Novel prototyping method for microfluidic devices based on thermoplastic polyurethane microcapillary film

Thermoplastic polyurethane microcapillary film (TPU-MCF), as a novel extruded product, inherently contains an array of circular micron-sized capillaries embedded inside the polymer matrix. With the aid of simple laser cutting and conventional sealing technologies, a rapid prototyping method for microfluidic devices is proposed based on the ready-made microstructure of MCFs. Two functionalized microfluidic devices: serpentine micromixer and multi-droplet generator, are rapidly fabricated to demonstrate the advantages and potential of employing this new method. The whole proof-of-concept fabrication process can be completed in 8–10 min in a simple way; each procedure is repeatable with stable performance control of microfluidic devices; and the material cost can be as low as $0.01 for each device. The TPU-MCF and this novel method are expected to provide a new perspective and alternative in microfluidic community with particular requirements.     

机载雷达级联降维空时自适应杂波抑制方法

提出一种机载雷达杂波抑制的级联降维空时自适应算法,即,先对全空时两维接收数据进行预滤波处理,将杂波局域化,降低杂波自由度;然后对预处理输出的信号的相关矩阵进行子阵划分,求解低维权向量,进一步降低运算量和采样要求。理论分析和实验仿真结果表明,所提算法具有良好的收敛性能和杂波抑制能力,并且对于阵元随机幅相误差和杂波起伏具有很好的容差能力。基于实测数据的实验验证了算法的有效性和稳健性。     

未来大数据环境下的配用电通信网虚拟网络架构及应用

根据智能配电网的通信需求,本文提出了基于无线 Mesh 网和电力线通信网的智能配电网通信框架。在此框架的基础上,设计了一个遗传算法和启发式算法,为不同类型的业务分别建立虚拟网络并将其映射到异构的物理网络中,通过增加传输分集来保证实时业务的可靠性。通过仿真,验证两种映射算法在保证实时业务可靠性的基础上,使尽力而为型业务的吞吐率达到最大化。     

具有耐火钢短梁的钢框架梁-柱连接抗火性能模拟

通过有限元模拟探讨将耐火钢短梁应用于钢框架梁 柱连接以提高连接抗火性能的可能性.考察三维有限元建模过程中模型简化、网格划分、求解方法和接触关系等关键技术,以提高计算效率并确保分析结果的可靠性.提出3个确定极限温度的准则并应用于钢框架,通过比较可知：对于采用普通钢的钢框架,采用准则1与准则2确定的极限温度与由最大挠度为L/20和L/10所确定的临界温度较接近,这有益于指导钢框架抗火设计.比较普通钢与耐火钢短梁的变形可知：耐火钢的使用可以有效控制跨中挠度变形,使最大变形值减小1/2以上.     

Protel DXP 2004应用中的常见问题及解决方法

Protel DXP 2004软件是一款应用广泛的印制电路板设计软件,学习该软件比较容易入门,但是要熟练掌握并且灵活处理软件使用中的常见问题并非易事。从电气规则检查、载入网络表、制作元件封装等方面,分析Protel DXP 2004软件操作中的常见问题及产生原因,并提出了解决这些问题的方法。     

旋转导向钻井稳定平台的广义预测控制研究

研究了旋转导向钻井稳定平台的摩擦问题,建立了带摩擦的稳定平台广义被控对象的数学模型。针对稳定平台摩擦的不确定性,为了提高稳定平台系统的控制精度和稳定性,提出了阶梯式广义预测控制算法,既保留了传统广义预测控制的优点,又避免了在线求解逆矩阵,同时使算法的安全性得到了提高。将改进算法应用于稳定平台的工具面角控制,并进行了仿真。仿真结果表明,上述算法具有较高的控制精度和良好的动态性能,并且对时变的摩擦干扰具有较强的抗干扰能力,对系统参数摄动具有良好的鲁棒性。     

基于双层安全体系的移动办公应用平台设计与实现

针对移动办公应用的需求,采用HTML5技术和Android系统进行开发,本文提出一种基于双层安全体系的移动办公应用平台。系统通过移动中间件实现了多类型移动设备的统一接入,对企业OA、报账系统、公文处理进行了接口设计,无缝接入企业原有系统的数据和报表,并提供了链路安全和应用安全双层安全体系的保障,为企业人员实时、有效地获取相关工作信息提供了便利。     

Characterizing and optimizing TPC-C workloads on large-scale systems using SSD arrays在大规模系统上优化 TPC-C 评测程序

Transaction processing performance council benchmark C (TPC-C) is the de facto standard for evaluating the performance of high-end computers running on-line transaction processing applications. Differing from other standard benchmarks, the transaction processing performance council only defines specifications for the TPC-C benchmark, but does not provide any standard implementation for end-users. Due to the complexity of the TPC-C workload, it is a challenging task to obtain optimal performance for TPC-C evaluation on a large-scale high-end computer. In this paper, we designed and implemented a large-scale TPC-C evaluation system based on the latest TPC-C specification using solid-state drive (SSD) storage devices. By analyzing the characteristics of the TPC-C workload, we propose a series of system-level optimization methods to improve the TPC-C performance. First, we propose an approach based on SmallFile table space to organize the test data in a round-robin method on all of the disk array partitions; this can make full use of the underlying disk arrays. Second, we propose using a NOOP-based disk scheduling algorithm to reduce the utilization rate of processors and improve the average input/output service time. Third, to improve the system translation lookaside buffer hit rate and reduce the processor overhead, we take advantage of the huge page technique to manage a large amount of memory resources. Lastly, we propose a locality-aware interrupt mapping strategy based on the asymmetry characteristic of non-uniform memory access systems to improve the system performance. Using these optimization methods, we performed the TPC-C test on two large-scale high-end computers using SSD arrays. The experimental results show that our methods can effectively improve the TPC-C performance. For example, the performance of the TPC-C test on an Intel Westmere server reached 1.018 million transactions per minute.     

Effects of residual motion compensation errors on the performance of airborne along-track interferometric SAR

Two approximations, center-beam approximation and reference digital elevation model (DEM) approximation, are used in synthetic aperture radar (SAR) motion compensation procedures. They usually introduce residual motion compensation errors for airborne single-antenna SAR imaging and SAR interferometry. In this paper, we investigate the effects of residual uncompensated motion errors, which are caused by the above two approximations, on the performance of airborne along-track interferometric SAR (ATI-SAR). The residual uncompensated errors caused by center-beam approximation in the absence and in the presence of elevation errors are derived, respectively. Airborne simulation parameters are used to verify the correctness of the analysis and to show the impacts of residual uncompensated errors on the interferometric phase errors for ATI-SAR. It is shown that the interferometric phase errors caused by the center-beam approximation with an accurate DEM could be neglected, while the interferometric phase errors caused by the center-beam approximation with an inaccurate DEM cannot be neglected when the elevation errors exceed a threshold. This research provides theoretical bases for the error source analysis and signal processing of airborne ATI-SAR.     

A semi-supervised privacy-preserving clustering algorithm for healthcare

With the proliferation of healthcare data, the cloud mining technology for E-health services and applications has become a hot research topic. While on the other hand, these rapidly evolving cloud mining technologies and their deployment in healthcare systems also pose potential threats to patient’s data privacy. In order to solve the privacy problem in the cloud mining technique, this paper proposes a semi-supervised privacy-preserving clustering algorithm. By employing a small amount of supervised information, the method first learns a Large Margin Nearest Cluster metric using convex optimization. Then according to the trained metric, the method imposes multiplicative perturbation on the original data, which can change the distribution shape of the original data and thus protect the privacy information as well as ensuring high data usability. The experimental results on the brain fiber dataset provided by the 2009 PBC demonstrated that the proposed method could not only protect data privacy towards secure attacks, but improve the clustering purity.