Spatial Filtering Research Articles

Do shifts in the racial and ethnic composition of a neighborhood lead to extreme changes in commuting times? evidence from New York City

From 2000 to 2016, NYC average commuting times increased by a marginal 0.24 s across all transit modes. Depending on one’s viewpoint, this could be a policy success due to no significant increase in commuting times or it could be a policy failure due to no substantial decrease in commuting time over almost two decades. Nevertheless, many census tracts actually experienced extreme increases or decreases in commuting times. The goal of this paper is to examine these extreme changes by focusing on understanding how changes in census tracts', racial and ethnic compositions influence commuting time changes. Analyzing NYC as a case study, we use logistic regressions with eigenvector spatial filtering (ESF) to identify the likelihood of extreme changes in commuting time from 2000 to 2016 given changes in various neighborhood characteristics. Overall, census tracts experiencing increases in Black, Asian, and Hispanic or Latino residents, female-headed households, and workers in professional occupations as well as higher highway densities are likely to have experienced extreme increases in commuting times. Meanwhile, census tracts experiencing increases in White residents, income, rental occupancy, housing burden, and proportions of their census tract within a subway shed are likely to have experienced extreme decreases in commuting times. The results for race and ethnicity support existing research indicating that racial and ethnic minorities are likelier to experience longer commuting times compared to White residents. The importance of our research, though, lies in showing that even when overall commuting times remain relatively stable, racial/ethnic minorities still face commuting disadvantages.

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%.

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%.

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

Extending the spatial extent of steady state broadband noise signals using time reversal

Exciting structures with broadband noise can be used to assess structural health by how it responds to the noise. Here time reversal (TR) is used to focus high-amplitude acoustic energy with the goal to use it to excite a structure. Equalization methods help achieve a desired spectral shape at the location of the TR focus. It is desired to increase the spatial extent of the focusing to excite larger regions. This study explores methods to expand the spatial extent first through the superposition of focusing to multiple locations and second by using a spatial inverse filter. These methods widen the spatial extent of the focusing but can come at the cost of the desired spectrum and/or overall level. The overall goal is to see if TR excitation yields higher amplitude equalized noise than the standard broadcast of it.

Profiling native pulmonary basement membrane stiffness using atomic force microscopy.

Mammalian cells sense and react to the mechanics of their immediate microenvironment. Therefore, the characterization of the biomechanical properties of tissues with high spatial resolution provides valuable insights into a broad variety of developmental, homeostatic and pathological processes within living organisms. The biomechanical properties of the basement membrane (BM), an extracellular matrix (ECM) substructure measuring only ∼100-400 nm across, are, among other things, pivotal to tumor progression and metastasis formation. Although the precise assignment of the Young's modulus E of such a thin ECM substructure especially in between two cell layers is still challenging, biomechanical data of the BM can provide information of eminent diagnostic potential. Here we present a detailed protocol to quantify the elastic modulus of the BM in murine and human lung tissue, which is one of the major organs prone to metastasis. This protocol describes a streamlined workflow to determine the Young's modulus E of the BM between the endothelial and epithelial cell layers shaping the alveolar wall in lung tissues using atomic force microscopy (AFM). Our step-by-step protocol provides instructions for murine and human lung tissue extraction, inflation of these tissues with cryogenic cutting medium, freezing and cryosectioning of the tissue samples, and AFM force-map recording. In addition, it guides the reader through a semi-automatic data analysis procedure to identify the pulmonary BM and extract its Young's modulus E using an in-house tailored user-friendly AFM data analysis software, the Center for Applied Tissue Engineering and Regenerative Medicine processing toolbox, which enables automatic loading of the recorded force maps, conversion of the force versus piezo-extension curves to force versus indentation curves, calculation of Young's moduli and generation of Young's modulus maps, where the pulmonary BM can be identified using a semi-automatic spatial filtering tool. The entire protocol takes 1-2 d.

Wave-based signal-to-noise enhancement and the shape of cochlear filters

Cochlear transfer functions measured at one location—a.k.a. “cochlear filters”—are characterized by a steep roll-off as the frequency increases above the local best (responding) frequency (BF). The functional role of the sharp “cut-off” is not well understood, although it was previously hypothesized that it plays a major role in encoding sound frequency. Less hypothetical in nature, our work elucidates the role of the steep cut-off in improving signal detection in cochlear-like amplification strategies. We study signal amplification in generic active gain media models and physics-based cochlear models as well. We demonstrate that the optimal strategy for boosting the signal-to-noise ratio (SNR) at a given location “x” requires high gain basal to “x,” followed by rapid attenuation (i.e., sharp cut-off) beyond it. This strategy of amplification followed by a sharp cut-off is precisely how the cochlea processes traveling waves of given frequencies; it boils down to a peculiar form of spatial filtering where waves coming from the “signal side” are amplified, while waves coming from the direction where there is noise but no signal are squelched. This noise “squelching” action manifests in the cochlear filters as the steep high-frequency roll-off.

Impact of adaptive filtering-based component analysis method on steady-state visual evoked potential based brain computer interface systems

<span lang="EN-US">The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.</span>

Anti-aliasing method based on rotated spatial filtering

Anti-aliasing method based on rotated spatial filtering

Sub‐Model Aggregation for Scalable Eigenvector Spatial Filtering: Application to Spatially Varying Coefficient Modeling

This study proposes a method for aggregating/synthesizing global and local sub‐models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was used to model spatially varying coefficients and spatial dependence in the residuals by sub‐model, while the generalized product‐of‐experts method was used to aggregate these sub‐models. The major advantages of the proposed method are as follows: (i) it is highly scalable for large samples in terms of accuracy and computational efficiency; (ii) it is easily implemented by estimating sub‐models independently first and aggregating/averaging them thereafter; and (iii) likelihood‐based inference is available because the marginal likelihood is available in closed‐form. The accuracy and computational efficiency of the proposed method are confirmed using Monte Carlo simulation experiments. This method was then applied to residential land price analysis in Japan. The results demonstrate the usefulness of this method for improving the interpretability of spatially varying coefficients. The proposed method is implemented in an R package spmoran.

Laser‐Induced Breakdown Spectroscopy Online Quantitative Analysis for Laser Processing

Plasma fluctuations, the uncertainty of laser ablation, and bremsstrahlung limit the identification of online element analysis during laser processing and cause difficulty in achieving concentration results with sufficient accuracy and repeatability. A laser‐induced breakdown spectroscopy (LIBS) online monitoring system with plasma spatial filtering and spectral screening is proposed to solve this problem. In this system, the high‐frequency ablation noise component of the plasma is eliminated using a specially designed optical Fourier filtering structure, and a spectral screening system based on plasma time waveform monitoring is used to suppress the influence of plasma fluctuations. Without noise filters or algorithm optimizations and based only on the basic internal standard method, the calibration curves of all nine elements in the alloy sample exhibits a Pearson's R2 value ranged from 0.91 to 0.99, with a mean of 0.94. The relative standard deviations are all in the range of 3.5%–8.4% with a mean of 5.4%. The accuracy and repeatability are comparable to those of typical LIBS systems.

Mammogram Images Using Noise Removal of Filtering Techniques

Historically, image denoising methods were proposed for reducing salt and pepper noise in mammography images through enhanced filtering techniques. The Mammogram Image Analysis Society (MIAS) benchmark database for DICOM visuals is used to detect benign and malignant breast cancer. Thus, mean and median filtering techniques, as well as spatial filtering methods, are used to implement medical image analysis with the modalities of low and high radiation for medical images. Similar to these experimental and suggested techniques for removing salt and pepper noise, these statistical techniques assess MSE, SNR, and PSNR data for enhanced quality of signal in the image.

A novel fast roll-off bandpass frequency selective surface for stealth applications

A novel frequency selective surface (FSS)-based spatial bandpass filter for TM-polarized waves is reported. The unit cell of the proposed FSS consists of a dielectric substrate with two conducting layers on opposite sides. One magnetic resonator is placed on the top conducting layer, while two pairs of electric resonators are positioned on the bottom conducting layer to provide a fast roll-off bandpass characteristic. The simulated values of the −3 dB to −10 dB roll-off factor (RF) for the proposed structure are 140 and 280 dB/GHz on the lower and upper sides of its passband, respectively. To validate the simulation results of the proposed structure, a prototype of the FSS-based filter was fabricated and tested experimentally. Furthermore, the present simulation results are benchmarked with the band-pass FSSs reported in the literature, demonstrating an advantage of the proposed filter over the filters mentioned in previous works.

Wide-field Fourier magnetic imaging with electron spins in diamond

Wide-field magnetic imaging based on nitrogen-vacancy (NV) centers in diamond has been shown the applicability in material and biological science. However, the spatial resolution is limited by the optical diffraction limit (>200 nm) due to the optical real-space localization and readout of NV centers. Here, we report the wide-field Fourier magnetic imaging technique to improve spatial resolution beyond the optical diffraction limit while maintaining the large field of view. Our method relies on wide-field pulsed magnetic field gradient encoding of NV spins and Fourier transform under pixel-dependent spatial filters. We have improved spatial resolution by a factor of 20 compared to the optical resolution and demonstrated the wide-field super-resolution magnetic imaging of a gradient magnetic field. This technique paves a way for efficient magnetic imaging of large-scale fine structures at the nanoscale.

A Differential Thermal Lens Spectrometry Method for Trace Detection.

Thermal lens spectroscopy (TLS) is a high-sensitivity method to determine the concentration of light-absorbing species in samples. Here, we implemented a transient configuration of the technique, with a focused pump and a collimated probe beam coaxially propagating. A Fabry-Perot optical resonator is incorporated allowing multi-passing of the probe beam through the sample to enhance sensitivity. We show how the low detection limit of the method can be reduced approximately by half by making differential measurements of the signal at a far field in the center point of the probe beam spot and that obtained by spatial filtering of the same beam, the so-called eclipsed signal. Measurements were performed in test samples of Deyman's organic dye, Strawberry 2143 v.7, dissolved in ethanol. The thermal lens signal measured as a function of the dye concentration in water at the center of the beam was compared with the differential signal resulting from this and the eclipsed beam.

Wavelength-tunable spatiotemporal mode-locking in a large-mode-area Er:ZBLAN fiber laser at 2.8 µm.

We report a tunable spatiotemporally mode-locked large-mode-area Er:ZBLAN fiber laser based on the nonlinear polarization rotation technique. A diffraction grating is introduced to select the operating wavelength. Under the spectral and spatial filtering effects provided by the grating and spatial coupling respectively, stable ps-level spatiotemporally mode-locked pulses around 2.8 µm with a repetition rate of 43.4 MHz are generated. Through a careful adjustment of the grating, a broad wavelength tuning range from 2747 to 2797 nm is realized. To the best of our knowledge, this is the first wavelength-tunable spatiotemporally mode-locked fiber laser in the mid-infrared region.

Rolling Bearing Fault Diagnosis with Distribution Shift Data Using Improved Spatial Distribution Filters and Constraint Feature Extraction

Rolling Bearing Fault Diagnosis with Distribution Shift Data Using Improved Spatial Distribution Filters and Constraint Feature Extraction

Triple band frequency selective surface design for 5G mm‐wave communication with artificial neural networks

AbstractHigh‐performance frequency selective surfaces (FSSs) have gained attention for their spatial filtering characteristics in 5G communication systems. In this work, we propose an efficient and accurate design methodology for the FSS. Three different artificial neural network methods (ANN) are employed, and their performances are compared for analysis and synthesis purposes. Results show that GRNN has the highest performance for both training and test phase of ANN based FSS analysis and synthesis. A novel, compact, low‐profile triple band FSS unit cell is introduced, and the working mechanism is described. By applying ANN based design procedure, the unit cell dimensions to resonate at the 5G mm‐wave frequency band is extracted. A unit cell with the extracted physical dimensions is simulated with a full‐wave analysis tool. The simulation results show that the FSS has the filtering feature at the predetermined mm‐wave frequencies of the 5G communication. The prototype of the FSS is fabricated, as well. The simulations are verified experimentally with measurement results. The results show that proposed ANN based analysis and synthesis method can be an effective tool for the design of FSS band‐pass filter for 5G applications.

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

AbstractContemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial‐temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance‐based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non‐spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision‐makers develop more effective urban development models and reduce future crash risks.