Sliding window method

Figure 2 shows the results of the IF analysis, revealing strong modulation at 50 on1 and other low frequency modes, similar to observations by Champion et al. [10][11] For comparison, the IF was also calculated using a sliding window FFT method, yielding similar results as shown in Figure 2. The observation of these low frequency modes is perhaps the most important result of the study. The 50 cm 1 mode in particular has been identified with the doming motion of the heme [12] and the lower frequencies can be correlated to the globin...

Preference functions were extracted from the data base of 63 membrane proteins selected by Rost et al. [9] and 37 soluble proteins of the P-class (SOLBl, Methods) by using the PREF algorithm versions with sliding window length from 7 to 19 residues. 

The best known and most used procedure is the Kyte-Doolittle method which computes within the sliding window of specific width the hydrophobicity/am-phiphilicity of the segment. This represents a certain probability that the specific segment will or will not be present in the membrane. 

A robust detection system should be able to provide both frequency and spatial information. While frequency serves to identify the presence of a defect, spatial information is necessary to identify the location of defect. It is well known that the Fourier transform suffers from a lack of spatial information while Gabor filtering methods are very computational expensive since the 2D convolution between the image and filter must be carried out in a sliding window throughout the entire image. These limitations could be eliminated by wavelets. 

The most common digital filtering polynomial methods, according to Savitzky and Golay [22, 23], involve a shortened least-square computation using a sliding window with variable data points but other algorithms are also sometimes practicable. 

Initial proposals for identification methods to detect vehicle stmctures in images were based on hand-crafted features such as contours [13], symmetry [14], comers [15], or edges [16]. However, these algorithms never achieved the detection rates of automatically trained detectors which use a sliding window scheme for hypothesis... 

In this chapter, the traditional method is used, with a sliding window of 40 m.y. and rejection of all poles >25° from the mean pole, a value suggested by the distribution of poles for the past 1 m.y. discussed above (Figure 3.8). The resulting UgjS are probably minimum values because the selection excludes those poles that lie beyond the 25° limit that otherwise meet the palaeomagnetic criteria. 

The results provided by three-dimensional MRTM are consistent with the numerical output of one-dimensional MRTM. The concentration-depth curves are shown to be similar for a nominal test case that is independent of temporal and spatial scales. Besides the numerical output that the model generates, the visualization component of the model gives an almost instantaneous look into the spatial distribution of the contaminant. This visualization is made by sliding three planes (horizontal, longitudinal, and transversal) across the entire simulation domain. Concentrations are scaled from 0.0 to the maximum values so that the trace concentrations can be easily visualized. The numerical value of the maximum concentration is also output in the visualization window, together with the current position of the visualization plane. When the trace compound is hazardous (e.g., a heavy metal such as mercury), it is also necessary to monitor the spatial distribution of very low concentrations. The current three-dimensional, MRTM visualization method provides the means to track these types of trace concentrations. 

Individual cells may also be deposited onto a Cap2 window support, but perhaps the more practical and most appropriate (in terms of its match to cytological practices) method nowadays is to use a low-e glass slide and record a transflection spectrum, see next section. 

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