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.     

Fabrication of paper based carbon/graphene/ZnO aerogel composite decorated by polyaniline nanostructure: Investigation of electrochemical properties

The cellulose derived carbon/graphene/ZnO aerogel composite was prepared as an electrode in order to investigate the electrochemical properties. Carbon aerogel was synthesized using paper as an available cellulose source, and the composite was obtained through a new and simple preparation method including the immersion of monolithic carbon aerogel in graphene oxide/Zn²⁺ suspension and subsequent chemical reduction and freeze drying. The morphology, functional groups and crystalline structure of the samples were studied with Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction Spectroscopy (XRD), respectively. Electrochemical performance of the prepared binder free electrodes was examined using Cyclic Voltammetry (CV), Galvanostatic Charge-Discharge (GCD) and Electrochemical Impedance Spectroscopy (EIS). The data revealed that flexible carbon/graphene/ZnO composite resulted in a low density (0.035 g cm⁻³) electrode with the capacitance of 900 mF cm⁻² at a high current density of 10 mA cm⁻², lower IR drop and high cyclic stability (capacitance retention of 96%) after 1000 cycles, at 10 mA cm⁻². These features were due to the presence of 3D porous conductive network, highly reduced graphene oxide, and the formation of ZnO nanoparticles on graphene sheets. Moreover, polyaniline (PANI) was introduced to carbon/graphene/ZnO composite electrode using electro-oxidation method at different reaction time and aniline concentration in order to achieve remarkably improved capacitance of 2500 mF cm⁻² (at 10 mA cm⁻²) and low charge transfer resistance. Also, after the supercapacitor device assembly, the capacitance was retained. Based on the results, the synthesized composite is a promising material for new generation of lightweight freestanding electrodes with the high electrochemical performance.     

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。     

Simply constructed highly dispersed cobalt nanoparticles in diverse N-doped graphitic carbon with remarkable performances for water electrolysis

Metal/carbon composite materials are highly promising electrocatalysts for water electrolysis. In this work, three composites of metal cobalt nanoparticles highly dispersed in N-doped carbon materials were facilely constructed by pyrolysis of different phenylenediamine based Schiff base-Co complexes (PDBs). Interestingly, the composites derived from PDBs based on different phenylenediamine exhibited different morphologies. The superior case is that rodlike composite catalyst was derived from o-phenylenediamine based PDBs. The obtained catalyst exhibited remarkable performances for both cathodic hydrogen evolution reaction (HER) and anodic oxygen evolution reaction (OER), as well as overall water electrolysis. Only 172 and 289 mV of overpotentials and 1.57 V of cell voltage were exhibited at 10 mA cm^?2 for HER, OER and water splitting in 1.0 M KOH, respectively. The catalyst also displayed robust stability and high Faraday efficiency, and thus are potential high-performance catalyst for commercial water electrolysis.     

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.     

基于可供性的文化意象交互式智能音箱设计研究

目的 探索将文化民俗有效融入现代产品中的技术思路，保留并延续中华传统文化的生命力，有助于提升用户体验。方法 分析可供性与文化意象之间的主客观互通性;挖掘交互式智能音箱中的可供性触点;构建形态、感官、认知及情境四类可供性的设计框架;以楚文化意象为例进行设计实践，对智能音箱的产品造型与交互操作方式进行设计，并进行设计评价。结果 在可供性理论指导下的楚文化意象交互式智能音箱产品外观及交互体验设计较好地满足了设计目标和用户需求。结论 可供性有助于文化意象的正确提取与转化，可将意象抽象为具有实际意义的设计要素，帮助用户在生理与认知两个层面理解产品的使用方法，同时进一步回味、体验文化内涵和文化精髓。     

Accelerating BPC-PaCo through Visually Lossless Techniques

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo.