Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.     

Theoretical and experimental characterization of wettability of various nanolens arrayed polymer surfaces replicated with nanodimpled aluminum mold insert

Various types of polymer surfaces with a nanolens array, which has an entrant shape with a low aspect ratio, were fabricated and the wettability of the fabricated surfaces was evaluated in both theoretical and experimental ways. The nanolens array was replicated on three different polymer surfaces of polydimethylsiloxane (PDMS), cyclic olefin copolymer (COC), and polymethylmethacrylate (PMMA) by means of replica molding and hot embossing with a nanodimpled aluminum mold that was manufactured by a chemical oxidation process. From the theoretical and experimental evaluations of the wettability it was found that the measured contact angles were very similar with the theoretically estimated ones and also the hydrophilicity and hydrophobicity of the hydrophilic PMMA and hydrophobic PDMS, respectively, surfaces were reinforced by the nanolens array within the Wenzel wetting state.     

Improved nonlinear trajectory tracking using RBFNN for a robotic helicopter

This paper presents a backstepping control method using radial‐basis‐function neural network (RBFNN) for improving trajectory tracking performance of a robotic helicopter. Many well‐known nonlinear controllers for robotic helicopters have been constructed based on the approximate dynamic model in which the coupling effect is neglected; their qualitative behavior must be further analyzed to ensure that the unmodeled dynamics do not destroy the stability of the closed‐loop system. In order to improve the controller design process, the proposed controller is developed based on the complete dynamic model of robotic helicopters by using an RBFNN function approximation to the neglected dynamic uncertainties, and then proving that all the trajectory tracking error variables are globally ultimately bounded and converge to a neighborhood of the origin. The merits of the proposal controller are exemplified by four numerical simulations, showing that the proposed controller outperforms a well‐known controller in (J. Robust Nonlinear Control 2004; 14 (12):1035–1059). Copyright © 2009 John Wiley & Sons, Ltd.     

A virtual subnet scheme on clustering algorithms for mobile ad hoc networks

Resolving the broadcast storm problem is an important issue in mobile ad hoc networks (MANETs). In this paper, we propose an approach for constructing a virtual subnet whose nodes are logically related. The virtual subnet can be spread upon clusters of a MANET. An intelligent agent with a routing filtering table is proposed to assist the best known clustering algorithms, the original Least ID algorithm and the original Highest Connection Cluster (HCC) algorithm, to improve group communication efficiency. Our simulation covers the network factors of hop count, deprave rate, and delay time. The simulation results show that when the proposed intelligent agent is used with the HCC algorithm, the delay time was reduced by 81.84% as compared with flooding, and by 49.25% as compared with the Ad Hoc On-Demand Distance Vector (AODV) routing algorithm. The delay time for the Least ID algorithm assisted by the proposed agents reduced by 81.84% compared to that of flooding and by 50% compared to that of AODV.     

A new strategy for defect inspection by the virtual inspection in semiconductor wafer fabrication

The defect of process equipments is a major factor that impairs the yields in the mass production of semiconductor wafer fabrication and it is a main supervision means to use high-resolution defect inspection tools to detect and monitor the defect damage. Due to the high investment costs of these inspection tools and the resulting decrease in the throughput, how to improve the sampling rate is an important issue for the associated inspection strategy. This paper proposes a new concept and implementation of virtual inspection (VI) to enhance the detection and monitoring of defect in semiconductor production process. The underlying theory of the VI concept is that the state variables identifications (SVIDs) of process equipments can reflect the process quality effectively and loyally. The approach of VI is to combine the application of the fault detection and classification (FDC), and the defect library and the re-engineering of inspection procedure to reach the full-scope of strategic objective. VI enables the defect monitoring to enter a new era by promoting the monitoring level of defect inspection from the previous lot-sampling basis to the wafer-sampling level, and hence upgrades the sampling strategy from random-sampling to full and right-sampling. In this study, various typical defect cases are utilized to illustrate how to create VI models and verify the reliability of the proposed approach. Furthermore, a feasible architecture of the VI implementation for mass production in semiconductor factory is presented in the paper.     

Implicit modeling from polygon soup using convolution

We present a novel method for creating implicit surfaces from polygonal models. The implicit function is defined by convolving a kernel with the triangles in the polygonal model. By adopting a piecewise quartic polynomial kernel function with a finite support, we derive a convolution model that has a closed-form solution, and thus can be efficiently evaluated. The user only needs to specify an effective radius of influence to generate an implicit surface of desired closeness to the polygonal model. The resulting implicit surface is fast to evaluate, not requiring accumulating evaluation results using any hierarchical data structure, and can be efficiently ray-traced to reveal the detailed features.     

Microinjection molded disposable microfluidic lab-on-a-chip for efficient detection of agglutination

Previous diagnosing methods based on agglutination have a limitation in view of emergency and point-of-care diagnoses due to the requirement of large scale equipments and much agglutination time. In this paper, we propose a low cost microfluidic lab-on-a-chip for more efficient detection of agglutination. In the present lab-on-a-chip, two inlet microwells, flow guiding microchannels, chaotic micromixer and reaction microwell are fully integrated. Mold inserts for the lab-on-a-chip were manufactured by UV photolithography and nickel electroplating process. The complete lab-on-a-chip was realized by the microinjection molding of cyclic olefin copolymer and the subsequent thermal bonding. The improved serpentine laminating micromixer, developed by our group, integrated in the lab-on-a-chip showed the high-level of chaotic mixing, thereby enabling us to get a reliable mixing of sample and reagent. The performance of the fabricated lab-on-a-chip was demonstrated by agglutination experiments with simulated bloods of 10 μl and simulated sera of 10 μl. The results of agglutination inside the reaction microwell were clearly read by means of the level of light transmission. The present microfluidic lab-on-a-chip could be widely applied to various clinical diagnostics based on agglutination tests.     

基于STM32的居民社区门窗禁控制系统

针对居民社区公共门窗管理不及时,耗费大量人力资源,且效率较低等问题,设计了一款基于STM32单片机的居民社区门窗禁控制系统.在传统的门禁控制系统的基础上,系统利用Zigbee无线传感网技术,实现门窗禁控制器之间的无线通讯.管理用户可以通过以太网远程控制系统,系统也可以根据传感器监测到的环境数据实现自动调节.同时,尝试采用一种基于频率捷变的分布式自适应干扰技术,解决系统在居民居住环境中遇到的Wifi网络信号干扰问题.最后,通过测试表明,该系统运行稳定,具有广泛的应用前景.     

Motion regularization for matting motion blurred objects

This paper addresses the problem of matting motion blurred objects from a single image. Existing single image matting methods are designed to extract static objects that have fractional pixel occupancy. This arises because the physical scene object has a finer resolution than the discrete image pixel and therefore only occupies a fraction of the pixel. For a motion blurred object, however, fractional pixel occupancy is attributed to the object’s motion over the exposure period. While conventional matting techniques can be used to matte motion blurred objects, they are not formulated in a manner that considers the object’s motion and tend to work only when the object is on a homogeneous background. We show how to obtain better alpha mattes by introducing a regularization term in the matting formulation to account for the object’s motion. In addition, we outline a method for estimating local object motion based on local gradient statistics from the original image. For the sake of completeness, we also discuss how user markup can be used to denote the local direction in lieu of motion estimation. Improvements to alpha mattes computed with our regularization are demonstrated on a variety of examples.     

Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization

This paper proposes an improved variational model, multiple piecewise constant with geodesic active contour (MPC-GAC) model, which generalizes the region-based active contour model by Chan and Vese, 2001 [11] and merges the edge-based active contour by Caselles et al., 1997 [7] to inherit the advantages of region-based and edge-based image segmentation models. We show that the new MPC-GAC energy functional can be iteratively minimized by graph cut algorithms with high computational efficiency compared with the level set framework. This iterative algorithm alternates between the piecewise constant functional learning and the foreground and background updating so that the energy value gradually decreases to the minimum of the energy functional. The k-means method is used to compute the piecewise constant values of the foreground and background of image. We use a graph cut method to detect and update the foreground and background. Numerical experiments show that the proposed interactive segmentation method based on the MPC-GAC model by graph cut optimization can effectively segment images with inhomogeneous objects and background.