Comprehensive two-dimensional gas chromatography (GC Â GC) presents information technology challenges in data handling, visualization, processing, analysis and reporting. With its significantly greater separation capacity, GC Â GC generates data sets that are two to three orders-of-magnitude larger than for conventional GC and that exhibit an order-of-magnitude or more distinct peaks in complex multidimensional patterns. GC Â GC is a much richer source of data for chemical analyses, but extracting relevant information from the large, complex data sets requires advanced information technologies. This paper reviews a range of state-of-the-art information technologies for GC Â GC, including graphical user-interfaces (GUIs), computer graphics for data visualization, data file formats for storage and interchange, digital image processing, image analysis and pattern recognition, data and information formats, and software architecture and engineering. D 2004 Published by Elsevier B.V.
We study the problem of tracking an object that is moving randomly through a dense network of wireless sensors. We assume that each sensor has a limited range for detecting the presence of the object, and that the network is sufficiently dense so that the sensors cover the area of interest. In order to conserve energy the sensors may be put into a sleep mode with a timer that determines the sleep duration. We assume that a sensor that is asleep cannot be communicated with or woken up. Thus, the sleep duration needs to be determined at the time the sensor goes to sleep based on all the information available to the sensor. The objective is to track the location of the object to within the accuracy of the range of the sensor. However, having sleeping sensors in the network could result in tracking errors, and hence there is a tradeoff between the energy savings and the tracking errors that result from the sleeping actions at the sensors. We consider the design of sleeping policies that optimize this tradeoff.
This paper describes a language for expressing criteria for chemical identification with comprehensive two-dimensional gas chromatography paired with mass spectrometry (GC×GC-MS) and presents computer-based tools implementing the language. The Computer Language for Indentifying Chemicals (CLIC) allows expressions that describe rules (or constraints) for selecting chemical peaks or data points based on multi-dimensional chromatographic properties and mass spectral characteristics. CLIC offers chromatographic functions of retention times, functions of mass spectra, numbers for quantitative and relational evaluation, and logical and arithmetic operators. The language is demonstrated with the compound-class selection rules described by Welthagen et al. [W. Welthagen, J. Schnelle-Kreis, R. Zimmermann, J. Chromatogr. A 1019 (2003) 233-249]. A software implementation of CLIC provides a calculator-like graphical user-interface (GUI) for building and applying selection expressions. From the selection calculator, expressions can be used to select chromatographic peaks that meet the criteria or create selection chromatograms that mask data points inconsistent with the criteria. Selection expressions can be combined with graphical, geometric constraints in the retention-time plane as a powerful component for chemical identification with template matching or used to speed and improve mass spectrum library searches.
This paper investigates methods for comparing datasets produced by comprehensive two-dimensional gas chromatography (GC × GC). Chemical comparisons are useful for process monitoring, sample classification or identification, correlative determinations, and other important tasks. GC × GC is a powerful new technology for chemical analysis, but methods for comparative visualization must address challenges posed by GC × GC data: inconsistency and complexity. The approach extends conventional techniques for image comparison by utilizing specific characteristics of GC × GC data and developing new methods for comparative visualization and analysis. The paper describes techniques that register (or align) GC × GC datasets to remove retention-time variations; normalize intensities to remove sample amount variations; compute differences in local regions to remove slight misregistrations and differences in peak shapes; employ color (hue), intensity, and saturation to simultaneously visualize differences and values; and use tools for masking, three-dimensional visualization, and tabular presentation with controls for graphical highlights to significantly improve comparative analysis of GC × GC datasets. Experimental results indicate that the comparative methods preserve chemical information and support qualitative and quantitative analyses.
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