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Low-dimensional material-based heterogeneous silicon photonics has attracted significant attention due to their applications in developing integrated optoelectronic devices from the telecommunication band to mid-infrared wavelengths. However, the study of waveguide components integrated with low-dimensional materials for mode-division multiplexing (MDM) applications mostly remains in its infancy. In this paper, we demonstrated waveguide-integrated spatial mode filters by integrating subtly designed ten-layer PtSe2 nanoribbons on an ultrathin silicon waveguide with a deep-subwavelength thickness to eliminate modal crosstalk. To be specific, the undesirable propagating mode can be filtered out due to its strong interaction with the PtSe2 nanoribbons on the silicon waveguide surface. Our results show that TE1-to-TE0 and TE2-to-TE0 modal extinction ratios of 12 dB and 14.5 dB were measured in 100 and 75-μm-long PtSe2-on-silicon waveguides at 2200-nm wavelengths. Our study paves the intriguing approach to developing waveguide-integrated spatial mode filters for on-chip MDM applications for optical interconnects and optical communications. [ABSTRACT FROM AUTHOR]

Background: In this study, we evaluated the potential of a network approach to electromyography and electroencephalography recordings to detect covert command-following in healthy participants. The motivation underlying this study was the development of a diagnostic tool that can be applied in common clinical settings to detect awareness in patients that are unable to convey explicit motor or verbal responses, such as patients that suffer from disorders of consciousness (DoC). Methods: We examined the brain and muscle response during movement and imagined movement of simple motor tasks, as well as during resting state. Brain-muscle networks were obtained using non-negative matrix factorization (NMF) of the coherence spectra for all the channel pairs. For the 15/38 participants who showed motor imagery, as indexed by common spatial filters and linear discriminant analysis, we contrasted the configuration of the networks during imagined movement and resting state at the group level, and subject-level classifiers were implemented using as features the weights of the NMF together with trial-wise power modulations and heart response to classify resting state from motor imagery. Results: Kinesthetic motor imagery produced decreases in the mu-beta band compared to resting state, and a small correlation was found between mu-beta power and the kinesthetic imagery scores of the Movement Imagery Questionnaire-Revised Second version. The full-feature classifiers successfully distinguished between motor imagery and resting state for all participants, and brain-muscle functional networks did not contribute to the overall classification. Nevertheless, heart activity and cortical power were crucial to detect when a participant was mentally rehearsing a movement. Conclusions: Our work highlights the importance of combining EEG and peripheral measurements to detect command-following, which could be important for improving the detection of covert responses consistent with volition in unresponsive patients. [ABSTRACT FROM AUTHOR]

Introduction: Rehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG–EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices. Methods: Eight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG–EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme. Results: Among the three functional connectivity methods investigated—coherence, Pearson correlation coefficient and mutual information (MI)—the MI-based EEG–EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p > 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications. Conclusion: Our results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems. [ABSTRACT FROM AUTHOR]

An internal tide model ZHAO30yr is developed using 30 years of satellite altimetry sea surface height measurements from 1993 to 2022 by a newly improved mapping technique that consists of two rounds of plane wave analysis with a spatial bandpass filter in between. Prerequisite wavelengths are obtained using climatological annual-mean hydrographic profiles in the World Ocean Atlas 2018. The model contains 12 internal tide constituents: 8 mode-1 constituents (M2, S2, N2, K2, K1, O1, P1, and Q1) and 4 mode-2 constituents (M2, S2, K1, and O1). Model errors are estimated to be lower than 1 mm in amplitude, thanks to the new mapping technique and long data record. The model is evaluated by making internal tide correction to independent altimetry data in 2023. Ten constituents (but for K2 and Q1) can reduce variance on global average; K2 and Q1 can cause variance reductions in their source regions. The model decomposes the multiconstituent multimodal multidirectional internal tide field into a series of plane waves at each grid point. The decomposition reveals unprecedented features previously masked by multiwave interference. The model divides each internal tide constituent into components by propagation direction. The directionally-decomposed components reveal numerous long-range internal tidal beams associated with notable topographic features. The semidiurnal beams off the Amazon shelf and the diurnal beams in the Arabian Sea are examined in detail. ZHAO30yr is available for download at http://doi.org/10.6084/m9.figshare.28078523 (Zhao, 2024). [ABSTRACT FROM AUTHOR]

The significant brightness contrast between stars and orbiting planets often hinders the detection of exoplanets. This paper presents the development and validation of an experimental setup and digital filtering techniques for a rotational shearing interferometer (RSI) aimed at enhancing exoplanet detection. The method leverages controlled phase shifts and spatial frequency modulation through Risley and Dove prisms to isolate faint planetary signals from dominant starlight. Laboratory experiments use HeNe lasers to simulate a star-planet system, and spatial filters ensure precise wavefront alignment. The interferometer's rotational shearing capabilities enhance the accuracy of phase alignment, allowing for significant suppression of starlight and improved detection of planetary signals. Additionally, applying Fourier-based digital filtering techniques further enhances detection sensitivity by reducing background noise. Experimental results demonstrate an 80% reduction in noise and up to a 20% increase in detection sensitivity compared to traditional interferometric methods. The RSI's performance represents a significant advancement in interferometric techniques, suggesting its potential for real-world astronomical applications. However, further optimization is required to address challenges associated with space-based observations. This work sets the foundation for future research aimed at refining optical configurations and digital filtering techniques for exoplanet detection. [ABSTRACT FROM AUTHOR]

A non-contact heart rate measurement method using low-cost RGB video is proposed in this study. Only an RGB video of a human wrist is required as input for the proposed method. The method consists of spatial processing, gray value compensation, temporal processing, and heart rate measurement. Spatial processing runs firstly for preliminary image noise reduction. Gray value compensation is then used to compensate for the impact of ambient light changes. Temporal processing is used for pixel-by-pixel bandpass filtering. The filtered data is used at last for heart rate measurement. Experiments under various camera-wrist distances and light intensities are conducted to verify the effectiveness of the proposed method. Results show that the average measurement accuracy improves by 2.2 % and 1.9 % respectively by the gray value compensation algorithm under different camera-wrist distances and light intensities. High measurement accuracy can be obtained when the camera-wrist distance is within 10 cm and the light intensity is more than 400 lux. [ABSTRACT FROM AUTHOR]

Ending poverty in all its forms everywhere remains the number one Sustainable Development Goal of the United Nations 2030 Agenda. Governments face challenges in measuring socioeconomic status with fine spatial resolution because traditional data collection methods, such as censuses and surveys, are time-consuming, labor-intensive, performed at long intervals, and cover only a limited population. This work is a data-driven study to analyze the digital traces left by humans in supermarket transactions and model the relationship between consumption behavior and the average per capita income, proposing a proxy to estimate socioeconomic status at the urban neighborhood level. We analyze more than 20 million supermarket shopping transactions in Guayaquil, the most populated city in Ecuador. Using customer consumption data, we created a basket graph and fed it into a graph neural network to predict neighborhood socioeconomic status. The model was trained with spectral and spatial convolutional filters using cross-validation to select the best approach for the prediction. The results show that the Chebyshev spectral convolutional filter has the highest predictive power to predict the socioeconomic status of the neighborhood, with R 2 = 0.91 . Our proposed approach contributes to measuring socioeconomic status at the neighborhood level to support policymakers in making informed decisions about resource allocation according to the needs of different geographical areas. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is useful for studying muscle function and controlling prosthetics, but cross talk from nearby muscles often limits its effectiveness. High-density surface EMG (HD-sEMG) improves spatial resolution, allowing for the isolation of M-waves in the densely packed forearm muscles. This study assessed HD-sEMG for localizing M-waves and evaluated the impact of spatial filters on cross talk reduction. We administered peripheral nerve stimulation to activate forearm muscles in five participants. We analyzed cross talk by correlating the shape of M-waves between electrodes and used ultrasound to confirm muscle identity and location. At low-stimulation intensities, we successfully isolated M-waves with minimal cross talk without spatial filtering. Higher recruitment levels produced significant cross talk, which was reduced by applying bipolar or tripolar spatial filters. M-waves from the monopolar HD-sEMG montage showed high correlations between electrodes (r = 0.97 transversely; r = 0.95 longitudinally), while bipolar and tripolar montages showed lower correlations (bipolar: r = 0.41 transversely; r = 0.19 longitudinally; tripolar: r = 0.17 transversely; r = 0.01 longitudinally). The tripolar filter significantly reduced cross talk (51.10% amplitude decay one electrode away) compared with no filter (10.32% amplitude decay one electrode away), effectively reducing cross talk to negligible levels at distances ≥2.55 cm. Ultrasound was crucial for distinguishing true activation from artifacts caused by converging signals along muscle boundaries. Spatially filtered HD-sEMG accurately detects and isolates M-waves in the forearm, and ultrasound imaging is useful for verifying the location and identity of the muscles underlying the HD-sEMG grids. NEW & NOTEWORTHY: This study introduces an innovative approach to enhancing evoked potential measurements using high-density surface electromyography (HD-sEMG). The precision and localization of evoked potentials are significantly improved by spatial filters and ultrasound imaging, offering a novel method for better assessing motor pathway integrity. These advancements could lead to more accurate tools for detecting and treating neurological deficits, making it a significant contribution to neurophysiological research. [ABSTRACT FROM AUTHOR]

Interferometric scattering (iSCAT) microscopy enables high‐speed and label‐free detection of individual molecules and small nanoparticles. Here we apply point spread function engineering to provide adaptive control of iSCAT images using spatial light modulation. With this approach, we demonstrate improved dynamic spatial filtering, real‐time background subtraction, focus control, and signal modulation based on sample orientation. LAY DESCRIPTION: In this paper, we apply spatial light modulation to manipulate the illumination properties of an interferometric scattering microscopy. This technique, called iSCAT, can image individual molecules and nanoparticles without the need for fluorescent labels. We added a digital display device that can modulate the pattern and phase of the laser illumination. This allows us to control the light used to make images. By changing how we use the light, we can optimise the contrast of our images, make rapid changes in focus without mechanical movement, and explore the alignment of gold nano rods. [ABSTRACT FROM AUTHOR]

This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected pixels through localized contextual analysis in the immediate surroundings. Our approach employs an iterative processing method, where additional iterations do not degrade the image quality achieved after the first filtration, even with high noise densities up to 97% spatial distribution. To ensure the results are measurable and comparable with other methods, the filter's performance was evaluated using standard image quality assessment metrics. Experimental evaluations across various image databases confirm that our filter consistently provides high-quality results. The code is implemented in the R programming language, and both data and code used for the experiments are available in a public repository, allowing for replication and verification of the findings. [ABSTRACT FROM AUTHOR]

This paper aims to provide a general review of the fundamental ideas, varieties, methods, and experimental research of the most advanced frequency selective surfaces available today. Frequency-selective surfaces are periodic structures engineered to work as spatial filters in interaction with electromagnetic (EM) waves with different frequencies, polarization, and incident angles in a desired and controlled way. They are usually made of periodic elements with dimensions less than the operational wavelength. The primary issue examined is the need for more efficient, compact, and adaptable electromagnetic filtering solutions. The research method involved a comprehensive review of recent advancements in FSS design, focusing on structural diversity, miniaturization, multiband operations, and the integration of active components for tunability and reconfigurability. Key findings include the development of highly selective miniaturized FSSs, innovative applications on flexible and textile substrates, and the exploration of FSSs for liquid and strain sensing. The conclusions emphasize the significant potential of FSS technology to enhance wireless communication, environmental monitoring, and defense applications. This study provides valuable insights into the design and application of FSSs, aiming to guide future research and development in this dynamic field.

Abstract Coal Mining enterprises deploy numerous monitoring devices to ensure safe and efficient production using target detection technologies. However, deploying deep detection models on edge devices poses challenges due to high computational loads, impacting detection speed and accuracy. A mining target detection dataset has been created to address these issues, featuring key targets in coal mining scenes such as miners, safety helmets, and coal gangue. A model is proposed to improve real‐time performance for edge mining detection tasks. Detection performance is enhanced by incorporating a Pixel‐wise Normalization Spatial Attention Module (PN‐SAM) into the MobileNet‐v3 bneck structure and replacing the h‐swish activation function with Mish, providing more prosperous gradient information transfer. The proposed model, YOLO‐v4‐LSAM, shows a 3.2% mAP improvement on the VOC2012 dataset and a 2.4% improvement on the mining target dataset compared to YOLO‐v4‐Tiny, demonstrating its effectiveness in mining environments. These enhancements enable more accurate and efficient detection in resource‐constrained edge environments, contributing to safer and more reliable monitoring in coal mining operations.

The concurrent optimization of topology and fibre orientation is a promising approach to pursue higher strength and lighter weight of variable-stiffness structures. This paper suggests a novel discrete-continuous scheme for the concurrent optimization. Considering the global convergence, Discrete Material Optimization is first utilized to select a dominant angle for each element from several predefined candidate angles. However, some regions with multiaxial stress make it difficult to select the definite angles for the elements. Therefore, the Sequential Binary-Phase Topology Optimization is suggested to guarantee the uniqueness of the element to candidate angle mapping. Moreover, to obtain better mechanical properties, Continuous Fibre Angle Optimization with a spatial filter is introduced to optimize fibre continuity. Several classic numerical examples are used to verify the excellent performance of the suggested method in terms of fibre angle convergence and show robustness regarding sensitivity to the initial setting. [ABSTRACT FROM AUTHOR]

Brain imaging performed in natural settings is known as mobile brain and body imaging (MoBI). One of the features which distinguishes MoBI and laboratory-based experiments is the body posture. Previous studies pointed to mechanical, autonomic, cortical and cognitive differences between upright stance and sitting or reclining. The purpose of this study was to analyse effects of posture on eye-movement related potentials (EMRP) recorded during free viewing of human faces. A 64-channel wireless EEG was recorded from 14 participants in either standing or reclining postures while they freely viewed pictures of emotional faces displaying fear, anger, sadness, and a neutral emotional state. Eye tracking data was used to insert triggers corresponding to the instant at which the gaze first landed on a face. Spatial filtering of the EEG data was performed using a group independent component analysis (ICA). Grand average EMRPs displayed the post-saccadic lambda component and the face-sensitive N170/vertex positive potential (VPP) complex. The lambda component but not the N170 component was stronger during reclining than upright posture. Emotional expression of faces showed no effects on EMRP components or subjective ratings. Results suggest that posture primarily affects early components of EMRPs recorded using wireless EEG recordings during free viewing of faces. Thus, findings from evoked potential data obtained in seated individuals, e.g., in laboratory experiments, should be interpreted with caution in MoBI experiments with posture affecting primarily the early latency component. [ABSTRACT FROM AUTHOR]

Key points A fundamental question in sensory neuroscience revolves around how neurons represent complex visual stimuli. In mammalian primary visual cortex (V1), neurons decode intricate visual features to identify objects, with most being selective for edge orientation, but with half of those also developing invariance to edge position within their receptive fields. Position invariance allows cells to continue to code an edge even when it moves around. Combining feature selectivity and invariance is integral to successful object recognition. Considering the marsupial–eutherian divergence 160 million years ago, we explored whether feature selectivity and invariance was similar in marsupials and eutherians. We recovered the spatial filters and non‐linear processing characteristics of the receptive fields of neurons in wallaby V1 and compared them with previous results from cat cortex. We stimulated the neurons in V1 with white Gaussian noise and analysed responses using the non‐linear input model. Wallabies exhibit the same high percentage of orientation selective neurons as cats. However, in wallabies we observed a notably higher prevalence of neurons with three or more filters compared to cats. We show that having three or more filters substantially increases phase invariance in the V1s of both species, but that wallaby V1 accentuates this feature, suggesting that the species condenses more processing into the earliest cortical stage. These findings suggest that evolution has led to more than one solution to the problem of creating complex visual processing strategies. Previous studies have shown that the primary visual cortex (V1) in mammals is essential for processing complex visual stimuli, with neurons displaying selectivity for edge orientation and position. This research explores whether the visual processing mechanisms in marsupials, such as wallabies, are similar to those in eutherian mammals (e.g. cats). The study found that wallabies have a higher prevalence of neurons with multiple spatial filters in V1, indicating more complex visual processing. Using a non‐linear input model, we demonstrated that neurons with three or more filters increase phase invariance. These findings suggest that marsupials and eutherian mammals have evolved similar strategies for visual processing, but marsupials have condensed more capacity to build phase invariance into the first step in the cortical pathway. [ABSTRACT FROM AUTHOR]

Ecological community structure, which has traditionally been described in terms of taxonomic units, is driven by dispersal and environmental filters. Traits have recently been recognized as alternative units for quantifying community parameters, but they may have important differences with taxonomic units. For example, as taxon‐based community structure is determined by the local species pool, it may be more dispersal‐limited, whereas trait‐based community structure may be more regulated by environmental conditions because traits are less tied to specific habitat locations. This implies that the relative importance of these two filters may vary depending on the units describing community structure, but no study has yet quantified how the contributions of these filters can differ. In this study, we examined zooplankton assemblages in 87 artificial reservoirs throughout the Japanese archipelago to quantify the relative importance of two filters by examining the effects of spatial configuration (reflecting dispersal filters) and biotic and abiotic variables (reflecting environmental filters) for taxon‐ and trait‐based community structure. Variation in the taxon‐based community structure was explained equally well by the spatial and the environmental variables, while variation in the trait‐based community structure was explained more by environmental variables. These results support the idea that environmental filters play a more central role in determining trait‐based community structures, and show that the relative importance of spatial and environmental filters changes with the way we define community structure. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs. [ABSTRACT FROM AUTHOR]

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

In recent years, significant advancements in high-repetition-rate, high-average-power mid-infrared laser pulses have enabled the generation of tabletop high-flux coherent soft x-ray harmonics for photon-hungry experiments. However, for practical applications, it is crucial to effectively filter out the driving beam from the high harmonics. In this study, we leverage the distinctive properties of a Bessel–Gauss (BG) beam to introduce a novel approach for spatial filtering, specifically targeting soft x-ray harmonics, releasing with a high-photon flux simultaneously. Our simulations reveal that by finely adjusting the focus geometry and gas pressure, the BG beam naturally adopts an annular shape, emitting high harmonics with minimal divergence in the far field. To achieve complete spatial separation of the driving beam and harmonic emissions, we pinpoint the optimal gas pressure and focusing geometry, particularly under overdriven laser intensities, for achieving good phase matching of harmonic emissions from short-trajectory electrons within the gas medium when the exact ionization level is higher than the "critical" value. Additionally, we establish scaling relations for sustaining optimal phase-matching conditions crucial for spatially separating the driving laser and the high-harmonic field, especially as the wavelength of the driving laser increases. Furthermore, our analysis demonstrates a substantial enhancement of harmonic yields by at least one order of magnitude compared to a truncated Gaussian annular beam. We also show that under accessible experimental conditions, soft x-ray photon flux up to 1010 photons/s at 250 eV can be achieved. The utilization of the BG beam opens up a promising pathway for the development of high-flux attosecond soft x-ray light sources, poised to serve a wide range of applications. [ABSTRACT FROM AUTHOR]

The effects of sea surface temperature (SST) changes induced by tropical cyclone (TC) Bilis (0604) and its preceding TC, TC Ewiniar (0603), on the intensity of TC Bilis were investigated by using numerical experiments. The observed SST changes induced by TCs Ewiniar (0603) and Bilis (0604) were first separated by using the smooth filtering and cylindrical filtering methods. The maximum SST cooling induced by TC Ewiniar (0603) exceeded 3°C, and that induced by the two TCs, Ewiniar (0603) and Bilis (0604), exceeded 6°C. The simulation results show that the SST cooling induced by TC Ewiniar (0603) and by two TCs reduced the intensity of TC Bilis (0604) by 6.2 m s-1 and 7.6 m s-1 within 60 h, respectively. Without the SST cooling induced by TC Ewiniar (0603), TC Bilis (0604) could have developed into a typhoon. The cold wake induced by TC Ewiniar (0603) was an important factor in inhibiting the intensity of TC Bilis (0604). Based on the analysis of the physical processes of the simulation results, a schematic diagram of the effects of SST changes induced by TCs on the intensity of TC Bilis (0604) was presented. The SST cooling reduced the upward heat flux and moisture flux at the surface of TC mainly within a radius of two latitudes, which in turn decreased the conditional instability and atmospheric moisture in the lower layer of TC, followed by weakening in TC precipitation, TC warm core, the upward vertical velocity of TC, and the inward radial wind in the lower layer of TC. The intensity of TC Bilis (0604) decreased accordingly. [ABSTRACT FROM AUTHOR]

This paper aims to provide a general review of the fundamental ideas, varieties, methods, and experimental research of the most advanced frequency selective surfaces available today. Frequency-selective surfaces are periodic structures engineered to work as spatial filters in interaction with electromagnetic (EM) waves with different frequencies, polarization, and incident angles in a desired and controlled way. They are usually made of periodic elements with dimensions less than the operational wavelength. The primary issue examined is the need for more efficient, compact, and adaptable electromagnetic filtering solutions. The research method involved a comprehensive review of recent advancements in FSS design, focusing on structural diversity, miniaturization, multiband operations, and the integration of active components for tunability and reconfigurability. Key findings include the development of highly selective miniaturized FSSs, innovative applications on flexible and textile substrates, and the exploration of FSSs for liquid and strain sensing. The conclusions emphasize the significant potential of FSS technology to enhance wireless communication, environmental monitoring, and defense applications. This study provides valuable insights into the design and application of FSSs, aiming to guide future research and development in this dynamic field. [ABSTRACT FROM AUTHOR]

The genus Zephyranthes is widely represented in Mexico, with 37 species of ornamental and medical importance. However, basic aspects of the genus, such as the environmental variables that determine its presence in certain sites, have not yet been addressed, which limits the knowledge of its ecology, potential applications and possible conservation strategies. Potential distribution models were generated with data on the presence of 13 species of the genus Zephyranthes, using 28 bioclimatic and edaphic variables with the maximum entropy method (Maxent). Of these variables, the most important and least correlated for each species were chosen by principal component analysis (PCA); the occurrence data were obtained from digital platforms and filtered to reduce spatial autocorrelation. The resulting models, had AUC values > 0.90 and Kappa index values > 0.6, in addition to being significant according to the results of the binomial test applied (p < 0.05). Maximum temperatures and humidity, as well as annual precipitation, are relevant environmental variables for the niche models. Most species are distributed in the biogeographic province of the Transmexican Volcanic Belt. Zephyranthes concolor and Zephyranthes lindleyana were the species with the largest potential range. The species with the most restricted potential distribution were Zephyranthes citrina and Zephyranthes sessilis. The most determinant variables for species with neotropical affinity are different from those identified for Nearctic species, reflecting niche differentiation, congruent with the evolutionary history of Zephyranthes. [ABSTRACT FROM AUTHOR]

Maintaining a high level of safety awareness among drivers is essential to ensure the safe operation of automated vehicles (AVs). Many factors can influence the model's performance in achieving accuracy, such as the application of spatial filters, the type of feature selection, and the classifier. Complex modeling, accuracy achievement, and learning time are also practically difficult. No study has discussed the application of the Laplacian Spatial Filter (LSF) to driver vigilance classification performance. Therefore, this study aimed to analyze driving vigilance detection using LSF and linear classification models. The study involved signal preprocessing and feature extraction in signal energy, followed by the application of Kruskal - Wallis (KW) and Minimum Redundancy - Maximum Relevance (MRMR) for feature selection. Finally, various classification models such as Linear Discriminant Analysis (LDA), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) were used for exploration. The results were significant, with SVM without LSF achieving the highest average accuracy of 84.26% in the intra-subject and 70.15% across the subject. Based on this study, LSF was not recommended for EEG-based driver vigilance detection. [ABSTRACT FROM AUTHOR]

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BackgroundMethodResultsConclusionAs the elderly population is growing worldwide and communication is increasingly relayed by visual interfaces, identifying age-related changes in the visual perception of complex stimuli is critical. We examined the effect of spatial frequency filtering on object categorization in young (mean 25 years) and older (mean 65 years) participants.The stimuli used were low spatial frequency (LSF, cutoff 8 cpi) or high spatial frequency (HSF, cutoff 24 cpi) images of objects of various categories, and hybrid images composed of a LSF object superimposed on a HSF object from a different semantic category. Participants were asked to press a key when they detected an object from a specified category (e.g., animal).Young participants categorized efficiently LSF and HSF images, and exhibited a bias towards the HSF component of hybrid images. Older participants showed a better performance on LSF than on HSF images and exhibited a strong bias towards the LSF component of hybrid image.The results show that LSF are better preserved than HSF in older people. The greater interference of LSF than HSF in older than in young adults suggests that LSF are available earlier than HSF in older people. [ABSTRACT FROM AUTHOR]

ABSTRACT Detecting hotspots in origin–destination (OD) flows is crucial for understanding spatial interaction patterns of geographical phenomena. Because of the influence of global spatial autocorrelation, local flow hotspots are often misidentified or overlooked. To address this issue, this study proposes a weighted moving average method based on binomial coefficients (i.e., flow Yang Chizhong filtering) to model the global spatial autocorrelation structure from a geometric perspective and detects flow clusters from the residuals obtained by removing the global spatial autocorrelation structure from the original flow data. Flow Yang Chizhong filtering upgrades a spatial filtering method, that is, Yang Chizhong filtering, from a spatial point and polygon data to spatial flow data through redefining the convolution kernel. A multiscale geometric approximation strategy is employed to identify the optimal global spatial autocorrelation structure. Experimental results on simulated and Beijing taxi OD flow datasets demonstrate that the global spatial autocorrelation structures constructed by flow Yang Chizhong filtering are more accurate than those constructed by the trend surface analysis. Consequently, a flow hotspot detection method combined with the flow Yang Chizhong filtering can effectively identify local flow hotspots masked by global autocorrelation structures. [ABSTRACT FROM AUTHOR]

Mask-based multichannel speech enhancement methods based on artificial neural networks estimate a mask that is applied to the multichannel input signal or a reference channel to obtain the estimated desired signal. For the estimation, both spectral and spatial cues from the multichannel input can be used. However, the interplay of the two inside the neural network is typically unknown. In this contribution, we propose a framework to analyze neural spatiospectral filters (NSSFs) with respect to their capabilities to extract and represent spatial information. We explicitly take the characteristics of the training target signal into account and analyze its effect on the functionality of the NSSF. Using two conceptually different NSSFs as example, we show that not all NSSFs use spatial information under all circumstances and that the training target signal has a significant influence on the spatial filtering behavior of an NSSF. These insights help to assess the signal processing capabilities of neural networks and allow to make informed decisions when configuring, training, and deploying NSSFs. [ABSTRACT FROM AUTHOR]

The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, preserving the natural spatial variability and enabling precise, high-resolution tracking of eddy dynamics. This dataset provides daily information on eddy characteristics, such as size, intensity, and polarity, over a 30-year period (1993–2023), capturing complex eddy interactions, including splitting and merging events that often produce networks of interconnected eddies. This unique approach challenges the traditional single-trajectory perspective, offering a nuanced view of eddy life cycles as dynamically linked trajectories. In addition to traditional metrics, TOEddies identifies both the eddy core (characterized by maximum azimuthal velocity) and the outer boundary, offering a detailed representation of eddy structure and enabling precise comparisons with in situ data. To demonstrate its value, we present a statistical overview of eddy characteristics and spatial distributions, including generation, disappearance, and merging/splitting events, alongside a comparative analysis with existing global eddy datasets. Among the multi-year observations, TOEddies captures coherent, long-lived eddies with lifetimes exceeding 1.5 years, while highlighting significant differences in the dynamic properties and spatial patterns across datasets. Furthermore, this study integrates TOEddies with 23 years of colocalized Argo profile data (2000–2023), allowing for a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties. This atlas aims to be a valuable resource for the oceanographic community, providing an open dataset that can support diverse applications in ocean dynamics, climate research, and marine resource management. [ABSTRACT FROM AUTHOR]

To address the performance and computational complexity issues in speech enhancement for hearing aids, a speech enhancement algorithm based on a microphone array and a lightweight two-stage convolutional recurrent network (CRN) is proposed. The algorithm consists of two main modules: a beamforming module and a post-filtering module. The beamforming module utilizes directional features and a complex time-frequency long short-term memory (CFT-LSTM) network to extract local representations and perform spatial filtering. The post-filtering module uses analogous encoding and two symmetric decoding structures, with stacked CFT-LSTM blocks in between. It further reduces residual noise and improves filtering performance by passing spatial information through an inter-channel masking module. Experimental results show that this algorithm outperforms existing methods on the generated hearing aid dataset and the CHIME-3 dataset, with fewer parameters and lower model complexity, making it suitable for hearing aid scenarios with limited computational resources. [ABSTRACT FROM AUTHOR]

Coal Mining enterprises deploy numerous monitoring devices to ensure safe and efficient production using target detection technologies. However, deploying deep detection models on edge devices poses challenges due to high computational loads, impacting detection speed and accuracy. A mining target detection dataset has been created to address these issues, featuring key targets in coal mining scenes such as miners, safety helmets, and coal gangue. A model is proposed to improve real‐time performance for edge mining detection tasks. Detection performance is enhanced by incorporating a Pixel‐wise Normalization Spatial Attention Module (PN‐SAM) into the MobileNet‐v3 bneck structure and replacing the h‐swish activation function with Mish, providing more prosperous gradient information transfer. The proposed model, YOLO‐v4‐LSAM, shows a 3.2% mAP improvement on the VOC2012 dataset and a 2.4% improvement on the mining target dataset compared to YOLO‐v4‐Tiny, demonstrating its effectiveness in mining environments. These enhancements enable more accurate and efficient detection in resource‐constrained edge environments, contributing to safer and more reliable monitoring in coal mining operations. [ABSTRACT FROM AUTHOR]

Background: The creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV). Methods: We trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math. Results: The initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time. Conclusions: The results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements. [ABSTRACT FROM AUTHOR]

The Australian Synchrotron Imaging and Medical Beamline (IMBL) uses a superconducting multipole wiggler (SCMPW) source, dual crystal Laue monochromator and 135 m propagation distance to enable imaging and computed tomography (CT) studies of large samples with mono‐energetic radiation. This study aimed to quantify two methods for CT dose reduction: wiggler source operation at reduced magnetic field strength, and beam modulation with spatial filters placed upstream from the sample. Transmission measurements with copper were used to indirectly quantify the influence of third harmonic radiation. Operation at lower wiggler magnetic field strength reduces dose rates by an order of magnitude, and suppresses the influence of harmonic radiation, which is of significance near 30 keV. Beam shaping filters modulate the incident beam profile for near constant transmitted signal, and offer protection to radio‐sensitive surface organs: the eye lens, thyroid and female breast. Their effect is to reduce the peripheral dose and the dose to the scanned volume by about 10% for biological samples of 35–50 mm diameter and by 20–30% for samples of up to 160 mm diameter. CT dosimetry results are presented as in‐air measurements that are specific to the IMBL, and as ratios to in‐air measurements that may be applied to other beamlines. As CT dose calculators for small animals are yet to be developed, results presented here and in a previous study may be used to estimate absorbed dose to organs near the surface and the isocentre. [ABSTRACT FROM AUTHOR]

Aiming at the problems of insufficient feature extraction ability, many mismatching points and low registration accuracy of some remote sensing image registration algorithms, this study proposes a remote sensing image registration algorithm via cyclic parameter synthesis spatial transformation network. (1) We propose a feature extraction network framework combined with the improved spatial transformation network and improved Densely Connected Networks (DenseNet), which can focus on important areas of images for feature extraction.This framework can effectively improve the feature extraction ability of the model, so as to improve the model accuracy. (2) In the matching stage, we design the coarse filter and fine filter double filter architecture. Thus, the false matching points are effectively filtered out, which not only improves the robustness of the model but also improves the registration accuracy. Compared with the two traditional methods and two deep learning methods, the experimental results of this model are better in many indexes. [ABSTRACT FROM AUTHOR]

The miniaturization of semiconductor chips creates discrepancies between the designed node size and physical values. It has resulted in a tightened on-product overlay (OPO) budget and increased the demand for improved measurement noise reduction and accuracy in optical systems. A solution utilizing moiré targets can address such challenges by enabling the amplification of small misalignments that cannot be achieved with conventional overlay targets using an image-based overlay (IBO) estimator. However, moiré patterns formed within a layer introduce noise sources and problems owing to interference from the reflected light, adversely affecting the precision of overlay measurements and limiting the effective utilization of moiré patterns. We investigate the problems associated with moiré patterns in the IBO measurement method and propose a novel overlay measurement algorithm to mitigate the problems. The proposed algorithm increases the accuracy of the filtering method in the spatial frequency domain and improves the overlay precision by approximately 2% compared with conventional measurement algorithms. The proposed low-frequency selection algorithm and signal indexing algorithm effectively address the challenges posed by high-frequency problems and signal strength degradation in moiré patterns. The proposed practical solution achieves more accurate overlay measurements in semiconductor manufacturing, enabling better control and optimization of chip fabrication processes. [ABSTRACT FROM AUTHOR]

This paper presents and evaluates an algorithm for extracting cerebral evoked potentials associated with the heartbeat (heartbeat-evoked potentials, HEP) using the independent components method. The algorithm includes simultaneous recording of the electroencephalogram (EEG) and photoplethysmogram (PPG), with extraction of EEG fragments corresponding to PPG pulse wave peaks, removal of cardiogenic activity from the resulting fragments by independent components analysis (ICA), and synchronization of the fragments with the cardiogram R-wave. The locations of sources of recorded potentials were clarified by applying a "current source density" (CSD) spatial filter. The algorithm was tested on 21 experimental participants and demonstrated a characteristic increase in the amplitude of the potential in the interval 0–400 msec after the heartbeat, i.e., the HEP. Thus, application of independent components analysis and spatial filtration to PPG-synchronized EEG fragments can isolate cerebral evoked potentials associated with heartbeats independently of the actual cardiac activity recorded by scalp leads. [ABSTRACT FROM AUTHOR]

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]

Background/Objectives: Coated drug pellets enjoy widespread use in hard gelatine capsules. In heterogeneous pellets, the drug substance is layered onto core pellets. Coatings are often applied to generate a retarded release or an enteric coating. Methods: In the present study, the thickness of a polymer coating layer on drug pellets was correlated to the drug release kinetics. Results: The question should be answered whether it is possible to stop the coating process when a layer thickness referring to an intended drug release is achieved. Inert pellets were first coated with sodium benzoate and second with different amounts of water insoluble polyacrylate in a fluidized bed apparatus equipped with a Wurster inlet. The whole process was controlled in-line and at-line with process analytical technology by the measurement of the particle size and the layer thickness. The in-vitro sodium benzoate release was investigated, and the data were linearized by different standard models and compared with the polyacrylate layer thickness. With increasing polyacrylate layer thickness the release rate diminishes. The superposition of several processes influencing the release results in release profiles corresponding approximately to first order kinetics. The coating layer thickness corresponds to a determined drug release profile. Conclusions: The manufacturing of coated drug pellets with intended drug release is possible by coating process control and layer thickness measurement. Preliminary investigations are necessary for different formulations. [ABSTRACT FROM AUTHOR]

With the wide application of electric energy storage component arrays, such as battery cell arrays, capacitor arrays, and inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot realize rapid, precise, and nondestructive localization of the faulty component within these large-scale arrays, especially for a component with an early stage short-circuit fault. To address this challenge, this paper proposes a magnetic field based method and realizes precise fault localization by inducing an alternating magnetic field from the target array, unlike previous research where a static magnetic field was induced. Through establishing a physical model of the short-circuit component as well as the whole array, a spatial filtering algorithm based on beamforming techniques is utilized to process the measured magnetic field data in real time. Both the simulation and experimental results demonstrate the capability of the proposed method in enhancing the security of electric energy storage component arrays. Within an imaging area of 80 × 80 mm2, the proposed method can accurately locate the faulty component out of a nine-component array, with an error of only 0.72 mm for capacitors and 0.91 mm for battery cells. [ABSTRACT FROM AUTHOR]

A crucial aspect of human social competence is the ability to spontaneously and rapidly infer from facial cues whether others are likely to approach us with friendliness or hostility—that is, trustworthiness. The rapid and automatic nature of these inferences has prompted the claim that they may originate from evolutionary pressures to detect potential threats, thus enhancing our chances of survival. However, the developmental origins of this social skill remain a topic of ongoing debate. Recent evidence shows that infants' brains and looking time behaviors differentiate between faces varying along the trustworthiness continuum, but the question about the nature of the facial cues they rely on remains open. In this review, we propose the spatial frequency (SF) filtering approach (i.e., the selective removal of spatial frequency bands from the image) as a useful tool for investigating this question, and specifically whether infants' discrimination of facial expressions of emotion share common visual and neural mechanisms with discrimination of facial cues associated with trustworthiness inferences. The SF filtering approach could shed light on the neural and perceptual mechanisms underlying trustworthiness perception in infancy, providing insights into whether and how these mechanisms change across development. The evidence gathered through this method would prove critical to the understanding of the developmental origins of trustworthiness perception. [ABSTRACT FROM AUTHOR]

This letter introduces a novel design approach for broadband radar cross section (RCS) reduction of frequency selective surface (FSS) radomes. The approach integrates the principles of phase cancellation and spatial filtering together through a hybrid design method. The phase cancellation is obtained through the checkerboard arrangement of units, and the spatial filtering characteristics is achieved by the slot etched on the ground plane. Experimental and simulation results demonstrate that etching slots on the ground plane maintains broadband in‐phase reflection and bandpass characteristics, thereby extending the bandwidth of RCS reduction through a combination of two operation bands. Theoretical analysis of the working mechanism is also provided using equivalent circuit models. In comparison to the conventional radomes, the proposed FSS radome achieves significant bandwidth improvement for RCS reduction, indicating that it has promising prospects in future low‐RCS radome applications. [ABSTRACT FROM AUTHOR]

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

The long‐wavelength correction (LWC) of SWOT data is intended to reduce errors related to the stability of the SWOT antenna and its attitude in orbit. The algorithms used to compute the LWC utilize SWOT KaRIn sea surface‐height (SSH) measurements and additional data, and the LWC may absorb geophysical SSH into the correction. Different LWC algorithms are used on the L2 and L3 SWOT products, which are analyzed here during the 1 day repeat (Cal/Val) mission phase lasting approximately 100 days. During this mission phase the SSH anomaly (SSHA) computed using the L3 LWC is much more realistic than the L2 LWC, as shown here by comparing spatial statistics of the L2 and L3 products. The L3 LWC algorithm is nonlinear insofar as it depends on second‐order statistics of the SSHA and multi‐satellite SSHA differences, making it difficult to quantify the extent to which it could absorb baroclinic tidal signals. To overcome this difficulty, a proxy L3 LWC algorithm is developed which mimics the L3 LWC but is strictly linear in the SSHA. The proxy LWC is applied to both idealized waveforms and to the predicted internal tide available on the products, and it is found to absorb 1% or less of the signal variance, leading to corresponding pointwise errors of 10% or less. Because the errors are at longer wavelengths and are significantly smaller amplitude than internal tide signals, the LWC impact on the measurement and interpretation of internal tides with SWOT is expected to be negligible in most applications. Plain Language Summary: In order to make the SWOT data useful for studies of sea level associated with horizontal length scales of roughly 100 km and larger, it is necessary to compute a correction which aligns the measured SSH with independent data from other satellite missions. The correction is implemented with a type of data‐driven spatial low‐pass filter; however, the data‐driven character of this filter means that it is nonlinear and its response cannot be characterized using standard techniques of linear filter analysis. In this paper a linear approximation of the long‐wavelength correction is developed which is amenable to standard linear analysis techniques. Using this linear approximation, the extent to which the long‐wavelength correction may absorb signals of interest arising from the baroclinic tides is quantified. It is found that the filter response is generally below 1% of the signal variance, which ought to be negligible in most studies of tides using data during the Cal/Val mission phase. Key Points: The long‐wavelength correction has the potential to remove geophysical signals of interestThe correction is implemented as a nonlinear data‐driven filter, making it difficult to characterize its response using linear techniquesA linear approximation of the filter is developed here and used to characterize the response to baroclinic tides in the open ocean [ABSTRACT FROM AUTHOR]

Abstract Beamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters’ outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam.

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]

This article presents an ultrathin chiral metasurface which can exhibit multiband asymmetric absorption as well as symmetric transmission in a specific frequency band outside the absorption regions. Unlike most electromagnetic metasurface absorbers, the proposed structure does not have a continuous conducting sheet at the bottom which also allows it to act as a bandpass spatial filter. The metasurface has a substrate thickness of only λhigh/62.5 and λlow/34 at the highest and lowest operational free‐space wavelengths, respectively. The transmission band is centered at 5.5 GHz, and the asymmetric absorption bands are centered at 3, 3.33, and 4.5 GHz, respectively. The operational bands can be tuned as per user requirements. The metasurface has an angular stability of 45° for both TE and TM incidence. It can be used for radar cross‐section (RCS) reduction, electromagnetic shielding, and as a spatial bandpass filter. [ABSTRACT FROM AUTHOR]

Large eddy simulations (LES) provide a methodology for both analyzing and simulating multi-scale flows when the smallest scales of motion cannot be resolved. Within environmental flows there exist numerous biogeochemical processes involving tracers undergoing reactions. In this study, we perform an a posteriori LES analysis on a direct numerical simulation of an idealized model reactive tracer subjected to three-dimensional turbulence induced by a Rayleigh–Taylor instability. The governing equations, including an advection–diffusion–reaction equation for the reactive tracer, are filtered, and the resulting sub-filter-scale terms are expressed in terms of interactions between scales. The procedure is demonstrated for a generalized degree N polynomial reaction function. Various spectral filters are applied to the data and compared. The preferential choice is to use the widest filter possible with a smoothed cutoff. The sub-filter-scale reaction term that results from filtering the reaction function is considered for each of the filter choices. When using a particularly harsh filter, local balances are found for the resolved scale and cross-scale components of the sub-filter-scale reaction term. The same result is shown for the vertical sub-filter-scale flux for both a reactive and a passive tracer. The components of the sub-filter-scale reaction and vertical flux terms involving interactions at the sub-filter-scale do not show any evidence of local balances and are distributed around the fine turbulent structures in the flow. This suggests that parameterizations for the sub-filter-scale terms would benefit from considering event specific dynamics. [ABSTRACT FROM AUTHOR]

In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. Theoretically, an implication of this fact is that a filter may know what it is looking at, but not where it is positioned in the image. In this paper, we first test this hypothesis and reveal that a surprising degree of absolute position information is encoded in commonly used CNNs. We show that zero padding drives CNNs to encode position information in their internal representations, while a lack of padding precludes position encoding. This observation gives rise to deeper questions about the role of position information in CNNs: (i) What boundary heuristics enable optimal position encoding for downstream tasks? (ii) Does position encoding affect the learning of semantic representations? (iii) Does position encoding always improve performance? To provide answers, we perform the largest case study to date on the role that padding and border heuristics play in CNNs. We design novel tasks that allow us to quantify boundary effects as a function of the distance to the border. Numerous semantic objectives reveal the effect of the border on semantic representations. Finally, we demonstrate the implications of these findings on multiple real-world tasks to show that position information can both help or hurt performance. [ABSTRACT FROM AUTHOR]

Forensic Science is the application of Scientific methods to resolve crime and legal issues. It involves various disciplines, such as Computer Science, Biology, Chemistry and Anthropology. Forensic scientists examine and analyze evidence from crime scenes, such as fingerprints, DNA, blood, or weapons. Digital proof is one of the forms of forensic evidence. It provide real time eye witness of the incident. Video recordings enable investigators to find out what exactly has transpired. Investigators use video evidence as a source for witness statements, and it aids in the search for the missing person or suspect. Video evidence is also used to testify in court and help with investigations and prosecutions. Failure of forensic science results in wrong judgement convicting innocent people and escaping criminals [1]. For most crimes high quality video recordings are often not available. video quality issues such as blurry, speckled, pixelated and low-resolution videos captured at low light are a real challenge in forensic analysis. To address such issues in this research a hybrid model using set of filters including triplemask spatial linear filter, median filter and bilateral filters are used. For denoising images, a novel image filter using sliding window convolution is proposed. For image sharpening a triplemask spatial linear filter is proposed. Triplemask spatial linear filter is created by cascading a series of filters. Identity, shift and fraction-based approach is used in mask processing. For image smoothing and to preserve the edges bilateral filter is used [2]. The performance of convolution operation is compared with distinct convolution, shift rotational convolution and scipy convolution. To handle uncertainty, imprecision, and ambiguity in real-world image data in a precise manner neutrosophic science is used in image analysis. By the generated neutrosophic set of the given input image ambiguous regions in the image are detected. Feature selection is made by calculating the entropy of different image regions. From the generated neutrosophic set entropy the degree of uncertainty, within the input image is quantified. The intensity distributions are measured using entropy values. In feature selection regions with highest and lowest entropy values containing face images are selected, visualized and processed to further aid in forensic analysis in detecting the culprits. Neutrosophic AHP is used for prioritizing criteria based on face detection and indeterminancy. Face detection is performed using single shot detector framework with a resnet base network, trained using caffe deep learning framework. face recognition process is performed using dlibs [9] state-of-the-art face recognition model built with deep learning [10]. Face recognition in this research distance-based similarity measure using neutrosophic sets is performed. These measures are used in conjunction with facenet[59] face recognition algorithm to improve the robustness and accuracy over traditional methods. The model has an accuracy of 99.38% on the labelled faces in the wild benchmark. [ABSTRACT FROM AUTHOR]

Conventional active SONAR systems often use beamformers and matched filters separately to extract bearing and range information from the received signal and offer a straightforward way of creating a two‐dimensional map of the environment. In SONAR systems the minimum‐variance‐distortionless‐response beamformer (MVDR beamformer) is a commonly used type of beamformer, which will reconstruct the receive signal from a certain direction optimally. In terms of detecting the transmit signal, the most used method is the conventional matched filter. Both algorithms are simple to implement and perform well under various noise scenarios. The proposed method combines the beamformer and matched filter by introducing an extended channel model that allows the derivation of a multichannel Wiener filter to solve for the unknown reflection coefficients of the complete two‐dimensional environment. This results in adaptively calculated filter weights that will drastically improve the performance compared to a separate MVDR beamformer and matched filter. In addition, a parameter is introduced with which one can arbitrarily adjust the focus between angular and temporal resolution depending on the application. After the derivation, the performance is demonstrated with simulations and measurements. [ABSTRACT FROM AUTHOR]