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In recent years, evidence has accumulated towards a distractor suppression mechanism that enables efficient selection of targets in a visual search task. According to these findings, the search for a target is faster in the presence of a salient distractor in a display among homogenous distractors as opposed to its absence. Studies have also shown that distractor suppression not only operates on the feature level but can also be spatially guided. The motivation of the current study was to examine if spatially guided distractor suppression can be goal-driven. We tested this across four experiments. In Experiment 1A, the task was to search for a shape target (e.g., a circle) and discriminate the orientation of the line within it. In some trials, a salient color distractor was presented in the display while participants were told that it appeared in one of the two locations on the horizontal axis (or the vertical axis, counterbalanced across participants). We expected enhanced distractor suppression when the salient distractor appeared within this "spatial filter" but did not find it since the target was also presented at the filtered locations. Experiment 1B replicated Experiment 1A, except that the target was always presented outside the filter; filtering enhanced search performance. In Experiment 2 even when the filter contained the salient distractor in only 65% of the filtered trials, filtering benefited search performance. In Experiment 3, the filter changed on every trial and did not benefit suppression. [ABSTRACT FROM AUTHOR]

Due to the evolution of graphics processors over recent decades it has now become possible to produce high-quality, realistic three-dimensional scenes. However, aliasing occurs during the sampling process performed during rasterization, which causes a serrated effect around the edges of the objects presented in the scene, highlighting an unreal aspect of the image and causing displeasure to the viewer. This paper aims to develop an anti-aliasing treatment based on rotated spatial filtering. It undertakes edge detection by applying spatial filtering with a simple linear regression technique. A smoothing spatial filter is then rotated to match the direction of the inspected edge and is applied to the affected regions. Testing was performed on an OpenGL application, processing the rendered image from the framebuffer, and Blender, a 3D modeling software that enables scenes with more complex graphics. The results have demonstrated the effectiveness of the proposed method in smoothing aliasing with good quality while preserving the details of the scene. Hence, the problem was managed effectively with a post-filtering approach and without oversampling. The running time of the algorithm is O(n), and the memory used is O(n). [ABSTRACT FROM AUTHOR]

Aim: The continuous development of statistical tools applied to ecology has contributed to great advances for modelling species' niches and distributions from opportunistic observations. However, as these observations are subject to biases caused by spatial variation in sampling effort, ecological niche models (ENMs) are also frequently biased. Among several bias correction methods that have been proposed, spatial filtering—imposing a minimum distance between occurrences—is widely used, yet lacks clear guidelines for choosing the filtering distance. Here, we aimed to explore the impact of spatial filtering distances on the performance of ENMs. Location: Europe. Taxon: Virtual species. Methods: We applied ENMs to two virtual species with contrasting levels of specialisation, across a spectrum of modelling conditions, bias types and sample sizes. Results: Models applied to the specialist species had on average a lower performance than those applied to the generalist species. Using a biased sample reduced model performance, especially when the bias was strong, and when the sample size was large. In many cases, spatial filtering failed to improve model performance or even reduced it. We did find an improvement for the generalist species modelled with large and strongly biased datasets. However, there was no optimal filtering distance, as this improvement was linearly and positively associated with filtering distance. Moreover, because the initial bias was strong and the filtered dataset became very small, the resulting models had only very low accuracy. Main Conclusions: Our results suggest that there is no optimal filtering distance for dealing with sampling bias in ENMs, and that spatial filtering never improves model performance enough to draw accurate predictions. We therefore recommend spatial filtering to be employed cautiously, only when enough data are available, and bearing in mind that its effectiveness remains highly uncertain. [ABSTRACT FROM AUTHOR]

Reduced-order models (ROMs) have achieved a lot of success in reducing the computational cost of traditional numerical methods across many disciplines. In fluid dynamics, ROMs have been successful in providing efficient and relatively accurate solutions for the numerical simulation of laminar flows. For convection-dominated (e.g., turbulent) flows, however, standard ROMs generally yield inaccurate results, usually affected by spurious oscillations. Thus, ROMs are usually equipped with numerical stabilization or closure models in order to account for the effect of the discarded modes. The literature on ROM closures and stabilizations is large and growing fast. In this paper, instead of reviewing all the ROM closures and stabilizations, we took a more modest step and focused on one particular type of ROM closure and stabilization that is inspired by large eddy simulation (LES), a classical strategy in computational fluid dynamics (CFD). These ROMs, which we call LES-ROMs, are extremely easy to implement, very efficient, and accurate. Indeed, LES-ROMs are modular and generally require minimal modifications to standard ("legacy") ROM formulations. Furthermore, the computational overhead of these modifications is minimal. Finally, carefully tuned LES-ROMs can accurately capture the average physical quantities of interest in challenging convection-dominated flows in science and engineering applications. LES-ROMs are constructed by leveraging spatial filtering, which is the same principle used to build classical LES models. This ensures a modeling consistency between LES-ROMs and the approaches that generated the data used to train them. It also "bridges" two distinct research fields (LES and ROMs) that have been disconnected until now. This paper is a review of LES-ROMs, with a particular focus on the LES concepts and models that enable the construction of LES-inspired ROMs and the bridging of LES and reduced-order modeling. This paper starts with a description of a versatile LES strategy called evolve–filter–relax (EFR) that has been successfully used as a full-order method for both incompressible and compressible convection-dominated flows. We present evidence of this success. We then show how the EFR strategy, and spatial filtering in general, can be leveraged to construct LES-ROMs (e.g., EFR-ROM). Several applications of LES-ROMs to the numerical simulation of incompressible and compressible convection-dominated flows are presented. Finally, we draw conclusions and outline several research directions and open questions in LES-ROM development. While we do not claim this review to be comprehensive, we certainly hope it serves as a brief and friendly introduction to this exciting research area, which we believe has a lot of potential in the practical numerical simulation of convection-dominated flows in science, engineering, and medicine. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy and Glaucoma are two common diabetic retinal pathogens. It has the potential to damage human retina and result in vision impairment. Based on the International Diabetes Federation (IDF) reports, In India 77 million people were affected with diabetics 2019 and approximately 147.2 million people are expected by 2045. By effectively managing diabetes and undergoing routine eye examinations, would avoid or reduce the risk of eye complications such as diabetic retinopathy, cataracts, and Glaucoma. The Timely identification and precise delineation of affected areas in retinal pathogen images are crucial for effective disease management. To address this, we propose a novel hybrid diabetic retinal pathogen classification mechanism using Artificial Fish Swarm and Deep Convolutional Radial Basis Function network (AFS-DCRBF). This method utilizes retinal images containing normal, Diabetic-Retinopathy, and Glaucoma as inputs. Preprocessing of raw input images is performed using a spatial filtering technique in the initial phase. A vector-auto regression method is then employed for feature engineering, followed by retinal pathogen classification. The Deep-Convolutional Neural Network (DCNN) selects most informative traits from the extracted feature sequence, while the Radial Basis Function (RBF) module performs the classification task. The Artificial Fish Swarm (AFS) optimization fine-tunes the hyperparameters of DCRBF and improves classification performance. The proposed study was evaluated using ORIGA data set and publicly available datasets for DR. The proposed method required a total time of 1.12 seconds and achieved 99.40% accuracy and 99.61%specificity, and dice coefficient of 0.97. [ABSTRACT FROM AUTHOR]

The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn't require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4 N + 2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) is a new promising technology for control and communication, the BCI system aims to decode the measured brain activity into a command signal. This paper proposes a hybrid approach to improve the classification performance of motor imagery BCI (MI BCI). Our proposed method aims to take the advantage of two principal feature extraction approaches. The first approach named Multi-Band common spatial patterns (MBCSP) consists of decomposing the MI trial into multiple sub-bands, for each sub-band CSP is applied to extract the features. Then, the subject-specific frequency bands are selected. Simultaneously, the selected frequency bands are used as input to the second approach named boosted tangent space mapping (BTSM), which extracts the features from each sub-band. An automatic feature selection algorithm is introduced to select the subject-specific frequency bands and to reduce the high dimensionality space of extracted features. Finally, the LogitBoost (LB) classifiers are learned on the extracted features by each approach and the linear combination of these classifiers is used to identify the class of MI trial. The proposed model is evaluated on three public MI-BCI datasets, including multiclass motor imagery datasets (BCI competition IV dataset 2a and BCI competition III dataset 3a) and binary class motor imagery dataset (BCI competition III dataset 4a), The average accuracy attained on the three datasets are respectively 73.61%, 86.66%, and 86.68%. The conducted comparative study between the proposed approach and some state-of-the-the art methods showed a statistically significant improvement (p-value < 0.05) of the classification performance of MI BCI. The experiment results showed a significant improvement when using the proposed hybrid approach against the use of a single-feature extraction method. [ABSTRACT FROM AUTHOR]

We 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 104$$ 1{0}^4 $$–105$$ 1{0}^5 $$ 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 [−51∘,−41∘]$$ \left[-5{1}^{\circ },-4{1}^{\circ}\right] $$, [17∘,27∘]$$ \left[1{7}^{\circ },2{7}^{\circ}\right] $$, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation. Methods: We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory. Results: Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the secondM20 forMEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in di [ABSTRACT FROM AUTHOR]

In the civil structural health monitoring fields, monitored data suffer from noise and sensor faults. In practice, redundant sensors are usually deployed to monitor structural condition to obtain more accurate and robust information. This paper proposes a beamforming-based spatial filtering method to improve the data quality by using the information redundancy within sensor networks. Data pre-processing is first implemented, including missing data imputation and thermal response separation. Subsequently, short-term Fourier transform is used to transform the measured time sequences into time–frequency domain to obtain more useful features. Finally, signals in the time and frequency domain are processed using the beamforming algorithm. In the beamformers, a linear filter is applied to suppress noise signals, which is formulated as a constrained optimization problem. Herein, interior point algorithm is used to optimize the allocation of the linear filter, wherein the objective function is to minimize the power of the noise component at the beamformer output. The effectiveness of the proposed method is verified by using signals from strain gauges installed on steel deck plates of the 3rd Nanjing Yangtze River Bridge. Results through the case study show that signals after spatial filtering have a satisfactory de-noising, which indicates the effectiveness of the proposed beamforming algorithm. We believe that the proposed beamforming algorithm has substantial potential applications, such as providing high quality data source for further investigations. [ABSTRACT FROM AUTHOR]

Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties. [ABSTRACT FROM AUTHOR]

This paper presents a comparative benchmarking of different beamforming techniques for robust multipath and spoofing mitigation in handheld receivers equipped with a small number of antenna elements. In the field of Global Navigation Satellite Systems (GNSSs), multipath and spoofing have become major sources of signal degradation, whereby the presence of closely spaced replicas of the signal of interest hampers the receiver's performance. Spatial filtering through the use of antenna arrays is one appealing approach to combat these effects. However, the upward trend in device miniaturization poses a serious concern for spatial filtering in handheld devices such as tablets or smartphones, particularly for the case of decimeter-wave signals such as in GNSSs. The terminal size constraints lead to deploying arrays with very few antennas, thus degrading the mitigation performance of the above impairments. This work explores the feasibility of performing robust multipath and spoofing mitigation in GNSS receivers with antenna arrays of very small sizes. Simulation results on four-antenna rectangular and two-antenna linear array distributions are provided, showing that the reduction in array size can be counteracted by the use of advanced beamformers. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by reconstructing the background. This study proposed a novel spatial–spectral joint HAD method based on a two-branch 3D convolutional autoencoder and spatial filtering. We used the two-branch 3D convolutional autoencoder to fully extract the spatial–spectral joint features and spectral interband features of HSI. In addition, we used a morphological filter and a total variance curvature filter for spatial detection. Currently, most of the datasets used to validate the performance of HAD methods are airborne HSI, and there are few available satellite-borne HSI. For this reason, we constructed a dataset of satellite-borne HSI based on the GF-5 satellite for experimental validation of our anomaly detection method. The experimental results for the airborne and satellite-borne HSI demonstrated the superior performance of the proposed method compared with six state-of-the-art methods. The area under the curve (AUC) values of our proposed method on different HSI reached above 0.9, which is higher than those of the other methods. [ABSTRACT FROM AUTHOR]

This study presents a new estimate for subsidence in the northeast of the country through the time series analysis of 11-year (from the beginning of 2006 till the end of 2016) of 31 stations of the Khorasan network, as part of the Iranian Permanent Geodynamic & GNSS Network (IPGN), located in northeastern Iran. The mentioned estimation is obtained from the velocity vector of network stations in the International Terrestrial Reference Frame of ITRF2014 based on time series analysis in two realms, i.e., deterministic model analysis and stochastic model analysis. The deterministic model analysis is comprised of jump detection, determination of station motion model parameters, the study of station trend, outlier detection, and statistical significance test to check the jumps magnitude. Due to the interdependence of these steps, the related calculations are performed iteratively. Noise analysis includes two phases, namely, spatial filtering and temporal filtering. In the first phase, the Common Mode Error (CME) parameter is calculated using the weighted stacking method and taking into account the data correlation coefficient and stations distance. In the second phase, using the maximum likelihood estimation (MLE) method, the optimal noise model is derived as a combination of white noise and flicker noise. As a result, the reliable velocities of the stations (resulting from a complete analysis of the deterministic model) and their realistic uncertainties (resulting from the selection of optimal stochastic models) are calculated. Based on this study we found that: (1) Each station during the 11-year study period has on average nine jumps, all of which are of non-seismic origin. (2) Including the data from all IPGN stations in spatial filtering, leads to better results and on average reduces the norm of post-fit residual vectors for east, north, and up coordinate components by 30.17%, 29.40%, and 17.90%, respectively. (3) Concerning the temporal filtering, we found that the noise of the up-component is significantly higher than the noise of the horizontal components. (4) Stochastic model analysis showed the realistic uncertainties of the east, north, and up components are 4.33, 4.44, and 3.70 times, respectively, greater than the uncertainties which are derived without application of stochastic modeling (optimistic uncertainties). (5) The vertical velocity of most of stations was found to be in the normal range of -5 to 5 mm/yr. (6) Five stations, namely, GOLM, GRGN, NFRD, NISH, and SHRN are having anomalous subsidence (up to 9 mm/yr). (7) The proximity of the three stations GOLM, NFRD, and NISH allows us to infer a regional subsidence for the area of their location. (8) The station GRGN, in addition to anomalous subsidence, shows distinctive features such as the nonlinear trend as well as large periodic signals in the up component of the station. Therefore, to find the reason for such vertical behavior of the earth's crust more permanent GNSS stations must be established in that area. (9) The estimated parameters of periodic signal of the stations demonstrate that the annual and draconitic year signals have the largest amplitudes in the three coordinate components. In addition, amplitude of the periodic signals of the up component is significantly larger than the other components. [ABSTRACT FROM AUTHOR]

Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus. [ABSTRACT FROM AUTHOR]

Digital holographic microscopy has significant advantages over traditional 3D measurements. In order to obtain a reconstructed image of superior quality, the zero-order-term spectrum and the −1 term spectrum must be eliminated during the digital reconstruction. At the same time, digital holographic microscopy has changed from the traditional manual selection of the +1 term spectrum to the automatic selection of the +1 term spectrum. In the process of spatial filtering of the +1 term spectrum, the filtering window is crucial, and the shape of the filtering window and the robustness of the filtering window to noise will ultimately affect the reconstruction results. In this paper, we propose an automatic spatial filtering method that combines wavelet transform and mathematical morphology to automatically extract the +1 term spectrum. Compared with conventional methods, the reconstruction results are more accurate and robust to noise, and the experimental results verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Thermal imaging plays a vital role in structural health monitoring of various materials and provides insight into the defect present due to aging, deterioration, and fault during construction. This study investigated the effectiveness of spatial filters during pre-processing of thermal images and a correlation technique in post-processing, as well as exploited its application in non-destructive testing and evaluation of defects in steel structures. Two linear filters (i.e., Gaussian and Window Averaging) and a non-linear filter (i.e., Median) were implemented during pre-processing of a pulsed thermography image sequence. The effectiveness of implemented filters was then assessed using signal to noise ratio as a quality metric. The result of pre-processing revealed that each implemented filter is capable of reducing impulse noise and producing high-quality images; additionally, when comparing the signal to noise ratio, the Gaussian filter dominated both Window Averaging and Median filters. Defect size was determined using a correlation technique on a sequence of pulsed thermography images that had been pre-processed with a Gaussian filter. Finally, it is concluded that the correlation technique could be applied to the fast measurement of defect size, even though the accuracy may depend on the detection limit of thermography and defect size to depth ratio. [ABSTRACT FROM AUTHOR]

Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject's calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain–computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin. [ABSTRACT FROM AUTHOR]

Simple Summary: With the development of artificial intelligence, researchers can roughly predict the crystal structure of a protein by computer without the need for biological experiments, which provides new ideas and solutions to problems, such as protein-protein interaction and drug-target predictions. In this study, we proposed strategies to combine predicted protein structures with deep learning networks and evaluated them on different protein binding site prediction tasks. Our computational experiment results showed that all proposed strategies could effectively encode structural information for deep learning models. Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations. [ABSTRACT FROM AUTHOR]

As pixel resolution continues to increase in images acquired from different sensors, the measurement of the correlation between pixels and their properties requires numerous processing steps over multiple iterations. Quantum image processing (QIP) is a technology capable of processing exponentially large volumes of data within polynomial time. One such application is image fusion. In the field of radiography, computerized tomography (CT) images provide the structural information of bone tissues, and magnetic resonance MR images can provide the visualization of gray‐scale anatomical structures of soft tissues. With the help of CT and MR fusion, a composite image can be obtained containing information on both hard and soft tissues. Clinicians often have to switch between these modalities to trace various patterns for effective staging and diagnosis of various oncological diseases. In this study, a multilevel filtering‐based image fusion algorithm that efficiently fuses information from CT and MR images into a single image has been proposed. First, source images are processed with a rolling guidance filter, and the detail layer is calculated. The base layer is then computed using guided filtering to ensure a high level of edge preservation. Furthermore, to minimize noise and artifacts, the detail layers are fused using the Karhunen–Loeve transform, and the base layers are combined using the weighted superimposition principle. Experimental results demonstrate that the proposed methodology is subjectively more efficient than the existing state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

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

INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF). OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc. METHODS, RESULTS AND CONCLUSION: The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities levels. [ABSTRACT FROM AUTHOR]

Biomedical imaging has helped the medical practitioners to facilitate treatments faster and more accurate than conventional methods. Automated technologies suffice the default preprocessing and identification from the images of the retina and their vessel vascular network. Human retinal diseases, leading to blindness, will be identified by the segmentation of blood vessels to enhance the statistical accuracy of intra-retinal layer thickness. This proposal overcomes the drawbacks of traditional angiogram techniques by coloring the retinal paths by fluorescent dyes. From the obtained results, automated segmentation by using the proposed technique has improved then compared methodologies. Employing Gabor filters in the preprocessing functions, elimination of false positives is greatly achieved with some modifications. The followed processes divide the retinal images into linear and curvature segments after implying Gabor constraints to obtain a predicted trajectory of vessels. Limiting the major chances of false positives and better vessel construction in this automated proposal are proved and discussed. [ABSTRACT FROM AUTHOR]

This paper presents a novel and robust algorithm to estimate the direction of arrival of sources by combining spatial time–frequency distributions and spatial filtering. The proposed algorithm first analyzes a given signal using robust spatial time–frequency distribution. Then, the strongest time–frequency points are selected and clustered to find out the direction of the strongest source. The signal corresponding to the strongest source is then removed through spatial filtering, and this process is iterated till the directions of all the sources are estimated. Numerical results indicate that the proposed method achieves better performance compared to other state-of-the-art methods for both complete signals and sparsely sampled signals. For example, in direction of arrival estimation, the proposed method achieves a mean square error of - 37 dB which is 2 dB lower than the mean square error achieved by the next best method. [ABSTRACT FROM AUTHOR]

Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD) task. This article proposes a hyperspectral AD method based on Wasserstein distance (WD) and spatial filtering (called AD-WDSF). Based on the assumption that both background and anomaly targets obey the multivariate Gaussian distribution, background and anomaly target distributions are estimated in the local regions of HSIs. Subsequently, the anomaly intensity of test pixels centered in the local regions are determined via measuring the WD between background and anomaly target distributions. Lastly, spatial filters, i.e., guided filter (GF), total variation curvature filter (TVCF), and Maxtree filter, are exploited to further refine detection results. Experimental results conducted on two real hyperspectral data sets demonstrate that the proposed method achieves competitive detection performance compared with the state-of-the-art AD methods. [ABSTRACT FROM AUTHOR]

With the increase in mobility in technology, artists are looking for ways to create paintings using their tablets and smartphones while on the move. Digital oil painting is a revolutionary technique where an artist uses digital paintbrush to simulate the traditional bristle paintbrush of a painting to get an oil paint image. In this article, we propose a novel realistic technique for reversible digital oil painting to accelerate the users to (re)paint an oil paint image to convey the surrounding atmosphere, express their emotions and feelings, or showcase a time of day or type of weather through that painting. Realism is all about accuracy. To repaint, the original image is retrieved from the existing oil paint image by reversing the pixels to its original value. We derive a mathematical expression to determine the probability of a pixel, with a set of neighbor pixels, being oil painted to get an oil paint image of the desired quality, which reduces the time and space complexities of the technique. We also develop prototypes on Android, Windows, and Ubuntu operating systems that successfully implement the proposed technique. [ABSTRACT FROM AUTHOR]

Prevalent north–south striping (NSS) noise in the spherical harmonic coefficient products of the satellite missions gravity recovery and climate experiment greatly impedes the interpretation of signals. The overwhelming NSS noise always leads to excessive smoothing of the data, allowing a large room for improvement in the spatial resolution if this particular NSS noise can be mitigated beforehand. Here, we put forward a new spatial filter that can effectively remove NSS noise while remaining orthogonal to physical signals. This new approach overcomes the limitations of the previous method proposed by Swenson and Wahr (2006), where signal distortion was large and high-order coefficients were uncorrectable. The filter is based on autocorrelation in the longitude direction and cross-correlation in the latitude direction. The NSS-type noise identified by our method is mainly located in coefficients of spherical harmonic order larger than about 20 and degree beyond 30, spatially between latitudes ± 60°. After removing the dominating NSS noise with our method, a weaker filter than before is added to handle the residual noise. Thereby, the spatial resolution can be increased and the amplitude damping can be reduced. Our method can coincidentally reduce outliers in time series without significant trend bias, which underpins its effectiveness and reliability. [ABSTRACT FROM AUTHOR]

The isotropic Gaussian filter has been used extensively in Gravity Recovery and Climate Experiment (GRACE) temporal gravity field solutions, and is still being applied to GRACE Follow-On products to remove high-frequency errors and improve the estimation of mass transport events on the Earth's surface. For such applications, the only known rigorous method to calculate the spherical harmonic coefficients of an isotropic Gaussian filter is by the use of a second-order recurrence relation. As an alternative, an approximate expression is also used frequently. In this paper, we provide some additional expressions for the calculation of isotropic Gaussian filter kernels in the spherical harmonic domain. Specifically, we derive a new recurrence relation, a closed-form expression, expressions involving modified Bessel functions of the first kind, and a new approximate expression. We also examine and compare them from a computational viewpoint. The results of our numerical investigations indicate that the new recurrence relation and the closed-form expression are unstable in a way similar to the second-order recurrence relation that has been used so far. The expressions involving modified Bessel functions, and particularly the ones using exponentially scaled modified Bessel functions, provide a simple, elegant and stable way of calculating isotropic Gaussian filter coefficients, since routines for their stable evaluation are readily available in many programming languages. Alternatively, the new approximate expression can be used, which is also stable and offers better accuracy than previous approximations. [ABSTRACT FROM AUTHOR]

Changing and often class-dependent non-stationarities of signals are a big challenge in the transfer of common findings in cognitive workload estimation using Electroencephalography (EEG) from laboratory experiments to realistic scenarios or other experiments. Additionally, it often remains an open question whether actual cognitive workload reflected by brain signals was the main contribution to the estimation or discriminative and class-dependent muscle and eye activity, which can be secondary effects of changing workload levels. Within this study, we investigated a novel approach to spatial filtering based on beamforming adapted to changing settings. We compare it to no spatial filtering and Common Spatial Patterns (CSP). We used a realistic maneuvering task, as well as an auditory n-back secondary task on a tugboat simulator as two different conditions to induce workload changes on professional tugboat captains. Apart from the typical within condition classification, we investigated the ability of the different classification methods to transfer between the n-back condition and the maneuvering task. The results show a clear advantage of the proposed approach over the others in the challenging transfer setting. While no filtering leads to lowest within-condition normalized classification loss on average in two scenarios (22 and 10%), our approach using adaptive beamforming (30 and 18%) performs comparably to CSP (33 and 15%). Importantly, in the transfer from one to another setting, no filtering and CSP lead to performance around chance level (45 to 53%), while our approach in contrast is the only one capable of classifying in all other scenarios (34 and 35%) with a significant difference from chance level. The changing signal composition over the scenarios leads to a need to adapt the spatial filtering in order to be transferable. With our approach, the transfer is successful due to filtering being optimized for the extraction of neural components and additional investigation of their scalp patterns revealed mainly neural origin. Interesting findings are that rather the patterns slightly change between conditions. We conclude that the approaches with low normalized loss depend on eye and muscle activity which is successful for classification within conditions, but fail in the classifier transfer since eye and muscle contributions are highly condition-specific. [ABSTRACT FROM AUTHOR]

Efficient beam supercollimation is proposed and shown experimentally by axisymmetric aperiodic photonic structures. The structure consists of equidistant layers of concentric refraction index rings, with the transverse period of the rings varying from layer to layer along the structure. Numerically designed and optimized axisymmetric photonic structures are inscribed in silica glass by femtosecond pulses. Strong enhancement (more than by one order of magnitude) of the axial beam component is demonstrated, resulting in the record performance of the beam supercollimation. [ABSTRACT FROM AUTHOR]

This paper presents a new dataset of measured multichannel room impulse responses (RIRs) named dEchorate. It includes annotations of early echo timings and 3D positions of microphones, real sources, and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods in echo-aware speech enhancement, room geometry estimation, RIR estimation, acoustic echo retrieval, microphone calibration, echo labeling, and reflector position estimation. The dataset is provided with software utilities to easily access, manipulate, and visualize the data as well as baseline methods for echo-related tasks. [ABSTRACT FROM AUTHOR]

We describe a new way to apply a spatial filter to gridded data from models or observations, focusing on low‐pass filters. The new method is analogous to smoothing via diffusion, and its implementation requires only a discrete Laplacian operator appropriate to the data. The new method can approximate arbitrary filter shapes, including Gaussian filters, and can be extended to spatially varying and anisotropic filters. The new diffusion‐based smoother's properties are illustrated with examples from ocean model data and ocean observational products. An open‐source Python package implementing this algorithm, called gcm‐filters, is currently under development. Plain Language Summary: "The large scale part" and "the small scale part" of quantities like velocity, temperature, and pressure fluctuations are important for a range of questions in Earth system science. This study describes a precise way of defining these quantities, as well as an efficient method for diagnosing them from gridded data, especially the data produced by Earth system models. Key Points: A new way to apply a spatial low‐pass filter to gridded data is developedThe new method can be applied in any geometry since it only requires a discrete Laplacian operatorThe algorithm's flexibility is illustrated using a range of examples from simulation and observation data [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland–Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (−0.095 logMAR), CCA (0.039 logMAR), and MSI (−0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the role of spatial dependence, spatial heterogeneity and spatial scale in principal component analysis for geographically distributed data. It considers spatial heterogeneity by adopting geographically weighted principal component analysis at a fine spatial resolution. Moreover, it focuses on dependence by introducing a novel approach based on spatial filtering. These methods are applied in order to derive a composite indicator of socioeconomic deprivation in the Italian province of Rome while considering two spatial scales: municipalities and localities. The results show that considering spatial information uncovers a range of issues, including neighbourhood effects, which are useful in order to improve local policies. [ABSTRACT FROM AUTHOR]

In an analytical form, the features of the image formation of the faces of a three-dimensional asymmetric edge of an object with an absolutely reflective inner surface are studied in relation to the dimensional inspection. Formulas are obtained for calculating the fields in images of object faces in ideal and diffraction-limited systems, depending on the value of the object's bevel , the phase shift of the wave reflected from the inner surface of the object, and the angular aperture of the coherent optical image formation and filtering system. It is found that for metallic three-dimensional (3D) objects (), the field value in the image of the back face at the point corresponding to the position of its boundary is negligible when the depth of focus of the system is much less than the thickness of the object. It is shown that at bevels of the object, much smaller than the Fresnel zone ( is the wavelength of light, and is the thickness of the object) and greater than the depth of focus , the shift of the intensity profile in the image of the front face is proportional to and depends on the phase . For large bevels, when and , the active face is the back one and the displacement of the back face boundary is inversely proportional to the value of . These offsets can lead to systematic errors in the measurement of the position of the boundaries of the 3D-object faces, and they must be taken into account during precision dimensional inspection. [ABSTRACT FROM AUTHOR]

A highly reconfigurable direct-conversion software-defined multiple-input multiple-output (MIMO) receiver with four RF inputs and four I/Q baseband outputs is proposed. It allows for digital MIMO but also analog interference rejection by spatial notch filtering through four flexible and simultaneous orthogonal beams. A segmented constant-Gm vector modulator (VM) with improved interference tolerance and wide RF frequency range targeting the sub-6-GHz bands is proposed. It exploits current-domain beamforming before I – V conversion by transimpedance amplifiers. A 0.7–5.7-GHz 22-nm fully depleted silicon-on-insulator (FD-SOI) prototype chip achieves >29 dB spatial filtering for a single notch and an ultrawideband 20-dB notch suppression bandwidth of 2.3 GHz at broadside excitation at an local oscillator (LO) frequency of 2.5 GHz. In the notches, an IIP3 of +16 dBm and B1dB of −11.5 dBm at a 41-dB gain is achieved, improving IIP3 and B1dB by 35 and 27 dB, respectively, by spatial filtering. A single-element noise figure (NF) of 5.5–7 dB is achieved on the VM constellation corners, degrading about 2 dB on the points nearby the biggest circle fitting into a square constellation. However, sub-3-dB system NF is potentially achievable, taking into account up to 6-dB improvement by the four-element beamforming. Given both gain and phase control provided by the VM, spatial patterns with up to three independent nulls can be synthesized with the four-element antenna array. The chip of 0.52 mm2 active area consumes 77–139 mW at an LO-frequency of 0.7–5.7 GHz from a 0.8-V supply. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCI) based on steady-state visually evoked potentials (SSVEP) have been increasingly used in different applications, ranging from entertainment to rehabilitation. Filtering techniques are crucial to detect the SSVEP response since they can increase the accuracy of the system. Here, we present an analysis of a space-time filter based on the Minimum Variance Distortionless Response (MVDR). We have compared the performance of a BCI-SSVEP using the MVDR filter to other classical approaches: Common Average Reference (CAR) and Canonical Correlation Analysis (CCA). Moreover, we combined the CAR and MVDR techniques, totalling four filtering scenarios. Feature extraction was performed using Welch periodogram, Fast Fourier transform, and CCA (as extractor) with one and two harmonics. Feature selection was performed by forward wrappers, and a linear classifier was employed for discrimination. The main analyses were carried out over a database of ten volunteers, considering two cases: four and six visual stimuli. The results show that the BCI-SSVEP using the MVDR filter achieves the best performance among the analysed scenarios. Interestingly, the system's accuracy using the MVDR filter is practically constant even when the number of visual stimuli was increased, whereas degradation was observed for the other techniques. [ABSTRACT FROM AUTHOR]

Spatial filtering is an important mechanism to improve the spatial quality of laser beams. Typically, a confocal arrangement of lenses with a diaphragm in the focal plane is used for intracavity spatial filtering. Such conventional filtering requires access to the far‐field domain. In microlasers, however, conventional filtering is impossible due to the lack of space in microresonators to access the far‐field. Therefore, a novel concept for more compact and efficient spatial filtering is necessary. In this study, a conceptually novel mechanism of spatial filtering in the near‐field domain is proposed and demonstrated, by a nanostructured multilayer coating—a 2D photonic crystal structure with a periodic index modulation along the longitudinal and transverse directions to the beam propagation. The structure is built on a nanomodulated substrate, to provide the transverse periodicity. The physical vapor deposition is used to provide self‐repeating modulation in the longitudinal direction. A 5 µm thick photonic multilayer structure composed of nanostructured multiple layers of alternating high‐ and low‐index materials providing spatial filtering in the near‐infrared frequencies with 2° low angle passband is experimentally demonstrated. The proposed photonic structure can be considered as an ideal component for intracavity spatial filtering in microlasers. [ABSTRACT FROM AUTHOR]

The article proposes a direct algorithm for reconstructing the electromagnetic field at the antenna aperture, which can be used to solve problems of diagnostics and spatial filtering. The algorithm is based on the solution of Maxwell's equations in the form of advanced potentials of the electromagnetic field. It is shown that advanced electromagnetic waves, which are a solution to Maxwell's equations, are equivalent to retarded electromagnetic waves when the parameters of the medium are stationary and linear. The direct algorithm is formulated as the multiplication of the transformation operator matrix by the known vector of the electromagnetic field on a closed surface. In this case, the transformation outward (to the far field) or inward (to the aperture) of the surface is described by the same operator and differs only in the signs of the input quantities. Unlike the known algorithms, the direct one does not require scanning the electromagnetic field on canonical surfaces or solving a large system of integral equations. It makes the new algorithm optimal for use in new near-field antenna measurement systems based on industrial robots, unmanned aerial vehicles, etc. Verification of the developed algorithm using experimental data has shown the possibility of reconstructing the normalized distribution of the electric field strength at the antenna aperture with an error less than 2 dB. [ABSTRACT FROM AUTHOR]

Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases. [ABSTRACT FROM AUTHOR]

A key assumption in species distribution modeling (SDM) with presence‐background (PB) methods is that sampling of occurrence localities is unbiased and that any sampling bias is proportional to the background distribution of environmental covariates. This assumption is rarely met when SDM practitioners rely on federated museum records from natural history collections for geo‐located occurrences due to inherent sampling bias found in these collections. We use a simulation approach to explore the effectiveness of three methods developed to account for sampling bias in SDM with PB frameworks. Two of the methods rely on careful filtering of observation data—geographic thinning (G‐Filter) and environmental thinning (E‐Filter)—while a third, FactorBiasOut, creates selection weights for background data to bias locations toward areas where the observation dataset was sampled. While these methods have been assessed previously, evaluation has emphasized spatial predictions of habitat potential. Here, we dig deeper into the effectiveness of these methods by exploring how sampling bias not only affects predictions of habitat potential, but also our understanding of niche characteristics such as which explanatory variables and response curves best represent species–environment relationships. We simulate 100 virtual species ranging from generalist to specialist in their habitat preferences and introduce geographic and environmental bias at three intensity levels to measure the effectiveness of each correction method to (1) predict true probability of occurrence across a study area, (2) recover true species–environment relationships, and (3) identify true explanatory variables. We find that the FactorBiasOut most often showed the greatest improvement in recreating known distributions but did no better at correctly identifying environmental covariates or recreating species–environment relationships than G‐Filter or E‐Filter methods. Narrow niche species are most problematic for biased calibration datasets, such that correction methods can, in some cases, make predictions worse. [ABSTRACT FROM AUTHOR]

Spatial EEG filters are widely used to isolate event-related potential (ERP) components. The most commonly used spatial filters (e.g., the average reference and the surface Laplacian) are "stationary." Stationary filters are conceptually simple, easy to use, and fast to compute, but all assume that the EEG signal does not change across sensors and time. Given that ERPs are intrinsically nonstationary, applying stationary filters can lead to misinterpretations of the measured neural activity. In contrast, "adaptive" spatial filters (e.g., independent component analysis, ICA; and principal component analysis, PCA) infer their weights directly from the spatial properties of the data. They are, thus, not affected by the shortcomings of stationary filters. The issue with adaptive filters is that understanding how they work and how to interpret their output require advanced statistical and physiological knowledge. Here, we describe a novel, easy-to-use, and conceptually simple adaptive filter (local spatial analysis, LSA) for highlighting local components masked by large widespread activity. This approach exploits the statistical information stored in the trial-by-trial variability of stimulus-evoked neural activity to estimate the spatial filter parameters adaptively at each time point. Using both simulated data and real ERPs elicited by stimuli of four different sensory modalities (audition, vision, touch, and pain), we show that this method outperforms widely used stationary filters and allows to identify novel ERP components masked by large widespread activity. Implementation of the LSA filter in MATLAB is freely available to download. NEW & NOTEWORTHY EEG spatial filtering is important for exploring brain function. Two classes of filters are commonly used: stationary and adaptive. Stationary filters are simple to use but wrongly assume that stimulus-evoked EEG responses (ERPs) are stationary. Adaptive filters do not make this assumption but require solid statistical and physiological knowledge. Bridging this gap, we present local spatial analysis (LSA), an adaptive, yet computationally simple, spatial filter based on linear regression that separates local and widespread brain activity (https://www.iannettilab.net/lsa.html or https://github.com/rorybufacchi/LSA-filter). [ABSTRACT FROM AUTHOR]

A compact high-isolation multiple-input multiple-output (MIMO) microbase station antenna consisting of two dual-polarized antenna elements is presented. The symmetrical magnetic coupling feeding network is used to feed the radiation patch of the dual-polarized antenna and to improve the isolation of the antenna element. By introducing parasitic square patches, the radiation characteristics and isolation of the dual-polarized antenna are improved. In addition, a modified closed-loop metasurface unit is designed and used to construct an isolation wall. When dual-polarized antenna elements are used to assemble a MIMO antenna, the proposed metasurface structure is inserted between them to reduce the coupling between MIMO antenna elements, regarded as a spatial band-stop filter to suppress the spatial electromagnetic waveform of the elements. After loading the metasurface isolation wall, the maximum increase of isolation is 9.17 dB at the center frequency point of 2.6 GHz. For antenna element ports and cross-polarized ports, the isolations are greater than 30 dB, and the isolation of the same polarized ports is better than 25 dB within the frequency band from 2.45 to 2.8 GHz. At the same time, the reflection coefficients of the ports of the elements are less than −15 dB. Finally, a prototype of the proposed MIMO microbase station antenna was fabricated and tested, and the experimental results are proximity with the simulation responses. [ABSTRACT FROM AUTHOR]

Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A method is proposed in this paper for life detection and non-contact respiratory rate measurement in cluttered environments. Only an RGB video of the detection area is required. In the method, spatial filtering is firstly applied to each frame of the video for image denoising. Gray level compensation follows to compensate for the change of gray level caused by the environment light. Thirdly, the gray levels of each pixel over time are filtered separately by a low-pass filter. At last, the human is located and the respiratory rate is measured. Tests on a self-made dataset show that an accuracy of 76.7% is achieved by the proposed method, which is better than that of the Convolutional Neural Networks (30%) and the histogram of oriented gradients (3.3%). [ABSTRACT FROM AUTHOR]

Deflection, a basic functionality of wavefront manipulation is usually associated with the phase-gradient metasurfaces and the classical blazed gratings. We numerically and experimentally demonstrate an unusually wideband and simultaneously wide-angle deflection achieved at near-infrared in reflection mode for a periodic (nongradient), ultrathin meta-array comprising only one silicon nanorod (Mie resonator) per period. It occurs in the range where only the first negative diffraction order and zero order may propagate. Deflection serves as the enabler for multifunctional operation. Being designed with the main goal to obtain ultra-wideband and wide-angle deflection, the proposed meta-array is also capable in spatial filtering and wide-angle splitting. Spatial filtering of various types can be obtained in one structure by exploiting either deflection in nonzero diffraction orders, or the specular-reflection (zero-order) regime. Thus, the role of different diffraction orders is clarified. Moreover, on–off switching of deflection and related functionalities is possible by changing polarization state of the incident wave. The suggested device is simple to fabricate and only requires cost-effective materials, so it is particularly appropriate for the large-area fabrication using nanoprint lithography. Ultra-wideband wide-angle and other deflection scenarios, along with the other functionalities, are promising for applications in optical communications, laser optics, sensing, detection, and imaging. [ABSTRACT FROM AUTHOR]