RTSPC: a software utility for real-time SPC and tool data analysis

Competition in the semiconductor industry is forcing manufacturers to continuously improve the capability of their equipment. The analysis of real-time sensor data from semiconductor manufacturing equipment presents the opportunity to reduce the cost of ownership of the equipment. Previous work by the authors showed that time series filtering in combination with multivariate analysis techniques can be utilized to perform statistical process control, and thereby generate real-time alarms in the case of equipment malfunction. A more robust version of this fault detection algorithm is presented. The algorithm is implemented through RTSPC, a software utility which collects real-time sensor data from the equipment and generates real-time alarms, Examples of alarm generation using RTSPC on a plasma etcher are presented     

DBMap: a space-conscious data visualization and knowledge discovery framework for biomedical data warehouse

Advances in digital imaging modalities as well as other diagnosis and therapeutic techniques have generated a massive amount of diverse data for clinical research. The purpose of this study is to investigate and implement a new intuitive and space-conscious visualization framework, called DBMap, to facilitate efficient multidimensional data visualization and knowledge discovery against the large-scale data warehouses of integrated image and nonimage data. The DBMap framework is built upon the TreeMap concept. TreeMap is a space constrained graphical representation of large hierarchical data sets, mapped to a matrix of rectangles, whose size and color represent interested database fields. It allows the display of a large amount of numerical and categorical information in limited real estate of the computer screen with an intuitive user interface. DBMap has been implemented and integrated into a large brain research data warehouse to support neurologic and neuroradiologic research at the University of California, San Francisco Medical Center. For imaging specialists and clinical researchers, this novel DBMap framework facilitates another way to better explore and classify the hidden knowledge embedded in medical image data warehouses.     

Adaptive diffusion as a versatile tool for time-frequency and time-scale representations processing: a review

Inspired by the work on image processing by Perona and Malik, diffusion-based models were first investigated by Goncalve/spl grave/s and Payot to improve the readability of Cohen class time-frequency representations. They rely on signal-dependent partial differential equations that yield adaptive smoothed representations with sharpened time-frequency components. Here, we demonstrate the versatility and utility of this family of methods, and we propose new adaptive diffusion processes to locally control both the orientation and the strength of smoothing. Our approach is an improvement on previous works as it provides a unified framework not only for the Cohen class but for the affine class as well. The latter is of particular interest because, except for some special techniques such as the reassignment method, no signal-dependent smoothing technique exists to process bilinear time-scale distributions, nor even a transposition of the adaptive optimal-kernel method proposed by Baraniuk and Jones.     

Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis.

Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection with subsequent SL on microbiological data. The results of our study show that local DR within natural clusters may result in better representation for SL in comparison with the global DR on the whole data.     

The short proposal: versatile tool for communicating corporateculture in competitive climates

The authors maintain that proposals are typically overwritten, poorly researched documents that force recipients to search for ideas of pertinent concern and rarely accomplish their objectives. They suggest that short proposals, introduced early in the negotiation process, can act as replacements for formal proposals or as interim documents leading to more productive, persuasive formal proposals. They explain why many long proposals fail and short proposals succeed     

Near-field optics: a new tool for data storage

Evanescent energy can be used to produce extremely small optical spots. Two practical implementations that use evanescent energy are aperture probes and solid immersion lenses (SILs). For data storage, the optical near field is defined in terms of evanescent coupling between the system used to read data and the recording layer. Because of the small spot size, near-field techniques are applied to optical data storage systems in order to increase recording density. Both aperture-type systems and SIL systems show good promise of achieving densities of more than 150 Gb/in/sup 2/. The characteristics and performance of several systems are compared, and future near-field technologies are discussed.     

Rough set theory: a data mining tool for semiconductormanufacturing

The growing volume of information poses interesting challenges and calls for tools that discover properties of data. Data mining has emerged as a discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decisionmaking. In this paper, the basic concepts of rough set theory and other aspects of data mining are introduced. The rough set theory offers a viable approach for extraction of decision rules from data sets. The extracted rules can be used for making predictions in the semiconductor industry and other applications. This contrasts other approaches such as regression analysis and neural networks where a single model is built. One of the goals of data mining is to extract meaningful knowledge. The power, generality, accuracy, and longevity of decision rules can be increased by the application of concepts from systems engineering and evolutionary computation introduced in this paper. A new rule-structuring algorithm is proposed. The concepts presented in the paper are illustrated with examples     

配电自动化终端数据质量分析工具应用研究

本文通过在配电主站开发数据质量分析工具分析配电自动化终端三遥信息数据质量问题,通过工具分析配电自动化终端遥控失败,遥信抖动、频发,遥测存在死区设置过小、不合理和不刷新等情况原因,解决配电自动化终端数据质量问题,提高配电主站数据质量.     

Medical informatics and bioinformatics: a bibliometric study.

This paper reports on an analysis of the bioinformatics and medical informatics literature with the objective to identify upcoming trends that are shared among both research fields to derive benefits from potential collaborative initiatives for their future. Our results present the main characteristics of the two fields and show that these domains are still relatively separated.     

XFACT: a furnace analysis and characterization tool

The diffusion furnace is one the most important and complex process equipment in the process line and efforts towards characterizing it will be well worth the effort. A CAM package for wafer temperature map reconstruction using only a few sampled data points is proposed. The software is completely generic and has no affiliation to any make of furnace. The data required for simulation can easily be obtained from the actual piece of equipment. Interpolation techniques with high accuracy are used for developing the temperature contours inside the furnace using a modified version of the Lagrange's interpolation technique     

Imaging of biomedical data using a multiplicative regularizedcontrast source inversion method

In this paper, the recently developed multiplicative regularized contrast source inversion method is applied to microwave biomedical applications. The inversion method is fully iterative and avoids solving any forward problem in each iterative step. In this way, the inverse scattering problem can efficiently be solved. Moreover, the recently developed multiplicative regularizer allows us to apply the method blindly to experimental data. We demonstrate inversion from experimental data collected by a 2.33-GHz circular microwave scanner using a two-dimensional (2-D) TM polarization measurement setup. Further some results of a feasibility study of the present inversion method to the 2-D TE polarization and the full-vectorial three-dimensional measurement will be presented as well     

Developing a low-cost high-quality software tool for dynamicfault-tree analysis

Sophisticated modeling and analysis methods are being developed in academic and industrial research labs for reliability engineering and other domains. The evaluation and evolution of such methods based on use in practice is critical to research progress, but few such methods see widespread use. A critical impediment to disseminating new methods is the inability to produce, at a reasonable cost, supporting software tools that have the: usability and dependability characteristics that industrial users require; and evolvability to accommodate software change as the underlying analysis methods are refined and enhanced. The difficulty of software development thus emerges as a key impediment to advances in engineering modeling and analysis. This paper presents an approach to tool development that attacks these problems. Progress requires synergistic, interdisciplinary collaborations between application-domain and software-engineering researchers. The authors have pursued such an approach in developing Galileo: a fault tree modeling and analysis tool. These innovations are described in two dimensions: (1) the Galileo core reliability modeling and analysis function; and (2) the authors' work on software engineering for high-quality, low-cost modeling and analysis tools     

RELVEC — a tool for control system reliability analysis

This article will present a computerized reliability analysis tool for large control systems. It will also show a new dynamic representation of system structure. It enables us to model the physical system only once for any number of control tasks. The algorithm for computing minimal cut sets for the control tasks has been developed and automated. The result is RELVEC, an interactive computer program that performs reliability/availability calculation, sensitivity analysis and critical component identification. It can handle two repair policies and common mode failures. Reconfiquring of the physical system or the control tasks is simple. RELVEC is becoming an everyday tool in control system reliability analysis at VTT.     

Bayesian nonstationary autoregressive models for biomedical signal analysis

We describe a variational Bayesian algorithm for the estimation of a multivariate autoregressive model with time-varying coefficients that adapt according to a linear dynamical system. The algorithm allows for time and frequency domain characterization of nonstationary multivariate signals and is especially suited to the analysis of event-related data. Results are presented on synthetic data and real electroencephalogram data recorded in event-related desynchronization and photic synchronization scenarios.     

New analysis tool for graphs

The variable incidence set of a graph is defined and its usefulness demonstrated in reducing a graph by identifying specified vertices and removing self loops, testing for connectedness, and generating specified segs, cosegs and cutsets.     

TDAT-time domain analysis tool for EEG analysis

An interactive design and analysis tool for displaying and quantifying multiple channels of data is presented. The system allows one to easily visualize multiple data channels and simultaneously observe the effects of filters on the data and to evaluate signal detection algorithms. The software is designed for a workstation environment; it will find application in a variety of applications where one needs to simultaneously visualize multiple data channels. TDAT is being used for the design and evaluation of filters and detection algorithms for electroencephalogram (EEG) waveforms, and it is serving as a prototype of a paperless system to be used by electroencephalographers. This paper describes the general software structure of the system and illustrates many of the system features with examples.     

An efficient data routing scheme for multi-patient monitoring in a biomedical sensor network through energy equalization strategy

Wireless Networks - Biomedical sensor networks find wide applications in human health monitoring. In such applications, routing strategies in the sensor nodes play a key role towards energy...     

Large-scale biomedical image analysis in grid environments

This paper presents the application of a component-based Grid middleware system for processing extremely large images obtained from digital microscopy devices. We have developed parallel, out-of-core techniques for different classes of data processing operations employed on images from confocal microscopy scanners. These techniques are combined into a data preprocessing and analysis pipeline using the component-based middleware system. The experimental results show that: 1) our implementation achieves good performance and can handle very large datasets on high-performance Grid nodes, consisting of computation and/or storage clusters and 2) it can take advantage of Grid nodes connected over high-bandwidth wide-area networks by combining task and data parallelism.     

Spectroscopic photon emission microscopy: a unique tool for failure analysis of microelectronics devices

Photon emission microscopy (PEM) is a technique used commonly for failure analysis of microelectronics chips. This technique has it limitations: it can only be used to indicate the place of the failure. In most cases, this is not enough to allow a definition of the failure, i.e. to find out whether it is due to a gate oxide breakdown, a metal short, a junction spiking, etc. In this paper spectral PEM is discussed. It is shown that the spectrum of the light emitted by the failure may offer valuable information about the identity of the failure.     

PLANET: a tool for telecommunication network

PLANET is an integrated software package aiding several steps of the transmission network planning, as well as the traffic simulation. The database of PLANET consists of the structural and the cost models of the transmission systems, the topological alternatives, the circuit routing and grouping rules, etc. The topological and transmission plans of the networks are obtained based on the traffic planning results checked by simulation and they are stored systematically with the input data in the database of plans. The results describe the network topology, the circuit routes meeting different kinds of routing conditions, the grouping solutions and the equipment assignments to the circuit demands of different services. The users of PLANET are provided with lists and graphical documentation that can be directly used in the development of the network. The global structure of the software package as well as the algorithms of specific phases are discussed and the results obtained by the application of PLANET are demonstrated