Ballistic target recognition based on multiple data representations and deep-learning algorithms

Target recognition is a significant part of a Ballistic Missile Defense System(BMDS).However,most existing ballistic target recognition methods overlook the impact of data representation on recognition outcomes.This paper focuses on systematically investigating the influences of three novel data representations in the Range-Doppler(RD)domain.Initially,the Radar Cross Section(RCS)and micro-Doppler(m-D)characteristics of a cone-shaped ballistic target are analyzed.Then,three different data representations are proposed:RD data,RD sequence tensor data,and RD trajectory data.To accommodate various data inputs,deep-learning models are designed,including a two-Dimensional Residual Dense Network(2D RDN),a three-Dimensional Residual Dense Network-Gated Recurrent Unit(3D RDN-GRU),and a Dynamic Trajectory Recognition Network(DTRN).Finally,an Electromagnetic(EM)computation dataset is collected to verify the performances of the networks.A broad range of experimental results demonstrates the effectiveness of the proposed framework.Moreover,several key parameters of the proposed networks and datasets are extensively studied in this research.

Low temperature specific heat of 12442-type KCa2Fe4As4F2 single crystals

Low-temperature specific heat(SH)is measured for the 12442-type KCa2Fe4As4F2 single crystal under different magnetic fields.A clear SH jump with the height of?C/T|Tc=130 mJ mol-1 K-2 is observed at the superconducting transition temperature Tc.It is found that the electronic SH coefficient?γ(H)quickly increases when the field is in the low-field region below 3T and then considerably slows down the increase with a further increase in the field,which indicates a rather strong anisotropy or multi-gap feature with a small minimum in the superconducting gap(s).The temperature-dependent SH data indicate the presence of the T2 term,which supplies further information and supports the picture with a line-nodal gap structure.Moreover,the onset point of the SH transition remains almost unchanged under the field as high as 9 T,which is similar to that observed in cuprates,and places this system in the middle between the BCS limit and the Bose-Einstein condensation.

Smart structures with embedded flexible sensors fabricated by fused deposition modeling-based multimaterial 3D printing

Smart structures have the advantages of high system integrity and diverse sensing capabilities.However,the labor-intensive and timeconsuming fabrication process hinders the large-scale adoption of smart structures.Despite recent attempts to develop sensorembedded structures using 3D printing technologies,the reported smart structures generally suffer from the complex fabrication process,constrained part size,and limited sensing modality.Herein,we propose a workflow to design and fabricate novel smart structures via multi-material fused deposition modeling(FDM)-based 3D printing.More specifically,conductive filaments with tailorable mechanical and elec-trical properties,e.g.piezoresistive effects,were developed.Additionally,the printing process was optimized for processing soft filaments with Young’s modulus around 2 MPa,resolving the issue of filament buckling.Furthermore,the potential applications of the proposed workflow were showcased using three design cases,i.e.biaxial strain sensor,smart tire,and cable-driven soft finger with multiple sensing capabilities.This workflow provides a cost-effective and rapid solution for developing novel smart structures with soft materials.

High-throughput first-principle prediction of collinear magnetic topological materials

The success of topological band theory and symmetry-based topological classification significantly advances our understanding of the Berry phase.Based on the critical concept of topological obstruction,efficient theoretical frameworks,including topological quantum chemistry and symmetry indicator theory,were developed,making a massive characterization of real materials possible.However,the classification of magnetic materials often involves the complexity of their unknown magnetic structures,which are often hard to know from experiments,thus,hindering the topological classification.In this paper,we design a high-throughput workflow to classify magnetic topological materials by automating the search for collinear magnetic structures and the characterization of their topological natures.We computed 1049 chosen transition-metal compounds(TMCs)without oxygen and identified 64 topological insulators and 53 semimetals,which become 73 and 26 when U correction is further considered.Due to the lack of magnetic structure information from experiments,our high-throughput predictions provide insightful reference results and make the step toward a complete diagnosis of magnetic topological materials.