ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two-dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid-like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid-like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks.     

Rolling Kanban: a new visual tool to schedule family batch manufacturing processes with kanban

Rolling Kanban is the name of a kanban management methodology for batch processes manufacturing environment. Proposed in the early 2000s by FESTO Consulting, for two decades we have lost knowledge both from operative and scientific point of view. Basically, Rolling Kanban means a visual planning methodology based on the production of product-families and variants where: (i) set-up times are reduced between the products of the same family, and (ii) relevant times (dozens of minutes if not even hours) must be considered for changeover between products of different families. In addition, the cyclic production sequence between different product families cannot be maintained. Considering that very few technical information and documents are available about this approach, the main objective of this paper is to retrieve and present, for the first time to the scientific community, the Rolling Kanban methodology. Besides, a real industrial implementation concerning a manufacturer of domestic fittings is discussed as a case study. More specifically, two novel versions of the original Rolling Kanban technique are fully presented to effectively overcome certain limits and criticalities found during its operative use, such as the difficulty to realise a pull production, considering increased set-up time for changeover between products of different families.     

同频段混合信号中的无人机信号盲检测识别

无人机信号的探测识别技术是应对无人机黑飞滥用的关键技术之一。在实际信号监测环境中，经常会接收到多个信号的混合信号，它们在时域和频域上混叠且各信号分量调制样式相同。为解决在同频段混合信号中检测识别出无人机信号的问题，提出了一种通过谱特征分析判断无人机信号存在性的方法。分别采用基于二次方谱特征的无人机图传和WiFi混合信号检测识别算法以及基于频谱带宽特征的多无人机混合信号检测识别算法，通过对射频电路采集的信号进行仿真验证，实现了从同频段混合信号中检测识别出无人机信号分量。理论分析和实验测试结果证实了所提检测识别算法的有效性。     

EmotionMap: Visual Analysis of Video Emotional Content on a Map

Journal of Computer Science and Technology - Emotion plays a crucial role in gratifying users’ needs during their experience of movies and TV series, and may be underutilized as a framework...     

High-roubustness keystroke recognition method based on acoustic spatial gradient

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.     

智能网联汽车APP中控制模块的视觉设计与应用

目的研究智能网联汽车手机APP中控制模块的视觉设计与应用,强化视觉设计过程的合理性,为智能汽车HMI设计实践工作提供启示与借鉴。方法首先从用户体验的角度分析APP控制模块存在的视觉设计问题,主要是视觉结构单一、视觉层级混乱、视觉特征缺失,再从行为逻辑的角度思考视觉设计方法,"由面及点"地提出控制模块的视觉设计思路,并结合设计应用案例论证其可行性。结果从合理视觉布局、明确视觉层级和提炼视觉语言的角度提出APP控制模块的视觉设计思路。结论智能网联汽车APP控制模块的视觉设计应该重视行为逻辑的影响。从任务角度思考视觉布局、从行为逻辑的角度思考视觉层级、从行为特征的角度思考视觉语言,提高视觉设计工作的合理性,减少与交互设计工作脱节的现象,强化控制求助的高效性和安全性。     

选矿生产指标可视化监控平台研究

针对选矿工业过程流程长、工序多、生产指标多的特点,结合数据可视化及可视分析技术,开发一种选矿生产指标可视化监控组态平台。该平台包括生产工艺可视化组态设计环境、生产指标监控和可视及可视分析3个工具。组态设计环境支持以组态方式绘制工艺流程图,并能通过可视界面自定义生产工序的输入输出指标、触发事件、约束规则、工序状态属性、提示信息等;支持集成专家知识、经验和规则以实现基于知识的生产指标监控;提供算法配置接口,方便集成指标监控算法。其组态出的每一个工序都可以复用和扩展,可以构建选矿行业的基础工序单元组件库,形成选矿行业的知识积累。基于可视的工艺流程,利用指标监控工具为指标配置可视化方案,实现指标可视化监控。可视化方案由可视及可视分析工具提供,包括实时数据、历史数据及其统计特性、多指标综合对比分析、指标关联关系分析、多视图等可视方案。此外,为了提升工序指标监控效率和减轻操作人员监控强度,系统提供因子分析、Pearson相关分析、互信息、信息熵等分析手段,以辅助人们提取出工序关键监控指标,从而实现对监控生产指标的约简。由于该平台以组态方式提供,使其可以快速应用于其他流程行业,实现生产指标可视化监控的组态化。最后,该系统作为选矿生产执行系统的一部分成功应用到某选矿厂生产过程中,取得了良好的应用效果。     

面向自动化精密装配的视觉定位引导系统

针对总装生产线精密装配现状,项目设计开发了一种机器视觉定位引导系统,主要实现工业机器人在运动过程中完成在线采集不规则工件图像、视觉定位、角度补偿及精密装配作业。该系统首先采集实时工件图像,利用改进优化的九点标定算法对机器人进行视觉标定可以提高算法的精度;通过预处理操作获取质量较好的图像,按顺序创建卡尺工具,采用加权最小二乘法拟合工件亚像素边缘,再根据相关二维测量算法获取工件中心坐标。最终将得到的位姿数据传送给6轴机器人进行精密装配任务。实验结果表明,该视觉定位引导系统具有现场环境下的高精度定位检测功能,自动化装配定位精度在X方向最大定位误差为0.0281mm、Y方向最大定位误差为0.026mm,足以达到自动化装配生产线的技术要求,具有较好的应用前景和参考价值。     

金属有机框架化合物在荧光识别方面的研究进展

通过荧光识别化合物是一种简单可靠、方便快捷的一种分析手段。金属有机框架(metalorganic frameworks, MOFs)通过框架和客体分子之间的相互作用,在荧光识别和传感等领域有着广阔的应用前景而成为一个热点研究领域。主要综述了金属有机框架在荧光识别溶剂小分子、离子、芳香有机分子以及气体分子等领域的研究工作。     

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.