Multi-scale and multi-patch transformer for sandstorm image enhancement

Sandstorm is a meteorological phenomenon common in arid and semi-arid regions. A sandstorm can carry large volumes of sand unexpectedly, which leads to severe color deviations and significantly degraded visibility when an image is taken in such a scenario. However, existing image enhancement methods cannot enhance sandstorm images well due to the challenging degradations and the scarcity of sandstorm training data. In this paper, we propose a Transformer with rotary position embedding to perform sandstorm image enhancement via building multi-scale and multi-patch dependencies. Our key insights in this work are 1) a multi-scale Transformer can globally eliminate the color deviations of sandstorm images via aggregating global information, 2) a multi-patch Transformer can recover local details well via learning the spatial variant degradations, and 3) a U-shape Transformer with rotary position embedding as the core unit of multi-scale and multi-patch Transformer can effectively build the long-range dependencies. We also contribute a real-world Sandstorm Image Enhancement (SIE) dataset including 1,400 sandstorm images with different degrees of degradations and various scenes. Experiments performed on synthetic images and real-world sandstorm images demonstrate that our proposed method not only obtains visually pleasing results but also outperforms state-of-the-art methods qualitatively and quantitatively.     

基于章水泉竹艺非物质文化遗产的APP设计与研究

目的 针对章水泉竹艺非物质文化遗产在目前所遭遇的传承困境，利用数字媒体技术与互联网平台，打造推动其产品创新与文化传承的APP，助力其文化与产品的传播与推广，进而为湖北地区的非遗文化发展提供参考。方法 从章水泉竹器的本源出发，对其主要产品门类、工艺特点与艺术形式进行整理与分类，分析制约其传承与发展的主要原因，进而提出设计策略，并通过数字化传承、合作创新、成果推广3个策略模块针对性地进行APP设计。结论 章水泉竹艺APP设计通过利用数字媒体技术与互联网平台优势，将其历史与工艺资料进行数字化保存与展示，实现了文化保护功能；将其传统技艺与新锐设计思维通过线上平台进行融合与创新，实现了设计创新功能；将其制作流程与文化精髓通过直播与精准推送展现于公众视野，实现了产品推广功能。最终，为章水泉竹艺的传承与推广提供了新的路径。     

“黄金分割法”在爆破工程中的应用探讨

"黄金分割法"是一种古老的数学方法,它无处不在并造就了事物的和谐美,多年来经常被应用到艺术、摄影、绘画、设计等多个领域,而且得到了意想不到的效果。根据多年的爆破工程实践,将"黄金分割法"与工程爆破实例相结合,应用到露天爆破、高耸建(构)筑物爆破拆除等工程中,取得了很好的爆破效果。     

Impact of TiO2 nanoparticles and nanowires on corrosion protection performance of chemically bonded phosphate ceramic coatings

The impact of the addition of TiO₂ nanoparticles and nanowires on the morphology, phase characteristics, contact angle, and electrochemical performance of chemically bonded phosphate ceramic coatings (CBPCs) was investigated. The chemical composition and surface morphology of the TiO₂ nanoparticle and nanowire modified with and without (heptadecafluoro-1,1,2,2-tetradecyl) trimethoxysilane were characterized. Results indicated that the hydrophobic –CF₂– and –CF₃ groups were successfully introduced into the TiO₂ nanoparticles and nanowires after modification. Corrosion resistance of CBPCs with TiO₂ was evidently improved compared with that without TiO₂. Such improvement was mainly due to the combined effects of low surface energy materials and micro/nano structures. In addition, CBPCs with TiO₂ nanowires exhibited higher hydrophobicity and corrosion resistance than those with TiO₂ nanoparticles because of the special columnar structure of the nanowires.     

Neural Style Transfer for image within images and conditional GANs for destylization

In this paper, the feature representation of an image by CNN is used to hide the secret image into the cover image. The style of the cover image hides the content of the secret image and produce a stego image using Neural Style Transfer (NST) algorithm, which resembles the cover image and also contains the semantic content of secret image. The main technical contributions are to hide the content of the secret image in the in-between hidden layered style features of the cover image, which is the first of its kind in the present state-of-art-technique. Also, to recover the secret image from the stego image, destylization is done with the help of conditional generative adversarial networks (GANs) using Residual in Residual Dense Blocks (RRDBs). Further, stego images from different layer combinations of content and style features are obtained and evaluated. Evaluation is based on the visual similarity and quality loss between the cover-stego pair and the secret-reconstructed secret pair of images. From the experiments, it has been observed that the proposed algorithm has 43.95 dB Peak Signal-to-Noise Ratio (PSNR)), .995 Structural Similarity Index (SSIM), and .993 Visual Information Fidelity (VIF) for the ImageNet dataset. The proposed algorithm is found to be more robust against StegExpose than the traditional methods.     

使能未来工厂的5G能力综述

5G系统将移动通信服务从移动电话、移动宽带和大规模机器通信扩展到新的应用领域，即所谓对通信服务有特殊要求的垂直领域。对使能未来工厂的5G能力进行了全面的分析总结，包括弹性网络架构、灵活频谱、超可靠低时延通信、时间敏感网络、安全和定位，而弹性网络架构又包括对网络切片、非公共网络、5G局域网和边缘计算的支持。希望从广度到深度，对相关的理论及技术应用做透彻、全面的梳理，对其挑战做清晰的总结，从而为相关研究和工程技术人员提供借鉴。     

高光谱成像用高帧频CMOS图像传感器北大核心

针对高帧频、全局曝光和光谱平坦等成像应用需求,设计了一款高光谱成像用CMOS图像传感器。其光敏元采用PN型光电二极管,读出电路采用5T像素结构。采用列读出电路以及高速多通道模拟信号并行读出的设计方案来获得低像素固定图像噪声(FPN)和非均匀性抑制。芯片采用ASMC 0.35μm三层金属两层多晶硅标准CMOS工艺流片,为了抑制光电二极管的光谱干涉效应,后续进行了光谱平坦化VAE特殊工艺,并对器件的光电性能进行了测试评估。电路测试结果符合理论设计预期,成像效果良好,像素具备积分可调和全局快门功能,最终实现的像素规模为512×256,像元尺寸为30μm×30μm,最大满阱电子为400 ke^(-),FPN小于0.2%,动态范围为72 dB,帧频为450 f/s,相邻10 nm波段范围内量子效率相差小于10%,可满足高光谱成像系统对CMOS成像器件的要求。     

钢混结构大型厂房整体式爆破拆除

采用整体式控制爆破拆除方案对华电扬州电厂钢筋混凝土框-排架结构厂房进行了爆破拆除。爆破前,对汽机房、厂房外墙和楼梯进行了预处理。将厂房划分为3个爆区,采用非电多回路网格式爆破网路,依靠东侧锅炉房倒塌施加在西侧锅炉房上的倾覆力矩,有效地克服了西侧锅炉房向西倒的难题,可为同类工程提供一定的参考。     

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。