TBPM) without diagonalization. Especially, the TBPM is based on the numerical solution of time-dependent Schr\"odinger equation, achieving linear scaling with system size in both memory and CPU costs. Consequently, TBPLaS provides a numerically cheap approach to calculate the electronic, transport and optical properties of large tight-binding models with billions of atomic orbitals. Current capabilities of TBPLaS include the calculation of band structure, density of states, local density of states, quasi-eigenstates, optical conductivity, electrical conductivity, Hall conductivity, polarization function, dielectric function, plasmon dispersion, carrier mobility and velocity, localization length and free path, Z2 topological invariant, wave-packet propagation, etc. All the properties can be obtained with only a few lines of code. Other algorithms involving tight-binding Hamiltonians can be implemented easily thanks to its extensible and modular nature. In this paper, we discuss the theoretical framework, implementation details and common workflow of TBPLaS, and give a few demonstrations of its applications.Comment: 54 pages, 16 figure