Temporal Filtering Research Articles

Cavity-assisted resonance fluorescence from a nitrogen-vacancy center in diamond

The nitrogen-vacancy center in diamond is an attractive resource for the generation of remote entangled states owing to its optically addressable and long-lived electronic spin. However, its low native fraction of coherent photon emission, ~3%, undermines the achievable spin-photon entanglement rates. Here, we couple a nitrogen-vacancy center with a narrow extrinsically-broadened linewidth (159 MHz), hosted in a micron-thin membrane, to an open microcavity. The resulting Purcell factor of ~1.8 increases the zero-phonon line fraction to over 44%. Operation in the Purcell regime, together with an efficient collection of the zero-phonon-line photons, allows resonance fluorescence to be detected for the first time without any temporal filtering. We achieve a >10 signal-to-laser background ratio. This selective enhancement of the center’s zero-phonon transitions could increase spin-spin entanglement success probabilities beyond an order of magnitude compared to state-of-the-art implementations, and enable powerful quantum optics techniques such as wave-packet shaping or all-optical spin manipulation.

The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain.

The ability to detect fast responses with functional MRI depends on the speed of hemodynamic responses to neural activity, because hemodynamic responses act as a temporal low-pass filter which blurs rapid changes. However, the shape and timing of hemodynamic responses are highly variable across the brain and across stimuli. This heterogeneity of responses implies that the temporal specificity of fMRI signals, or the ability of fMRI to preserve fast information, could also vary substantially across the cortex. In this work we investigated how local differences in hemodynamic response timing affect the temporal specificity of fMRI. We used ultra-high field (7T) fMRI at high spatiotemporal resolution, studying the primary visual cortex (V1) as a model area for investigation. We used visual stimuli oscillating at slow and fast frequencies to probe the temporal specificity of individual voxels. As expected, we identified substantial variability in temporal specificity, with some voxels preserving their responses to fast neural activity more effectively than others. We investigated which voxels had the highest temporal specificity, and tested whether voxel timing was related to anatomical and vascular features. We found that low temporal specificity is only weakly explained by the presence of large veins or cerebral cortical depth. Notably, however, temporal specificity depended strongly on a voxel's position along the anterior-posterior anatomical axis of V1, with voxels within the calcarine sulcus being capable of preserving close to 25% of their amplitude as the frequency of stimulation increased from 0.05Hz to 0.20Hz, and voxels nearest to the occipital pole preserving less than 18%. These results indicate that detection biases in high-resolution fMRI will depend on the anatomical and vascular features of the area being imaged, and that these biases will differ depending on the timing of the underlying neuronal activity. While we attribute this variance primarily to hemodynamic effects, neuronal nonlinearities may also influence response timing. Importantly, this spatial heterogeneity of temporal specificity suggests that it could be exploited to achieve higher specificity in some locations, and that tailored data analysis strategies may help improve the detection and interpretation of fast fMRI responses.

Observation of Standing Slow Magneto-acoustic Waves in a Flaring Active Region Corona Loop

Intensity fluctuations are frequently observed in different regions and structures of the solar corona. These fluctuations may be caused by magneto-hydrodynamic (MHD) waves in coronal plasma. MHD waves are prime candidates for the dynamics, energy transfer, and anomalous temperature of the solar corona. In this paper, analysis is conducted on intensity and temperature fluctuations along the active region coronal loop (NOAA AR 13599) near solar flares. The intensity and temperature as functions of time and distance along the loop are extracted using images captured by the Atmospheric Imaging Assembly (AIA) instrument onboard the Solar Dynamics Observatory (SDO) space telescope. To observe and comprehend the causes of intensity and temperature fluctuations, after conducting initial processing, and applying spatial and temporal frequency filters to data, enhanced distance-time maps of these variables are drawn. The space-time maps of intensities show standing oscillations at wavelengths of 171, 193, and 211 Å with greater precision and clarity than earlier findings. The amplitude of these standing oscillations (waves) decreases and increases over time. The average values of the oscillation period, damping time, damping quality, projected wavelength, and projected phase speed of standing intensity oscillations are in the range of 15–18 minutes, 24–31 minutes, 1.46″–2″, 132″–134″, and 81–100 km s−1, respectively. Also, the differential emission measure peak temperature values along the loop are found in the range of 0.51–3.98 MK, using six AIA passbands, including 94, 131, 171, 193, 211, and 335 Å. Based on the values of oscillation periods, phase speeds, damping time, and damping quality, it is inferred that the fluctuations in intensity are related to standing slow magneto-acoustic waves with weak damping.

Dynamic convolutional time series forecasting based on adaptive temporal bilateral filtering

Time series data typically contain complex dynamic patterns, which not only include linear trends and seasonal variations but also significant nonlinear changes and complex dependencies. Currently, feature extraction methods for time series data primarily employ mixed-mode extraction within the temporal domain, neglecting the effective extraction and analysis of nonlinear characteristics between different observation points and the interdependencies between variables. To address these issues, we designed an adaptive temporal bilateral filtering module that effectively preserves and highlights the nonlinear features and patterns in time series while filtering out noise and redundant information. We also designed a nonlinear feature adaptive extraction module that integrates a gating mechanism and deformable convolutions. This design allows the model to adaptively adjust the shape of the convolutional kernels according to different time steps and conditions. This enables accurate capture and extraction of nonlinear features between various time observations and dependencies between different variables. Additionally, we use stacked convolutional layers to extract local contextual features, addressing fluctuations in local features caused by changes in data distribution in real-world scenarios. In summary, we propose DCNet, a dynamic convolutional network based on an adaptive temporal bilateral filter, and evaluated its performance on twelve real-world datasets. The results indicate that DCNet consistently achieves state-of-the-art performance in both short-term and long-term forecasting tasks, with favorable runtime efficiency.

HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalized POI recommendation

The presence of user-generated ratings has dramatically facilitated the development of recommendation systems to aid users in discovering relevant and personalized points of interest (POI). It is worth mentioning that users’ choices and preferences are not static but rather dynamic, reflecting the ever-changing nature of human experiences and influences. Furthermore, the utilization of social influence and geographical proximity of users is still insufficient to capture the homophily effect within networks. In this paper, an interesting Hybrid Gate-based Graph Convolutional Network (HyGate-GCN) combining with feature vectors embedding and interaction, where a modified gated-GCN is proposed for personalized recommendations by adequately employing the behavior of users’ check-ins, temporal properties of users’ decisions, social properties of users, as well as the user/POI profile information data. Specifically, a novel POI graph reflecting the geographical proximity is first established to describe the behavior of users’ check-ins and, at the same time, an improved overlap ratio about POIs is employed to effectively describe temporal properties of users’ decisions. Then, an attention mechanism is developed to encode feature vectors of both the users and POIs, with the objective of assigning higher importance to features that are deemed relevant. Furthermore, a temporal Kalman filter dynamically estimating ratings is developed to exploit the information about the evolving preferences of users over time. Finally, a modified gated-GCN model with merging and refining gates is constructed to effectively acquire the homophily phenomenon in both trust network graphs and spatial adjacency matrix graphs of users and POIs respectively. Experimental results provide evidence of the effectiveness of our approach in improving accuracy and personalization.

A Global Mosaic of Temporally Stable Pixels for Radiometric Calibration of Optical Satellite Sensors Using Landsat 8

Calibrating optical sensors has become a priority to maintain data quality and ensure consistency among sensors from different agencies. Achieving and monitoring radiometric calibration often involves the identification of temporally stable targets on the Earth’s surface. Although some locations across North Africa have traditionally been used as primary targets for calibration purposes, it is crucial to explore alternative options to account for potential changes in these sites over time. This study conducted a global assessment of pixel-level temporal stability using Landsat 8 OLI data, with the primary goal of identifying regions suitable for global radiometric calibration efforts. This work followed a two-stage approach, including the testing and selection of an effective combination of statistical tests to differentiate between temporally stable and unstable pixels and the generation of a worldwide mosaic of temporally stable pixels through a per-pixel statistical analysis employing a combination of Spearman’s rho and Pettitt’s test for assessing long-term trends and detecting change points. Notably, comparing the temporal mean top-of-atmosphere (TOA) reflectance before and after applying the generated temporal filter to a site with documented unstable pixels revealed a substantial reduction in mean variation, up to 6%. In addition, slopes observed in the pre-filter mean TOA reflectance, ranging between −0.002 and −0.005, became zero or near-zero and statistically insignificant after the temporal filter was applied, demonstrating a reduction in total uncertainties by 3 to 4%. These findings evidence the potential of this work, placing it as a potential foundation in the continuous search to identify additional targets for global radiometric calibration efforts.

Multiplexed MicroRNA biomarker detection by bridging lifetime filtering imaging and dynamic chemical labeling

MicroRNAs (miRs) have emerged as promising biomarkers for early disease diagnosis and personalized treatment monitoring. However, their clinical utility has been hampered by technical limitations. Dynamic chemical labeling (DCL) based on capturing abasic PNA probes and reactive nucleobases, known as SMART bases, is a PCR-free approach that has proven very useful for the direct interrogation of circulating miRs. In this work, we expand the palette of tools available for the detection of DCL miR by synthesizing a new SMART nucleobase called SMART-C-Eu. This nucleobase contains a stable lanthanide cryptate. Using this SMART-C-Eu base and time-gated (TG) luminescence imaging, we successfully detect and quantify miR-122–5p in human serum samples. miR-122–5p is a well-known biomarker for drug-induced liver injury. Through a bead-counting analysis approach, statistical robustness is improved and miR-122–5p concentrations are detected in the nanomolar range. Furthermore, we extend this approach to multiplexed detection of three different miRs (miR-371a-3p, miR-451a-5p, and miR-122–5p) using spectral and temporal filtering. Importantly, we designed a user-independent multiplexed analysis using machine learning algorithms for automatic bead classification. Although the sensitivity of this technique must be further improved to detect miRs at lower concentrations, the method represents a significant advancement in miR analysis by combining ML segmentation using lifetime and intensity images. In addition, the technique offers multiplexing capabilities and the potential for automation, paving the way for more accurate and robust clinical applications in the future.

Eddy–Internal Wave Interactions: Stimulated Cascades in Cross-Scale Kinetic Energy and Enstrophy Fluxes

Abstract The interactions between oceanic mesoscale eddies, submesoscale currents, and internal gravity waves (IWs) are investigated in submesoscale-resolving realistic simulations in the North Atlantic Ocean. Using a novel analysis framework that couples the coarse-graining method in space with temporal filtering and a Helmholtz decomposition, we quantify the effects of the interactions on the cross-scale kinetic energy (KE) and enstrophy fluxes. By systematically comparing solutions with and without IW forcing, we show that externally forced IWs stimulate a reduction in the KE inverse cascade associated with mesoscale rotational motions and an enhancement in the KE forward cascade associated with divergent submesoscale currents, i.e., a “stimulated cascade” process. The corresponding IW effects on the enstrophy fluxes are seasonally dependent, with a stimulated reduction (enhancement) in the forward enstrophy cascade during summer (winter). Direct KE and enstrophy transfers from currents to IWs are also found, albeit with weaker magnitudes compared with the stimulated cascades. We further find that the forward KE and enstrophy fluxes associated with IW motions are almost entirely driven by the scattering of the waves by the rotational eddy field, rather than by wave–wave interactions. This process is investigated in detail in a companion manuscript. Finally, we demonstrate that the stimulated cascades are spatially localized in coherent structures. Specifically, the magnitude and direction of the bidirectional KE fluxes at submesoscales are highly correlated with, and inversely proportional to, divergence-dominated circulations, and the inverse KE fluxes at mesoscales are highly correlated with strain-dominated circulations. The predominantly forward enstrophy fluxes in both seasons are also correlated with strain-dominated flow structures.

Solution to the cocktail party problem: A time-reversal active metasurface for multipoint focusing

The cocktail party effect is the capability to focus one’s auditory attention on particular audio sources while ignoring other audio sources. We propose an experimental strategy to reproduce this ability by designing a time-dependent metasurface composed of independent active mirrors. Each active mirror is a programmable acoustic unit cell capable of hearing, computing, and re-emitting acoustic signals: each of them acts as a convolution filter. The proper configuration of the metasurface temporal filters allows one to establish an acoustic communication link between groups of individuals immersed in the noisy environment: a multiple-user multiple-input, multiple-output acoustic system is built. Published by the American Physical Society 2024

Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.

The widely adopted paradigm in brain-computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels.

Cavity-enhanced photon indistinguishability at room temperature and telecom wavelengths

Indistinguishable single photons in the telecom-bandwidth of optical fibers are indispensable for long-distance quantum communication. Solid-state single photon emitters have achieved excellent performance in key benchmarks, however, the demonstration of indistinguishability at room-temperature remains a major challenge. Here, we report room-temperature photon indistinguishability at telecom wavelengths from individual nanotube defects in a fiber-based microcavity operated in the regime of incoherent good cavity-coupling. The efficiency of the coupled system outperforms spectral or temporal filtering, and the photon indistinguishability is increased by more than two orders of magnitude compared to the free-space limit. Our results highlight a promising strategy to attain optimized non-classical light sources.

Influence of wildfires on the conflict (2006–2022) in eastern Ukraine using remote sensing techniques (MODIS and Sentinel-2 images)

Humans primarily alter fire regimes in three ways: changing the distribution and density of ignitions, shifting the seasonality of burning, or altering available fuels. Remote sensing techniques are a highly feasible and effective tool for monitoring wildfires, delineating burn perimeters, describing patterns of fire occurrence, characterizing the degree of intensity, or fire risk mapping. In this study MODIS and Sentinel-2 satellite images were used to analyze the burned areas in the Luhansk and Donetsk Regions of Ukraine (2006–2022), and specifically how the political and military hostilities in 2014 impacted fire activity. The burned areas detected by the MODIS sensor were selected using a size (32 ha) and temporal (May–October) filter. The Relativized Burn Ratio (RBR) index was used to identify burned areas, while the Normalized Difference Water Index (NDWI) was employed to discriminate the water bodies in Sentinel-2 images. We analyzed the burned area data over the 17-year period, eight of them during the years before the conflict and the other nine after the conflict. The results show that burned area has increased from the pre-conflict period (2008–2013) by 689,898 ha to the post-conflict period (2014–2022) around the contact line. In addition, the frequency of fires has increased in the post-war period, especially towards the south of Donetsk. Finally, Sentinel-2 imagery was used to map Large Wildfires (LAWF), which means fires with a burned area >500 ha. Our study concluded that MODIS MCD64A1 underestimates the burned area with a recorded difference of up to 40% of the area in one of the analyzed fires. In addition, an analysis of the burned areas classified according to the main land use has been performed for both sensors. The results obtained show a regression for the total area of R2 = 0,97 and both sensors show high correlation between them for each land use analyzed. Grasslands and agricultural land covers were most affected by LAWFs during the conflict period (2014–2022), noting that fires near urban entities were also more frequent in the war period.

High-energy, low-jitter, narrowband ps probe laser for kHz-rate fs/ps coherent anti-Stokes Raman scattering.

Hybrid fs/ps coherent anti-Stokes Raman scattering (CARS) thermometry often utilizes ps probe pulses derived from pulse shaping or spectrally filtering the primary laser source or by synchronization with a low repetition rate external laser. This results in limited energy, spectral resolution, and/or repetition rate of the ps probe. In this work, a master-oscillator power-amplifier (MOPA) laser was synchronized to the oscillator of a Ti:sapphire regenerative amplifier to achieve high-energy (600 µJ), narrowband (58 ps) probe pulses at kHz repetition rates. Temporal filtering allows the pulse characteristics to be adjusted for each application. At 25 Torr, relevant to high-speed flows, the kHz-rate MOPA system generated signal-to-noise ratios 3× higher in nitrogen and had improved precision relative to a 10 ps probe derived from spectral filtering and the power-amplifier. The MOPA system also enabled single-shot ro-vibrational hybrid fs/ps CARS thermometry in 650 K heated air.

Tau-Cell-Based Analog Silicon Retina With Spatio- Temporal Filtering and Contrast Gain Control.

Developing precise artificial retinas is crucial because they hold the potential to restore vision, improve visual prosthetics, and enhance computer vision systems. Emulating the luminance and contrast adaption features of the retina is essential to improve visual perception and efficiency to provide an environment realistic representation to the user. In this article, we introduce an artificial retina model that leverages its potent adaptation to luminance and contrast to enhance vision sensing and information processing. The model has the ability to achieve the realization of both tonic and phasic cells in the simplest manner. We have implemented the retina model using 0.18 μm process technology and validated the accuracy of the hardware implementation through circuit simulation that closely matches the software retina model. Additionally, we have characterized a single pixel fabricated using the same 0.18 μm process. This pixel demonstrates an 87.7-% ratio of variance with the temporal software model and operates with a power consumption of 369 nW.

Arbitrary-Scale Video Super-resolution Guided by Dynamic Context

We propose a Dynamic Context-Guided Upsampling (DCGU) module for video super-resolution (VSR) that leverages temporal context guidance to achieve efficient and effective arbitrary-scale VSR. While most VSR research focuses on backbone design, the importance of the upsampling part is often overlooked. Existing methods rely on pixelshuffle-based upsampling, which has limited capabilities in handling arbitrary upsampling scales. Recent attempts to replace pixelshuffle-based modules with implicit neural function-based and filter-based approaches suffer from slow inference speeds and limited representation capacity, respectively. To overcome these limitations, our DCGU module predicts non-local sampling locations and content-dependent filter weights, enabling efficient and effective arbitrary-scale VSR. Our proposed multi-granularity location search module efficiently identifies non-local sampling locations across the entire low-resolution grid, and the temporal bilateral filter modulation module integrates content information with the filter weight to enhance textual details. Extensive experiments demonstrate the superiority of our method in terms of performance and speed on arbitrary-scale VSR.

Motion Capture in Mixed-Reality Applications: A Deep Denoising Approach

Motion capture is a fundamental technique in the development of video games and in film production to animate a virtual character based on the movements of an actor, creating more realistic animations in a short amount of time. One of the ways to obtain this movement from an actor is to capture the motion of the player through an optical sensor to interact with the virtual world. However, during movement some parts of the human body can be occluded by others and there can be noise caused by difficulties in sensor capture, reducing the user experience. This work presents a solution to correct the motion capture errors from the Microsoft Kinect sensor or similar through a deep neural network (DNN) trained with a pre-processed dataset of poses offered by Carnegie Mellon University (CMU) Graphics Lab. A temporal filter is implemented to smooth the movement, given by a set of poses returned by the deep neural network. This system is implemented in Python with the TensorFlow application programming interface (API), which supports the machine learning techniques and the Unity game engine to visualize and interact with the obtained skeletons. The results are evaluated using the mean absolute error (MAE) metric where ground truth is available and with the feedback of 12 participants through a questionnaire for the Kinect data.

TelEnfermagem na Prevenção e Controlo do Risco Vascular na Pessoa Idosa com Hipertensão Arterial: uma Revisão Scoping

Introduction The global increase in the older adult population has led to a higher demand for healthcare services, as greater longevity is associated with a rise in the prevalence of chronic conditions like hypertension (HTN). Geographic barriers and current limitations in healthcare services pose significant challenges to access, especially for older adults. Various national and international organizations advocate that telenursing should be widely available to mitigate these challenges by enhancing care delivery, improving proximity to nursing services, and achieving better health outcomes. Objective To map the available evidence on the contribution of telenursing to the prevention and management of cardiovascular risk in older adults with HTN. Methods Following the Joanna Briggs Institute methodology, we conducted the scoping review and used the PRISMA-ScR checklist as a complementary guide. We searched the databases MEDLINE, CINAHL, Open Access Scientific Repository, and Google Scholar for articles and documents up to May 2023, with no temporal filter. Results Seven studies met the inclusion criteria. Six major dimensions characterize the potential of telenursing in older adults with HTN: cardiovascular risk prevention and management, self-management of hypertension, improved quality of life, therapeutic adherence, prevention of clinical inertia, and monitoring of adverse events. Conclusion Telenursing contributes to the prevention and management of cardiovascular risk and fosters a partnership relationship with older adults, supporting self-management of their health and promoting self-care.

Estimates of Temporal Edge Detection Filters in Human Vision

Edge detection is an important process in human visual processing. However, as far as we know, few attempts have been made to map the temporal edge detection filters in human vision. To that end, we devised a user study and collected data from which we derived estimates of human temporal edge detection filters based on three different models, including the derivative of the infinite symmetric exponential function and temporal contrast sensitivity function. We analyze our findings using several different methods, including extending the filter to higher frequencies than were shown during the experiment. In addition, we show a proof of concept that our filter may be used in spatiotemporal image quality metrics by incorporating it into a flicker detection pipeline.

Neuronal temporal filters as normal mode extractors

Physical Review Research To generate actions in the face of physiological delays, the brain must predict the future. Here we explore how prediction may lie at the core of brain function by considering a neuron predicting the future of a scalar time series input. Assuming that the dynamics of the lag vector (a vector composed of several consecutive elements of the time series) are locally linear, normal mode decomposition decomposes the dynamics into independently evolving (eigen)modes allowing for straightforward prediction. We propose that a neuron learns the top mode and projects its input onto the associated subspace. Under this interpretation, the temporal filter of a neuron corresponds to the left eigenvector of a generalized eigenvalue problem. We mathematically analyze the operation of such an algorithm on noisy observations of synthetic data generated by a linear system. Interestingly, the shape of the temporal filter varies with the signal-to-noise ratio (SNR): a noisy input yields a monophasic filter and a growing SNR leads to multiphasic filters with progressively greater number of phases. Such variation in the temporal filter with input SNR resembles that observed experimentally in biological neurons.Published by the American Physical Society2024

SAR interferometry on full scatterers: Mapping ground deformation with ultra-high density from space

Spaceborne time series SAR interferometry (TS-InSAR) technology has been widely applied in ground deformation monitoring. The current popular TS-InSAR are carried out mainly on permanent scatterers (PS) or distributed scaterrers (DS), which can map the ground deformation on sparse targets with high or moderate coherence. However, high-precision and high-density deformation monitoring is always restricted by atmospheric artifacts and surface decorrelation, especially over nonurban areas. Here we show that the ground deformation can be precisely mapped with high density by spaceborne InSAR technique. We proposed a full scatterers InSAR (FS-InSAR) methodology, which can significantly improve the quality of the interferograms by applying a dual-scale temporal low-pass filter (DTLF) to separate both atmospheric and noisy phases from deformation phases, without external atmospheric data. Simulation experiments were conducted to determine the small-scale and large-scale window sizes and evaluate the effectiveness of DTLF. Then we applied the FS-InSAR method to the Tianjin-Tangshan region, China using Sentinel-1 data, assessed the regenerated differential low-frequency phases, and validated the results with leveling measurements and groundwater depth data. What's more, detailed ground deformation was retrieved over the Tangshan mining area with an unprecedented density of 98.55%. Our results demonstrate that the FS-InSAR strategy contrasts sharply with the previous PS-InSAR or DS-InSAR methods by simultaneously solving the two bottleneck problems of atmospheric artifacts and decorrelation in most cases except for water bodies or dense vegetations such as rainforest, thereby it is of great importance for future monitoring and understanding the ground deformation to prevent and control geological disasters.