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.

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.

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.

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.

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.

Modelling of Wideband Concentric Ring Frequency Selective Surface for 5G Devices

Frequency selective surfaces(FSSs)play an important role in wireless systems as these can be used as filters,in isolating the unwanted radiation,in microstrip patch antennas for improving the performance of these antennas and in other 5G applications.The analysis and design of the double concentric ring frequency selective surface(DCRFSS)is presented in this research.In the sub-6 GHz 5G FR1 spectrum,a computational synthesis technique for creating DCRFSS based spatial filters is proposed.The analytical tools presented in this study can be used to gain a better understanding of filtering processes and for constructing the spatial filters.Variation of the loop sizes,angles of incidence,and polarization of the concentric rings are the factors which influence the transmission coefficient as per the thorough investigation performed in this paper.A novel synthesis approach based on mathematical equations that may be used to determine the physical parameters ofDCRFSSbased spatial filters is presented.The proposed synthesis technique is validated by comparing results from high frequency structure simulator(HFSS),Ansys electronic desktop circuit editor,and an experimental setup.Furthermore,the findings acquired from a unit cell are expanded to a 2×2 array,which shows identical performance and therefore proves its stability.

A True-Time-Delay Array Architecture for Wideband Multi-Beam Tracking

The true-time delay(TTD)units are critical for solving beam squint and frequency selective fading inWideband Large-Scale Antenna Systems(LSASs).In this work,we propose a TTD array architecture for wideband multi-beam tracking that eliminates the beam squint phenomenon and filters out interference signals by applying a spatial filter and time delay estimations(TDEs).The paper presents a novel approach to spatial filter design by introducing a transformation matrix that can optimize the beam response in a specific direction and at a specific frequency.Using the variable fractional delay(VFD)filters,we propose a TDE algorithm with a Newton-Raphson iteration update process that corrects the arrival time delay difference between sensors.Simulations and examples have demonstrated that the proposed architecture can achieve beam tracking within 10 ms at the low signalto-noise ratio(SNR)and demodulation loss is less than 0.5 dB in wideband multi-beam scenarios.

A variational successive corrections approach for the sea ice concentration analysis

The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone.A successive corrections analysis using variational optimization method,called spatial multi-scale recursive filter(SMRF),has been designed in this paper to extract multi-scale information resolved by sea ice observations.It is a combination of successive correction methods(SCM)and minimization algorithms,in which various observational scales,from longer to shorter wavelengths,can be extracted successively.As a variational objective analysis scheme,it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time,and also,the specification of parameters is more convenient.Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals.Further,it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration(SIC)experiment with the real observations from Special Sensor Microwave/Imager SIC(SSMI).

Obtaining vehicle parameters from bridge dynamic response:a combined semi-analytical and particle filtering approach

Dynamic load imposed on the bridge by mov- ing vehicle depends on several bridge-vehicle parameters with various uncertainties. In the present paper, particle filter technique based on conditional probability has been used to identify vehicle mass, suspension stiffness, and damping including tyre parameters from simulated bridge accelerations at different locations. A closed-form expres- sion is derived to generate independent response samples for the idealized bridge-vehicle coupled system consider- ing spatially non-homogeneous pavement unevenness. Thereafter, it is interfaced with the iterative process of particle filtering algorithm. The generated response sam- ples are contaminated by adding artificial noise in order to reflect field condition. The mean acceleration time history is utilized in particle filtering technique. The vehicle- imposed dynamic load is reconstructed with the identified parameters and compared with the simulated results. The present identification technique is examined in the presence of different levels of artificial noise with bridge response simulated at different locations. The effect of vehicle velocity, bridge surface roughness, and choice of prior probability density parameters on the efficiency of the method is discussed.

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.

Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

We present a general formulation based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Gray-level images degraded with Gaussian white noise have been considered. Based on the pixel local neighborhood, fuzzy logic has been employed intelligently to avoid unnecessary estimation of a pixel. The intensity estimation of the selected pixels is then carried out by employing punctual kriging in conjunction with the method of Lagrange multipliers and estimates of local semi-variances. Application of such a hybrid technique performing both selection and intensity estimation of a pixel demonstrates substantial improvement in the image quality as compared to the adaptive Wiener filter and existing fuzzykriging approaches. It has been found that these filters achieve noise reduction without loss of structural detail information, as indicated by their higher structure similarity indices, peak signal to noise ratios and the new variogram based quality measures.

Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling

Population spatialization is widely used for spatially downscaling census population data to finer-scale.The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level(AUlevel)and transfer it to generate the gridded population.However,the statistical characteristic of attributes at the pixel-level differs from that at the AU-level,thus leading to prediction bias via the cross-scale modeling(i.e.scale mismatch problem).In addition,integrating multi-source data simply as covariates may underutilize spatial semantics,and lead to incorrect population disaggregation;while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation.To address the scale mismatch in downscaling,this paper proposes a Cross-Scale Feature Construction(CSFC)method.More specifically,by grading pixel-level attributes,we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level.Meanwhile,fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling.Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization.Through the comparison with traditional feature construction method and the ablation experiments,the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps.Furthermore,accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization.

Development of high damage threshold laser-machined apodizers and gain filters for laser applications

We have developed high damage threshold filters to modify the spatial profile of a high energy laser beam. The filters are formed by laser ablation of a transmissive window. The ablation sites constitute scattering centers which can be filtered in a subsequent spatial filter. By creating the filters in dielectric materials, we see an increased laser-induced damage threshold from previous filters created using ‘metal on glass' lithography.

Towed line array sonar platform noise suppression based on spatial matrix filtering technology

The spatial matrix filter was designed and used for solving the problem to detect a weak target who was influenced by the strong nearby platform noise interference of the towed line array sonar. The MFP technology and the DOA estimation technology were combined together by using the sound propagation characteristics of both target and interference. The spatial matrix filter with platform noise zero response constraint was designed by the near-field platform noise normal modes copy vectors and the far-field plane wave bearing vectors together. The optimal solution of the optimization problem for designing the spatial matrix filter was deduced directly, and it was simplified by the generalized singular value decomposition. The total response error to the plane wave bearing vectors and the total response to the platform noise copy vectors were given. The phenomena that strong interferences existed in the bearing course and blind areas existed after filtering were analyzed by the correlation between the plat- form noise copy vectors and the plane wave bearing vectors. It could be found from simulations that it has less blind area and higher detection ability by using the spatial matrix filtering technology.

Radar Sea Clutter Suppression and Target Indication with a Spatial Tracking Filter

It is an important issue to study sea clutter suppression because it could interfere with the detection of targets above the sea surface severely. Spatial spectrum analyses show that the majority of sea clutter has low-frequency characteristics, compared to the high-frequency characteristics of the targets. This paper proposes a frequency-based spatial tracking filter to suppress sea clutter to facilitate targets identification. Experimental results show that the signal-to-clutter ratio can increase by more than 10 dB after filtering and the algorithm is feasible for practical use. In addition, the filtering equation can be optimized to maximize the signal-to-clutter ratio improvement. The equation parameters can also be adjusted to give a proper cut-off frequency for different targets and clutter.

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.

Investigations into sensing characteristics of circular thin-plate electrostatic sensors for gas path monitoring

Circular thin-plate electrostatic sensors are promising in gas path monitoring due to their advantages of non-intrusiveness and easy installation. The spatial sensitivity and filtering effect are two important performance parameters. In this paper, an analytically mathematical model of induced charge on a circular thin-plate electrode is first derived. Then the spatial sensitivity and filtering effect of the circular electrostatic sensor are investigated by numerical calculations. Finally,experimental studies are performed to testify the theoretical results. Both theoretical and experimental results demonstrate that circular thin-plate electrostatic sensors act as a low-pass filter in the spatial frequency domain, and both the spatial filtering effect and the temporal frequency response characteristics depend strongly on the spatial position and velocity of the charged particle. These conclusions can provide guidelines for the optimal design of circular thin-plate electrostatic sensors.

1995-2015年中国老年人口迁移的空间格局与影响因素

Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in China.The full picture of their migration in the entire country over time remains unknown.This study examines the spatial patterns of older interprovincial migration flows and their drivers in China over the period 1995 to 2015,using four waves of census data and intercensal population sample survey data.Results from eigenvector spatial filtering negative binomial regressions indicate that older adults tend to migrate away from low cost-of-living rural areas to high cost-of-living urban and rural areas,moving away from areas with extreme temperature differences.The location of their grandchildren is among the most important attractions.Our findings suggest that family-oriented migration is more common than amenity-led migration among retired Chinese older adults,and the cost-of-living is an indicator of economic opportunities for adult children and the quality of senior care services.