Choice Of Window Size Research Articles

Localized Downscaling of Urban Land Surface Temperature—A Case Study in Beijing, China

High-resolution land surface temperature (LST) data are essential for fine-scale urban thermal environment studies. Urban LST downscaling studies mostly remain focused on only two-dimensional (2-D) data, and neglect the impact of three-dimensional (3-D) surface structure on LST. In addition, the choice of window size is also important for LST downscaling over heterogeneous surfaces. In this study, we downscaled Landsat-LST using localized and stepwise approaches in a random forest model (RF). In addition, both 2- and 3-D building morphologies were included. Our results show that: (1) The performances of a local moving window and stepwise downscaling are dependent on the extent of surface heterogeneity. For mixed surfaces, a localized window performed better than the global window, and a stepwise approach performed better than a single-step approach. However, for monotonous surfaces (e.g., urban impervious surfaces), the global window performed better than a localized window; (2) That multi-scale geographically weighted regression (MGWR) could provide a possibility for selection of the optimal moving window. 7 × 7 windows derived from MGWR by the minimum bandwidth of predictors, performed better than other windows (3 × 3, 5 × 5, and 11 × 11) in the Beijing area; (3) That the morphology of buildings has a non-negligible impact and scaling effect on urban LST. When building morphologies were included in downscaling, the performance of the RF model improved. Furthermore, the importance of the sky view factor, building height, and building density was greater at a higher resolution than at a lower resolution.

Influence of Sliding Time Window Size Selection Based on Heart Rate Variability Signal Analysis on Intelligent Monitoring of Noxious Stimulation under Anesthesia.

In recent decades, little progress of objective evaluation of pain and noxious stimulation has been achieved under anesthesia. Some researches based on medical signals have failed to provide a general understanding of this problem. This paper presents a feature extraction method for heart rate variability signals, aiming at further improving the evaluation of noxious stimulation. In the process of data processing, the empirical mode decomposition is used to decompose and recombine heart rate variability signals, and the sliding time window approach is used to extract the signal features of noxious stimulation, respectively. The influence of window size on feature extraction is studied by changing the window size. By comparing the results, the feature extraction in the process of data processing is valuable, and the selection of window size has a significant impact. With the increase of selected window sizes, we can get better detection results. But for the best choice of window size, to ensure the accuracy of the results and to make it easy to use, then, we need to get just a suitable window size.

Constraining the geotherm beneath the British Isles from Bayesian inversion of Curie depth: integrated modelling of magnetic, geothermal, and seismic data

Abstract. Curie depth offers a valuable constraint on the thermal structure of the lithosphere, based on its interpretation as the depth to 580 ∘C, but current methods underestimate the range of uncertainty. We formulate the estimation of Curie depth within a Bayesian framework to quantify its uncertainty across the British Isles. Uncertainty increases exponentially with Curie depth but this can be moderated by increasing the size of the spatial window taken from the magnetic anomaly. The choice of window size needed to resolve the magnetic thickness is often ambiguous but, based on our chosen spectral method, we determine that significant gains in precision can be obtained with window sizes 15–30 times larger than the deepest magnetic source. Our Curie depth map of the British Isles includes a combination of window sizes: smaller windows are used where the magnetic base is shallow to resolve small-scale features, and larger window sizes are used where the magnetic base is deep in order to improve precision. On average, the Curie depth increases from Laurentian crust (22.2±5.3 km) to Avalonian crust (31.2±9.2 km). The temperature distribution in the crust, and associated uncertainty, was simulated from the ensemble of Curie depth realizations assigned to a lower thermal boundary condition of a crustal model (sedimentary thickness, Moho depth, heat production, thermal conductivity), constructed from various geophysical and geochemical datasets. The uncertainty in the simulated heat flow field substantially increases from ±10 mW m−2 for shallow Curie depths at ∼15 km to ±80 mW m−2 for Curie depths >40 km. Surface heat flow observations are concordant with the simulated heat flow field except in regions that contain igneous bodies. Heat flow data within large batholiths in the British Isles exceed the simulated heat flow by ∼25 mW m−2 as a result of their high rates of heat production (4–6 µW m−3). Conversely, heat refraction around thermally resistive mafic volcanics and thick sedimentary layers induce a negative heat flow misfit of a similar magnitude. A northward thinning of the lithosphere is supported by shallower Curie depths on the northern side of the Iapetus Suture, which separates Laurentian and Avalonian terranes. Cenozoic volcanism in Northern Britain and Ireland has previously been attributed to a lateral branch of the proto-Icelandic mantle plume. Our results show that high surface heat flow (>90 mW m−2) and shallow Curie depth (∼15 km) occur within the same region, which supports the hypothesis that lithospheric thinning occurred due to the influence of a mantle plume. The fact that the uncertainty is only ±3–8 km in this region demonstrates that Curie depths are more reliable in hotter regions of the crust where the magnetic base is shallow.

Spectral analysis of time-dependent market-adjusted return correlation matrix

We present an adjusted method for calculating the eigenvalues of a time-dependent return correlation matrix in a moving window. First, we compare the normalized maximum eigenvalue time series of the market-adjusted return correlation matrix to that of the logarithmic return correlation matrix on an 18-year dataset of 310 S&P 500-listed stocks for small and large window or memory sizes. We observe that the resulting new eigenvalue time series is more stationary than the time series obtained without the adjustment. Second, we perform this analysis while sweeping the window size τ∈{5,…,100}∪{500} in order to examine the dependence on the choice of window size. This approach demonstrates the multi-modality of the eigenvalue distributions. We find that the three dimensional distribution of the eigenvalue time series for our market-adjusted return is significantly more stationary than that produced by classic method. Finally, our model offers an approximate polarization domain characterized by a smooth L-shaped strip. The polarization with large amplitude is revealed, while there is persistence in agreement of individual stock returns’ movement with market with small amplitude most of the time.

ON THE NON-KOLMOGOROV NATURE OF FLARE-PRODUCTIVE SOLAR ACTIVE REGIONS

ABSTRACT A magnetic power spectral analysis is performed on 53 solar active regions, observed from 2011 August to 2012 July. Magnetic field data obtained from the Helioseismic and Magnetic Imager, inverted as Active Region Patches, are used to study the evolution of the magnetic power index as each region rotates across the solar disk. Active regions are classified based on the numbers and sizes of solar flares they produce in order to study the relationship between flare productivity and the magnetic power index. The choice of window size and inertial range plays a key role in determining the correct magnetic power index. The overall distribution of magnetic power indices has a range of 1.0–2.5. Flare-quiet regions peak at a value of 1.6. However, flare-productive regions peak at a value of 2.2. Overall, the histogram of the distribution of power indices of flare-productive active regions is well separated from flare-quiet active regions. Only 12% of flare-quiet regions exhibit an index greater than 2, whereas 90% of flare-productive regions exhibit an index greater than 2. Flare-quiet regions exhibit a high temporal variance (i.e., the index fluctuates between high and low values), whereas flare-productive regions maintain an index greater than 2 for several days. This shows the importance of including the temporal evolution of active regions in flare prediction studies, and highlights the potential of a 2–3 day prediction window for space weather applications.

Quantitative comparison of analysis methods for spectroscopic optical coherence tomography: reply to comment.

We reply to the comment by Kraszewski et al on "Quantitative comparison of analysis methods for spectroscopic optical coherence tomography." We present additional simulations evaluating the proposed window function. We conclude that our simulations show good qualitative agreement with the results of Kraszewski, in support of their conclusion that SOCT optimization should include window shape, next to choice of window size and analysis algorithm.

Estimating Genome‐Wide Significance for Whole‐Genome Sequencing Studies

Although a standard genome-wide significance level has been accepted for the testing of association between common genetic variants and disease, the era of whole-genome sequencing (WGS) requires a new threshold. The allele frequency spectrum of sequence-identified variants is very different from common variants, and the identified rare genetic variation is usually jointly analyzed in a series of genomic windows or regions. In nearby or overlapping windows, these test statistics will be correlated, and the degree of correlation is likely to depend on the choice of window size, overlap, and the test statistic. Furthermore, multiple analyses may be performed using different windows or test statistics. Here we propose an empirical approach for estimating genome-wide significance thresholds for data arising from WGS studies, and we demonstrate that the empirical threshold can be efficiently estimated by extrapolating from calculations performed on a small genomic region. Because analysis of WGS may need to be repeated with different choices of test statistics or windows, this prediction approach makes it computationally feasible to estimate genome-wide significance thresholds for different analysis choices. Based on UK10K whole-genome sequence data, we derive genome-wide significance thresholds ranging between 2.5 × 10−8 and 8 × 10−8 for our analytic choices in window-based testing, and thresholds of 0.6 × 10−8–1.5 × 10−8 for a combined analytic strategy of testing common variants using single-SNP tests together with rare variants analyzed with our sliding-window test strategy.

Spectral Analysis of Time-Dependent Market-Adjusted Return Correlation Matrix

We present an adjusted method for calculating the eigenvalues of a time-dependent return correlation matrix that produces a more stationary distribution of eigenvalues. First, we compare the normalized maximum eigenvalue time series of the market-adjusted return correlation matrix to that of logarithmic return correlation matrix on an 18-year dataset of 310 S&P 500-listed stocks for two (small and large) window or memory sizes. We observe that the resulting new eigenvalue time series is more stationary than time series obtained through the use of existing method for each memory. Later, we perform this analysis while sweeping the window size τ e {5, ..., 100} in order to examine the dependence on the choice of window size. We find that the three dimensional distribution of the eigenvalue time series for our market-adjusted return is significantly more stationary than that produced by classic method. Moreover, our model offers an approximate polarization domain of smooth L-shaped strip. The polarization with large amplitude is revealed, while there is persistence in agreement with small amplitude most of the time.

Multi-resolution decomposition in relation to characteristic scales and local window sizes using an operational wavelet algorithm

Data from an IKONOS image acquired over Dallas were used to demonstrate the use of an operational wavelet-based algorithm to examine the performance of different texture measures and window sizes at various resolutions in connection with characteristic scales. It was found that a 63 × 63 window was the optimal size, and energy measure produced the highest accuracy. Results from this study suggest that the choice of window size in wavelet-based classification affects the accuracy. Larger window sizes significantly improve the overall accuracy when using homogeneous samples. In the real-world situation, a larger window may not necessarily produce higher accuracy since a larger window tends to cover more land-use and land-cover classes and therefore may miss smaller regions of classes that could lead to poorer accuracy. On the other hand, a smaller window tends to be incomplete in its coverage of texture features that represent a complex class. The classification accuracy can be improved by using more combinations of sub-images at different scales. However, smaller sub-images at the last two levels may lower the classification accuracy. The characteristic scale of the most complex feature among all selected classes could be the optimal local window size necessary to achieve the highest accuracy.

The choice of window size in approximating topographic surfaces from Digital Elevation Models

Quantitative surface analysis through quadratic modelling of Digital Elevation Models (DEMs) is a promising tool for automatically describing the physical environment in ecological studies of terrestrial landscapes. Fundamental topographic variables such as slope, aspect, plan and profile curvature can be simply calculated from the parameters of a conic equation fitted to a DEM window through the least-squares method. The scale of the analysis, defined by the size of the DEM window used to fit the conic equation, affects both the estimated value of the topographic variables and the propagation of elevation errors to derived topographic variables. The least-squares method is amenable to the analytical treatment of the propagation of elevation errors to the derived topographical variables. A general analytical model of error propagation is presented that accounts for the effects of window size and of spatial autocorrelation in elevation errors. The method is based on the Taylor approximation of the least-square fitting equation and allows for the presence of stationary autocorrelation in the elevation errors. In numerical simulations with DEMs from British Columbia, Canada, it is shown that increasing the size of evaluation windows effectively reduces the propagation of elevation errors to the derived topographic variables. However, this was obtained at the expense of topographic detail. A methodology is proposed to evaluate quantitatively the loss of topographic detail through a χ 2-test of the corrected residuals in the immediate neighbourhood of the evaluation point. This methodology, in combination with the analytical model of error propagation, can be used to select the scale or range of scales at which to calculate topographic variables from a DEM.

TM Finder: a prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales.

Based on the principle of dual prediction by segment hydrophobicity and nonpolar phase helicity, in concert with imposed threshold values of these two parameters, we developed the automated prediction program TM Finder that can successfully locate most transmembrane (TM) segments in proteins. The program uses the results of experiments on a series of host-guest TM segment mimic peptides of prototypic sequence KK AAAXAAAAAXAAWAAXAAAKKKK-amide (where X = each of the 20 commonly occurring amino acids) through which an HPLC-derived hydropathy scale, a hydrophobicity threshold for spontaneous membrane insertion, and a nonpolar phase helical propensity scale were determined. Using these scales, the optimized prediction algorithm of TM Finder defines TM segments by first searching for competent core segments using the combination of hydrophobicity and helicity scales, and then performs a gap-joining operation, which minimizes prediction bias caused by local hydrophilic residues and/or the choice of window size. In addition, the hydrophobicity threshold requirement enables TM Finder to distinguish reliably between membrane proteins and globular proteins, thereby adding an important dimension to the program. A full web version of the TM Finder program can be accessed at http://www.bioinformatics-canada.org/TM/.

The intrinsic asymmetry component of the “total background” in XP spectra

Abstract The XP spectrum consists of peaks on a characteristic stepped background. The simplistic interpretation of this, used in most data systems, assumes that the step derives entirely from extrinsic losses and can be removed by a “Shirley” type of algorithm. Tougaard has demonstrated the weakness of this assumption, showing how removal of the background by the simple Shirley method will subtract, also, important components of the peak intensity itself. The reason for this is that both peak asymmetries and other intrinsic losses contribute intensity in the same form as that resulting from extrinsic losses. The Tougaard background was developed to account for extrinsic losses alone and has been shown to merge with the general loss background at a point some 50 eV distant from the peak position. The use of the Tougaard universal background for the transition elements removes very little intensity from the vicinity of the main peak and this poses a problem for quantitative analysis since use of a window wide enough to yield a peak intensity which is independent of the integration limits adds in a great deal of intensity, which has probably not been included in the standards used for quantification, whereas the reduction of the range to that of a conventional narrow window leaves, after subtraction of the Tougaard background, an asymmetric peak still retaining a step-like background and an integrated area which is not closed but depends on the choice of window size. In this work we show that the asymmetry associated with intrinsic, final state, losses in the photoelectron emission can be defined by a single shape parameter. This parameter, κ , is shown to be independent of the kinetic energy of the peak, is similar for different peaks of a given element and does not depends on the spectrometer type or operating parameters. It varies uniformly with atomic number in a manner which is similar to that of the melting point of the transition elements, i.e. reflects the internal energy in those cases where this is defined by the density of overlapping valence levels. The use of this parameter in curve fitting provides a means of characterising the intrinsic component of the losses. We have handled this by a Shirley type routine after subtraction of the Tougaard background. The advantage in quantification is that a closed area under the peak is obtained, i.e. the advantage of the Shirley background is retained but in a defined consistent manner.

Spatial discrimination and classification maps

A method of constructing maps through spatial discrimination is given. The discrimination depends basically on the assumption of local spatial continuity, and a factorized covariance matrix. Given an autocovariance function, this formulation in particular, leads to a deeper insight into the pioneering work of Switzer (1980). Certain windows for the maps are examined, and choice of window size is discussed in relation to the classification error when the variables are dependent versus independent. When a training data is given, we give a method of estimating the parameters in the model. Some numerical examples are also given.

Queuing network analysis for a mixed voice and data communication network with end‐to‐end window flow control policy

Abstract A Mixed Voice and Data Communication Network (MVDNET) with end‐to‐end (ETE) window flow control policy is modeled by a closed multichain queuing network. Using an approximate solution of the Mean Value Analysis (MVA) algorithm, the computational complexity of analysing a large‐scale MVDNET and various commodities is dramatically reduced by several orders of magnitude over that required by traditional product‐form solution. We consider the problems concerned with the choice of window size, the effect of voice interruption, the number of voice‐grade (VG) channels per internode link and the selection of appropriate packet length. It is shown that the analytic results are very close to the simulation ones using GPSS language, and that the extensive MVA algorithm is a practical and valuable tool for solving the MVDNET analytical model with ETE window flow control policy.