Geography of Talent in China During 2000-2015:An Eigenvector Spatial Filtering Negative Binomial Approach

The increase in China’s skilled labor force has drawn much attention from policymakers,national and international firms and media.Understanding how educated talent locates and re-locates across the country can guide future policy discussions of equality,firm localization and service allocation.Prior studies have tended to adopt a static cross-national approach providing valuable insights into the relative importance of economic and amenity differentials driving the distribution of talent in China.Yet,few adopt longitudinal analysis to examine the temporal dynamics in the stregnth of existing associations.Recently released official statistical data now enables space-time analysis of the geographic distribution of talent and its determinants in China.Using four-year city-level data from national population censuses and 1%population sample surveys conducted every five years between 2000 and 2015,we examine the spatial patterns of talent across Chinese cities and their underpinning drivers evolve over time.Results reveal that the spatial distribution of talent in China is persistently unequal and spatially concentrated between 2000 and 2015.It also shows gradually strengthened and significantly positive spatial autocorrelation in the distribution of talent.An eigenvector spatial filtering negative binomial panel is employed to model the spatial determinants of talent distribution.Results indicate the influences of both economic opportunities and urban amenities,particularly urban public services and greening rate,on the distribution of talent.These results highlight that urban economic-and amenity-related factors have simultaneously driven China’s talent’s settlement patterns over the first fifteen years of the 21st century.

Application of GIS-Based Spatial Filtering Method for Neural Tube Defects Disease Mapping

This study is to assess the prevalence rates spatial pattern of neural tube defects with geographic information system and spatial filtering technique. A total of 80 infants who diagnosed from neural tube defects in the area being studied between 1998 and 2001 were analyzed. Firstly, the geographic information system （GIS） software ArcGIS was used to map the crude prevalence rates. Secondly, the data were smoothed by the method of spatial filtering. We evaluated that the effect of changes in spatial filtering radius size was assessed by creating maps based on various filtering radius sizes. The 3 miles or larger filtering radius gives better section variability than the 2 and 2.5 miles or smaller ones. The maps produced by the spatial filtering technique indicate that prevalence rates in the villages in the southeastern region are to produce higher prevalence than that in the other regions. The smoothed maps based on Heshun County display a more adequate data representation than the raw prevalence rate map.

Improved spatial filtering velocimetry and its application in granular flow measurement

Spatial filtering velocimetry(SFV)has the advantages of simple structure,good stability,and wide applications.However,the traditional linear CCD-based SFV method requires an accurate angle between the direction of linear CCD and the direction of moving object,so it is not suitable for measuring a complex flow field or two-dimensional speed in a granular media.In this paper,a new extension of spatial filtering method(SFM)based on high speed array CCD camera is proposed as simple and effective technique for measuring two-dimensional speed field of granular media.In particular,we analyzed the resolution and range of array CCD-based SFV so that the reader can clarify the application scene of this method.This method has a particular advantage for using orthogonal measurement to avoid the angle measurement,which were problematic when using linear CCD to measure the movement.Finally,the end-wall effects of the granular flow in rotating drum is studied with different experimental conditions by using this improved technique.

Two-dimensional non-spatial filtering based on holographic Bragg gratings

The filter made up of two gratings performs as a two-dimensional non-spatial filtering. This paper reports that the volume Bragg gratings are fabricated by interfering two collimated coherent laser beams in photopolymer. Diffraction efficiency of a single grating is up to 78% in Bragg＇s condition, then a two-dimensional non-spatial filter, which consists of two volume Bragg gratings and a half-wave plate, enables the laser beam filtered in two dimensions with the diffraction efficiency of 54%. The Bragg＇s condition and effect of polarisation on performances of the two-dimension filter are also discussed.

An enhanced method for estimating snow water equivalent in the central part of the Tibetan Plateau using raster segmentation and eigenvector spatial filtering regression model

Snow water equivalent(SWE)is an important factor reflecting the variability of snow.It is important to estimate SWE based on remote sensing data while taking spatial autocorrelation into account.Based on the segmentation method,the relationship between SWE and environmental factors in the central part of the Tibetan Plateau was explored using the eigenvector spatial filtering(ESF)regression model,and the influence of different factors on the SWE was explored.Three sizes of 16×16,24×24 and 32×32 were selected to segment raster datasets into blocks.The eigenvectors of the spatial adjacency matrix of the segmented size were selected to be added into the model as spatial factors,and the ESF regression model was constructed for each block in parallel.Results show that precipitation has a great influence on SWE,while surface temperature and NDVI have little influence.Air temperature,elevation and surface temperature have completely different effects in different areas.Compared with the ordinary least square(OLS)linear regression model,geographically weighted regression(GWR)model,spatial lag model(SLM)and spatial error model(SEM),ESF model can eliminate spatial autocorrelation with the highest accuracy.As the segmentation size increases,the complexity of ESF model increases,but the accuracy is improved.

Robust Spatial Filters on Three-Class Motor Imagery EEG Data Using Independent Component Analysis

Independent Component Analysis (ICA) was often used to separate movement related independent components (MRICs) from Electroencephalogram (EEG) data.?However, to obtain robust spatial filters, complex characteristic features, which were manually selected in most cases, have been commonly used. This study proposed a new simple algorithm to extract MRICs automatically, which just utilized the spatial distribution pattern of ICs. The main goal of this study was to show the relationship between spatial filters performance and designing samples. The EEG data which contain?mixed brain states (preparing, motor imagery and rest) were used to design spatial filters. Meanwhile, the single class data was also used to calculate spatial filters to assess whether the MRICs extracted on different class motor imagery spatial filters are similar. Furthermore, the spatial filters constructed on one subject’s EEG data were applied to extract the others’ MRICs. Finally, the different spatial filters were then applied to single-trial EEG to extract MRICs, and Support Vector Machine (SVM) classifiers were used to discriminate left hand、right-hand and foot imagery movements of BCI Competition IV Dataset 2a, which recorded four motor imagery data of nine subjects. The results suggested that any segment of finite motor imagery EEG samples could be used to design ICA spatial filters, and the extracted MRICs are consistent if the position of electrodes are the same, which confirmed the robustness and practicality of ICA used in the motor imagery Brain Computer Interfaces (MI-BCI) systems.

Improved Wavelet-based Spatial Filter of Damage Imaging Method on Composite Structures

Piezoelectric sensor array-based spatial filter technology is a new promising method presented in research area of structural health monitoring （SHM） in the recent years. To apply this method to composite structures and give the actual position of damage, this paper proposes a spatial filter-based damage imaging method improved by complex Shannon wavelet transform. The basic principle of spatial filter is analyzed first. Then, this paper proposes a method of using complex Shannon wavelet transform to construct analytic signals of time domain signals of PZT sensors array. The analytic signals are synthesized depending on the principle of the spatial filter to give a damage imaging in the form of angle-time. A method of converting the damage imaging to the form of angle-distance is discussed. Finally, an aircraft composite oil tank is adopted to validate the damage imaging method. The validating results show that this method can recognize angle and distance of damage successfully.

Optimal design and verification of temporal and spatial filters using second-order cone programming approach

Temporal filters and spatial filters are widely used in many areas of signal processing. A number of optimal design criteria to these problems are available in the literature. Various computational techniques are also presented to optimize these criteria chosen. There are many drawbacks in these methods. In this paper, we introduce a unified framework for optimal design of temporal and spatial filters. Most of the optimal design problems of FIR filters and beamformers are included in the framework. It is shown that all the design problems can be reformulated as convex optimization form as the second-order cone programming （SOCP） and solved efficiently via the well-established interior point methods. The main advantage of our SOCP approach as compared with earlier approaches is that it can include most of the existing methods as its special cases, which leads to more flexible designs. Furthermore, the SOCP approach can optimize multiple required performance measures, which is the drawback of earlier approaches. The SOCP approach is also developed to optimally design temporal and spatial two-dimensional filter and spatial matrix filter. Numerical results demonstrate the effectiveness of the proposed approach.

A position sensor based on grating projection with spatial filtering and polarization modulation

A position sensor based on grating projection with spatial filtering and polarization modulation is presented. A grating is projected onto the object to be measured through a 4f optical system with a spatial filter. After reflected by the object, the grating projection is imaged on a detection grating through another 4f optical system to form moiré fringes, The polarization modulated moiré signal is detected to obtain the position information of the object. In the position sensor, the moiré signal varies sinusoidally with the position of object. The measurement is independent of the incident intensity on the projection grating and the reflectivity of the object to be measured, In experiments, the effectiveness of the position sensor is proved, and the root mean square （RMS） error at each measurement position is less than 13 nm.

3D image reconstruction with controllable spatial filtering based on correlation of multiple periodic functions in computational integral imaging

We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging. The multiple PDFAs, whose spatial periods correspond to object＇s depths with the elemental image array （EIA）, can generate a set of spatially filtered EIAs for multiple object depths compared with the conventional method for the depth of a single object. We analyze a controllable spatial filtering effect by the proposed method. To show the feasibility of the proposed method, we carry out preliminary experiments for multiple objects and present the results.

Matched field noise suppression: A generalized spatial filtering approach

An approach to improving the performance of matched field localization by introducing a generalized spa-tial filter to suppress interference noise and pass the signal of interest with minimal distortion is presented. The spatial filter is designed by minimizing the maximum distortion of the filtered replica vectors within passband while guaran- teeing the norm of the filter response within the stopband to be lower than some given threshold values. We show that the design problem can be formulated as the second-order cone programming (SOCP) which can be solved efficiently via the well-established interior point method. A modified matched field processor is given to ultimately eliminate the effect of the distortion in the spatially filtered replica vectors. Computer simulation results confirm the effectiveness of the proposed approach by localizing a weak source in the pres-ence of a strong interferer and noise.

Boosting the Information Transfer Rate of an SSVEP-BCI System Using Maximal-Phase-Locking Value and Minimal-Distance Spatial Filter Banks

For Brain-Computer Interface(BCI) systems, improving the Information Transfer Rate(ITR) is a very critical issue. This study focuses on a Steady-State Visually Evoked Potential(SSVEP)-based BCI because of its advantage of high ITR. Unsupervised Canonical Correlation Analysis(CCA)-based method has been widely employed because of its high efficiency and easy implementation. In a recent study, an ensemble-CCA method based on individual training data was proposed and achieved an excellent performance with ITR of 267 bit/min.A 40-target SSVEP-BCI speller was investigated in this study, using an integration of Minimal-Distance(MD) and Maximal-Phase-locking value(MP) approaches. In the MD approach, a spatial filter is developed to minimize the distance between the training data and the reference sine signal, and in this study, two different types of distance were compared. In the MP approach, a spatial filter is developed to maximize the Phase-Locking Value(PLV)between the training calibration data and the reference sine signal. In addition to the fundamental frequency of stimulation, the harmonics were used to train MD and MP spatial filters, which formed spatial filter banks. The test data epoch was multiplied by the MP and MD spatial filter banks, and the distances and PLVs were extracted as features for recognition. Across 12 subjects with a 0.4 s-data length, the proposed method realized an average classification accuracy and ITR of 93% and 307 bit/min, respectively, which is significantly higher than the current state-of-the-art method. To the best of our knowledge, these results suggest that the proposed method has achieved the highest ITR in SSVEP-BCI studies.

Evolution of low-frequency noise passing through a spatial filter in a high power laser system

The theoretical model of spatial noise passing through a spatial filter is established in high power laser system under the small signal approximation. The transmission characteristic for a noise signal passing through spatial filters with different magnifications is analyzed by numerical simulation, according to the actual structure of the high power laser system. The results show that the spatial modulation period of low-frequency noise getting through the pinhole will be proportional to the magnification of the spatial filter. When the magnification is less than 1, the safe low-frequency noise will be extruded into the high-frequency region, which is the fast increasing part, and finally develops into the most dangerous part which can damage the optical devices. The conclusion of this research improves the relay imaging theory of a spatial filter and provides an important theoretical basis for a general design of high power laser systems.

NLoS Mitigation in ToA Localization Based on Spatial Correlation Filter and Iterative Minimum Residual

To mitigate the Non-Line-of-Sight (NLoS) error which seriously affects the localization accuracy and robustness in complex indoor environment,a novel Iterative Minimum Residual (IMR) based on the consistency hypothesis of the residual and the error is proposed in this paper.It chooses the best subset of measurements to calculate the coordinates of the unknown node by comparing the residuals obtained with different subsets of beacons.To reduce the time complexity of the IMR algorithm,Spatial Correlation Filter (SCF) is also proposed,which can remove the most serious NLoS distance with low calculation cost.Combined with the proposed SCF and IMR algorithm,nodes can be localized with high accuracy and low time complexity.Experimental results with real dataset demonstrate that the proposed algorithm can identify the NLoS range effectively with about 50% time cost of employing SCF only.

Application of Spatial Filtering Techniques in the Measurement of the Velocity

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SPATIAL-TEMPORAL JOINT FILTERP ROCESSING APPROACH FOR PHASED ARRAY AIRBORNE EARLY WARNING RADARS

To suppress the ground clutter for airborne early warning (AEW) radars is the key technique in radar signal processing. In this paper, a spatial-temporal nonadaptive joint filter processing approach is proposed to suppress the clutter for AEW radars, which can significantly reduce the computation compared with other optimal or suboptimal methods. The performance of this approach is better than that of the conventional cascaded nonadaptive processing, especially.

Spatial mask filtering algorithm for partial discharge pulse extraction of large generators

A spatial mask filter algorithm (SMF) for partial discharge (PD) pulse extraction is proposed in this paper. In this algorithm, firstly, a ‘Teager’ operator is used to strengthen wavelet coefficient local energy; then direct multiplication of coefficients at two adjacent scales is used to detect singularity points of the signal and to obtain scale based spatial mask filter; finally, an ‘AND’ logic operator is used in different filters to obtain the last spatial mask filter. By multiplication of wavelet coefficients with the final mask filter and wavelet reconstruction process, partial discharge pulses are extracted. The results of digital simulation and practical experiment show that this method is superior to traditional wavelet shrinkage method (TWS). This algorithm not only can increase the signal to noise ratio (SNR), but also can preserve the energy and pulse amplitude.

Improvement of instantaneous positioning by spatial stacking and modified sidereal filtering methods with application to the 2008 Ms8.0 Wenchuan earthquake

We obtained several displacement time series from the Sichuan permanent GPS net and processed the 1-Hz data observed during a few days before the 2008 Ms8.0 Wenchuan earthquake by double-difference instantaneous positioning technique. We filtered the data by the spatial stacking and the modified sidereal filte- ring methods to reduce correlation bias in space and time. The results indicate that these methods can improve the precision significantly.

基于环形阵列的空间滤波器结构损伤扫描监测方法研究

本文提出了一种基于环形阵列的空间滤波器结构损伤扫描监测方法,在结构中央布置环形压电传感器阵列实现对结构的全方位扫描监测,消除了主动Lamb波空间滤波器监测方法中盲区及远场对监测效果的影响。环形阵列由直径方向不同角度的4个线阵组成,每个线阵采用主动Lamb波空间滤波监测方法对结构损伤进行监测、成像,各阵列成像结果通过PCA-小波变换进行融合对损伤位置进行判别,从而实现结构全方位损伤扫描监测,消除单个阵列损伤监测的盲区、远场及虚假成像等影响,进而获得更加准确的损伤监测结果。通过实验研究,验证了该方法的有效性和实用性。

Improved SMB speckle filtering of polarimetric SAR data with synergistic use of orientation angle compensation and spatial majority rule

The scattering-model-based(SMB)speckle filtering for polarimetric SAR(Pol SAR)data is reasonably effective in preserving dominant scattering mechanisms.However,the efficiency strongly depends on the accuracies of both the decomposition and classification of the scattering properties.In addition,a relatively weak speckle reduction particularly in distributed media was reported in the related literatures.In this work,an improved SMB filtering strategy is proposed considering the aforementioned deficiencies.First,the orientation angle compensation is incorporated into the SMB filtering process to remedy the overestimation of the volume scattering contribution in the Freeman-Durden decomposition.In addition,an algorithm to select the homogenous pixels is developed based on the spatial majority rule for adaptive speckle reduction.We demonstrate the superiority of the proposed methods in terms of scattering property preservation and speckle noise reduction using L-band Pol SAR data sets of San Francisco that were acquired by the NASA/JPL airborne SAR(AIRSAR)system.