A microcomputer system for routine and research use in cardiac pacing

A microcomputer stimulator for routine and research use in cardiac pacing is described. Emphasis is given to software aspects of the design including both the user-computer interface and the computer-patient interface and the multitasking features of the operating system. The main programming language is FORTRAN. Assembly language is used where speed of control or precise timing are necessary. The microcomputer supplies the intelligence to control an external isolated stimulation unit which is connected to the patient. The system is used to investigate the efficacy of cardiac pacing for the termination of arrhythmias, in the testing of cardiac electrophysiological function and for other research involving cardiac stimulation.     

A microcomputer program for the identification of tRNA genes

A microcomputer program which locates tRNA genes within long DNA sequences is described. The search is performed either by identifying tRNA-like secondary structures or by locating eukaryotic RNA polymerase III promoter consensus sequences. The program is also useful in finding inverted repeats allowing the formation of stem-loop secondary structures in tRNA. The program has been developed in BASIC and 6502 Assembler and runs on the Apple II plus and IIe microcomputers. The execution is quite fast; all the operations are carried out in 1-90 s, depending on the required task and on the sequence length.     

RIAPC: a microcomputer program for the analysis of radioimmunoassay data

A microcomputer program for analysis of radioimmunoassays has been developed for use on microcomputers which operate under MS-DOS system software. The program messages are contained in an ASCII text file in French, English, and Spanish and can be modified by the user. The parameters and data can be entered manually into screen tables, or read from external files. An unweighted log/logit transformation is used for regression analysis of the standard curve. Provision is made for correction of the sample measurements for procedural losses (recovery). All results are written to an ASCII text file which can printed and/or reduced in order to pass the sample concentrations to other programs.     

A new algorithm for dominant points detection and polygonization of digital curves

A new algorithm for detecting dominant points and polygonal approximation of digitized closed curves is presented. It uses an optimal criterion for determining the region-of-support of each boundary point, and a new mechanism for selecting the dominant points. The algorithm does not require an input parameter, and can handle shapes that contain features of multiple sizes efficiently. In addition, the approximating polygon preserves the symmetry of the shape.     

A framework for robust parametric set membership identification

This paper proposes a new framework for studying robust parametric set membership identification. The authors derive some new results on the fundamental limitations of algorithms in this framework, given a particular model structure. The new idea is to quantify uncertainty only with respect to the (finite dimensional) parametric part of the model and not the (fixed size) unmodeled dynamics. Thus, the measure of uncertainty is different from the measures used in previous robust identification work where system norms are used to quantify uncertainty. As an example, the results are used to assess the fidelity of a certain approximate robust parametric set membership identification algorithm     

Resolution-based outlier factor: detecting the top-n most outlying data points in engineering data

One of the common endeavours in engineering applications is outlier detection, which aims to identify inconsistent records from large amounts of data. Although outlier detection schemes in data mining discipline are acknowledged as a more viable solution to efficient identification of anomalies from these data repository, current outlier mining algorithms require the input of domain parameters. These parameters are often unknown, difficult to determine and vary across different datasets containing different cluster features. This paper presents a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify and rank top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into account. Experiments are conducted using both synthetic datasets and a real life construction equipment dataset from a large road building contractor. Comparison with the current outlier mining algorithms indicates that the proposed algorithm is more effective and can be integrated into a decision support system to serve as a universal detector of potentially inconsistent records.     

A robust algorithm for identification of the frequency of a sinusoidal signal

The problem of identification of an unknown frequency of a sinusoidal signal is considered. A new approach to estimating the frequency of a sinusoidal signal that is robust relative to unaccounted perturbations in the measurement of the useful signal is proposed.     

Parametric spline curves in integer arithmetic designed for use in microcomputer controlled plotters

An algorithm for generating a parametric spline curve through a set of points using only integer arithmetic is derived. The algorithm is especially suited for implementation on simple computers with a short wordlength and does not require multiplication or division. It is an incremental step algorithm suited for digital plotting and numerical machine tool control. It has been used to draw optimally “nice” alphanumeric characters independent of scale in a plotting system for the RC4000 computer where a microcomputer controls the plotter. The splined alphanumeric characters do not require more computer time on the main processor than the simple characters using straight line segments, and only increases the load on the microcomputer.     

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.     

A robust adaptive clustering analysis method for automatic identification of clusters

Identifying the optimal cluster number and generating reliable clustering results are necessary but challenging tasks in cluster analysis. The effectiveness of clustering analysis relies not only on the assumption of cluster number but also on the clustering algorithm employed. This paper proposes a new clustering analysis method that identifies the desired cluster number and produces, at the same time, reliable clustering solutions. It first obtains many clustering results from a specific algorithm, such as Fuzzy C-Means (FCM), and then integrates these different results as a judgement matrix. An iterative graph-partitioning process is implemented to identify the desired cluster number and the final result. The proposed method is a robust approach as it is demonstrated its effectiveness in clustering 2D data sets and multi-dimensional real-world data sets of different shapes. The method is compared with cluster validity analysis and other methods such as spectral clustering and cluster ensemble methods. The method is also shown efficient in mesh segmentation applications. The proposed method is also adaptive because it not only works with the FCM algorithm but also other clustering methods like the k-means algorithm.     

一类推广的两步鲁棒辨识算法

基于传统的两步辨识方法，提出一类当频域数据样本在系统频率段上非均匀分布时的H∞鲁棒辨识方法，分析了相应的最差情况的辨识误差，并给出了采用Lidstone插值样条算子和三角窗函数时显式最差情况辨识误差上界。     

Drawing curves onto a cloud of points for point-based modelling

Point-based geometric models are gaining popularity in both the computer graphics and CAD fields. A related design/modelling problem is the focus of the reported research: drawing curves onto digital surfaces represented by clouds of points. The problem is analyzed and solved, and a set of ‘design tools’ are proposed which allow the user/designer to efficiently perform ‘product development’ (alternative name: ‘detail design’) tasks which require efficient processing of a ‘digital surface’. The primary tool is a robust and efficient point projection algorithm combined with a smoothing technique for producing smooth ‘digital curves’ lying onto the cloud surface. The new design tools are tested on a real-life industrial example with very satisfactory results, which are thoroughly presented in the paper.     

基于特征提取和匹配的图像倾斜校正算法

针对图像中不包含明显直线的情况,提出一种基于特征点提取的图像倾斜校正算法。该算法建立在与无倾斜的训练图像比对基础上,利用特征点构造直线,不依赖于原图中是否存在直线进行倾斜检测,具有尺度、平移无关性。使用双向最大相关系数匹配,匹配正确率较高。利用大数原理对数据进行处理,去除误匹配的影响。该算法最少可以利用两个匹配对,检测出图像倾斜角度。结合本文的应用背景,本文还设计了一种用于特征提取的圆形模板,具有类似于旋转不变的性质。     

On the detection of dominant points on digital curves

A parallel algorithm is presented for detecting dominant points on a digital closed curve. The procedure requires no input parameter and remains reliable even when features of multiple sizes are present on the digital curve. The procedure first determines the region of support for each point based on its local properties, then computes measures of relative significance (e.g. curvature) of each point, and finally detects dominant points by a process of nonmaximum suppression. This procedure leads to the observation that the performance of dominant points detection depends not only on the accuracy of the measure of significance, but also on the precise determination of the region of support. This solves the fundamental problem of scale factor selection encountered in various dominant point detection algorithms. The inherent nature of scale-space filtering in the procedure is addressed, and the performance of the procedure is compared to those of several other dominant point detection algorithms, using a number of examples     

Parallel social behavior-based algorithm for identification of influential users in social network

Applied Intelligence - Influence maximization in social networks refers to the process of finding influential users who make the most of information or product adoption. The social networks is...     

参数凸曲线的性质及拐点判别算法

依据参数曲线凸性的原始几何定义,讨论了参数曲线的局部凸和全局凸性,得到了参数曲线局部凸和全局凸的若干性质。给出了参数曲线的拐点定义,讨论了参数曲线的拐点与局部性之间的关系,导出了参数曲线拐点判别的充要条件及算法。