Optimization of multipoint phase retrieval in edge illumination X-ray imaging: A theoretical and experimental analysis.

BackgroundIn the Point-Centred Quarter Method (PCQM), the mean distance of the first nearest plants in each quadrant of a number of random sample points is converted to plant density. It is a quick method for plant density estimation. In recent publications the estimator equations of simple PCQM (PCQM1) and higher order ones (PCQM2 and PCQM3, which uses the distance of the second and third nearest plants, respectively) show discrepancy. This study attempts to review PCQM estimators in order to find the most accurate equation form. We tested the accuracy of different PCQM equations using Monte Carlo Simulations in simulated (having ‘random’, ‘aggregated’ and ‘regular’ spatial patterns) plant populations and empirical ones.Principal FindingsPCQM requires at least 50 sample points to ensure a desired level of accuracy. PCQM with a corrected estimator is more accurate than with a previously published estimator. The published PCQM versions (PCQM1, PCQM2 and PCQM3) show significant differences in accuracy of density estimation, i.e. the higher order PCQM provides higher accuracy. However, the corrected PCQM versions show no significant differences among them as tested in various spatial patterns except in plant assemblages with a strong repulsion (plant competition). If N is number of sample points and R is distance, the corrected estimator of PCQM1 is 4(4N − 1)/(π ∑ R2) but not 12N/(π ∑ R2), of PCQM2 is 4(8N − 1)/(π ∑ R2) but not 28N/(π ∑ R2) and of PCQM3 is 4(12N − 1)/(π ∑ R2) but not 44N/(π ∑ R2) as published.SignificanceIf the spatial pattern of a plant association is random, PCQM1 with a corrected equation estimator and over 50 sample points would be sufficient to provide accurate density estimation. PCQM using just the nearest tree in each quadrant is therefore sufficient, which facilitates sampling of trees, particularly in areas with just a few hundred trees per hectare. PCQM3 provides the best density estimations for all types of plant assemblages including the repulsion process. Since in practice, the spatial pattern of a plant association remains unknown before starting a vegetation survey, for field applications the use of PCQM3 along with the corrected estimator is recommended. However, for sparse plant populations, where the use of PCQM3 may pose practical limitations, the PCQM2 or PCQM1 would be applied. During application of PCQM in the field, care should be taken to summarize the distance data based on ‘the inverse summation of squared distances’ but not ‘the summation of inverse squared distances’ as erroneously published.

PurposeTo enable a preliminary assessment of the suitability of edge illumination (EI) x-ray phase contrast (XPC) micro x-ray computed tomography (micro-CT) to preclinical imaging. Specifically, to understand how different acquisition schemes and their combination with dedicated data processing affect contrast-to-noise ratio (CNR) and spatial resolution, while providing control over scan time and radiation dose delivery.ProceduresDeceased mice (n = 3) were scanned with an EI XPC micro-CT setup operated under different settings, leading to scan times between 18 h and 13 min. For the shortest scan, the entrance dose was measured with a calibrated PTW 23344 ion chamber. Different data processing methods were applied, retrieving either separate attenuation and phase images, or hybrid (combined attenuation and phase) images. A quantitative comparison was performed based on CNR and spatial resolution measurements for a soft tissue interface.ResultsAll phase-based images have led to a higher CNR for the considered soft tissue interface than the attenuation image, independent of scan time. The best relative CNR (a sixfold increase) was observed in one of the hybrid images. Spatial resolution was found to be connected to scan time, with a resolution of approximately 20 μm and 60 μm achieved for the longest and shortest scans, respectively. An entrance dose of approximately 300 mGy was estimated for the scan performed within 13 min.ConclusionsDespite their preliminary nature, our results suggest that EI XPC bears potential for enhancing the utility of preclinical micro-CT, and, pending further research and development, could ultimately become a valuable technique in this field.

This paper describes a representation method of a single closed loop defined by two-dimensional CAD system based on G-type Fourier descriptor. Traditionally, equal length polygonal approximation method is used to get sampling points for calculating discrete fourier descriptor. However, this approach requires many sampling points depending on the complexity of the shape. This study describes a method for the reduction of the number of sampling points and discusses the geometrical error between the original sampling points and the reconstructed points using inverse fourier transformation. First, an equal angle polygonal approximation method is proposed to reduce the number of sampling points. It means that the length between neighboring two sampling points is unequal. As a result of experiments with thirty-one shapes, the equal angle method is concluded to be sufficient for the reconstruction of the shapes. Second, to improve the geometrical error of inverse fourier transformation of the shape with acute angle, a fourier descriptor with multiple vertices is proposed. Using this method, it is possible to reconstruct the shape without higher order harmonics. Finally, it is proposed that the fourier synthesis method for generating a new shape from fourier descriptors of two shapes.

In this study, the combined use of 2 new techniques, nonuniform sampling (NUS) and carbon direct-detection (CDD), for the measurement of multidimensional NMR has been demonstrated. NUS is a method for sampling time domain data of indirect dimensions at random sparse points, whereas canonical multidimensional NMR spectra require all the regular grids as sampling points. Of several sparse sampling methods, NUS is advantageous, because it can reduce the errors that originate from the lack of sampling points by randomizing the sampling points. NUS can have denser sampling points at the beginning, where signals decay less than those in the end of sampling, and in turn enhance the signal-to-noise ratio (SNR) in the frequency dimensions per unit time. NUS is applicable to proteins that have been difficult to study using conventional NMR approaches. CDD is another area where applications have advanced considerably over the last several years. CDD is an effective technique for detecting NMR signals from proteins that exist under unfavorable conditions to observe or discriminate proton signals, which include paramagnetic proteins, highly deuterated samples, proteins of high salts, and disordered proteins. Despite the advances in NMR hardware, however, CDD has not been easily employed for practical applications, because the SNR of its result is insufficient. In this paper, NUS and CDD have been used in a combination in a 3D CBCACON experiment, expecting the supporting role of NUS in improving the SNR of CDD. The 3D CBCACON experiment provides the chemical shifts information of Cα/β(i-1)-N(i)-C'(i-1). The Bruker’s default pulse sequence of “c_cbcacon_ia3d” was modified to incorporate the NUS scheme. The sample used in the present study was ubiquitin (approximately 2.0 mM) and the reference and NUS experiments were performed using a Bruker AV 600 MHz NMR machine equipped with a TCI cryoprobe. The time domains of reference 3D CBCACON data consisted of 256* (F3: C') × 28* (F2: N) × 24* (F1: Cα/β) × 2 (IPAP) (*means complex point), and the number of transients was 8, leading to the total measurement time of 26 hours. The NUS schedule comprised 270 points, and it corresponded to about 40% of the intact points and required an 11-hour measuring time. As the chemical shifts of the Cα/β atoms evolved under constant time, the points for the Cα/β dimension were designed to have equal distribution, whereas the points for N dimension distributed to a Gaussian shape with higher density at the beginning (Fig. S1). To process the NUS data, multidimensional Fourier transformation (MFT) and the maximum entropy (MaxEnt) algorithms by using in-house written MFT processing program (J-G. Jee, unpublished data) and the Rowland NMR Toolkit (version 3.0), respectively, were employed. For conventional fast discrete Fourier transformation (FFT) processing and analyses of spectra, NMRPipe software was used. The overlaid 2D spectra of [C', N] and [C, Cα/β] in FFT, MFT, and MaxEnt results are shown in Figure 1. Because the number of sampling points in the current NUS decreased more than a half, a lower SNR was expected in the results. Although some noise peaks were observed in both the MFT and MaxEnt data due to the lower sensitivity caused by smaller points (Fig. 1), they are tolerable for the backbone chemical shifts assignment. Generally, the spectra reproduced by MaxEnt display a better resolution than those reproduced by MFT. However, insufficient quality or quantity of the input data may alter the chemical shifts. MFT is expected to generate fewer erroneous results despite its

Abstract. A reasonably-complete bird inventory is the crucial starting point for the analysis of the bird community. We evaluated the efficiency of point counts in detecting forest birds and verified how many sampling points or occasions are needed to adequately characterize the bird community. We sampled birds in 5 forest stands (conifer and beech forests) from northern to southern Italy in 2012. Sampling (through aural and visual clues) lasted 5 minutes, during which species were recorded. Data were analysed in relation to both the number of sampling points and the number of sampling occasions. Then, estimates of species richness were compared to random resampling of subsets of the original data. Results showed that after 3.8 sampling occasions (out of 19–24 sampling points) or 10.4 sampling points (given points are sampled 5 times), the species coverage of each community approached, or exceeded, the 90% threshold. Also, no difference in the mean values emerged with the subset estimates, but the latter ...

In this paper, a probability algorithm is proposed to track the global power peak (GP) of PV arrays under partially shaded conditions (PSCs). It tries to find the mathematical relationship between GP and sampling points of PV curve. In other words, there is a probability for GP located into the vicinity of maximal sampling points. But the number of sampling points must be large enough to make the probability greater than one predetermined value. From this view, some traditional methods can be easily unified in the same math system. Finally, simulations and experiments demonstrate that this method can track global maximum power point (MPP) effectively.

The acquisition of signals is a precondition for tracking and solution calculation in software-based Global Navigation Satellite System (GNSS) receivers. The Parallel Code phase Acquisition (PCA) algorithm can simultaneously obtain the correlation results at every sampling point. However, if the number of sampling points that needs processing is large, this method will lead to a heavy computational load. Thus, we improve the process of the PCA algorithm and propose a novel algorithm that divides the signals intoK(Kis a constant) parts to achieve correlation and obtains the correlation results with a fusion algorithm. This algorithm can simultaneously obtain the correlation results for sampling points at an interval ofKpoints. If theKvalue is selected appropriately, the computational load can be decreased by about 50%. Also, the Receiver Operating Characteristic (ROC) curves show that under a certain probability of false alarm, the detection probability of the proposed algorithms is 5% lower than that of the PCA algorithm. Therefore, the proposed algorithm can speed up the acquisition process with a slight decrease in detection probability.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Research on the regional variability of soil organic carbon (SOC) has focused mostly on the influence of the number of soil sampling points and interpolation methods. Little attention has typically been paid to the influence of sampling point discretization. Based on dense soil sampling points in the red soil area of Southern China, we obtained four sample discretization levels by a resampling operation. Then, regional SOC distributions were obtained at four levels by two interpolation methods: ordinary Kriging (OK) and Kriging combined with land use information (LuK). To evaluate the influence of sample discretization on revealing SOC variability, we compared the interpolation accuracies at four discretization levels with uniformly distributed validation points. The results demonstrated that the spatial distribution patterns of SOC were roughly similar, but the contour details in some local areas were different at the various discretization levels. Moreover, the predicted mean absolute errors (MAE) and root mean square errors (RMSE) of the two Kriging methods all rose with an increase in discretization. From the lowest to the largest discretization level, the MAEs of OK and LuK rose from 4.47 and 3.02 g kg−1 to 5.46 and 3.54 g kg−1, and the RMSEs rose from 5.13 and 3.95 g kg−1 to 5.76 and 4.76 g kg−1, respectively. Though the trend of prediction errors varied with discretization levels, the interpolation accuracies of the two Kriging methods were both influenced by the sample discretization level. Furthermore, the spatial interpolation uncertainty of OK was more sensitive to the discretization level than that of the LuK method. Therefore, when the spatial distribution of SOC is predicted using Kriging methods based on the same sample quantity, the more uniformly distributed sampling points are, the more accurate the spatial prediction accuracy of SOC will be, and vice versa. The results of this study can act as a useful reference for evaluating the uncertainty of SOC spatial interpolation and making a soil sampling scheme in the red soil region of China.

Surrogate models are used to dramatically improve the design efficiency of numerical aerodynamic shape optimization, where high-fidelity, expensive computational fluid dynamics (CFD) is often employed. Traditionally, in adaptation, only one single sample point is chosen to update the surrogate model during each updating cycle, after the initial surrogate model is built. To enable the selection of multiple new samples at each updating cycle, a few parallel infilling strategies have been developed in recent years, in order to reduce the optimization wall clock time. In this article, an alternative parallel infilling strategy for surrogate-based constrained optimization is presented and demonstrated by the aerodynamic shape optimization of transonic wings. Different from existing methods in which multiple sample points are chosen by a single infill criterion, this article uses a combination of multiple infill criteria, with each criterion choosing a different sample point. Constrained drag minimizations of the ONERA-M6 and DLR-F4 wings are exercised to demonstrate the proposed method, including low-dimensional (6 design variables) and higher-dimensional problems (up to 48 design variables). The results show that, for surrogate-based optimization of transonic wings, the proposed method is more effective than the existing parallel infilling strategies, when the number of initial sample points are in the range from Nv to 8Nv (Nv here denotes the number of design variables). Each case is repeated 50 times to eliminate the effect of randomness in our results.

Due to the relative motion between adjacent blade rows the aerodynamic flow fields within turbomachinery are usually dominated by deterministic, periodic phenomena. In the numerical simulation of such unsteady flows, (nonlinear) frequency-domain methods are therefore attractive as they are capable of fully exploiting the given spatial and temporal periodicity, as well as modelling flow nonlinearities. A nontrivial issue in the application of frequency-domain methods to turbomachinery flows is to simultaneously capture disturbances with multiple fundamental frequencies in one relative system. In case of harmonically related frequencies, the interval spanned by the sampling points typically resolves the common fundamental frequency. To avoid signal aliasing the highest harmonic of the common frequency should be sampled with an appropriate number of sampling points. However, when the common fundamental frequency is very low in relation to the frequencies of primary interest, equidistant time sampling leads to a high number of sampling points, hence frequency-domain methods can become computationally inefficient. Furthermore, when a problem can no longer be described by harmonic perturbations that are integer multiples of one fundamental frequency, as it may occur in two-shaft configurations, the standard discrete Fourier transform is no longer suitable and the basic harmonic balance method requires extension. In this article two nonlinear frequency-domain approaches for dealing with the accounted issues are demonstrated and compared. The first approach is a generalized harmonic balance method based on almost periodic Fourier transforms with non-equidistant time sampling. Then the so-called harmonic set approach, developed by the authors, is evaluated. Based on the neglection of the nonlinear, quadratic cross-coupling terms between higher harmonics of different fundamental frequencies, the harmonic set approach allows the superposition of periodic disturbances with different fundamental frequencies as well as the separated, equidistant sampling of the highest harmonic of each base frequency. The aim of this paper is to compare the computational efficiency and accuracy of the two methods and assess the impact of neglecting the quadratic cross-coupling terms.

The aim of surface reconstruction is to transfer the shape of physical objects, which have been sampled by tactile or optical scanning techniques, into computer-processable descriptions. Triangulation is the most common surface model used in CAD/CAM systems. The processing time of a triangulation is decisively influenced by the number of sampling points. Hence, either the sampling points have to be reduced or efficient triangulations have to be found. Due to the fact that for interpolating triangulations the optimal distribution of the sampling points is generally difficult to find, here, self-organizing dynamic meshes are applied. The complex problem to find the best discrete approximation of a surface using a dynamic triangulation and a simple distance measure is solved by an evolution strategy. A special node-spring description always encodes valid triangulations.

In this study, the effective method for reliability estimation is proposed using tow-staged kriging metamodel and genetic algorithm. Kriging metamodel can be determined by appropriate sampling range and the number of sampling points. The first kriging metamodel is made based on the proposed sampling points. The advanced f`

Multidisciplinary optimization of an offshore aquaculture vessel hull form based on the support vector regression surrogate model

The present work aims to improve the comfort of architectural interior design and reduce indoor energy consumption. The Weight K-Nearest Neighborhood (WKNN) algorithm and Nondominated Sorting Genetic algorithm are proposed to locate and analyze the spatial location of indoor personnel and optimize the indoor energy consumption in combination with residential behavior. Firstly, the indoor human behavior data and energy-saving problems are analyzed based on residential behavior theory and architectural physics. The indoor positioning algorithm is proposed to identify the personnel activities to realize the optimization of indoor energy distribution. Secondly, mean filtering and cluster analysis are adopted to optimize sampling points' data and fingerprint database to eliminate data noise. Besides, the WKNN algorithm is used for Wireless Fidelity (Wi-Fi) indoor location fingerprint location. Then, aiming at the multiobjective optimization problem of building indoor energy consumption, the Nondominated Sorting Genetic algorithm obtains the optimal solution of the model. Combined with the indoor location information of personnel, the indoor heating and cooling system is monitored and distributed to reduce the energy consumption in the building while ensuring the living comfort of personnel. The test and simulation results demonstrate that the mean filtering algorithm can solve the room's fluctuation problem of Wi-Fi signals. The cluster analysis algorithm can eliminate the data noise of the fingerprint database and improve the positioning accuracy of the positioning algorithm. Moreover, the location result is independent of the number of nodes; the number of sampling points does not affect the location result. The positioning accuracy of the WKNN algorithm is 2 m, and the positioning error rate within 2 m is about 60%. Compared with other algorithms, the WKNN algorithm has better positioning accuracy and positioning stability. Therefore, the location algorithm designed here can be applied to indoor location optimization. This study provides a reference for optimizing buildings' indoor positioning and energy consumption.