Temporal Modulation Research Articles

Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots

Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in an edge computing mobile device using an intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an improvement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety.

Multi-Attention Recurrent Neural Network for Multi-Step Prediction of Chlorophyll Concentration

Abstract: Chlorophyll prediction facilitates the comprehension of red tide characteristics and enables early warning. In practice, it is formulated as a multivariate time series forecasting problem aimed at forecasting future chlorophyll concentrations by considering both exogenous factors and chlorophyll. However, the multi-step prediction of chlorophyll concentration poses a formidable challenge due to the intricate interaction between factors and the long temporal dependence between input sequences. In this work, we propose a Multi-attention Recurrent Neural Network (MaRNN) for the multi-step prediction of chlorophyll concentration. The MaRNN comprises an encoder incorporating two-stage spatial attention and a decoder employing temporal attention. The encoder first learns the significance of exogenous factors for prediction in the first phase, and subsequently captures the spatial correlation between the exogenous sequence and chlorophyll sequence in the second phase. The decoder further excavates input sequences that exhibit a strong correlation with the task through temporal attention module, thereby enhancing the prediction accuracy of the model. Experiments conducted on two real-world datasets reveal that MaRNN not only surpasses state-of-the-art methods in performance, but also offers interpretability for chlorophyll prediction.

Characteristic of temporal light modulation reference meter and standard light source for calibration and verification

Abstract The measurement of the temporal light modulation (TLM) of a light source is gaining increasing importance owing to its detrimental effects on human vision and health. To establish traceability in TLM measurements, the National Institute of Metrology, China (NIM), established a TLM reference meter (TLMM) and standard TLM light source (STLS) for calibration and verification services. The TLMM and STLS employ detector- and source-based approaches, respectively, to define the standards for TLM quantities. The TLMM rapidly acquires light waveform data from the TLM light sources and calculates corresponding TLM quantities. Frequency calibration result is with an expanded uncertainty of 1 × 10−5, frequency response function is almost flat in range of 1 kHz–25 kHz, and response time is less than 8 × 10−6 s. The electrical noise was evaluated at approximately 1000:1–5000:1. The STLS generates an arbitrary TLM waveform by converting the virtual waveform into an actual LED output light waveform. For the frequency response function, the relative amplitude decreased by 1% with a frequency of 15 kHz, and response time was less than 5 × 10−6 s. The linearity of the frequency dependency was studied with ratio varied by approximately 0.19% when the periodic triangle excitation in frequency band from 1 Hz to 1 kHz. Experimental comparisons demonstrated that TLM quantities (frequency, percentage flicker, flicker index, short-term flicker severity P st LM , and stroboscopic effect visibility measure ( SVM )) are consistent between the TLMM and STLS devices on 14 typical TLM waveforms. For P st LM , most of the deviations were in the range of −0.019–0.079 (except for one with 0.182). For SVM , most of the deviations were in the range of −0.001–0.003 (except for one with 0.014).

Spatially and Temporally Optimized Audio‐Driven Talking Face Generation

AbstractAudio‐driven talking face generation is essentially a cross‐modal mapping from audio to video frames. The main challenge lies in the intricate one‐to‐many mapping, which affects lip sync accuracy. And the loss of facial details during image reconstruction often results in visual artifacts in the generated video. To overcome these challenges, this paper proposes to enhance the quality of generated talking faces with a new spatio‐temporal consistency. Specifically, the temporal consistency is achieved through consecutive frames of the each phoneme, which form temporal modules that exhibit similar lip appearance changes. This allows for adaptive adjustment in the lip movement for accurate sync. The spatial consistency pertains to the uniform distribution of textures within local regions, which form spatial modules and regulate the texture distribution in the generator. This yields fine details in the reconstructed facial images. Extensive experiments show that our method can generate more natural talking faces than previous state‐of‐the‐art methods in both accurate lip sync and realistic facial details.

Visibility and annoyance of the phantom array effect varies with age and history of migraine

The phantom array effect (PAE) is a series of repeated images that may be perceived when a person moves their eyes in large saccades across a light source (or a specular reflection of that light source) that is modulating in output over time. Fifty-five people, including a group of 25 who experience migraine, evaluated the visibility and annoyingness of phantom arrays produced by 85 unique temporal light modulation waveforms (including sine, rectangular, complex and DC waveforms) generated using an LED placed against a black background. Those with migraine exhibited higher average visibility compared to those without migraine ( p = 0.019) and were relatively more sensitive at higher frequencies ( p < 0.001). Younger participants also found more stimuli to be visible ( p < 0.001). The threshold sensitivity function was similar to that developed for the phantom array visibility measure (PAVM), and PAVM was effective in predicting visibility ( R2 = 0.87 for the relevant region of PAVM < 3). While those in the migraine group did not report seeing the PAE more often in everyday life at a statistically significant level, they reported being more annoyed by it and having more unwanted physiological responses (headaches, eye fatigue and distraction/disorientation). Members of the migraine group were also more likely to have changed their behaviour in architectural spaces (such as leaving a restaurant with ‘flickering’ lights). In the four hours after completing the experiment, 64% of the migraine group (vs. 19% of the non-migraine group) reported experiencing discomfort or an adverse reaction. In particular, 41% reported experiencing a headache (vs. 8% for those in the non-migraine group).

Improvement of temporal light artefact effects in commercial LED lamps

Temporal light modulation and its potential adverse visual effects on human observers, collectively known as temporal light artefacts (TLAs), have been limited by the European Union Ecodesign regulation. Limitations on two key TLA metrics, flicker and stroboscopic effect, were introduced in September 2021. This study analyses the impact of this regulation on commercial LED lamps by measuring multiple different consumer-grade LED E27 retrofit lamps by several manufacturers before and after the regulation. The lamps purchased after the Ecodesign regulation seem to favour a certain type of LED driver that provides the best TLA behaviour of all lamp types. The regulation, with respect to TLA, has resulted in the replacement of ill-behaving lamp types in the market by newer lamps with better TLA characteristics.

SAPPNet: students’ academic performance prediction during COVID-19 using neural network

A variety of reasons have made it more difficult for educators and tutors to anticipate students’ performance. Numerous researchers have used various predictive models to identify students who may be at-risk of dropping out early. Additionally, these methods were used to forecast final semester grades based on various datasets. However, these prediction models still fall short of meeting educational management requirements. In this paper, we propose the deep learning (DL) based model named students academic performance prediction network (SAPPNet) to predict the students’ grades. We consider the questionnaire-based Jordan University dataset which contains demographic information, usage of digital tools before and after COVID-19, sleep times before and after COVID-19, social interaction, psychological state, and academic performance. SAPPNet consists of spatial convolution modules which are designed to extract spatial dependencies includes categorical and numerical attributes that represent static features (gender, level/year, age, digital tools used before and after COVID-19, psychological condition using prolonged e-learning tools) and temporal module for temporal dependencies involves sequences that capture changes before and after COVID-19. Additionally, we also try to implement classical machine learning (ML) models including support vector machine, k nearest neighbor, decision tree, and random forest, and DL models named artificial neural network, convolutional neural network, long short-term memory, and students learning prediction network. Simulation results show that SAPPNet achieved the best performance compared to state-of-the-art methods, with an accuracy, precision, recall, and an F1-score of 93%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93\\%$$\\end{document}. The proposed model with spatial and temporal modules improves the prediction performance, and it implies new aspect of the educational dataset.

Agile manipulation of the time-frequency distribution of high-speed electromagnetic waves

Controlling the temporal evolution of an electromagnetic (EM) wave’s frequency components, the so-called time-frequency (TF) distribution, in a versatile and real-time fashion remains very challenging, especially at the high speeds (> GHz regime) required in contemporary communication, imaging, and sensing applications. We propose a general framework for manipulating the TF properties of high-speed EM waves. Specifically, the TF distribution is continuously mapped along the time domain through phase-only processing, enabling its user-defined manipulation via widely-available temporal modulation techniques. The time-mapping operations can then be inverted to reconstruct the TF-processed signal. Using off-the-shelf telecommunication components, we demonstrate arbitrary control of the TF distribution of EM waves over a full bandwidth approaching 100 GHz with nanosecond-scale programmability and MHz-level frequency resolution. We further demonstrate applications for mitigating rapidly changing frequency interference terms and the direct synthesis of fast waveforms with customized TF distributions. The reported method represents a significant advancement in TF processing of EM waves and it fulfills the stringent requirements for many modern and emerging applications.

Generation of patterns and higher harmonics in 1D quantum droplets in tilted and driven quasi-periodic confinements

The generation of patterns by breaking the spatial symmetry in external confinement is a captivating area of physics. The emergence of patterns is a fundamental inquiry spanning various disciplines such as nonlinear optics, condensed matter physics, and fluid dynamics. The article investigates the generation of a variety of patterns in a one-dimensional binary mixture of Bose–Einstein condensate forming quantum droplets. By solving the extended Gross–Pitaevskii equation in the presence of tilted and driven engineered bi-chromatic optical lattices (BOL), the out-of-equilibrium dynamics of droplets under strong dc and ac fields are illustrated. Under the influence of a dc field, a stripe-like pattern emerges in the temporal domain, while the scenario with ac fields demonstrates temporal periodic and bi-periodic oscillations of density waves. The width and period of formed patterns are directly correlated with the strength of ac and dc fields. Moreover, temporal modulation of the BOL potential depth yields various harmonics in the oscillations of the condensate density pattern. Through Fast Fourier Transform (FFT) analysis, it is confirmed that these harmonics encompass multiple and combinational frequencies, suggesting potential applications in generating desired frequency combs within quantum droplets. We have also carried out a thorough numerical stability check of the obtained solutions and found them sufficiently stable.

Temporally enhanced abnormal behavior detection based on multi-channel coupling

Abnormal behavior detection in surveillance videos based on deep learning has becomea hot topic in current research. However, due to the complexity and variability of crowd movement and external environment, detecting abnormal behavior faces great challenges. Current abnormal behavior recognition models have limitations in feature extraction and pay insufficient attention to dynamic temporal features. To address these problems, a spatiotemporal enhancement anomaly detection method based on multi-channel coupling is proposed. Based on the SlowFast network, a multi-channel coupled temporal enhancement module and a spatial enhancement module are introduced respectively to extract more discriminative static and dynamic feature information. A large number of experiments are carried out on three benchmark datasets (Violent Flow, Hockey Fight, and Real-life Violence Situations). The results show that the prediction accuracy of the proposed abnormal behavior recognition method reaches 95.3%、97.3%and respectively 94%, thus verifying the effectiveness of the method.

Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models.

Role of chirping in pulsed single-photon spectroscopy

We investigate the precision of estimating the interaction strength between a two-level system (TLS) and a single-photon pulse when the latter is subject to chirping. We consider linear, quadratic, and sinusoidal temporal phases applied to Gaussian and exponential temporal profiles. At the asymptotic time, when the TLS has fully decayed to its ground state, the fundamental precision depends solely on the magnitude of its spectral amplitude. For quadratically phase-modulated Gaussian pulses, this is entirely determined by the spectral bandwidth. We provide expressions for evaluating the fundamental precision for general temporal profiles and phase modulations. Finally, we show that experimentally feasible mode-resolved measurements are optimal, or close to it, for chirped, pulsed single-photon spectroscopy. Published by the American Physical Society 2024

Spatial–Temporal-Correlation-Constrained Dynamic Graph Convolutional Network for Traffic Flow Forecasting

Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is regarded as a spatial–temporal prediction task. With the powerful ability to model the non-Euclidean data, the graph convolutional network (GCN)-based models have become the mainstream framework for traffic forecasting. However, existing GCN-based models either use the manually predefined graph structure to capture the spatial features, ignoring the heterogeneity of road networks, or simply perform 1-D convolution with fixed kernel to capture the temporal dependencies of traffic data, resulting in insufficient long-term temporal feature extraction. To solve those issues, a spatial–temporal correlation constrained dynamic graph convolutional network (STC-DGCN) is proposed for traffic flow forecasting. In STC-DGCN, a spatial–temporal embedding encoder module (STEM) is first constructed to encode the dynamic spatial relationships for road networks at different time steps. Then, a temporal feature encoder module with heterogeneous time series correlation modeling (TFE-HCM) and a spatial feature encoder module with dynamic multi-graph modeling (SFE-DCM) are designed to generate dynamic graph structures for effectively capturing the dynamic spatial and temporal correlations. Finally, a spatial–temporal feature fusion module based on a gating fusion mechanism (STM-GM) is proposed to effectively learn and leverage the inherent spatial–temporal relationships for traffic flow forecasting. Experimental results from three real-world traffic flow datasets demonstrate the superior performance of the proposed STC-DGCN compared with state-of-the-art traffic flow forecasting models.

Magnetospheric Line Radiation: Temporal Modulation Corresponding to a Bouncing Wave

AbstractMagnetospheric line radiation (MLR) is a peculiar type of whistler‐mode emissions formed by several similarly spaced spectral lines, whose frequencies may vary with time. These emissions typically occur at frequencies between about 1 and 8 kHz and are observed in both ground‐based and satellite measurements. However, their origin and source locations are not yet understood, as their detection by spacecraft instruments is challenging due to the limited frequency resolution of onboard wave measurements. We use high‐resolution multicomponent wave data obtained by the Van Allen Probes to demonstrate that, in addition to the frequency modulation, MLR events possess a temporal modulation with periods on the order of seconds, which may be related to the wave bouncing between hemispheres. Observed modulation periods are used to estimate a tentative source L‐shell. The modulation periods are shorter for events with larger frequency spacing, and the estimated source L‐shells correlate with model plasmapause locations.

Holistic and in situ calibration method for a spectral-temporally modulated Mueller spectropolarimeter

The spectral-temporally modulated Mueller spectropolarimeter (STMSP) offers advantages of broader band limitation, better resolution, and faster detection speed. However, the current STMSP calibration method separates the polarization state generator and analyzer, necessitating subsequent recombination, which is inefficient and unstable. In this paper, a holistic and in situ calibration method for STMSP is proposed. It only requires insertion of a polarizer as a reference sample, eliminating the need for separate calibration and recombination. The STMSP is calibrated in situ as a whole, addressing the misalignment error of the spectral modulation module, the total polarimetric errors of the temporal modulation module, and the spectral modulation transfer function of the spectrometer. Experimental results demonstrate high accuracy, with a root mean square error (RMSE) of 0.0004, which is an order of magnitude lower than that of the dual-rotating retarder spectropolarimeter (DRRSP) after eigenvalue calibration. This demonstrates its potential for enabling faster and more accurate acquisition of the Mueller spectra.

Error-robust hybrid atom-photon entangling gate via Gaussian soft control

Hybrid atom-photon gates play an important role for the realization of a quantum interface capable of mapping atomic states to photons for communication across quantum networks. Here, we propose a feasible theoretical scheme for implementing a hybrid atom-photon controlled-Z gate between an atom and a microwave photon in a superconducting coplanar waveguide resonator based on the Gaussian soft control technique. The gate protocol employs a classical auxiliary field that induces an atomic transition between one state of the atomic qubit and Rydberg states for obtaining strong coupling of the atom and microwave resonator. By tailoring the amplitude of this field with Gaussian temporal modulation, the gate performances are improved in various aspects. Numerical simulations demonstrate that the controlled-Z gate based on Gaussian soft control is resilient to the variation of the atom-photon coupling strength and deviation in the gate time, and it is less sensitive to the Rydberg level shifts caused by stray electric fields.

Dynamic control of Lamb modes in phononic crystals

This study explores the possibilities of temporal control of one-dimensional phononic crystal consisting of a homogeneous piezoelectric plate on which metallic electrodes are structured. Previous works [1] have shown that Lamb-type guided modes in these periodically structured piezoelectric ceramic plates exhibit a tunable frequency dispersion that can be controlled by external electric circuits connected to the electrodes. In this study we develop a device enabling changes in the electric boundary conditions (EBC) from one condition to another by making use of switches driven by a microcontroller. This device agility is exploited to perform dynamic control of the crystal properties. To our knowledge, this is the first study devoted to the dynamic modulation of Lamb-type guided waves. Here, we do not simply change the EBCs as a function of time, but we create a "wave" of modulation of properties. Broadband translation of Lamb-wave dispersion curves in the frequency-wavenumber space (f, k) are observed, depending on the modulation speed. Experimentally, the translation of the S0 mode is detected. The relationship between the position of static bands and the position of bands after temporal modulation obeys a very simple vector rule in the (f, k) space. [1] Phys. Rev. B 99, 094302 (2019).

Endogenous pain modulation after sleep restriction in migraine: a blinded crossover study

BackgroundPatients with migraine are vulnerable to insufficient sleep, but the impact of sleep restriction is largely unknown. In addition, the importance of sleep may be different in patients with migraine who mostly have attack onsets during sleep, so called sleep-related migraine, compared to patients with non-sleep-related migraine. In this study we investigate the effect of sleep restriction on endogenous pain modulation in patients with migraine and healthy controls. We also compared the effect of sleep restriction in sleep-related and in non-sleep-related migraine.MethodsMeasurements were conducted in 39 patients with migraine between attacks and 31 controls, once after habitual sleep and once after two consecutive nights of partial sleep restriction. There were 29 and 10 patients with non-sleep-related and sleep-related migraine respectively. Test stimulus was 2-min tonic noxious heat to the left volar forearm. Temporal summation was calculated as the regression coefficient for rated pain in the late part of this 2-min stimulation. Conditioning stimulus was right hand-immersion in 7 °C water. Conditioned pain modulation was defined as the difference in rated pain with and without the conditioning stimulus and was calculated for temporal summation and mean rated pain for the test stimulus. The effect of sleep restriction on temporal summation and conditioned pain modulation was compared in migraine subjects and controls using two-level models with recordings nested in subjects.ResultsConditioned pain modulation for temporal summation of heat pain tended to be reduced after sleep restriction in patients with migraine compared to controls (p = 0.060) and, in an exploratory analysis, was reduced more after sleep restriction in sleep-related than in non-sleep-related migraine (p = 0.017). No other differences between groups after sleep restriction were found for temporal summation or conditioned pain modulation.ConclusionPatients with migraine may have a subtly altered endogenous pain modulation system. Sleep restriction may have an increased pronociceptive effect on this system, suggesting a mechanism for vulnerability to insufficient sleep in migraine. This effect seems to be larger in sleep-related migraine than in non-sleep-related migraine.

Multimodal vehicle trajectory prediction and integrated threat assessment algorithm based on adaptive driving intention

As autonomous driving and connected communication technologies advance swiftly, vehicle trajectory prediction has become increasingly significant. The motion of a vehicle is contingent not only on its historical trajectory but is also subject to the influence of surrounding vehicles, thereby exhibiting intricate social and temporal interdependencies. Furthermore, the inherent randomness and uncertainty in driver behavior render vehicle trajectory prediction inherently multimodal, a factor that is frequently neglected in current research. Against this backdrop, a multimodal vehicle trajectory prediction (MTP) model based on an encoder-decoder architecture is proposed to hierarchically extract historical features of vehicles. The model consists of five key components: temporal feature encoder module, spatial interaction module, spatial-temporal dependence module, driving intention fusion module and multimodal trajectory output module. Experiments on the NGSIM dataset show that the predictive performance of the model has been improved to varying degrees, especially at 3–5 s, where the improvement is more significant. Compared with state-of-the-art models, the Root Mean Square Error (RMSE) error at 5 s time horizon is 3.38 m on NGSIM dataset, which represents a 25 % improvement. To measure the safety of predicted trajectories, we propose a comprehensive threat assessment model that combines collision time (TTC), headway (TH) and time to lateral collision (TLC) metrics based on safe distance theory. This model not only evaluates the longitudinal collision threat in the following state, but also evaluates the lateral collision threat during driving maneuvers in multi lane scenarios, thereby comprehensively improving the safety of vehicle driving. This research also offers new perspectives and insights for the development of autonomous driving.

Time of day affects MrgD-dependent modulation of cardiomyocyte contractility.

The renin-angiotensin system (RAS) is composed of a series of peptides, receptors, and enzymes that play a pivotal role in maintaining cardiovascular homeostasis. Among the most important players in this system are the angiotensin-II and angiotensin-(1-7) peptides. Our group has recently demonstrated that alamandine (ALA), a peptide with structural and functional similarities to angiotensin-(1-7), interacts with cardiomyocytes, enhancing contractility via the Mas-related G protein-coupled receptor member D (MrgD). It is currently unknown whether this modulation varies along the distinct phases of the day. To address this issue, we assessed the ALA-induced contractility response of cardiomyocytes from mice at four Zeitgeber times (ZTs). At ZT2 (light phase), ALA enhanced cardiomyocyte shortening in an MrgD receptor-dependent manner, which was associated with nitric oxide (NO) production. At ZT14 (dark phase), ALA induced a negative modulation on the cardiomyocyte contraction. β-Alanine, an MrgD agonist, reproduced the time-of-day effects of ALA on myocyte shortening. NG-nitro-l-arginine methyl ester, an NO synthase inhibitor, blocked the increase in fractional shortening induced by ALA at ZT2. No effect of ALA on myocyte shortening was observed at ZT8 and ZT20. Our results show that ALA/MrgD signaling in cardiomyocytes is subject to temporal modulation. This finding has significant implications for pharmacological approaches that combine chronotherapy for cardiac conditions triggered by disruption of circadian rhythms and hormonal signaling.NEW & NOTEWORTHY Alamandine, a member of the renin-angiotensin system, serves critical roles in cardioprotection, including the modulation of cardiomyocyte contractility. Whether this effect varies along the day is unknown. Our results provide evidence that alamandine via receptor MrgD exerts opposing actions on cardiomyocyte shortening, enhancing, or reducing contraction depending on the time of day. These findings may have significant implications for the development and effectiveness of future cardiac therapies.