Traditional Spatial Filtering Research Articles

Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm.

As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. In addition, the CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited. To make up for these deficiencies, this study introduces a novel spatial filter-solving paradigm named adaptive spatial pattern (ASP), which aims to minimize the energy intra-class matrix and maximize the inter-class matrix of MI-EEG after spatial filtering. The filter bank adaptive and common spatial pattern (FBACSP), our proposed method for MI-EEG decoding, amalgamates ASP spatial filters with CSP features across multiple frequency bands. Through a dual-stage feature selection strategy, it employs the Particle Swarm Optimization algorithm for spatial filter optimization, surpassing traditional CSP approaches in MI classification. To streamline feature sets and enhance recognition efficiency, it first prunes CSP features in each frequency band using mutual information, followed by merging these with ASP features. Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBACSP. The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm, filter bank common spatial pattern (FBCSP), the proposed algorithm improves by 11.44 and 7.11% on two datasets, respectively (p < 0.05). It is demonstrated that FBACSP has a strong ability to decode MI-EEG. In addition, the analysis based on mutual information, t-SNE, and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals and explains the improvement of classification performance by the introduction of ASP features. These findings may provide useful information to optimize EEG-based BCI systems and further improve the performance of non-invasive BCI.

DETECTING THE TEXTURE FOR MOBILE IMAGE BY USING PROPOSED TECHNIQUE

In this proposed search, a new technique was applied as an attempt to detect texture and the edges of a test image for mobile, lines drawn on a draft paper. Then was applied traditional spatial filters such as Sobel and Canny, comparison between them, and proposed method to detect the line edge and texture for the same image were applied. The results were that the detection method using the Canny filter showed more visual information and better accuracy than the Sobel spatial filter method, and when using the proposed technique, it gaves more information about the texture of the paper and more accurate results than the Canny filter, which was unable to detect the texture of the image.

Moving Objects Detection Based on Frequency Domain

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

Temporal Filters for Mapping Phragmites with C-HH SAR Data

We compared traditional spatial filters and multi-temporal filters to remove speckle from synthetic aperture radar (SAR) data for mapping Phragmites australis. SAR constellations, with more rapid revisit capability, allow one to generate stacks of SAR data and to use multi-temporal filters for speckle reduction. GAMMA software offers multi-temporal filters for SAR processing, two of which we compared to the traditional Enhanced Lee and the Lee filters. We evaluated the filters using three criteria: (1) visual inspection, (2) signal level ratio, and (3) the equivalent number of looks (ENL). The results of this study show that multi-temporal filters were able to reduce speckle from areas of surface water and land, as well as to improve the detection of Phragmites patches due to preserving the resolution and texture which helped in the detection of the patch boundaries. The signal level ratio was approximately 1.0 with the GAMMA Multi-temporal filter and approximately 0.9 with the other filters. The enhanced Lee and the two multi-temporal filters produced an equal ENL of about 6. However, due to small patch sizes and backscatter similarity with cattails and other cover types Phragmites patches were difficult to separate from other types of flooded vegetation with C-HH intensity only.

Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion

The steady-state motion visual evoked potential (SSMVEP) collected from the scalp suffers from strong noise and is contaminated by artifacts such as the electrooculogram (EOG) and the electromyogram (EMG). Spatial filtering methods can fuse the information of different brain regions, which is beneficial for the enhancement of the active components of the SSMVEP. Traditional spatial filtering methods fuse electroencephalogram (EEG) in the time domain. Based on the idea of image fusion, this study proposed an SSMVEP enhancement method based on time-frequency (T-F) image fusion. The purpose is to fuse SSMVEP in the T-F domain and improve the enhancement effect of the traditional spatial filtering method on SSMVEP active components. Firstly, two electrode signals were transformed from the time domain to the T-F domain via short-time Fourier transform (STFT). The transformed T-F signals can be regarded as T-F images. Then, two T-F images were decomposed via two-dimensional multiscale wavelet decomposition, and both the high-frequency coefficients and low-frequency coefficients of the wavelet were fused by specific fusion rules. The two images were fused into one image via two-dimensional wavelet reconstruction. The fused image was subjected to mean filtering, and finally, the fused time-domain signal was obtained by inverse STFT (ISTFT). The experimental results show that the proposed method has better enhancement effect on SSMVEP active components than the traditional spatial filtering methods. This study indicates that it is feasible to fuse SSMVEP in the T-F domain, which provides a new idea for SSMVEP analysis.

Low-pass spatial filters with small angle-domain bandwidth based on one-dimensional metamaterial photonic crystals

By using the spatial properties of zero-index gaps in one-dimensional metamaterial photonic crystals, we have designed low-pass spatial filters with small angle-domain bandwidth less than 10 degrees. It is demonstrated by transfer matrix method that the small angle-domain bandwidth and roll-off factor of the spatial filters can be adjusted by changing the structure parameters of the metamaterial photonic crystals. In addition, the spatial filters have absolute polarization-independent properties compared with previous photonic crystal spatial filters. These spatial filters with small angle-domain bandwidth may be applied to the laser science in the future, and decrease the space occupied by the traditional spatial filters.

An improved transmitting multi-layer thin-film filter

The angular resolutions of the phase-shifted Rugate thin-film filters, the ultra-narrow bandpass filters, and a cutoff filter-combination device are discussed, and the electric field distributions of the filters are compared. The results show that the three transmitting multi-layer thin-film filters can realize the same angular resolution, but the electric field in the cutoff filter-combination device is the lowest. Because a lower electric-field distribution corresponds to a higher laser-induced damage threshold of the thin films, the cutoff filter-combination device may replace the traditional spatial filters in high power laser systems.

Effect of the span of Australian GPS coordinate time series in establishing an optimal noise model

The span of coordinate time series affects the determination of an optimal noise model. We analyzed position data recorded for 10 continuous Global Positioning System (GPS) sites from 1998.0 to mid-2009 on the Australian Plate to estimate the best noise model and thereafter obtain the true uncertainties of the velocity, employing the maximum likelihood estimation (MLE) method. MLE was employed to analyze the data in four ways. In the first two analyses, the noise was assumed to be a combination of flicker noise and white noise for the raw time series and spatially filtered time series. In the final two analyses, the spectral indices and amplitudes were simultaneously estimated for a power law noise plus white noise model for the raw time series and spatially filtered time series. We conclude that the noise model of GPS time series in Australia can be best described as the combination of flicker noise and white noise. Velocity uncertainties fall below ∼0.2 mm/yr when the time span exceeds ∼9.5 years. A comparison of noise amplitudes and maximum likelihood estimation values between the raw and spatially filtered time series suggests that traditional spatial filtering to remove common-mode errors might not be applicable to the raw time series of this region.

Optimization of spatial filter with volume Bragg gratings in photo-thermo-refractive glass

We present a method for optimizing spatial filter performance by inserting volume Bragg gratings (VBGs) in front of the traditional spatial filters. The experimental results show that the cutoff frequency is modified with the insertion of VBGs. We also demonstrate the optimization of filtering performance in both the spatial and frequency domains, with detailed comparison of near-field modulation, contrast ratio, and power spectral density of output laser beams.

Multiple-point geostatistical simulation for post-processing a remotely sensed land cover classification

A post-processing method for increasing the accuracy of a remote sensing classification was developed and tested based on the theory of multiple-point geostatistics. Training images are used to characterise the joint variability and joint continuity of a target spatial pattern, overcoming the limitations of two-point statistical models. Conditional multiple-point simulation (MPS) was applied to a land cover classification derived from a remotely sensed image. Training data were provided in the form of “hard” (land cover labels), and “soft” constraints (class probability surfaces estimated using soft classification). The MPS post-processing method was compared to two alternatives: traditional spatial filtering (also a post-processing method) and the contextual Markov random field (MRF) classifier. The MPS approach increased the accuracy of classification relative to these alternatives, primarily as a result of increasing the accuracy of classification for curvilinear classes. Key advantages of the MPS approach are that, unlike spatial filtering and the MRF classifier, (i) it incorporates a rich model of spatial correlation in the process of smoothing the spectral classification and (ii) it has the advantage of capturing and utilising class-specific spatial training patterns, for example, classes with curvilinear distributions.

Adaptive spatial filtering of multichannel surface electromyogram signals

Spatial filtering of surface electromyography (EMG) signals can be used to enhance single motor unit action potentials (MUAPs). Traditional spatial filters for surface EMG do not take into consideration that some electrodes could have poor skin contact. In contrast to the traditional a priori defined filters, this study introduces an adaptive spatial filtering method that adapts to the signal characteristics. The adaptive filter, the maximum kurtosis filter (MKF), was obtained by using the linear combination of surrounding channels that maximises kurtosis. The MKF and conventional filters were applied to simulated EMG signals and to real EMG signals recorded with an electrode grid to evaluate their performance in detecting single motor units. The MKF was compared with conventional spatial filtering methods. Simulated signals, with different levels of spatially correlated noise, were used for comparison. The influence of one electrode with poor skin contact was also investigated. The MKF was found to be considerably better at enhancing a single MUAP than conventional methods for all levels of spatial correlation of the noise. For a spatial correlation of 0.97 of the noise, the improvement in the signal-to-noise ratio, where a MUAP could be detected, was at least 6dB. With a simulated poor skin contact for one electrode, the improvement over the other methods was at least 19 dB.

Edge enhancement of a phase-conjugate image with a mutually pumped phase conjugator

Usually edge enhancement of optical images is produced by introduction of loss into the low spatial frequency components of the image-bearing beam in the Fourier plane of a lens. We report on edge-enhanced phase-conjugate images from a mutually pumped conjugator accomplished by spatial filtering in the Fraunhofer diffraction region of the input beams. High-resolution (128-160 lines/mm) edge-enhanced images are obtained through traditional spatial filtering in the Fourier plane and in the Fraunhofer regime. Amplification of these edge-enhanced images is observed with some loss of high spatial resolution (50 lines/mm).