Research on multi-robot open architecture of an intelligent CNC system based on parameter-driven technology

This paper presents a multi-robot open architecture of an intelligent computer numerical control (CNC) system based on parameter-driven technology that has been developed for flexible and high-efficiency manipulation. An open architecture control system capable of distributed processing of decision-making and extraction of task information provides a premise for intelligent control and flexible operation. Intelligent detection with database feedback based on real-time assignment of tasks is proposed to achieve dynamic modification of the processing trajectory. In the context of flexible task control, a multi-robot architecture with collision-free path planning and a novel programming approach based on parameter-driven technology are developed. The proposed CNC system has been successfully implemented and demonstrated on an H-beam steel-cutting task that requires flexible and accurate machining.     

A stream processing architecture for heterogeneous data sources in the Internet of Things

The number of Internet of Things (IoT) and smart devices capable of producing, consuming and exchanging information is constantly increasing. It is estimated there will be around 30 billion of them in 2020. In most cases, the structures of the information produced by such devices are completely different, thus providing heterogeneous information. This is becoming a challenge for researchers working on IoT, who need to perform homogenisation and pre-processing tasks before using the IoT data. This paper aims to provide an architecture for processing and analysing data from heterogeneous sources with different structures in IoT scopes, allowing researchers to focus on data analysis, without having to worry about the structure of the data sources. This architecture combines the real-time stream processing paradigm for information processing and transforming, together with the complex event processing for information analysis. This provides us with capability of processing, transforming and analysing large amounts of information in real time. The results obtained from the evaluation of a real-world case study about water supply network management show that the architecture can be applied to an IoT water management scenario to analyse the information in real time. Additionally, the stress tests successfully conducted for this architecture highlight that a large incoming rate of input events could be processed without latency, resulting in efficient performance of the proposed architecture. This novel software architecture is adequate for automatically detecting situations of interest in the IoT through the processing, transformation and analysis of large amounts of heterogeneous information in real time.     

A secure framework of the surveillance video network integrating heterogeneous video formats and protocols

Recently, IT technologies are becoming focused on different traditional industries. The large scaled intelligent video surveillance system is one of them and it integrates a large number of digitalized CCTV [1] devices through the Web. However, existing video devices have been deployed for many years by different vendors as different models with different specifications. To integrate these heterogeneous devices, the centralized management server (CMS) and its clients need a specialized architecture to deal with different types of media encodings and connection protocols etc. In particular, well-defined access control mechanism is required for a large scaled surveillance video network. In this paper, we designed and implemented a server that incorporates the architecture integrating and delivering multiple video streams from different types of video devices to multiple clients and securing the access to the video streams.     

Design and implementation of a cloud-based event-driven architecture for real-time data processing in wireless sensor networks

The growth of the Internet of Things (IoTs) and the number of connected devices is driven by emerging applications and business models. One common aim is to provide systems able to synchronize these devices, handle the big amount of daily generated data and meet business demands. This paper proposes a cost-effective cloud-based architecture using an event-driven backbone to process many applications’ data in real-time, called REDA. It supports the Amazon Web Service (AWS) IoT core, and it opens the door as a free software-based implementation. Measured data from several wireless sensor nodes are transmitted to the cloud running application through the lightweight publisher/subscriber messaging transport protocol, MQTT. The real-time stream processing platform, Apache Kafka, is used as a message broker to receive data from the producer and forward it to the correspondent consumer. Micro-services design patterns, as an event consumer, are implemented with Java spring and managed with Apache Maven to avoid the monolithic applications’ problem. The Apache Kafka cluster co-located with Zookeeper is deployed over three availability zones and optimized for high throughput and low latency. To guarantee no message loss and to simulate the system performances, different load tests are carried out. The proposed architecture is reliable in stress cases and can handle records goes to 8000 messages in a second with low latency in a cheap hosted and configured architecture.

     

基于嵌入式Internet 技术的Webit体系结构研究与实现

基于嵌入式Intenret技术，提出一种面向设备动态驱动的异种网络互连体系结构－Webit,并建立Webit模型，该体系结构采用thinTCP/IP协议栈技术建立Embedded Web Server,实现了将现场设备接入Internet的目的，构建嵌入式XML中间件，实现实时多任务系统与Embedded Web Server的集成，使现场可编程设备的远程监控，管理及驱动程序动态下载得以实现。Webit体系结构使工业生产自动化系统和智能家电的远程化成为可能，并为其提供了技术上的支持。     

Performance analysis of a peer-to-peer I/O architecture in video server environments

In the personal computing (PC) and workstation environments, more and more I/O adapters are becoming complete functional subsystems that are intelligent enough to handle I/O operations on their own without much intervention from the host processor. The IBM Subsystem Control Block (SCB) architecture has been defined to enhance the potential of these intelligent adapters by defining services and conventions that deliver command information and data to and from the adapters. The SCB architecture has two operating modes. The Locate Mode represents the conventional, interrupt-driven I/O protocol used in many current personal computers. The Move Mode embodies an advanced, peer-to-peer I/O protocol proposed for the next generation of personal computers. In this paper, we would like to discuss and present a performance analysis of the SCB architecture in typical video server environments. In particular, we study a video server capable of providing support for simultaneous MPEG-1 video streams to multiple clients on a 16-Mbps token-ring network. We also consider the performance impact of a hypothetical 100-Mbps token-ring technology on the video server performance.     

基于Netty和Kafka的物联网数据接入系统

当前在物联网应用中,大量采集终端被用于感知环境、定位服务、状态监测等应用,并源源不断地上传数据,在提产助效的同时,给远程服务端数据收集及实时处理带来巨大挑战。利用Netty网络通信库构造高性能的收集端网络通信处理服务并设计出可供采集终端与收集端使用的通信协议,剥离出网络通信业务中耗时操作并将数据推送至流式消息处理系统Kafka中,再由Kafka消费者负责后续数据持久化、实时分析工作。由此实现一个支持高并发、低延迟的数据接入系统。实验结果表明,在万级别连接情况下,该系统能正常工作且保持较快的响应速度。     

A low power and real-time hardware recurrent neural network for time series analysis on wearable devices

The research presented in this paper addresses the exploitation of Deep Learning methods on wearable devices. We propose a hardware architecture capable of analyzing time series signals through a Recurrent Neural Network implemented on FPGA technology. This architecture has been validated using a real dataset, which includes three-axial accelerometer data acquired by a wearable device used for fall detection. The experiments have been conducted considering different devices and demonstrates that the proposed hardware architecture outperforms the state of the art solutions both in terms of processing time and power consumption. Indeed, the proposed architecture is real-time compliant in the elaboration of the fall detection dataset adopted for the validation. The power consumption is in the order of dozens μW. Finally, futher functionalities could be added in the same chip since the resource usage is low.     

基于区块链的边缘计算IIoT架构研究

在智能制造系统中,工业物联网通过先进的管理技术将制造设备互连,实现了信息的实时传输、设备的范在化感知和数据的快速分析处理。但是由于制造设备的异构性、物联网网关（IoT网关）数据分析能力的有限性、制造设备的存储力低下,设备和数据的低安全性等缺陷严重阻碍了智能制造的发展。BEIIoT架构从制造企业的实际生产过程与应用角度出发,将区块链技术与边缘计算相结合,通过对服务器进行P2P组网以实现对设备去中心化管理;通过对边缘设备进行服务化封装,增强设备的安全性与实时分析能力,降低设备的异构性;使用DAG双链式数据存储结构,提高数据的冗余度与安全性,实现生产线数据的异步并发备份存储。BEIIoT架构为智能制造的实施提供了体系支持。     

Design of intention-based assistive robot for upper limb

Robotic assistive devices for rehabilitation have proven to be beneficial for many individuals with impaired limbs by providing accessible, consistent and cost-effective therapy. However, it is important to acknowledge that many people need an alternative form of assistance for physical impairments, both while undergoing rehabilitation and in the common scenario of rehabilitation providing insufficient improvements. The aim of this paper is to present an intention-detecting assistive robot for upper limb motion, which may serve as a complement to rehabilitation procedures. The proposed system collects real-time data from an eye-tracker and force-sensing resistors to determine the intended movement of the arm of a user and physically assists the said movement. We show that the system is capable of moving an impaired upper limb to a desired position with average errors of less than 5 cm, and in this manner may assist motion required by various activities of daily living for someone suffering from physical impairments in their upper limbs.     

脑机接口中脑电信号的特征提取和模式分类

从智能处理与不确定性的角度, 探讨了脑机接口中的核心问题-EEG模式特征的识别和分类. 针对EEG模式分类中所存在的不确定性问题, 从EEG的特征提取和分类模型构建两个方面进行了分析, 并提出了解决问题的方法和对策. 以P300成分为例, 从导联选择、滤波处理和时间窗处理三方面进行特征提取, 采用贝叶斯线性判别分析的方法进行模式分类. 最后以第三届脑机接口竞赛P300字符输入的数据为实验, 分别采用3种不同的方法进行数据分析, 通过分类准确率和不同重复次数下性能的比较, 实验结果表明了本文特征提取和模式分类方法的有效性.     

NVM Storage in IoT Devices: Opportunities and Challenges

Edge storage stores the data directly at the data collection point, and does not need to transmit the collected data to the storage central server through the network. It is a critical technology that supports applications such as edge computing and 5G network applications, with lower network communication overhead, lower interaction delay and lower bandwidth cost. However, with the explosion of data and higher real-time requirements, the traditional Internet of Things (IoT) storage architecture cannot meet the requirements of low latency and large capacity. Non-volatile memory (NVM) presents new possibilities regarding this aspect. This paper classifies the different storage architectures based on NVM and compares the system goals, architectures, features, and limitations to explore new research opportunities. Moreover, the existing solutions to reduce the write latency and energy consumption and increase the lifetime of NVM IoT storage devices are analyzed. Furthermore, we discuss the security and privacy issues of IoT devices and compare the mainstream solutions. Finally, we present the opportunities and challenges of building IoT storage systems based on NVM.     

VGITS: ITS based on intervehicle communication networks and grid technology

The intelligent traffic system based on intervehicle communication networks and grid technology is proposed. This system adopts hybrid architecture, and diverse real-time traffic services are provided in a centralized or decentralized way. Grid technology is introduced to provide the high performance computing platform for massive traffic data processing and real-time traffic service presenting. Moreover, the computation and communication capacity of the IVC network is developed and exploited to provide decentralized traffic services to drivers. Therefore, the system load is balanced among grid nodes and in-vehicle terminal, and better performance is achieved. At last, a prototype is implemented to demonstrate the efficiency and effectivity.     

基于CVI与Matlab相结合的BCI在线应用系统

结合CVI与Matlab,设计了一个脑机接口在线应用控制系统。首先,在CVI中通过TCP/IP实时接收脑电信号,通过触发回调函数对接收到的数据进行实时更新;其次,通过软件接口启用Matlab,将更新后的数据送入Matlab,在Matlab中对数据进行预处理、特征提取及分类;最后,在CⅥ中通过软件接口获取Matlab中识别出的类别号,并实现对虚拟车在线实时控制。系统采用多线程技术,充分发挥了软件优势互补的特点,可推动BCI的在线应用。     

基于多Agent的动态数据并行计算方法研究

为实现动态数据的快速处理和计算,提出一种基于多Agent和分布式并行计算的动态数据处理方法。该方法以智能Agent作为计算和控制的最小单元,构建有向无环的动态网络拓扑结构,采用基于信任度的合同网模型,并与阈值相结合,实现动态数据处理和快速求解,体现系统的智能性和实时性。实验结果表明,该计算方式与传统计算方式相比,系统的计算速率显著提高,实现了动态数据的实时性处理。     

基于Spark Streaming的实时能耗分项计量系统

能耗分项计量能够准确、及时、有效地发现能源使用问题,形成和实现最有效的节能措施。能耗分项计量系统需要对各项能源使用量在不同粒度上进行统计,既有实时性的需求,又需要涉及到聚合、去重、连接等较为复杂的统计需求。由于数据产生快、实时性强、数据量大,所以很难统一采集并入库存储后再作处理,这便导致传统的数据处理架构不能满足需求。为此,提出基于Spark Streaming大数据流式技术构建一个实时能耗分项计量系统,对实时能耗分项计量的系统架构和内部结构进行了详细介绍,并通过实验数据分析了系统的实时数据处理能力。与传统架构不同,实时能耗分项计量系统在数据流动的过程中实时地进行捕捉和处理,一方面把捕捉到的异常信息及时报警到前端,同时把分类分项统计处理的结果保存到数据库,以便进行离线分析和数据挖掘,能有效地解决上述数据处理过程中遇到的问题。     

基于ARM的嵌入式Web服务器设计

将嵌入式技术与Web技术相结合,已成为目前嵌入式系统的一个重要发展方向和必然趋势。利用源代码开放的uClinux为操作系统平台,实现了以ARM微处理器为核心的嵌入式Web服务器,在分析了嵌入式Web服务器的体系结构后,给出了系统的硬件结构组成和软件设计。对Web服务器的测试结果表明,该服务器运行稳定、控制方便,将在智能家居、远程数据采集、工业控制等领域有着广泛的应用前景。     

PTC: Proxies that Transcode and Cache in Heterogeneous Web Client Environments

Advances in computing and communication technologies have resulted in a wide variety of networked mobile devices that access data over the Internet. In this paper, we argue that servers by themselves may not be able to handle this diversity in client characteristics and so intermediaries, such as proxies, should be employed to handle the mismatch between the server-supplied data and the client capabilities. Since existing proxies are primarily designed to handle traditional wired hosts, such proxy architectures will need to be enhanced to handle mobile devices. We propose such an enhanced proxy architecture that is capable of handling the heterogeneity in client needs—specifically the variations in client bandwidth and display capabilities. Our architecture combines transcoding (which is used to match the fidelity of the requested object to client capabilities) and caching (which is used to reduce the latency for accessing popular objects). Proxies that Transcode and Cache, PTCs, intelligently adapt to prevailing system conditions using learning techniques to decide whether to transcode locally or fetch an appropriate version from the server. Our experimental results indicate that the use of PTCs produces significant improvements in the client response times. We show that such results hold true for a variety of data content types like images and video data. Further, we find that even simple learning techniques can lead to significant performance improvements.     

A Semi-Autonomous Reactive Control Architecture

This paper presents a novel intelligent control architecture for semi-autonomous systems. A semi-autonomous system is defined here as that autonomous system (machine) which interacts intelligently with a human user (collaborator) who might command, modify, or override its behavior. This work has been motivated by the need for a control architecture that can interact with human users of different perceptual and cognitive capabilities. A dynamic arbitration layer forms the core of the proposed architecture. Accordingly, the architecture evolves around three main variables: degree of autonomy to reflect the user's capabilities, user's level of confidence in commanding the machine, and strength of conflict between the user's command and the machine's autonomous command. The analogy between this architecture and horseback riding is presented and finally a demonstrative application example of a robotic wheelchair is given.     

基于NB-IoT的UPS智能在线监测系统的设计

针对UPS机房在运行过程中无人监守,工作人员无法及时排查故障的问题,设计并实现了一套基于ARM和NB-IoT的UPS智能在线监测系统,由检测终端、云服务器和监管中心组成;检测终端使用各类传感器和检测电路获取多个UPS设备的输入输出电压和电流、负载率、蓄电池电量等数据以及机房温湿度环境参数,通过无线模块发送给云服务器,监管中心从云服务器获取数据,用户可以登录监管中心远程查询UPS的工作数据;经测试,系统能够实现对UPS各项数据的采集和无线传输,可以通过监管中心实时查询UPS工作状态以及下发指令,能达到实时监控的效果;具有数据测量准确,实时性响应良好等特点,减少了管理人员的工作量,提高了UPS的管理效率。