An Introduction to the Analysis and Processing of Signals

Lynn, Paul A.

doi:10.1007/978-1-349-19719-4

Cited by 70 publications

(31 citation statements)

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“…Spectra shown are averages of 4-10 scans with an average of an equal number of baseline scans subtracted. The resulting spectra were smoothed using a digital low-pass filter (9). All software used in the data collection and handling was carried out using a BBC+ microcomputer linked to the Dichrograph.…”

Section: Pur$cation Of Human Wild Type and Mutant Recom-mentioning

confidence: 99%

Determination of the contributions of individual aromatic residues to the CD spectrum of IL-lβ using site directed mutagenesis

Craig

Schmeissner

et al. 2009

International Journal of Peptide and Protein Research

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Circular dichroism (CD) spectra have been measured in the aromatic region for recombinant human interleukin IL‐1β and for site specific mutants in which each of the four tyrosines and the single tryptophan residue have been replaced one at a time by phenylalanine. These mutant proteins have been shown to have conformations that are closely similar to that of the wild type protein. By comparing the spectrum of each mutant with that of the wild type human protein it has been possible to assess the contribution of each aromatic residue to the CD spectrum of the latter. It has been shown that the spectrum is largely a result of contributions from Trp 120 and from Tyr 68.

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Section: Pur$cation Of Human Wild Type and Mutant Recom-mentioning

confidence: 99%

Determination of the contributions of individual aromatic residues to the CD spectrum of IL-lβ using site directed mutagenesis

Craig

Schmeissner

et al. 2009

International Journal of Peptide and Protein Research

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“…This procedure, which is based on the two-dimensional ͑2D͒ autocorrelation function ͑ACF͒, 24 can provide a real time indication of focus quality for manual ͑and passive automatic͒ focusing 25 in proton beam lithography and imaging with quadruple multiplet lens systems. This procedure, which is based on the two-dimensional ͑2D͒ autocorrelation function ͑ACF͒, 24 can provide a real time indication of focus quality for manual ͑and passive automatic͒ focusing 25 in proton beam lithography and imaging with quadruple multiplet lens systems.…”

Section: Figures Of Merit For Focusing Mega-electron-volt Ion Beams Imentioning

confidence: 99%

Figures of merit for focusing mega-electron-volt ion beams in biomedical imaging and proton beam writing

Ren

Whitlow

Sagari

et al. 2008

Journal of Applied Physics

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Articles you may be interested inA simple double-focusing electrostatic ion beam deflector Rev. Sci. Instrum. 81, 063304 (2010); 10.1063/1.3433485Focusing of laser-generated ion beams by a plasma cylinder: Similarity theory and the thick lens formula Phys. Plasmas 13, 063103 (2006); Characteristics of focused beam spots using negative ion beams from a compact surface plasma source and merits for new applications A figure of merit ͑FOM͒ has been developed for focusing quadrupole multiplet lenses for ion microand nanobeam systems. The method which is based on measurement of the central peak of the two-dimensional autocorrelation function of an image provides separate FOM for the horizontal and vertical directions. The approach has been tested by comparison with the edge widths obtained by nonlinear fitting the edge widths of a Ni grid and found to be reliable. The FOM has the important advantage for ion beam imaging of biomedical samples that the fluence needed is considerably lower than for edge fitting.

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“…However, the physical interpretation of these parameters may be incorrect. Spongberg [2000] Time domain filters are most often used and expressed as recursive recurrence formulas [Lynn, 1989]. These filters are calibrated to produce base flow estimates from streamflow data, which means that constants in a filter are adjusted until the time series produced from it resembles base flow.…”

Section: Filter Approachmentioning

confidence: 99%

A physically based filter for separating base flow from streamflow time series

Furey¹,

Gupta²

2001

Water Resources Research

144

163

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Abstract. A filter for separating base flow from streamflow in a river basin is derived from a mass balance equation for base flow from a hillside. Unlike other existing filters, this filter is physically based both during streamflow recession and rise, and each of the parameters in it has a well-defined physical meaning. Unlike other approaches to base flow separation, the filter is applied without any calibration or constraints. Yet, using two criteria as measures of filter performance, it performs as well as or better than two existing filters that require calibration. Using the same two criteria, weaknesses in the filter are identified and attributed mainly to a ratio of two parameters that are related to groundwater recharge and overland flow. This ratio is estimated using precipitation and streamflow data, and its estimate is conjectured to be inaccurate because of the poor spatial resolution of the precipitation data. Moreover, this ratio is assumed to be constant in time, yet, on physical grounds, it should be treated as a time-dependent quantity. IntroductionBase flow time series are needed to understand the spatial and temporal variability of runoff processes in river basins, to extrapolate runoff processes to ungauged locations [Gupta and Waymire, 1998], and, in practice, to manage water quantity and quality. However, there is no direct way to continuously measure base flow throughout a basin or processes that affect base flow such as overland flow, evapotranspiration, interflow, and groundwater recharge. Consequently, many approaches have been developed to estimate or separate base flow from streamflow continuously in time [e.g., see Wittenberg, 1999;Chapman, 1999;Birtles, 1978]. None of these approaches are physically based under all streamflow conditions and consist of only a few parameters. As a result, they rely heavily on calibration which masks the physics behind base flow estimation particularly during times when streamflow is rising. In this paper, a mathematical filter for continuous base flow separation is developed that is physically based under all streamflow conditions. It consists of four well-defined physical parameters, three of which can be estimated using both rainfall and streamflow data. Our filter is physically based both during streamflow recession and rise, and its physical basis requires that it be applied once and only forward in time to produce an estimate of base flow. By contrast, application of some other filters to streamflow data appears arbitrary [e.g., Nathan and McMahon, 1990; Chapman, 1991]. Unlike other filters, our filter is not founded on the assumption that base flow and overland flow are the low-and high-frequency components of streamflow, respectively [Spongberg, 2000]. Thus it accounts for the possibility that low-frequency variations exist in overland flow because of those in precipitation. The filter consists of four physical constants that must be evaluated before using it. Three of these are estimated from streamflow and precipitation data, and the filter...

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An Introduction to the Analysis and Processing of Signals

Cited by 70 publications

References 0 publications

Determination of the contributions of individual aromatic residues to the CD spectrum of IL-lβ using site directed mutagenesis

Determination of the contributions of individual aromatic residues to the CD spectrum of IL-lβ using site directed mutagenesis

Figures of merit for focusing mega-electron-volt ion beams in biomedical imaging and proton beam writing

A physically based filter for separating base flow from streamflow time series

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