基于邻层数据匹配的工业 CT 图像生成 G 代码方法

针对目前工业CT图像转换为3D打印G代码方法效率低的问题,提出一种基于邻层数据匹配的工业CT图像直接转换成G代码的方法。首先采用Canny算子提取工业CT图像的轮廓,然后处理轮廓分叉问题,实现邻层间几何信息数据匹配,其次进行邻层间轮廓插值以满足3D打印层间厚度要求,从而避免"阶梯效应",最后通过填充编码得到用于3D打印的G代码。使用本文提出的方法,轮毂CT图像转换为G代码的时间为10.5 s,耗时远小于其他间接转换方法;3D打印出的轮毂无"阶梯效应",平均尺寸误差率为0.25%。实验结果表明,该方法不涉及中间格式,转换效率高,转换误差与传统方法相当,适用于具有复杂内腔结构的零件。     

Underwater image super-resolution using multi-stage information distillation networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.     

A Biologically Muscle-Inspired Polyurethane with Super-Tough,Thermal Reparable and Self-Healing Capabilities for Stretchable Electronics

Polymeric elastomers play an increasingly important role in the development of stretchable electronics. A highly demanded elastic matrix is preferred to own not only excellent mechanical properties, but also additional features like high toughness and fast self-healing. Here, a polyurethane (DA-PU) is synthesized with donor and acceptor groups alternately distributed along the main chain to achieve both intra-chain and inter-chain donor-acceptor self-assembly, which endow the polyurethane with toughness, self-healing, and, more interestingly, thermal repair, like human muscle. In detail, DA-PU exhibits an amazing mechanical performance with elongation at break of 1900% and toughness of 175.9 MJ m⁻³. Moreover, it shows remarkable anti-fatigue and anti-stress relaxation properties as manifested by cyclic tensile and stress relaxation tests, respectively. Even in case of large strain deformation or long-time stretch, it can almost completely restore to original length by thermal repair at 60 °C in 60 s. The self-healing speed of DA-PU is gradually enhanced with the increasing temperature, and can be 1.0–6.15 µm min⁻¹ from 60 to 80 °C. At last, a stretchable and self-healable capacitive sensor is constructed and evaluated to prove that DA-PU matrix can ensure the stability of electronics even after critical deformation and cut off.     

Translesion Synthesis or Repair by Specialized DNA Polymerases Limits Excessive Genomic Instability upon Replication Stress

DNA can experience “replication stress”, an important source of genome instability, induced by various external or endogenous impediments that slow down or stall DNA synthesis. While genome instability is largely documented to favor both tumor formation and heterogeneity, as well as drug resistance, conversely, excessive instability appears to suppress tumorigenesis and is associated with improved prognosis. These findings support the view that karyotypic diversity, necessary to adapt to selective pressures, may be limited in tumors so as to reduce the risk of excessive instability. This review aims to highlight the contribution of specialized DNA polymerases in limiting extreme genetic instability by allowing DNA replication to occur even in the presence of DNA damage, to either avoid broken forks or favor their repair after collapse. These mechanisms and their key regulators Rad18 and Polθ not only offer diversity and evolutionary advantage by increasing mutagenic events, but also provide cancer cells with a way to escape anti-cancer therapies that target replication forks.     

Engineering Bioactive M2 Macrophage-Polarized Anti-Inflammatory,Antioxidant, and Antibacterial Scaffolds for Rapid Angiogenesis and Diabetic Wound Repair

Diabetic wound healing still faces great challenges due to the excessive inflammation, easy infection, and impaired angiogenesis in wound beds. The immunoregulation of macrophages polarization toward M2 phenotype that facilitates the transition from inflammation to proliferation phase has been proved to be an effective way to improve diabetic wound healing. Herein, an M2 phenotype-enabled anti-inflammatory, antioxidant, and antibacterial conductive hydrogel scaffolds (GDFE) for producing rapid angiogenesis and diabetic wound repair are reported. The GDFE scaffolds are fabricated facilely through the dynamic crosslinking between polypeptide and polydopamine and graphene oxide. The GDFE scaffolds possess thermosensitivity, self-healing behavior, injectability, broad-spectrum antibacterial activity, antioxidant and anti-inflammatory ability, and electronic conductivity. GDFE effectively activates the polarization of macrophages toward M2 phenotype and significantly promotes the proliferation of dermal fibroblasts, the migration, and in vitro angiogenesis of endothelial cells through paracrine mechanisms. The in vivo results from a full-thickness diabetic wound model demonstrate that GDFE can rapidly promote the diabetic wound repair and skin regeneration, through fast anti-inflammation and angiogenesis and M2 macrophage polarization. This study provides highly efficient strategy for treating diabetic wound repair through designing the M2 polarization-enabled anti-inflammatory, antioxidant, and antibacterial bioactive materials.     

基于光刀图像的机器人示教纠偏方法

为了解决机器人辅助在线检测系统示教过程中效率低、人为干预多的问题,针对孔类特征,提出一种通过处理光刀图像获取机器人位姿纠正参数的方法。在该方法中,纠偏过程按照将图像光刀线调整为水平方向、将被测特征调整至图像中央、根据不同特征调整最优扫描方向3个步骤进行。根据光刀图像可以求出测量坐标系与被测特征的位姿关系,并得到该位姿与理想测量位姿的偏差。经过坐标变换即可得到在机器人基坐标系下的调整参数。另外,可利用电脑控制机器人运动,从而实现了纠偏过程的自动化。实验结果表明,该方法可将初始的示教位姿修正为理想位姿,且比传统方式效率更高。     

Sub-band coding of hexagonal images

According to the circle-packing theorem, the packing efficiency of a hexagonal lattice is higher than an equivalent square tessellation. Consequently, in several contexts, hexagonally sampled images compared to their Cartesian counterparts are better at preserving information content. In this paper, novel mapping techniques alongside the wavelet compression scheme are presented for hexagonal images. Specifically, we introduce two tree-based coding schemes, referred to as SBHex (spirally-mapped branch-coding for hexagonal images) and BBHex (breadth-first block-coding for hexagonal images). Both of these coding schemes respect the geometry of the hexagonal lattice and yield better compression results. Our empirical results show that the proposed algorithms for hexagonal images produce better reconstruction quality at low bits per pixel representations compared to the tree-based coding counterparts for the Cartesian grid.     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.