Collapse-free thermal bonding technique for large area microchambers in plastic lab-on-a-chip applications

Bonding is an essential step to form microchannels or microchambers in lab-on-a-chip applications. In this paper, we present a novel plastic thermal bonding technique to seal and form large area microchambers (planar characteristic width and length on the order of 1 mm and characteristic thickness on the order of 10–100 μm) without collapse by introducing a holed pressure equalizing plate (HPEP) that includes holes of the same size and shape as the microchambers. To demonstrate the proposed technique, two types of large area microchambers [(1) 20 × 10 mm and 40 μm thick and (2) 12 × 2.5 mm and 120 μm thick] with microchannels were designed and replicated on plastic substrates by means of hot embossing and injection molding processes with prepared two nickel mold inserts. The replicated large area microchambers as well as the microchannels in the plastic lab-on-a-chip were successfully sealed (i.e., no leakage) and formed without any collapse by the proposed thermal bonding technique with the help of the HPEP.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits

Optimization of the wire bonding process of an integrated circuit (IC) is a multi-objective optimization problem (MOOP). In this research, an integrated multi-objective immune algorithm (MOIA) that combines an artificial immune algorithm (IA) with an artificial neural network (ANN) and a generalized Pareto-based scale-independent fitness function (GPSIFF) is developed to find the optimal process parameters for the first bond of an IC wire bonding. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and the multi-responses, and is applied to generate the multiple response values for each antibody generated by the IA. The GPSIFF is then used to evaluate the affinity for each antibody and to find the non-dominated solutions. The “Error Ratio” is then applied to measure the convergence of the integrated approach. The “Spread Metric” is used to measure the diversity of the proposed approach. Implementation results show that the integrated MOIA approach does generate the Pareto-optimal solutions for the decision maker, and the Pareto-optimal solutions have good convergence and diversity performance.     

一种基于C#的通用软件安装程序的架构设计与实现

可以说每个商业化软件都必须具备安装工具,使用系统软件自带的打包程序有时很难满足实际需求,为此提出了一种基于C#的通用软件安装程序的架构,给出了实现这一架构的设计方法和关键代码.通过该架构,可以非常容易地实现安装程序的功能扩展和代码维护.     

数据中心管理不再复杂

随着数据中心基础设施以及应用系统的增多,IT人员的工作量逐渐加大,自动化管理让这些变得更容易。     

基于DSP的数字图像增强技术

本文采用目前工程中最通用的空域滤波增强法理论,通过DSP实现对数字图像的增强处理,使得图像对比度变大,目标和背景易于分离,对系统的后续操作提供工程的可实现性.     

基于LEADTOOLS的医学影像学习平台的设计与实现

LEADTOOLS是LeadTools公司开发的优秀的医学软件开发包,DICOM是医院PACS系统的标准图像格式,如何将各大医院的医学影像库中的数据作为医学院校学生的学习资料,已成为一个问题.本文基于LEADTOOLS V14.0设计实现了医学影像学习平台,大大加强了医学专业学生们的专业知识的学习.     

区域科技发展状况评价体系的软件实现

区域科技发展状况评价体系包括科技进步贡献率与科技进步速度、科技实力、科技创新能力三部分指标的计算。通过对这些指标的计算结果进行考查可以对区域的科技发展状况有一个定量的研究结果。介绍了区域科技发展状况评价体系软件系统的开发思想,以及此系统的实现方法。     

局部PCA参数约束的Hough多椭圆分层检测算法

针对随机Hough变换(RHT)在复杂图像中检测圆及椭圆时随机采样所造成的大量无效采样、无效累积以及运算时间长等问题,提出基于局部PCA感兴趣参数约束Hough多椭圆分层检测思路。首先利用边缘检测算子获得边缘信息并去除边缘交叉点,在边缘图像中标记并提取出满足一定长度的连续曲线段;其次利用线段PCA方向分析确定是否属于有效曲线段;然后,对所有感兴趣曲线段按照标记顺序依次利用椭圆拟合办法初步得到感兴趣椭圆粗略参数,根据拟合结果进而模糊约束Hough变换参数搜索范围,得到精确椭圆参数;最后利用检测结果更新图像空间,删除已经检测到的椭圆,依次进行,直到所有椭圆检测完毕。实验结果表明,该算法在计算、存储消耗上均大大减少。     

IPv6下基于病毒过滤防火墙的设计与实现

包过滤防火墙无法检测出网络病毒,因此对其研究很有必要.设计的防火墙屏蔽了Linux自身的TCP/IP协议栈,重新构建了适合防火墙专用的TCP/IP协议栈,完成了防火墙上TCP协议的连接保持、数据包确认、文件传输等功能.防火墙主要考虑了HTTP协议下的文件过滤,使得内网主机在通过HTTP协议下栽文件时自动过滤病毒文件,保证内网主机的安全;防火墙以Linux可加栽内核模块形式实现,可以过滤链路层以上的各层;为提高病毒检测速度,提出了将病毒检测软件运行在核心态的方法.实验结果表明:设计的防火墙在性能和功能上都达到了预期目的.