Perspective:Societally connected multimedia across cultures

The advance of the Internet in the past decade has radically changed the way people communicate and col- laborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences (at the individual, community, as well as societal levels) still govern human-human interactions and must be con- sidered and leveraged in the online world. The rapid de-ployment of high-speed Internet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ’connected multimedia’, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.     

一种基于相关函数法铁磁谐振波形研究

对铁磁谐振波形进行了系统的分类分级,提出了一种基于Matlab的相关函数方法,并且利用大量数据进行对比,已经得到了效果验证,经过桂林市电力电容器责任有限公司认证,具有良好的实际效果。     

An efficient and scalable parallel mapping of pulse-Doppler radar signal processing chain on a multi-core DSP

Digital Pulse-Doppler radar chain consists of signal processing algorithms that require high computing power. Multi-processor and multi-core parallel embedded machines are one of the solutions to meet real-time constraints of many radar applications. In this paper, we proposed efficient and scalable parallelization methods of the Pulse-Doppler radar signal processing chain. First, we evaluated Open Multi Processing (OpenMP) to identify its best scheduling technique in order to exploit efficiently the available computing cores. Then, we have proposed new parallel and scalable approaches based on direct memory access (DMA) and inter-processor communication (IPC) techniques, combined with the best OpenMP scheduling method to accelerate radar signal processing chain. To prove the scalability of our proposed parallel approaches, two radar use cases with different real-time and memory constraints have been experienced. We used the eight cores C6678 digital signal processor (DSP) as a target for all our implementations. The obtained results show an overall parallel efficiency of 95%, which is better than the best state-of-the-art implementations.     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

Neural Computing and Applications - This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with...     

基于混合蛙跳算法的时序电路测试生成

阐述了将混合蛙跳算法应用在时序电路自动测试生成的实现方法和结果;根据时序电路自身的结构特点,构造了测试生成的混合蛙跳表达方式,设计了自动测试生成离散混合蛙跳模型,通过群体中青蛙间的合作和竞争产生的群智能指导快速优化收敛;最后,根据国际标准时序电路进行了验证试验;结果表明,与同类算法相比,该算法获得了较小的测试集和较高的故障覆盖率。     

紧凑型边界扫描技术的功能与现状

芯片一体化进程的加剧以及对功耗控制的关注日益增加,边界扫描技术面临新的挑战,这些挑战来源于调试、应用、测试等方面,紧凑型边界扫描技术对此提出了有效的解决方法;以IEEE1149.7标准以及国内外文献报道为依据,对精简引脚与增强功能边界扫描技术的测试规范进行系统的分析,重点对该技术的新功能、新特性和新应用进行详细介绍;针对符合IEEE1149.1标准的测试系统与被测系统如何升级以实现IEEE1149.7标准新功能的问题提出了相应的建议;最后总结了该技术的发展方向和应用前景。     

通用视觉检测设备的专家在线平台设计

针对视觉检测设备在复杂的生产环境下存在图像处理参数设置问题,设计了一套B/S架构的通用的专家在线平台,通过在原有的视觉检测平台上增加一个普通的USB摄像头及相应的应用软件,并通过网络将出现的新问题的图像实时传输给生产厂家或外部专家,由专家通过浏览器设置合理的图像处理参数,平台综合了数据协同、远程培训等功能,达到了远程解决问题的目的。