Spatial Filtering Method Research Articles

Enhancing detection of SSVEPs using discriminant compacted network.

This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning. Specifically, this study enhanced SSVEP features using Global template alignment (GTA) and Discriminant Spatial Pattern (DSP), and then designed a Compacted Temporal-Spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset. The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, deep learning methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset.The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively. This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI 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 Least-Square Unified Framework for Spatial Filtering in SSVEP-Based BCIs.

The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS) unified framework to summarize the correlation analysis (CA)-based SSVEP spatial filtering methods from a machine learning perspective. Within this framework, the commonalities and differences between various spatial filtering methods appear apparent, the interpretation of computational factors becomes intuitive, and spatial filters can be determined by solving a generalized optimization problem with non-linear and regularization items. Moreover, the proposed LS framework provides the foundation of utilizing the knowledge behind these spatial filtering methods in further classification/regression model designs. Through a comparative analysis of existing representative spatial filtering methods, recommendations are made for the superior and robust design strategies. These recommended strategies are further integrated to fill the research gaps and demonstrate the ability of the proposed LS framework to promote algorithmic improvements, resulting in five new spatial filtering methods. This study could offer significant insights in understanding the relationships between various design strategies in the spatial filtering methods from the machine learning perspective, and would also contribute to the development of the SSVEP recognition methods with high performance.

Evaluation of electrical activity at the carpal tunnel area in response to median nerve elbow stimulation using magnetoneurography

ObjectiveTo develop a magnetic field measurement method in the carpal tunnel area in response to electrical median nerve stimulation at the elbow and evaluate its usefulness. MethodsFive healthy subjects participated. The magnetic field recording evoked by median nerve electrical stimulation at the elbow with electrical intensity that minimized the pronator quadratus and flexor carpi radialis muscle response was measured with 132-channel superconducting quantum interference device magnetoneurography. The current distributions were reconstructed from magnetic fields with the spatial filter method. The magnetic fields evoked by electrical stimulation at the index distal interphalangeal joint were also measured. ResultsAfter median nerve elbow stimulation, intra-axonal current and inward currents flowing perpendicularly to the nerve pathway were visualized. The mean ± SD inward current intensity at the center of the carpal tunnel area was significantly larger with elbow stimulation than with index distal interphalangeal joint stimulation: 1.11 ± 0.65 nAm versus 0.51 ± 0.15 nAm (p = 0.047). ConclusionsMagnetic field measurement in the carpal tunnel area with adjusted submaximal stimulation at the elbow can detect larger signals compared with digit stimulation. SignificanceThis method has the potential to be more useful for the examination of carpal tunnel syndrome patients.

An optimal spatial-filtering method derived from eigenvalue perturbation for extending the Courant-Friedrichs-Lewy stability limit

Explicit time-marching schemes are widely used in numerical simulations. However, their maximum time step is constrained by the Courant-Friedrichs-Lewy (CFL) stability limit. Two methods have been developed to extend this limit: (1) the eigenvalue perturbation method, which exhibits high numerical accuracy but incurs unaffordable memory demand and computational costs, even for middle-scale models, and (2) the spatial-filtering method, which can be implemented easily but results in significant numerical errors under large time steps. However, the intrinsic relationship between these two methods remains unknown. We reveal the intrinsic relationship between these two methods by considering the eigenvalue perturbation method for a homogeneous model. Using this relationship, we derive an analytical spatial filter for the homogeneous model and develop an optimal spatial filter for heterogeneous models. Compared to the classical spatial-filtering method, which removes all high-wavenumber components, our method retains all high-wavenumber components that contribute to wave propagation while eliminating other high-wavenumber components that cause instability. For the same numerical accuracy, the maximum time step allowed by the proposed method is approximately twice that of the classical spatial-filtering method. Compared to the eigenvalue perturbation method, our method can be used in large-scale models without additional memory consumption and computational cost. This can significantly accelerate the pseudospectral method with significantly larger time steps beyond the CFL stability limit, which is particularly promising for large-scale models with a fine grid.

Strain fields measurement using frequency domain Savitzky–Golay filters in digital image correlation

Savitzky–Golay (SG) filter is an effective and convenient gradient calculation method employed for full-field strain measurement in digital image correlation (DIC). Currently, the strain field can be conveniently obtained by moving smoothing filtering with SG filters. This is a method of spatial filtering, which offers the advantages of easy implementation and high accuracy. This study proposed a strain calculation method based on frequency domain SG (FD-SG) filtering. Prior to performing FD-SG filtering, data extension operations involving outer-boundary padding and zero-padding were performed on the displacement field data. Similarly, the SG filters template was extended with zero-padding and circularly shift operations. Subsequently, FD-SG filters were generated by applying the fast Fourier transform (FFT) to the expanded SG template. Thereafter, FD-SG filtering was implemented through the multiplication of the displacement in the Fourier-domain by an FD-SG filter. Finally, the strain field was obtained via inverse FFT and valid data interception operation. The simulation and practical experiments confirmed the equivalent accuracy exhibited by FD-SG and spatial domain SG filtering. Thus, the proposed FD-SG filtering method has great potential for real-time strain measurement in DIC.

Correction performance uniformity on ground-layer adaptive optics

ABSTRACTGround-layer adaptive optics (GLAO) is generally used to improve the seeing for large-aperture ground-based optical telescopes across a wide field of view with uniform imaging quality. In this paper, a new criterion, which is defined as the weighted sum of the mean and mean-square-error values of the full width at half-maximum of the field-dependent point spread function of the image over the whole field of view, is proposed to evaluate GLAO performance uniformity. An analytic model of the GLAO-corrected point spread function based on spatial filtering methods is used to investigate the uniformity and image quality over the whole field of view for different guide-star layouts, telescope apertures, numbers of actuators for the deformable mirror, and turbulence profiles. Simulations are performed on the GLAO configurations for the 1-m New Vacuum Solar Telescope at Fuxian Solar Observatory. The results show that, compared with that of the classical evaluation, the correction uniformity improves by 63 per cent, with the resolution sacrificing only 5 per cent for a measured turbulence profile at Fuxian Solar Observatory. Furthermore, this criterion is applied to various requirements, and some critical system parameters are suggested for the wide field-of-view GLAO.

Improvement in magnetic nanoparticle tomography estimation accuracy by combining sLORETA and non-negative least squares methods

Imaging methods that can detect biofunctionalized magnetic nanoparticles (MNPs) accumulated at cancerous tumor sites are expected to be part of the in vivo cancer diagnostic techniques. An imaging technique called magnetic nanoparticle tomography (MNT), which uses a magnetic sensor array, has been proposed. High sensitivity and spatial resolution were achieved using the non-negative least-squares (NNLS) inverse solution in MNT. However, owing to the presence of measurement noise, the concentration and position of certain MNPs were estimated inaccurately, i.e., artifacts were generated. To overcome this issue, this study first applied standardized low-resolution brain electromagnetic tomography (sLORETA), a spatial filter method with no location bias, to approximate the position of MNPs. The region of analysis was restricted to where the estimated value exceeded the threshold. Subsequently, the NNLS method was applied to estimate the concentration and position of MNPs in the restricted region. In the experiment, two Resovist MNP samples (300 or 500 μgFe) were arranged at a distance of 25–502 mm, and the concentration and position estimation were performed. The estimation results demonstrated that the proposed method successfully suppresses artifacts and adequately estimates the concentration and position of MNPs within a position error of 10 mm and a concentration error of 20 %.

Separation of coherent and incoherent light by using optical vortex via spatial mode projection

We present a spatial light filter method which can distinguish light by its coherency. The unique orbital angular momentum and spiral phase front properties of the vortex beam generate a null intensity center area that can serve as a spatial window to filter out incoherent light. A spatial light modulator can be used to modulate the wavefront of the incident light beam. Thanks to diffraction, the spectra from the coherent and incoherent part are spatially separated at the backfocal plane of a lens. The coherent part forms a ring, and the incoherent part forms a spot in the null intensity center of the ring. This method offers a new perspective to enhance the signal-to-noise ratio in the field of applied optics, such as optical communication, and active remote sensing, such as light detection and ranging.

Effects of injection pressure on the flame front growth in an optical direct injection spark ignition engine

• Optical diagnostics performed at wide open throttle and 1200 rpm conditions. • Elevated injection pressure does not monotonically increase engine output. • Higher spatial variation in turbulence measured for very high injection pressure. • Local overmixing and increased mixture inhomogeneity are likely causes. • Despite decreased power, soot is still lower at very high injection pressure. The fuel injection pressure of direct injection spark ignition (DISI) petrol engines has increased significantly to improve engine efficiency and reduce air-polluting emissions. The present study performs a detailed analysis of spray penetration, burn duration, flame front vectors and turbulence intensity to provide scientific explanations about the optimal injection pressure observed for the maximum power output/efficiency in a single-cylinder optically accessible DISI engine. Five injection pressures of 5, 10, 15, 20 and 25 MPa were tested based on an equal-split double injection strategy using a side-mounted six-hole direct injector. A high-speed camera with a 20 kHz frame rate was used to record spray development, propagating petrol flame and soot luminosity. The Mie-scattering technique was used to image the fuel dispersion under varied injection pressure and to calculate spray penetration. For time-resolved, two-dimensional flame front vector extraction, a newly developed flame image velocimetry (FIV) was applied to high-speed natural combustion luminosity movies. A spatial filtering method was also used for the flow decomposition to acquire flame front turbulence intensity. A total of 100 combustion cycles were FIV processed for each test pressure to tackle the inherent cyclic variations. The spray images showed that higher fuel injection pressure leads to faster spray tip penetration and enhanced spray evaporation for better air/fuel mixing. However, the in-cylinder pressure and power output do not continue to increase with an increase in injection pressure; they peak at 15 MPa and decrease at higher injection pressure. The spatially averaged flame front vector magnitude and turbulence intensity also peak at 15 MPa, indicating the optimal injection pressure was due to the maximum turbulent flame front growth. The distribution of turbulence intensity along the flame boundary shows increased variations at injection pressure higher than 15 MPa, because local over-mixing caused low turbulent flame growth regions. Although the highest turbulent flame front growth was not measured at 25 MPa, the soot luminosity was much lower than that of 15 MPa, which explains the development trend towards higher injection pressure.

Accurate extraction of the +1 term spectrum with spurious spectrum elimination in off-axis digital holography.

In off-axis digital holography, spatial filtering is a key problem limiting the quality of reconstructed image, especially in the case of spurious spectrum generated by coherent noise in the hologram spectrum. In this paper, a new spatial filtering method with spurious spectrum elimination is proposed. Side band centering judgment is firstly implemented to locate the center point of the +1 term in the hologram spectrum. Then by roughly recognizing the region of +1 term spectrum, most of the -1 term, 0 term and the spurious spectral components are eliminated. Finally, Butterworth filtering is performed to extract the +1 term spectrum as enough as possible without introducing the spurious spectrum. Simulated hologram of E-shaped specimen with the spurious spectrum is generated to evaluate the performance of the proposed method. Experimental data of USAF 1951 resolution target, ovarian slice and microlens array are adopted to verify the effectiveness of the proposed method. Simulation and experimental results demonstrated that the proposed method is able to accurately extract the +1 term spectrum with spurious spectrum elimination and achieve a relatively good balance between the structural detail characterization and noise suppression.

In-Flame And In-Cylinder Flow/Turbulence Measurements Near The Glow Plug Using Flame Image Velocimetry And Particle Image Velocimetry In An Optical Compression-Ignition Engine

The present study implements two diagnostic methods based on tracking of seeded olive oil droplets (PIV: particle image velocimetry) and pattern changes detected in high-speed flame movies (FIV: flame image velocimetry) in a small-bore optical diesel engine. For each measurement, a total of 100 engine cycles are recorded and processed to address the inherent cyclic variations. The ensemble-averaged flow fields and turbulence intensity distribution extracted from individual cycles via the spatial filtering method are discussed with a particular interest in the influence of glow plug on flow and turbulence, i.e. rigid body and fluid interaction. The PIV results show a swirl flow structure forms and rotates with its centre shifted towards the exhaust side, leading to an asymmetric swirl structure. By comparing a PIV laser plane tilted towards the glow plug and a 10 mm horizontal plane below the cylinder head with no glow flow-plug interaction, it is observed that the flow-plug interaction causes the flow winding around the plug tip to generate complex flow structures and new vortices downstream of the plug. The tilted plane and 10-mm plane show similar bulk flow magnitude distribution patterns; however, the flow-plug interaction generates high turbulence in the tilted plane right downstream of the plug tip where new vortices form, which lasts for a few crank angles. The spatially averaged flow magnitude and turbulence intensity are measured higher in the 10-mm plane where there is no flow-plug interaction, suggesting the increased turbulence is a localised behaviour. The flame-plug interaction is also investigated during the combustion event using the FIV method. The level of flame-plug interaction is adjusted by changing the inter-jet spacing angle of two nozzle holes with one case showing high interaction and the other displaying low interaction. From the FIV measurements, the most significant effect of the flame-plug interaction is observed as the further penetration of the wall bounced flame for the high interaction case. This is due to the glow plug as a rigid body blocking the swirl flow and promoting the flame penetration back towards the centre of the combustion chamber upon the piston-bowl wall impingement. The measured turbulence intensity is also higher thanks to the enhanced wall bounced flame in addition to more significant flame-plug interaction at the interface.

Revisiting sea-level budget by considering all potential impact factors for global mean sea-level change estimation

Accurate estimates of global sea-level change from the observations of Altimetry, Argo and Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) are of great value for investigating the global sea-level budget. In this study, we analyzed the global sea-level change over the period from January 2005 to December 2019 by considering all potential impact factors, i.e. three factors for Altimetry observations (two Altimetry products, ocean bottom deformation (OBD) and glacial isostatic adjustment (GIA)), three factors for Argo observations (four Argo products, salinity product error and deep-ocean steric sea-level change), and seven factors for GRACE/GRACE-FO observations including three official RL06 solutions, five spatial filtering methods, three GIA models, two C20 (degree 2 order 0) products, Geocenter motion, GAD field and global mass conservation. The seven impact factors of GRACE/GRACE-FO observations lead to ninety combinations for the post-procession of global mean barystatic sea-level change estimation, whose rates range from 2.00 to 2.45 mm/year. The total uncertainty of global barystatic sea-level change rate is ± 0.27 mm/year at the 95% confidence level, estimated as the standard deviation of the differences between the different datasets constituting the ensembles. The statistical results show that the preferred GIA model developed by Caron et al. in 2018 can improve the closure of the global sea-level budget by 0.20–0.30 mm/year, which is comparable with that of neglecting the halosteric component. About 30.8% of total combinations (GRACE/GRACE-FO plus Argo) can close the global sea-level budget within 1-sigma (0.23 mm/year) of Altimetry observations, 88.9% within 2-sigma. Once the adopted factors including GRACE/GRACE-FO solutions from Center for Space Research (CSR), Caron18 GIA model, SWENSON filtering and Argo product from China Second Institute of Oceanography, the linear trend of global sterodynamic sea-level change derived from GRACE/GRACE-FO plus Argo observations is 3.85 ± 0.14 mm/year, nearly closed to 3.90 ± 0.23 mm/year of Altimetry observations.

Regularised task‐related component analysis for robust SSVEP‐based brain‐computer interface

Recent advancements in steady-state visual evoked potential-based brain-computer interface have been made possible by the widespread use of various spatial filtering methods. Task-related component analysis has superiority over existing subject-specific target frequency recognition methods. However, the optimised spatial filters of task-related component analysis generated from a small training dataset are susceptible to artefacts and noise, which can be overfitted, particularly in short time windows. To tackle this issue, the authors propose a regularised task-related component analysis that adopts three regularisation approaches to the objective function of task-related component analysis. Conventionally, the regularisation method is a simple and efficient way to overcome the overfitting problem, especially for a small training dataset. To this end, the proposed regularised task-related component analyses outperform the conventional task-related component analysis in terms of average classification accuracy and information transfer rate.

Incorporating spatial information in machine learning: The Moran eigenvector spatial filter approach

AbstractSpatial statistical models are highly effective for modeling geospatial data as they consider spatial information of geographic spaces and other non‐spatial covariates, enabling them to minimize spatial autocorrelation by addressing spatial dependence. In contrast, machine learning (ML) models are highly effective for predicting non‐spatial data, but they are not as effective for modeling and predicting geospatial data because of spatial autocorrelation issues. One of the frequently reported limitations of ML models for geospatial data modeling is that there is no standard method of incorporating spatial information of geographic space into the model, and consequently they cannot minimize spatial autocorrelation. In this study, we have presented a local spatial information‐embedded ML method capable of minimizing spatial autocorrelation by addressing spatial dependence while predicting a geospatial phenomenon. Our study applied the eigenvector spatial filter method to extract approximated eigenvectors from spatial coordinates and embed them within ML models as a set of vectors along with the selected non‐spatial covariates. We have also presented a comparison of relative prediction performance between traditional spatial statistical and ML‐based models. The experiment demonstrates that incorporating spatially filtered eigenvectors to represent spatial information in ML model specification significantly improves the prediction performance.

Incorporating Neighboring Stimuli Data for Enhanced SSVEP-Based BCIs

Various spatial filters have been proposed to enhance the target identification performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The current methods only extract the target-related information from the corresponding stimulus to learn the spatial filter parameter. However, the SSVEP data from neighboring stimuli also contain frequency information of the target stimulus, which could be utilized to further improve the target identification performance. In this paper, we propose a new method incorporating SSVEPs from the neighboring stimuli to strengthen the target-related frequency information. First, The spatial filter is obtained by maximizing the summation of covariances of SSVEP data corresponding to the target and its neighboring stimuli. Then the correlation features between spatially filtered templates and test data are calculated for target detection. For the performance evaluation, we implemented the offline experiment using the 40-class benchmark dataset from 35 subjects and the 12-target self-collected dataset from 11 subjects. Compared with the state-of-art spatial filtering methods, the proposed method showed superiority in classification accuracy and information transfer rate (ITR). The comparison results demonstrate the effectiveness of the proposed spatial filter for target identification in SSVEP-based BCIs.

Latency Aligning Task-Related Component Analysis Using Wave Propagation for Enhancing SSVEP-Based BCIs.

Due to the high robustness to artifacts, steady-state visual evoked potential (SSVEP) has been widely applied to construct high-speed brain-computer interfaces (BCIs). Thus far, many spatial filtering methods have been proposed to enhance the target identification performance for SSVEP-based BCIs, and task-related component analysis (TRCA) is among the most effective ones. In this paper, we further extend TRCA and propose a new method called Latency Aligning TRCA (LA-TRCA), which aligns visual latencies on channels to obtain accurate phase information from task-related signals. Based on the SSVEP wave propagation theory, SSVEP spreads from posterior occipital areas over the cortex with a fixed phase velocity. Via estimation of the phase velocity using phase shifts of channels, the visual latencies on different channels can be determined for inter-channel alignment. TRCA is then applied to aligned data epochs for target recognition. For the validation purpose, the classification performance comparison between the proposed LA-TRCA and TRCA-based expansions were performed on two different SSVEP datasets. The experimental results illustrated that the proposed LA-TRCA method outperformed the other TRCA-based expansions, which thus demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.

Study on organic molecular vibrational dynamics by TR-box-CARS spectroscopy

This paper reported a comparative study of molecular vibrational dynamics in different organic molecules by femtosecond time-resolved folded box-coherent anti-Stokes Raman scattering (TR-box-CARS) spectroscopy. As model system methanol and ethanol molecules are chosen, the coherence transfer between stretching vibration modes of C-H bonds in them are excited and detected at the same time. Different vibration periods and wavenumber spacing are found for the amplitudes of the coherent vibrations of C-H stretch modes in methanol and ethanol molecules. This study makes femtosecond TR-box-CARS possible as a prospective technique for detection and identification of organic molecular species in ambient environments. A simple spatial magic filter and frequency domain filter method with folded TR-box-CARS approach of suppressing the non-resonant FWM background is demonstrated.

Tunable GaAs metasurfaces for ultrafast image processing

The design and construction of optical semiconductor metasurfaces for various applications have become an important topic in the last decade. However, most metasurfaces are static; they are optimized for only one exact purpose and typically realize only one operation. In this work, we discuss the basic methods for creating dynamic metasurfaces giving special attention to ultrafast optical switching and provide numerical modeling of metasurfaces made of GaAs material realizing different amplitude-phase profiles under asymmetrical optical pumping. The metasurfaces are composed of semiconductor discs immersed in a fused silica medium. We demonstrate that based on Fourier transform and spatial filtering methods, these structures can be used for image processing and optical computing. Ultrafast switching is achieved by using an optical pump-probe scheme. The characteristic relaxation times between the pumped state and the relaxed state are on the order of several picoseconds.

The Application of Flame Image Velocimetry to After-injection Effects on Flow Fields in a Small-Bore Diesel Engine

This study implements Flame Image Velocimetry (FIV), a diagnostic technique based on post-processing of high-speed soot luminosity images, to show the in-flame flow field development impacted by after-injection in a single-cylinder, small-bore optical diesel engine. Two after-injection cases with different dwell times between the main injection and after-injection, namely, close-coupled and long-dwell, as well as a main-injection-only case are compared regarding flow fields, flow vector magnitude, and turbulence intensity distribution. For each case, high-speed soot luminosity movies from 100 individual combustion cycles are recorded at a high frame rate of 45 kHz for FIV processing. The Reynolds decomposition using a spatial filtering method is applied to the obtained flow vectors so that bulk flow structures and turbulence intensity distributions can be discussed. The results show significant after-injection-induced flow structures as a group of vectors travelling back toward the center of the combustion chamber upon the jet impingement on the piston bowl wall and then jet-to-jet collision. Despite higher cyclic variations caused by the after-injection, increased bulk flow magnitude and turbulence intensity upon the after-injection event suggests locally enhanced mixing. Compared to the long-dwell after-injection, the close-coupled after-injection shows more significant turbulence enhancements.