Signal Time Research Articles

High dynamic range spatial heterodyne one-dimensional imaging spectroscopy based on a digital micromirror device

Spatial heterodyne one-dimensional imaging spectrometer (SHIS) can simultaneously acquire hyperspectral information from different fields of view (FOVs). However, the dynamic range of SHIS is limited by the detector's performance. We propose a high dynamic range spatial heterodyne one-dimensional imaging spectroscopy (HD-SHIS) based on a digital micromirror device (DMD), which can control the exposure time of each FOV signal by adjusting the flip time of micromirrors on an M-bit DMD, realizing the simultaneous detection of strong and weak signals in FOVs with a theoretical improvement of the dynamic range by dB. Meanwhile, HD-SHIS utilizes a DMD to realize the Hadamard modulation of interference data in the spectral dimension, which can be used with the linear array detector to complete the detection of the imaging spectrum. We have built an HD-SHIS principle prototype and carried out dynamic range experiments. The experimental results show that HD-SHIS can achieve 48 dB dynamic range improvement by utilizing an 8-bit display width DMD.

A false trigger pulse elimination circuit for low-frequency energy harvesting active rectifiers

Abstract In this article, a false trigger pulse elimination circuit is proposed to be used for a low-frequency energy harvesting active rectifier. It is divided into two-stage circuits, each stage almost including a switching signal generation circuit, a switching inverter, and an inverter to eliminate false triggering pulses on the rising edge and falling edge respectively. There is an edge detection circuit in each switching signal generation circuit, generating a switching signal that controls the switching inverter’s on-off behavior. This allows the inverter to achieve inverter output voltage clamping by turning off its inverter function and turning it into a switch that is only turned on at the high or low level. At the same time, a thyristor delay element is used to determine and adjust the effective level time of the switching signal, which can reach over 150 μs, completely covering the rising or falling edge false trigger pulse so that no GΩ-level resistors are needed in the low-frequency field. The results show that the low-frequency false trigger pulse elimination circuit proposed in this article can better eliminate false trigger pulses on both rising and falling edges. It can also achieve on-chip integration, reduce the power consumption of the self-adaptive delay compensation loop, and enhance the stability of the circuit.

An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.

Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.

Free-Breathing Functional Pulmonary Proton MRI: A Novel Approach Using Voxel-Wise Lung Ventilation (VOLVE) Assessment in Healthy Volunteers and Patients With Chronic Obstructive Pulmonary Disease.

In respiratory medicine, there is a need for sensitive measures of regional lung function that can be performed using standard imaging technology, without the need for inhaled or intravenous contrast agents. To describe VOxel-wise Lung VEntilation (VOLVE), a new method for quantifying regional lung ventilation (V) and perfusion (Q) using free-breathing proton MRI, and to evaluate VOLVE in healthy never-smokers, healthy people with smoking history, and people with chronic obstructive pulmonary disease (COPD). Prospective pilot. Twelve healthy never-smoker participants (age 30.3 ± 12.5 years, five male), four healthy participants with smoking history (>10 pack-years) (age 42.5 ± 18.3 years, one male), and 12 participants with COPD (age 62.8 ± 11.1 years, seven male). Single-slice free-breathing two-dimensional fast field echo sequence at 3 T. A novel postprocessing was developed to evaluate the MR signal changes in the lung parenchyma using a linear regression-based approach, which makes use of all the data in the time series for maximum sensitivity. V/Q-weighted maps were produced by computing the cross-correlation, lag and gradient between the respiratory/cardiac phase time course and lung parenchyma signal time courses. A comparison of histogram median and skewness values and spirometry was performed. Kruskal-Wallis tests with Dunn's multiple comparison tests to compare VOLVE metrics between groups; Spearman correlation to assess the correlation between MRI and spirometry-derived parameters; and Bland-Altman analysis and coefficient of variation to evaluate repeatability were used. A P-value <0.05 was considered significant. Significant differences between the groups were found for ventilation between healthy never-smoker and COPD groups (median XCCV, LagV, and GradV) and perfusion (median XCCQ, LagQ, and GradQ). Minimal bias and no significant differences between intravisit scans were found (P range = 0.12-0.97). This preliminary study showed that VOLVE has potential to provide metrics of function quantification. 2 TECHNICAL EFFICACY: Stage 1.

Repolarization gradient induces local depolarization abnormalities in Brugada Syndrome

Abstract Background Transmural voltage gradient in right ventricular outflow tract (RVOT) induces diagnostic ECG-phenotype in Brugada syndrome(BrS). Moreover, RVOT depolarization abnormalities have been described as contributors to BrS phenotype and arrhythmogenesis. Purpose To investigate the effect of ajmaline administration on J-elevation of RVOT unipolar signals in BrS patients. Moreover, we wanted to assess whether ajmaline-induced unipolar J-point elevation variations induced local depolarization abnormalities. Methods 21 BrS patients with spontaneous type-1 ECG pattern were enrolled. Due to the absence of BrS ECG pattern at the study, patients underwent RV endocardial mapping with the CARTO3 system, before (PRE) and after (POST) ajmaline administration. The PRE and POST data were exported from CARTO and converted into Matlab format using OpenEP. J-elevation for each point was calculated as the amplitude of the unipolar signal at J wave with respect to baseline. Activation time (AT) of each point was defined as the difference between local depolarization (minimum dV/dt of the unipolar signal) and surface ECG depolarization (time of the minimum signal on V2). With an automatic algorithm, corresponding PRE and POST points were selected. The difference between POST and PRE of each parameter was calculated to obtain the differential values delta-J and delta-AT. Differential values were then interpolated on the mesh of each subject to obtain 3D maps. The deltaJ map was divided into four intervals based on quartiles. We then selected a ROI 'IN' on a region with the greatest deltaJ variation, and a second ROI 'OUT' on the zone of lowest variation. The same ROIs were applied to the deltaAT map. The mean value of the respective differential parameters was extracted in each ROI. Results Greatest delta-J values were found in the RVOT/anterior wall. delta-J in the ROI 'IN' was greater than in the ROI 'OUT' (1.68 [1.11-2.28] vs 0.56 [0.38-0.93] mV, p&amp;lt;0.001). delta-AT in the ROI ‘IN’ was greater than in the ROI ‘OUT’ (27.96 [20.39-44.89] vs 7.62 [4.24-16.69] ms, p&amp;lt;0.001). A good correlation was found between delta-J and delta-AT, considering the data for the two ROIs together (Spearman R coefficient=0.71, p&amp;lt;0.001). Conclusions Our study shows that the repolarization gradient, evaluated by localized delta-J increase in RVOT, justifies BrS ECG phenotype and local depolarization abnormalities. A strong correlation was present in these RVOT areas between J-elevation variation and slow conducting zones.

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method.

Алгоритм совместной оценки и компенсации ошибок временной и частотной синхронизации сигналов с возможностью регулирования точности оценки

Представлен анализ влияния ошибок синхронизации на обработку сигнала, дан обзор существующих подходов оценки и компенсации ошибок временной и частотной синхронизации, предложен новый подход, позволяющий совместно оценить смещения по времени и частоте, обеспечить более точную оценку по сравнению с существующими аналогами и, более того, регулировать точность оценки дробной ошибки и временной, и частотной синхронизации.

Bilayer piezoionic sensors for enhanced detection of dynamic, static, and directional forces with self-healing capabilities

Electronic skin (e-skin) aims to replicate the tactile feedback mechanism of natural skin, offering great potential across robotics, healthcare monitoring, and human-machine interfaces. Traditional e-skin systems utilize electron-based sensing through capacitance and piezoresistivity for static forces, and piezoelectricity and triboelectricity for dynamic forces. However, the former group struggles with dynamic stimuli and external power dependencies, while the latter excels in self-powering but fails in static detection. Addressing these gaps, iontronics emerges as a versatile solution, providing innovative avenues for energy harvesting, sensing, and actuation through ion redistribution in response to varied stimuli, but shows limitations in output signals and response times. We report herein the development of bilayerstructured piezoionic materials with significantly enhanced output signal and response time. The piezoionic bilayer structure not only harnesses an ion accumulation interface, leading to amplified output signals and fast response times, but also exhibits an ion-dipole interactive self-healing property. Our sensor generates a signal output of 95 mV and features a quick response time of 30 ms, with an impressive self-healing capability of ∼100%. It demonstrates the ability to accurately detect both static and dynamic forces, including specific movements like bending and vibrations, maintaining its robustness through 8000 cycles of repetitive deformation. Its utility is further proven in a practical setting with a braille reader device, highlighting its potential for incorporation into a wide array of soft, autonomous iontronic systems that require enhanced sensitivity, rapid detection capabilities, and self-sustainability.

REPOLARIZATION GRADIENT CAUSES LOCAL DEPOLARIZATION ABNORMALITIES IN BRUGADA SYNDROME

Abstract Background Transmural voltage gradient in RVOT induces ECG–phenotype in Brugada syndrome(BrS). Moreover,RVOT depolarization abnormalities have been described as contributors to BrS phenotype and arrhythmogenesis. Purpose To investigate the effect of ajmaline administration on J–elevation of RVOT unipolar signals in BrS patients. Moreover,we wanted to assess whether ajmaline–induced unipolar J–point elevation variations induced local depolarization abnormalities. Methods 21 BrS patients with spontaneous type–1 ECG pattern were enrolled. Due to the absence of BrS ECG pattern at the study, patients underwent RV endocardial mapping with the CARTO3 system, before (PRE) and after (POST) ajmaline administration. The data were exported from CARTO and converted into Matlab format using OpenEP. J–elevation for each point was calculated as the amplitude of the unipolar signal at J wave with respect to baseline. Activation time (AT) of each point was defined as the difference between local depolarization (minimum dV/dt of the unipolar signal) and surface ECG depolarization(time of the minimum signal on V2). With an automatic algorithm, corresponding PRE and POST points were selected. The difference between POST and PRE of each parameter was calculated to obtain the differential values delta–J and delta–AT. Differential values were then interpolated on the mesh of each subject to obtain 3D maps. The deltaJ map was divided into four intervals based on quartiles. We then selected a ROI ‘IN‘ on a region with the greatest deltaJ variation, and a second ROI ‘OUT‘ on the zone of lowest variation. The same ROIs were applied to the deltaAT map. The mean value of the respective differential parameters was extracted in each ROI. Results Greatest delta–J values were found in the RVOT/anterior wall. delta–J in the ROI ‘IN‘ was greater than in the ROI ‘OUT‘ (1.68 [1.11–2.28] vs 0.56 [0.38–0.93] mV, p&amp;lt;0.001). delta–AT in the ROI ‘IN’ was greater than in the ROI ‘OUT’ (27.96 [20.39–44.89] vs 7.62 [4.24–16.69] ms, p&amp;lt;0.001). A good correlation was found between delta–J and delta–AT, considering the data for the two ROIs together (Spearman R coefficient=0.71, p&amp;lt;0.001). Conclusions: Our study shows that the repolarization gradient, evaluated by localized delta–J increase in RVOT, justifies BrS ECG phenotype and local depolarization abnormalities. A strong correlation was present in these RVOT areas between J–elevation variation and slow conducting zones.

Engineering Cell Instructive Microenvironments for In Vitro Replication of Functional Barrier Organs.

Multicellular organisms exhibit synergistic effects among their components, giving rise to emergent properties crucial for their genesis and overall functionality and survival. Morphogenesis involves and relies upon intricate and biunivocal interactions among cells and their environment, that is, the extracellular matrix (ECM). Cells secrete their own ECM, which in turn, regulates their morphogenetic program by controlling time and space presentation of matricellular signals. The ECM, once considered passive, is now recognized as an informative space where both biochemical and biophysical signals are tightly orchestrated. Replicating this sophisticated and highly interconnected informative media in a synthetic scaffold for tissue engineering is unattainable with current technology and this limits the capability to engineer functional human organs in vitro and in vivo. This review explores current limitations to in vitro organ morphogenesis, emphasizing the interplay of gene regulatory networks, mechanical factors, and tissue microenvironment cues. In vitro efforts to replicate biological processes for barrier organs such as the lung and intestine, are examined. The importance of maintaining cells within their native microenvironmental context is highlighted to accurately replicate organ-specific properties. The review underscores the necessity for microphysiological systems that faithfully reproduce cell-native interactions, for advancing the understanding of developmental disorders and disease progression.

An efficient estimator for source localization in WSNs using RSSD and TDOA measurements

Range-based localization has received considerable attention in wireless sensor networks due to its ability to efficiently locate the unknown source of a signal. However, the localization accuracy with a single set of measurements may be inadequate, especially in dynamic and noisy environments. To mitigate this problem, received signal strength difference (RSSD) and time difference of arrival (TDOA) measurements are used to develop an efficient estimator to reduce the bias and improve localization accuracy. First, the RSSD/TDOA-based maximum likelihood (ML) localization problem is transformed into a hybrid information nonnegative constrained least squares (HI-NCLS) framework. Then, this framework is used to develop an effective bias-reduction localization approach (BRLA) with a two-step linearization process. The first step employs a linear solving method (LSM) which exploits an active set method to obtain a sub-optimal estimator. The second step uses a bias reduction method (BRM) to mitigate the correlation from linearization and a weighted instrumental variables matrix (IVM) which is weakly correlated with the noise but strongly correlated with the data matrix (DM) is used in place of the DM. Performance results are presented which demonstrate that the proposed BRLA provides better localization performance than state-of-the-art methods in the literature.

Ephemeris Matching Reveals False Positive Validated and Candidate Planets from the K2 Mission

Data from the Kepler space telescope have led to the discovery of thousands of planet candidates. Most of these candidates are likely to be real exoplanets, but a significant number of false positives still contaminate the sample, especially in candidate lists from the K2 mission. Identifying and rejecting the false positives lurking in the planet candidates sample is important for prioritizing follow-up resources and measuring the most accurate population statistics. Here, we identify false positives in the K2 planet candidate sample using a technique called “ephemeris matching,” in which we compare the period and transit time of different signals. When signals from different stars show the same period and time of transit, we can conclude that at least one of the two signals is contamination. We identify 43 false positives among published K2 planet candidates (nearly 2% of the complete list), one of which (K2-256 b) was previously validated as genuine exoplanet. This work increases the reliability of the K2 planet sample and helps boost confidence in the surviving planet candidates.

Noise Immunity of Binary Chirp Signals

The results of a study of the noise immunity of receiving signals with linear frequency modulation in telecommunication information transmission systems are considered. Analytical expressions for the synthesis of binary signals by linear frequency modulation with control parameters are obtained. The dependence of the structure of linear frequency modulation signals on the shaping parameters has been studied. The optimization problem of finding the maximum value of the Euclidean distance for linear frequency modulation signals of a binary structure has been solved by modeling. Differences have been established in the noise immunity of receiving linear frequency modulation signals in relation to the binary structures of opposite and orthogonal signals. Diagrams of time and spectral fragments of linear frequency modulation signals are presented, explaining the essence of the research results. Graphs are presented for comparative assessment of the noise immunity of receiving binary signals of various structures in terms of the bit error probability as a function of the signal-to-noise ratio in the channel. The essence of correlation processing of signals with a large base is revealed.

Modeling of traffic at a road crossing and optimization of waiting time of the vehicles

Traffic management is a critical activity, the population is increasing day by day and so the traffic on the road is also increasing. Traffic jams and long waiting queues of vehicles at the road crossing are now part of everyone's life. The traffic lights used at the crossing to regulate the traffic play a vital role in the smooth functioning of traffic movement. At a crossing of four roads, it has been observed that giving an equal amount of green light to all roads is meaningless since the arrival of traffic on different paths is different. Importantly, the arrival rate is responsible for all traffic jams, long queues, and increased waiting time. Therefore, this paper suggests a green light allocation scheme for all paths i depending on the arrival rate of the vehicles. Thus, the allocation of green light will be dynamic. Further, weight is also computed, where more arrival rate means more weight, thereby assigning more time to the green signal. This will help in reducing the long queue length, residual traffic, and long waiting times. On simulating the traffic with the traffic data, the proposed optimized green light allocation scheme to path i reduces the residue traffic to negligible, allowing smooth traffic flow even during peak hours. The work also provides a proficient optimization of the waiting time of vehicles accumulated during the red light. According to the simulation, the maximum time assigned for the green signal during the peak hour of 9:30 AM to 10:00 AM for paths i, where 1≤i≤4 is 39.96, 33.36, 26.64, and 20.04 seconds respectively. Similarly, during the second rush hour of 5:00 PM to 6:00 PM, the simulation assigns a green signal time of 41.4, 37.2, 24.84, and 16.56 seconds for corresponding paths 1–4. Thus, the proposed work suggests an effective traffic management scheme at the four-road crossing.

Resonance Detection Method of AC-DC Hybrid Micro-grid Based on Wavelet Transform and Fast Fourier Transform

The grid connection of a mass of distributed power sources in the AC/DC hybrid micro-grid will cause harmonics, voltage sags, voltage fluctuations, and flicker in the grid, which seriously affects the power quality of the grid. Therefore, the power quality problems such as the resonance generated by the AC/DC hybrid micro-grid need to be studied urgently. In this paper, a resonance detection technology based on the combination of wavelet transform (WT) and fast Fourier transform (FFT) is proposed. This method uses wavelet transform to process the high-frequency and low-frequency parts of the signal separately, and eliminates part of the interference signal. The method then uses the FFT to obtain the resonance content. The AC/DC hybrid micro-grid resonance detection method based on WT and FFT can not only determine the time and amplitude of the resonance signal, but also filter the interference signal and improve the accuracy of frequency domain analysis. It is verified by simulation that the algorithm can detect the resonance of the AC/DC hybrid micro-grid.

Measurement of distance and speed between transmitter and receiver using the propagation time of underwater acoustic communication signals with orthogonal signal division multiplexing

This paper proposes a procedure for measuring transmitter–receiver (Tx–Rx) distance using acoustic communication signals to realize communication and positioning underwater with the same signal and device. Specifically, we focus on an underwater acoustic communication using orthogonal signal division multiplexing, where a pilot signal and messages are placed in the time–frequency domain. In this paper, we show that measurements of both Tx–Rx distance and speed are available by utilizing the pilot signal. We conducted a sea trial and evaluated the performance of the proposed method. During the experiment, the Tx–Rx distance was 50–550 m. The obtained results suggest that the proposed method can measure the Tx–Rx distance and speed in a specific environment. The mean absolute error and the standard deviation error of ranging are 4.0 and 2.7 m, respectively, while those of measurement speed are 0.13 and 0.22 m s−1, respectively.

A New Data Processing Approach for the SHPB Test Based on PSO-TWER

This study addresses the challenge of accurately determining the arrival time of stress wave signals in SHPB test data processing. To eliminate human error, we introduce the time-window energy ratio method and evaluate six filters for noise reduction using box fractal dimensions. A mathematical model is established to optimize the stress equilibrium and impact process, which is solved using particle swarm optimization, resulting in the PSO-TWER method. We explore the impact of inertia weight and calculation methods on optimization outcomes, defining a stress equilibrium evaluation index. The results indicate that time-window length significantly affects arrival-time outputs, and the dynamic inertia weight factor enhances optimization convergence. The method accurately determines arrival times and effectively screens test data, providing a robust approach for SHPB test data processing.

Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors

Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown–Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.

Evaluation of physiological response and synchronisation errors during synchronous and pseudosynchronous stimulation trials

Rhythm perception and synchronisation is musical ability with neural basis defined as the ability to perceive rhythm in music and synchronise body movements with it. The study aimed to check the errors of synchronisation and physiological response as a reaction of the subjects to metrorhythmic stimuli of synchronous and pseudosynchronous stimulation (synchronisation with an externally controlled rhythm, but in reality controlled or produced tone by tapping) Nineteen subjects without diagnosed motor disorders participated in the study. Two tests were performed, where the electromyography signal and reaction time were recorded using the NORAXON system. In addition, physiological signals such as electrodermal activity and blood volume pulse were measured using the Empatica E4. Study 1 consisted of adapting the finger tapping test in pseudosynchrony with a given metrorhythmic stimulus with a selection of preferred, choices of decreasing and increasing tempo. Study 2 consisted of metrorhythmic synchronisation during the heel stomping test. Numerous correlations and statistically significant parameters were found between the response of the subjects with respect to their musical education, musical and sports activities. Most of the differentiating characteristics shown evidence of some group division in the undertaking of musical activities. The use of detailed analyses of synchronisation errors can contribute to the development of methods to improve the rehabilitation process of subjects with motor dysfunction, and this will contribute to the development of an expert system that considers personalised musical preferences.

Transfer of polarity information via diffusion of Wnt ligands in C. elegans embryos

Different signaling mechanisms concur to ensure robust tissue patterning and cell fate instruction during animal development. Most of these mechanisms rely on signaling proteins that are produced, transported, and detected. The spatiotemporal dynamics of signaling molecules are largely unknown, yet they determine signal activity's spatial range and time frame. Here, we use the Caenorhabditis elegans embryo to study how Wnt ligands, an evolutionarily conserved family of signaling proteins, dynamically organize to establish cell polarity in a developing tissue. We identify how Wnt ligands, produced in the posterior half of the embryos, spread extracellularly to transmit information to distant target cells in the anterior half. With quantitative live imaging and fluorescence correlation spectroscopy, we show that Wnt ligands diffuse through the embryo over a timescale shorter than the cell cycle, in the intercellular space, and outside the tissue below the eggshell. We extracted diffusion coefficients of Wnt ligands and their receptor Frizzled and characterized their co-localization. Integrating our different measurements and observations in a simple computational framework, we show how fast diffusion in the embryo can polarize individual cells through a time integration of the arrival of the ligands at the target cells. The polarity established at the tissue level by a posterior Wnt source can be transferred to the cellular level. Our results support a diffusion-based long-range Wnt signaling, which is consistent with the dynamics of developing processes.