网络公共资源防护技术研究

提出了一种网络公共资源防护系统,用于有效阻止对网络公共资源的恶意破坏、提高网络资源的末端防护能力、保证信息服务的安全可靠。首先,根据网络公共资源自身的特点和防护需求,为了使整个防护系统能够灵活、高效地运转,设计了一种“监测-处理-恢复-监测”闭环反馈运转机制;然后,介绍了系统中的体系化联动机制和主动防御方法。实验结果表明：平均资源修复速率为本地31.95KB/毫秒,网络16.10KB/毫秒。系统部署后,访问测试网站的平均响应时间仅降低了1.93%,不会影响资源的可用性。     

基于生理振动分析的手机用户身份识别

为保护手机用户的个人信息,对用户进行身份识别认证,提出一种生物识别方法。利用智能手机内置的传感器,采集人体的生理振动信息;通过对采集数据进行去重力等预处理,提取均值、方差、能量、峰度、偏度等特征指标,利用z‐score方法进行标准化处理;进行分类器设计,得出分类器的查准率、误检率等评价指标。分析60组不同用户的生理振动数据,分析结果表明,该分类器的查准率最高可达98?33％。     

实现系统安全的技术方案分析

首先讨论了安全模型P2DR的理论概念及其体系结构,依据安全模型的四要素关系及时间值概念,给出了系统安全的定义公式。对安全防御技术和入侵检测技术分别进行了优缺点分析,得出两种技术需有效结合才能符合P2DR安全模型的安全要求。安全联动技术是当前系统安全研究的热点,属于一种新型的安全整合技术,提出了该技术在研究过程中需要解决的相关问题,以满足技术的开放性要求。     

Potentiometric urea biosensor based on BSA embedded surface modified polypyrrole film

A potentiometric biosensor based on bovine serum albumin (BSA) embedded surface modified polypyrrole has been developed for the quantitative estimation of urea in aqueous solution. The enzyme, urease (Urs), was covalently linked to free amino groups present over the BSA embedded modified surface of the conducting polypyrrole film electrochemically deposited onto an indium–tin-oxide (ITO) coated glass plate. The biosensor has been characterized by UV–visible, infrared spectroscopy and SEM. Potentiometric and spectrophotometric response of the enzyme electrode (Urs/BSA-PPy/ITO) were measured as a function of urea concentration in Tris–HCl buffer (pH 7.0). It has been found that the electrode responds to low urea concentration with wider range of detection. The electrode showed a linear response range of 6.6 × 10⁻⁶ to 7.5 × 10⁻⁴ M urea. The response time is about 70–90 s reaching to a 95% steady-state potential value and 75% of the enzyme activity is retained for about 2 months. These results indicate an efficient covalent linkage of enzyme to free amino groups of the BSA molecules over the surface of polypyrrole film, which leads to high enzyme loading, an increased lifetime stability of the electrode and an improved wide range of detection of low urea concentration in aqueous solution.     

远程多生理参数监护系统通信协议的研究

根据移动通信网络中多生理信号的传输要求,修改远程心电监护系统原有通信协议的数据结构、数据补发机制和数据同步机制,提出一种新的适用于远程多生理参数监护系统的通信协议。该通信协议已在远程无线多生理参数实时监护系统中得到应用,实验表明其能满足多生理参数监护仪和监护服务器间的通信要求。     

支持审计与取证联动的日志系统设计

目前国内外缺乏综合数据提炼能力的日志搜索和分析系统,也还没有专门同时为安全审计与计算机取证目的设计的日志保护和分析工具,针对这一现状,分析并提出了一种日志综合分析平台,以支持网络审计与计算机取证.描述了这种安全日志文件系统的构建,采集、管理和保护,可以做到审计与取证的联动分析,形成了一个高可信审计与取证能力的基本通用模型.最后给出了系统实现的界面和系统的性能分析.     

粒子群算法用于装载机工作装置优化计算

装载机的工作装置是组成装载机的关键部件之一,其设计水平的高低直接影响工作装置性能的好坏,进而影响整机的工作效率与经济性指标。本文基于MTLAB,运用粒子群算法对ZL50装载机工作装置的连杆机构进行优化计算并对计算结果进行分析。     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre. The storage service is distributed as pay a service for accessing the size to collect the data. Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy, duplication leads to increase storage space. The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis. It creates a complex nature to increase the storage consumption under cost. To resolve this problem, this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication (HIBD) based on Segmented Bind Linkage (SBL) Methods for reducing storage in a cloud environment. Initially, preprocessing is done using the sparse augmentation technique. Further, the preprocessed files are segmented into blocks to make Hash-Index. The block of the contents is compared with other files through Semantic Content Source Deduplication (SCSD), which identifies the similar content presence between the file. Based on the content presence count, the Distance Vector Weightage Correlation (DVWC) estimates the document similarity weight, and related files are grouped into a cluster. Finally, the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case. This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.     

基于STM32和小波自适应滤波算法的生理参数监测系统的研究

针对心血管疾病的高死亡率以及人口老龄化的现象,本篇文章开发了基于STM32单片机和小波自适应阈值滤波算法的可穿戴式健康监测系统。系统可分为系统微处理器、数字系统模块、人机交互模块、信号采集模块和无线通信模块等几个部分,针对人体的心率、血氧、体温等重要生理参数进行处理分析,进而对人体实时监护。系统处理器选取STM32F103C8T6作为控制芯片,显示模块选用了OLED。生理参数采集系统选用了MAX30102传感器和Pulse sense传感器分别对人体腕部和指尖心率进行采集。生理参数采集完毕后,通过进一步的A/D转化,基于提出的一种改进小波自适应阈值滤波算法降噪滤波,从而将人体的生理特征参数记录下来。再将采集的生理数据通过蓝牙传输至手机端,其中的ZigBee模块主要是把获得的数据再次输送到远程控制端内,让患者能够远程得到更好的医疗监控。本系统通过软件与硬件相结合的方式。最后通过对比论证其中心率（BPM）结果误差为±2BPM,血氧含量监测结果误差在±2%以内。