安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

土木工程施工课程线上平台教学实践

土木工程施工课程作为土木工程及工程管理专业的学科基础课和核心专业课程,在疫情防控期间"停课不停教""停课不停学"的要求下,通过线上平台进行授课。由于土木工程施工课程内容庞杂、综合性强、实践性强,且章节之间关联性较弱,探究既能使学生快速适应,又能保证教学质量的在线教学方法至关重要。以华南某高校土木工程施工课程为例,基于中国大学MOOC、建筑云课、腾讯课堂、QQ群等线上平台讲授教学内容,并运用问卷调查对课程线上教学效果进行评价。结果表明,线上教学为学生提供了丰富灵活的学习方式,显著提高了学生的自主学习能力,扩大了学生的知识面,达到了较好的学习效果。     

Recovery of polyphenols from camu-camu (Myrciaria dubia H.B.K. McVaugh) depulping residue by cloud point extraction

In this study the potential of cloud point extraction formed by a non-ionic surfactant was used in order to separate polyphenols from industrial residues of camu-camu. The effects of operational conditions of the cloud point extraction (CPE) on the polyphenol recovery and volumetric ratio were investigated. The results showed a maximum recovery of 95.71% that was obtained using 7.0 wt% Triton X-114, native pH (3.25), and 80 wt% polyphenol extract at 30 °C. The use of cloud point extraction was successful to recover the polyphenols from agroindustrial residue since it is a simple as well as of low-cost technique.     

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.     

Using provenance to efficiently improve metadata searching performance in storage systems

In cloud storage systems, more than 50% of requests are metadata operations, and thus the file system metadata search performance has become increasingly important to different users. With the rapid growth of storage system scales in volume, traditional full-size index trees cannot offer high-performance metadata search due to hierarchical indexing bottleneck. In order to alleviate the long latency and improve the quality-of-service (QoS) in cloud storage service, we proposed a novel provenance based metadata-search system, called PROMES. The metadata search in PROMES is split into three phases: (i) leveraging correlation-aware metadata index tree to identify several files as seeds, most of which can satisfy the query requests, (ii) using the seeds to find the remaining query results via relationship graph search, (iii) continuing to refine and rerank the whole search results, and sending the final results to users. PROMES has the salient features of high query accuracy and low latency, due to files’ tight and lightweight indexing in relationship graph coming from provenance’s analysis. Compared with state-of-the-art metadata searching schemes, PROMES demonstrates its efficiency and efficacy in terms of query accuracy and response latency.     

Cloud asset for urban flood control

The performance of physical assets has become a major determinant success factor for urban flood control. However, managing these assets is always challenging as there are a huge number of diverse assets involved, which are distributed throughout the city, and owned by different agencies. Aiming at improving the management efficiency of these assets, and ensuring their performance, this paper proposes the concept of cloud asset based on cloud computing, mobile agent, and various smart devices. Through hardware integration and software encapsulation, cloud asset could sense its real-time status, adapt to varied working scenarios, be controlled remotely, and shared among agencies. It enables accurate real-time control of every asset, and thus improves the management efficiency and effectiveness. This paper first presents the concept of cloud asset with its technical architecture, and then analyses the software agent model for cloud asset, which is the key enabler to realize UPnP (Universal Plug and Play) management of assets, and provides mobility and intelligence for them. After that, the framework of cloud asset-enabled workflow management is built, in which cloud asset could be easily found and dynamically invoked by different workflows. Finally, a demonstrative case is provided to verify the effectiveness of cloud asset.     

A geospatial framework to support integrated biogeochemical modelling in the United Kingdom

Anthropogenic impacts on the aquatic environment, especially in the context of nutrients, provide a major challenge for water resource management. The heterogeneous nature of policy relevant management units (e.g. catchments), in terms of environmental controls on nutrient source and transport, leads to the need for holistic management. However, current strategies are limited by current understanding and knowledge that is transferable between spatial scales and landscape typologies. This study presents a spatially-explicit framework to support the modelling of nutrients from land to water, encompassing environmental and spatial complexities. The framework recognises nine homogeneous landscape units, distinct in terms of sensitivity of nutrient losses to waterbodies. The functionality of the framework is demonstrated by supporting an exemplar nutrient model, applied within the Environmental Virtual Observatory pilot (EVOp) cloud cyber-infrastructure. We demonstrate scope for the use of the framework as a management decision support tool and for further development of integrated biogeochemical modelling.     

Cloud based cyber-physical systems: Network evaluation study

In recent years, the Industry 4.0 concept brings new demands and trends in different areas; one of them is distributing computational power to the cloud. This concept also introduced the Reference Architectural Model for Industry 4.0 (RAMI 4.0). The efficiency of data communications within the RAMI 4.0 model is a critical issue. Aiming to evaluate the efficiency of data communication in the Cloud Based Cyber-Physical Systems (CB-CPS), this study analyzes the periods and data amount required to communicate with individual hierarchy levels of the RAMI 4.0 model. The evaluation of the network properties of the communication protocols eligible for CB-CPS is presented. The network properties to different cloud providers and data centers’ locations have been measured and interpreted. To test the findings, an architecture for cloud control of laboratory model was proposed. It was found that the time of the day; the day of the week; and data center utilization have a negligible impact on latency. The most significant impact lies in the data center distance and the speed of the communication channel. Moreover, the communication protocol also has impact on the latency. The feasibility of controlling each level of RAMI 4.0 through cloud services was investigated. Experimental results showed that control is possible in many solutions, but these solutions mostly cannot depend just on cloud services. The intelligence on the edge of the network will play a significant role. The main contribution is a thorough evaluation of different cloud providers, locations, and communication protocols to provide recommendations sufficient for different levels of the RAMI 4.0 architecture.     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

基于密钥共享的分层混合认证模型

随着信息时代的迅速发展,云计算数据访问安全已经成为了用户最关心的问题。身份认证技术是确保参与者在开放的网络环境中实现安全通信的一种重要手段,如何利用身份认证技术为云环境安全保驾护航,成为学者研究的热点。文中通过公钥基础设施(Public Key Infrastructure,PKI)颁发CA证书以在不同云服务间建立信任,将多个采用身份密码体制(Identity-Based Encryption,IBE)的云联合起来；采用分层身份加密体系,引入共享密钥技术,通过选取成环结构,提出一种PKI-IBE混合认证模型方案,并对方案的安全性进行分析,从理论上证明了云环境下PKI-IBE(Public Key Infrastructure-Identity-Based Encryption)同层成环模型提供服务的可行性。同时文中设计了一种基于该模型的签密技术,通过公私密钥对实现云内认证以及跨云认证。安全性理论证明与性能分析表明,该方案在计算量稍增加的前提下,保证了足够的安全性,更加满足云环境下的用户分属不同云域的认证以及用户安全访问的需求,有效解决了云环境中数据访问的安全问题。