Spatial Filtering Research Articles

Bearing fault diagnosis based on data missing and feature shift suppression strategy

To mitigate the impact of fault iconic feature shift and feature missing due to missing data values on bearing fault diagnosis, this paper proposes a fault diagnosis method based on a spatial frequency filter and a Multi-Scale feature recombination calibration network (MSRCN). First, the fault features are converted into frequency band features and feature enhancement is realized using Mel filters to weaken the effect of fault feature offset. Then, the spatial calibration module (SC) in the MSRCN is utilized to further improve the fault feature distribution and eliminate the fault feature offset problem. Next, to solve the fault feature missing problem, the remaining fault features are sampled by multi-scale reorganization using MSRCN to obtain new fault features, which overcomes the effect of fault feature missing on fault diagnosis. Finally, experiments are conducted on CWRU and XJTU-SY rolling bearing datasets to verify that the algorithm can effectively solve the fault feature offset and missing problem. Meanwhile, the experimental results prove that the algorithm proposed in this paper can realize high-precision fault diagnosis.

The method for noninvasive investigation of the boundaries of homogeneous magnetizable inclusions inside a biological object

Currently, in modern medicine and related areas of medical technology, the problem of noninvasive investigation of the internal structure of a biological object with the aim of detecting and subsequently visualizing hidden metallic foreign inclusions in the body is becoming increasingly important. Determining the spatial position and identifying the boundaries of such inclusions in the body can be a rather time-consuming process, as solving this problem is mainly done through noninvasive methods of introscopy, which require very careful application. Thus, a method for noninvasive investigation of hidden differently oriented homogeneous inclusions that allow for magnetization by an externally periodically time-varying magnetic field inside a biological object, is proposed in the study. This is achieved by representing the investigated volume as a set of parallel sections and measuring the distribution of the vector function of magnetic field induction in each of them using a working element in the form of spatially oriented contours based on a pair of inductance coils with an equal number of turns and opposing winding inclusion. This allows for non-invasive tomographic scanning of a biological object aimed at detecting the refraction effect of the altered vector field of magnetic field induction, followed by the reconstruction of inclusions based on projection data. The reconstruction is carried out using the back-projection method with prior spatial filtering in the original area through convolution of projection data with a convolution function.

Sequential Markov chain Monte Carlo for Lagrangian data assimilation with applications to unknown data locations

AbstractWe consider a class of high‐dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging, as not only is it high‐dimensional, but the model for the signal yields longer‐range time dependences through the observation locations. Motivated by this model, we revisit a lesser‐known and provably convergent computational methodology from Berzuini et al. (1997, Journal of the American Statistical Association, 92, 1403–1412); Centanniand Minozzo (2006, Journal of the American Statistical Association, 101, 1582–1597); Martin et al. (2013, Annals of the Institute of Statistical Mathematics, 65, 413–437) that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on linear Gaussian state‐space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high‐dimensional rotating shallow‐water model (of about – dimensions) with real and synthetic data. The data are provided by the National Oceanic and Atmospheric Administration (NOAA) and contain observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals , , respectively.

Optimizing the beam quality of high-energy pulse mid-IR Fe:ZnSe laser

The mid-IR Fe:ZnSe laser has gained widespread attention due to its enormous potential with high-energy output. However, applications of Fe:ZnSe laser are limited by the beam quality. To optimize the beam quality of high-energy pulse Fe:ZnSe laser, this study presents a method of using spatial filtering in the resonator. Based on the theory of the ABCD transfer matrix and simulation of resonator stability and beam radius properties, the structure of the resonator was designed. Additionally, we study the output properties of Fe:ZnSe laser with different aperture diameters. We demonstrate when the aperture diameter D = 0.5 mm, the Fe:ZnSe laser output energy is 45.7 mJ and the beam quality is Mx2=2.11, My2=2.29, which is the best beam quality of using a pulse-pumped source to our knowledge. This method will guide for optimizing the beam quality of high-energy pulse mid-IR laser.

Regional comparison of socio-demographic variation in urban E-scooter usage

In recent years we have witnessed explosive growth in the shared, free-floating, electric scooter industry. While still controversial in many North American cities, a number of large e-scooter operators have managed to carve out a piece of the urban transportation landscape. As these vehicles shift from novelty services to increasingly reliable modes of short personal travel, the discussion has turned to investigating who exactly benefits from these micromobility services and who are being left behind. Though population surveys have been administered to identify the socio-demographic characteristics of e-scooter riders in the past, little work has linked these characteristics through trips, or investigated the regional variation in these demographic factors. In this work we explore the variability and similarities in e-scooter rider characteristics across three major U.S. cities. To accomplish this, we apply a Moran’s Eigenvector Spatial Filtering linear regression model and compare our results to more commonly used spatial regression approaches. Our results indicate that the spatial filtering approach outperforms other methods in identifying socio-demographic characteristics of e-scooter users, across multiple regions. We find that many socio-demographics associated with e-scooter usage are regionally variant, despite younger users making up the core user base in all cities. There are variations in usage based on gender, income, and race across cities with Black and Hispanic populations remaining underserved. The implications of these findings are discussed.

Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals.

Objective.Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics of a freely moving human hand using sEMG from extrinsic hand muscles remains a challenge. Deep learning has been recently successfully applied to this problem by mapping raw sEMG signals into kinematics. Nonetheless, the optimal number of EMG signals and the type of pre-processing that would maximize performance have not been investigated yet.Approach.Here, we analyze the impact of these factors on the accuracy in kinematics estimates. For this purpose, we processed monopolar sEMG signals that were originally recorded from 320 electrodes over the forearm muscles of 13 subjects. We used a previously published deep learning method that can map the kinematics of the human hand with real-time resolution.Main results.While myocontrol algorithms essentially use the temporal envelope of the EMG signal as the only EMG feature, we show that our approach requires the full bandwidth of the signal in the temporal domain for accurate estimates. Spatial filtering however, had a smaller impact and low-order spatial filters may be suitable. Moreover, reducing the number of channels by ablation resulted in large performance losses. The highest accuracy was reached with the highest number of available sensors (n = 320). Importantly and unexpected, our results suggest that increasing the number of channels above those used in this study may further enhance the accuracy in predicting the kinematics of the human hand.Significance.We conclude that full bandwidth high-density EMG systems of hundreds of electrodes are needed for accurate kinematic estimates of the human hand.

Experimentally Closing the Balance of Progress of Reaction in Premixed Turbulent Combustion in the Thin Flame Regime

We investigate the possibility of determining the local turbulent flame speed by measuring the individual terms in the balance of a mean progress of reaction variable for the case of a low turbulence methane-air Bunsen flame in the thin flame regime. Velocity distributions and flame edge positions were measured by particle image velocimetry techniques at 3 kHz for a flame stabilized by a surrounding pilot of the same stoichiometry, for a turbulent Reynolds number around 66 and Karlovitz numbers of the order of 4. The conservation equation for mean progress variable was analyzed along different streamlines as a balance of terms expressed as velocities, including terms for convection, turbulent diffusion, mean reaction, and turbulent and molecular diffusion. Each term was estimated from local velocities and flame locations using a thin flame approximation, and their uncertainty was evaluated based on propagation of experimentally measured statistical correlations. The largest terms were the convective and reaction terms, as expected, with smaller roles for turbulent and molecular diffusion across the flame brush. Countergradient diffusion and transition to gradient diffusion were observed across the flame brush. Closure of the balance of terms in the conservation equations using independently measured terms was not consistently achieved across the flame brush within the reckoned uncertainties, arriving at a balance within 20–30% of the absolute value. Testable hypotheses are offered for the possible reasons for the mismatch, including the role of spatial filtering and 3D effects on the reaction rate term. Finally, the experiments identify the inaccuracies in measuring a true local turbulent flame speed, and suggest a consistent methodology to reduce errors in such estimations. This is the first time such a detailed experimental closure is attempted for any configuration. The results suggest that the significant improvements in spatial resolution are necessary for a full closure.

Controllable Spatial Filtering Method in Lensless Imaging

We propose a method for multiple-depth extraction in diffraction grating imaging. A diffraction grating can optically generate a diffraction image array (DIA) having parallax information about a three-dimensional (3D) object. The optically generated DIA has the characteristic of forming images periodically, and the period depends on the depth of the object, the wavelength of the light source, and the grating period of the diffraction grating. The depth image can be extracted through the convolution of the DIA and the periodic delta function array. Among the methods for extracting depth images through the convolution characteristics of a parallax image array (PIA) and delta function array, an advanced spatial filtering method for the controllable extract of multiple depths (CEMD) has been studied as one of the reconstruction methods. And that possibility was confirmed through a lens-array-based computational simulation. In this paper, we aim to perform multiple-depth extraction by applying the CEMD method to a DIA obtained optically through a diffraction grating. To demonstrate the application of the CEMD in diffraction grating imaging, a theoretical analysis is performed to apply the CEMD in diffraction grating imaging; the DIA is acquired optically, and the spatial filtering process is performed through computational methods and then compared with the conventional single-depth extraction method in diffraction grating imaging. The application of the CEMD to DIA enables the simultaneous reconstruction of images corresponding to multiple depths through a single spatial filtering process. To the best of our knowledge, this is the first research on the extraction of multiple-depth images in diffraction grating imaging.

Improving Digital Satellite Image for security purposes

Satellite imagery is employed in many different fields of study. These pictures have serious quality problems. Image enhancement algorithms, however, can improve it in terms of contrast, brightness, feature elimination from noise contents, etc. These algorithms present and analyse the picture's properties by sharpening, focusing, or smoothing the image. Therefore, the specific application determines the goal of picture enhancement. This paper briefly overviews picture enhancement methods that produce optimal and progressive outcomes for satellite images used for secured remote sensing. To do this, various image enhancement techniques are used, which are widely used today to improve image quality across various image processing applications. Some commonly used image enhancement techniques include spatial filtering, contrast stretching, and histogram equalisation. These techniques aim to enhance the visual quality of satellite images by adjusting brightness and contrast and reducing noise. These methods can also improve the interpretability of the images for remote sensing purposes. The enhancement of satellite images finds use in several fields, particularly security. It is essential for security applications, including threat detection, border control, and surveillance. Security professionals may more effectively analyse and understand data to spot any dangers or questionable activity by boosting the visual details and general quality. Keywords: Satellite image analysis; mean filter; secured application; SVM; wavelet transformation

Coherent Imaging with Photonic Lanterns

Photonic lanterns (PLs) are tapered waveguides that gradually transition from a multimode fiber geometry to a bundle of single-mode fibers (SMFs). They can efficiently couple multimode telescope light into a multimode fiber entrance at the focal plane and convert it into multiple single-mode beams. Thus, each SMF samples its unique mode (lantern principal mode) of the telescope light in the pupil, analogous to subapertures in aperture masking interferometry (AMI). Coherent imaging with PLs can be enabled by the interference of SMF outputs and applying phase modulation, which can be achieved using a photonic chip beam combiner at the backend (e.g., the ABCD beam combiner). In this study, we investigate the potential of coherent imaging by the interference of SMF outputs of a PL with a single telescope. We demonstrate that the visibilities that can be measured from a PL are mutual intensities incident on the pupil weighted by the cross correlation of a pair of lantern modes. From numerically simulated lantern principal modes of a 6-port PL, we find that interferometric observables using a PL behave similarly to separated-aperture visibilities for simple models on small angular scales (<λ/D) but with greater sensitivity to symmetries and capability to break phase angle degeneracies. Furthermore, we present simulated observations with wave front errors (WFEs) and compare them to AMI. Despite the redundancy caused by extended lantern principal modes, spatial filtering offers stability to WFEs. Our simulated observations suggest that PLs may offer significant benefits in the photon-noise-limited regime and in resolving small angular scales at the low-contrast regime.

Calculation position of the focal point in self-focusing media

Using a laser beam in a self-focusing environment with different power more and less than the critical one, more accurate coordinates of the point with the maximum radiation intensity were obtained. The results of the calculations corresponded to the experimental data obtained by spatial filtering of the angular spectra of forced Raman scattering. The agreement of the results was satisfactory for all beam power levels.

Multi-Algorithm Artifact Correction (MAAC) procedure part one: Algorithm and example

The Multi-Algorithm Artifact Correction (MAAC) procedure is presented for electroencephalographic (EEG) data, as made freely available in the open-source EP Toolkit (Dien, 2010). First the major EEG artifact correction methods (regression, spatial filters, principal components analysis, and independent components analysis) are reviewed. Contrary to the dominant approach of picking one method that is thought to be most effective, this review concludes that none are globally superior, but rather each has strengths and weaknesses. Then each of the major artifact types are reviewed (Blink, Corneo-Retinal Dipole, Saccadic Spike Potential, and Movement). For each one, it is proposed that one of the major correction methods is best matched to address it, resulting in the MAAC procedure. The MAAC itself is then presented, as implemented in the EP Toolkit, in order to provide a sense of the user experience. The primary goal of this present paper is to make the conceptual argument for the MAAC approach.

STUDY OF POISSON NOISE REDUCTION ON GAMMA CAMERA IMAGE USING SPATIAL DOMAIN FILTER

A gamma camera image is produced by a gamma camera that detects the gamma radiation emitted by the radioactive substance or radiopharmaceutical injected into the body. The gamma camera image sometimes has noise that can interfere with the diagnosis. This image is commonly affected by a Poisson-type random noise. This research proposes using a spatial domain filter to study Poisson noise reduction in gamma camera images. The image sample used is the image of a mouse injected with Lu-177-DOTA Trastuzumab with 100 µCi activity detected using a dual-head gamma camera with NaI(Tl) detectors. The grayscale image is treated with Poisson noise, then improved using a spatial domain filter. The spatial domain filters used include Mean, Median, Wiener, and Spatial Lowpass Filters. The mean filter is the best one that can reduce Poisson noise among the four applied filters. The best filter size for noise reduction is 3 with MSE 5.07, PSNR 41.08 dB, and SSIM 0.99.

Research on Fiber-Optic Optical Coherence Ranging System Based on Laser Frequency Scanning Interferometry.

In this paper, a system for absolute distance measurement is proposed based on laser frequency scanning interferometry (FSI). The system utilizes a digitally tunable laser as the light source and employs synchronized pulses to drive an analog-to-digital converter (ADC) for interference signal acquisition. The frequency domain demodulation for absolute distance measurement is achieved through a three-spectrum line interpolation method based on the Hanning window. The system takes advantage of the spatial filtering characteristics of a single-mode optical fiber and the diffuse reflection properties of light to achieve a high integration of the prism system that forms the interference optical path. The resulting integrated fiber-optic probe is capable of measuring the distance to a non-cooperative target even when oriented at a certain angle with the target. We designed and fabricated a portable prototype. Experimental validation demonstrated that the maximum measurement distance of the system is 73.51 mm with a standard deviation of less than 0.19 μm for optimal measurement results. Even when there is an offset angle, the system maintains good measurement repeatability.

Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation

EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.

Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals

Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8–19 Hz and 19–30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, and Kappa index of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.

A thin-walled cavity structure with double-layer tapered scatterer locally resonant metamaterial plates for extreme low-frequency attenuation

Locally resonant acoustic metamaterials (LRAMs) are effective spatial frequency filters due to their local resonance system. However, they own narrow stopbands, charge additional weight on the primary system, and operate only at the adjusted frequency range. In this paper, a novel dual-target LRAM is proposed based on coupling the cavity and convex mechanisms, utilizing the benefits of both sound-barrier and sound-absorbing types of acoustic metamaterials. A combination of the cavity and convex structures is presented and investigated for the first time, which exploits both the reflection and absorption theories simultaneously to improve the sound attenuation performance of acoustic metamaterials. By arranging two-layer finite periodic of 5×5 proposed convex unit cells along x and y directions and separating them by an air cavity, a supercell named hybrid locally resonant acoustic metamaterials (HLRAM) baffle is designed. The band structures, transmission spectrum, and displacement vector fields are calculated employing the finite element method (FEM). In addition, vibration modes at the edges of stopbands are computed and carefully analyzed to determine the formation mechanism, mechanics/dynamic response, and dispersion features of stopbands. Meanwhile, sensitivity analyses have been conducted on the model to investigate the influence of material and geometry parameters on dispersion characteristics. Equivalent spring-mass analytic models are used to direct the modifications of structure and the adjustment of structural and material parameters in order to achieve a wide stopband with a low opening frequency. Thereby, the effective stiffness and mass values influencing the starting and cutoff frequencies are adjusted in a desirable manner. The results show that the structure can generate a wide stopband that covers the frequency range of 64.9-805.6Hz, allowing the structure to block incoming acoustic waves with 35.42dB root mean square of noise attenuation (RMSNA) the given frequency range (0-1kHz). This study indicates that the HLRAM has a preponderance among conventional techniques as we can manipulate dispersion characteristics and the sound transmission loss (STL) based on the desired sound reduction level and shift stopbands to the intended frequency range. This can be achieved by altering cavity size, material, or geometry parameters.

The WIPI Model Based on Multi-Scale Local Contrast Post-Processing for Infrared Small Target Detection

According to the infrared patch image (IPI) model theory, the infrared image background has a low rank and the target is sparse. The low-rank model can be used to separate the background and identify the target. However, in a noisy environment, the recognition effect will be affected. The higher the noise, the harder it would be to detect a small target. The residual strong fault and background edges could reduce the detection rate and increase false alarms. The traditional IPI model is adaptable to the background with the lower noise. This paper combines weighted nuclear norm minimization (WNNM) optimization with sparse representation based on the local IPI model. The background details are described more prominently by improving the nuclear norm weighting factor. The target is much easier to detect under the specific bright clouds and ground buildings background with high noise. At the same time, post-processing with image local contrast analysis is performed to compare traditional spatial filtering and local infrared patch image model algorithms. Our method has a good suppression effect on complex noise backgrounds and achieves a higher signal to clutter ratio gain (SCRG). It could also improve the target detection rate and reduce false alarms.

Gradient-metasurface directional photodetectors.

Angle-sensitive photodetectors are a promising device technology for many advanced imaging functionalities, including lensless compound-eye vision, lightfield sensing, optical spatial filtering, and phase imaging. Here we demonstrate the use of plasmonic gradient metasurfaces to tailor the angular response of generic planar photodetectors. The resulting devices rely on the phase-matched coupling of light incident at select geometrically tunable angles into guided plasmonic modes, which are then scattered and absorbed in the underlying photodetector active layer. This approach naturally introduces sharp peaks in the angular response, with smaller footprint and reduced guided-mode radiative losses (and therefore improved spatial resolution and sensitivity) compared to analogous devices based on diffractive coupling. More broadly, these results highlight a promising new application space of flat optics, where gradient metasurfaces are integrated within image sensors to enable unconventional capabilities with enhanced system miniaturization and design flexibility.