Time-varying Signals Research Articles

Rapid and objective assessment of auditory temporal processing using dynamic amplitude-modulated stimuli.

Current tests of hearing fail to diagnose pathologies in ~10% of patients seeking help for hearing difficulties. Neural ensemble responses to perceptually relevant cues in the amplitude envelope, termed envelope following responses (EFR), hold promise as an objective diagnostic tool to probe these 'hidden' hearing difficulties. But clinical translation is impeded by current measurement approaches involving static amplitude modulated (AM) tones, which are time-consuming and lack optimal spectrotemporal resolution. Here we develop a framework to rapidly measure EFRs using dynamically varying AMs combined with spectrally specific analyses. These analyses offer 5x improvement in time and 30x improvement in spectrotemporal resolution, and more generally, are optimal for analyzing time-varying signals with known spectral trajectories of interest. We validate this approach across several mammalian species, including humans, and demonstrate robust responses that are highly correlated with traditional static EFRs. Our analytic technique facilitates rapid and objective neural assessment of temporal processing throughout the brain that can be applied to track auditory neurodegeneration using EFRs, as well as tracking recovery after therapeutic interventions.

Review on application of fractional Fourier transform in LinearFrequencyModulation signal and communication system

Traditional Fourier transform often apply to analyze and process stationary signals, however, it is weak for time-varying non-stationary signals, and fractional Fourier transform (FRFT) can better solve such problems. The FRFT can be comprehended as the expressive methods on the fractional Fourier domain constituted by the spinning coordinate axis of the signal anticlockwise about the origin at arbitrarily Angle in the time-frequency plane. In this paper, the improved fractional Fourier transform is combined with other calculation methods to achieve high precision estimation of chirp signal parameters. And the communication system built on weighted fractional Fourier transform and discrete fractional Fourier transform is studied and simulated respectively, which verifies the feasibility and improves the anti-jamming and anti-interception ability of the communication system.

Spike by spike frequency analysis of amperometry traces provides statistical validation of observations in the time domain

Amperometry is a commonly used electrochemical method for studying the process of exocytosis in real-time. Given the high precision of recording that amperometry procedures offer, the volume of data generated can span over several hundreds of megabytes to a few gigabytes and therefore necessitates systematic and reproducible methods for analysis. Though the spike characteristics of amperometry traces in the time domain hold information about the dynamics of exocytosis, these biochemical signals are, more often than not, characterized by time-varying signal properties. Such signals with time-variant properties may occur at different frequencies and therefore analyzing them in the frequency domain may provide statistical validation for observations already established in the time domain. This necessitates the use of time-variant, frequency-selective signal processing methods as well, which can adeptly quantify the dominant or mean frequencies in the signal. The Fast Fourier Transform (FFT) is a well-established computational tool that is commonly used to find the frequency components of a signal buried in noise. In this work, we outline a method for spike-based frequency analysis of amperometry traces using FFT that also provides statistical validation of observations on spike characteristics in the time domain. We demonstrate the method by utilizing simulated signals and by subsequently testing it on diverse amperometry datasets generated from different experiments with various chemical stimulations. To our knowledge, this is the first fully automated open-source tool available dedicated to the analysis of spikes extracted from amperometry signals in the frequency domain.

Assessing the influence of latency variability on EEG classifiers - a case study of face repetition priming

AbstractData-driven strategies have been widely used to distinguish experimental effects on single-trial EEG signals. However, how latency variability, such as within-condition jitter or latency shifts between conditions, affects the performance of EEG classifiers has not been well investigated. Without explicitly considering and disentangling such attributes of single trials, neural network-based classifiers have limitations in measuring their contributions. Inspired by domain knowledge of subcomponent latency and amplitude from traditional cognitive neuroscience, this study applies a stepwise latency correction method on single trials to control for their contributions to classifier behavior. As a case study demonstrating the value of this method, we measure repetition priming effects of faces, which induce large reaction time differences, latency shifts, and amplitude effects in averaged event-related potentials. The results show that within-condition jitter negatively impacts classifier performance, but between-condition latency shifts improve accuracy, whereas genuine amplitude differences have no significant influence. While demonstrated in the case of priming effects, this methodology can be generalized to experiments involving many kinds of time-varying signals to account for the contributions of latency variability to classifier performance.

Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.

GCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

High-precision global navigation satellite system (GNSS) time series data contain rich tectonic and non-tectonic motion information. However, such data also have common-mode errors (CME) related to spatiotemporal characteristics. CME will produce bias in accurate and reliable GNSS station positions and velocities estimations, ultimately deciphering erroneous geophysical signals. Currently, CME extraction methods can be divided into three categories: regional stack filtering, reference frame filtering, and statistical signal decomposition. The third method is widely used in geodetic data processing, including GNSS time series analysis, global time-varying gravity signal extraction, interferometric synthetic aperture radar (InSAR) time series analysis, etc. The demand for GNSS time series analysis software with interactive, visualization, and multifunctional features has also increased with the increase in GNSS stations. Therefore, we developed gCMEbox based on MATLAB. The gCMEbox’s primary functions include GNSS time series preprocessing, multiple statistical signal decomposition algorithms for extracting CME, GNSS time series analysis, visualization, velocity field construction, and generation of multiple time series post-processing products. Based on GNSS time series data, this study utilized the gCMEbox for data preprocessing, including format conversion, outlier removal, missing data recovery, etc. By applying statistical signal decomposition methods, gCMEbox successfully extracted CME. The primary objective of this study is to share this toolbox with the scientific community, providing a comprehensive tool for GNSS time series analysis and applications.

Iterative local maximum synchrosqueezing-extracting transform

Recently, there has been significant interest in time-varying signal analysis. Multisynchrosqueezing transform (MSST) and improved multisynchrosqueezing transform (IMSST) have shown promise in overcoming the limitations of conventional methods. Although progress has been made, challenges still exist, such as the presence of non-reassigned time–frequency (TF) points and the presence of noise in the TF plane when processing noisy time-varying signals. In this paper, we introduce an innovative TF analysis method known as the iterative local maximum synchrosqueezing-extracting transform (ILMSSTSEO). First, the proposed method extracts the local maximum value of the instantaneous frequency (IF) during each iteration to eliminate non-reassigned TF points. Additionally, a synchroextracting operator is introduced unconventionally to enhance the concentration of TF energy and reduce noise in the TF representation. By doing so, the method effectively solves many challenges faced by existing methods and provides a new extraction framework for synchrosqueezing transform-based TF analysis methods.

Tool wear feature extraction in BTA deep hole drilling process based on maximum probability multi-synchrosqueezing transform of spindle current signal

Multi-synchrosqueezing transform (MSST) enhances the concentration of the time–frequency representation (TFR) of time-varying signals by introducing multi-instantaneous frequency (IF) estimation operator. However, the existence of estimation bias may lead to uncertainty in the discrete values of IF during computer computations, causing issues with non-reassigned points. In this paper, a maximum probability multi-synchrosqueezing transform (MPMSST) is proposed, which incorporates distribution probability during the discretization process of the IF, taking the frequency corresponding to the maximum distribution probability as the determined value of IF. Through verification with multi-component time-varying simulation signals, MPMSST not only effectively eliminates non-reassigned points but also accurately estimates the theoretical time–frequency curve. By applying MPMSST to analyze spindle motor current signals during deep hole machining, TFRs under different tool wear states are obtained. The singular value decomposition (SVD) method is utilized to extract the principal component of the TFR, and a tool wear state feature index is constructed based on the sum of squares of the first 10 singular values. The correlation coefficient and covariance between the proposed feature index and the tool wear curve are 0.835 and 5.704 respectively, indicating a good consistency of the index with tool wear changes, and a high sensitivity to variations in tool wear state.

Environmental time-of-use scheme: Strategic leveraging of financial and environmental incentives for greener electric vehicle charging

If charged with green electricity, electric vehicles (EVs) can contribute greatly toward mitigation of greenhouse gas (GHG) emissions. To promote greener EV charging practices, this study proposes the environmental time-of-use (E-ToU) which provides time-varying prices and GHG intensity signals. Specifically, in E-ToU, GHG intensity is used as a nudge to steer drivers’ charging behavior, providing a mixed financial-environmental incentive. Conjoint-based choice experiments were conducted with participation of 1220 South Korean drivers (including 129 EV owners) to simulate EV charging under this novel scheme. Unit commitment was utilized to determine time-varying prices and GHG intensity, as well as evaluate the effectiveness of E-ToU. The results indicated that both charging costs and GHG intensity influenced the charging decisions (timing) of EV users. The E-ToU shifted EV charging to renewable-abundant daytime, reducing EV charging costs by 14.1 % and GHG emission from EV charging by 15.7 %. Additionally, it reduced the generation costs by 2.2 %, GHG emissions by 1.5 %, and renewable energy curtailments by 2.5 % in the power system. Notably, E-ToU outperformed flat-rate/ToU with respect to all evaluated metrics, highlighting its effectiveness. Therefore, this study recommends the mixed financial-environmental incentive approach for generating synergistic co-benefits for sustainability of power and transportation sectors.

Gegenbauer Graph Neural Networks for Time-Varying Signal Reconstruction.

Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addressing these tasks. However, existing approaches relying on smoothness assumptions of temporal differences and simple convex optimization techniques that have inherent limitations. To address these challenges, we propose a novel approach that incorporates a learning module to enhance the accuracy of the downstream task. To this end, we introduce the Gegenbauer-based graph convolutional (GegenConv) operator, which is a generalization of the conventional Chebyshev graph convolution by leveraging the theory of Gegenbauer polynomials. By deviating from traditional convex problems, we expand the complexity of the model and offer a more accurate solution for recovering time-varying graph signals. Building upon GegenConv, we design the Gegenbauer-based time graph neural network (GegenGNN) architecture, which adopts an encoder-decoder structure. Likewise, our approach also uses a dedicated loss function that incorporates a mean squared error (MSE) component alongside Sobolev smoothness regularization. This combination enables GegenGNN to capture both the fidelity to ground truth and the underlying smoothness properties of the signals, enhancing the reconstruction performance. We conduct extensive experiments on real datasets to evaluate the effectiveness of our proposed approach. The experimental results demonstrate that GegenGNN outperforms state-of-the-art methods, showcasing its superior capability in recovering time-varying graph signals.

Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control.

This article proposes a deep learning (DL)-based control algorithm-DL velocity-based model predictive control (VMPC)-for reducing traffic congestion with slowly time-varying traffic signal controls. This control algorithm consists of system identification using DL and traffic signal control using VMPC. For the training process of DL, we established a modeling error entropy loss as the criteria inspired by the theory of stochastic distribution control (SDC) originated by the fourth author. Simulation results show that the proposed algorithm can reduce traffic congestion with a slowly varying traffic signal control input. Results of an ablation study demonstrate that this algorithm compares favorably to other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.

Data-Driven-Based Cooperative Resilient Learning Method for Nonlinear MASs Under DoS Attacks.

In this article, we consider the cooperative tracking problem for a class of nonlinear multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. To solve such a problem, a hierarchical cooperative resilient learning method, which involves a distributed resilient observer and a decentralized learning controller, is introduced in this article. Due to the existence of communication layers in the hierarchical control architecture, it may lead to communication delays and DoS attacks. Motivated by this consideration, a resilient model-free adaptive control (MFAC) method is developed to withstand the influence of communication delays and DoS attacks. First, a virtual reference signal is designed for each agent to estimate the time-varying reference signal under DoS attacks. To facilitate the tracking of each agent, the virtual reference signal is discretized. Then, a decentralized MFAC algorithm is designed for each agent such that each agent can track the reference signal by only using the obtained local information. Finally, a simulation example is proposed to verify the effectiveness of the developed method.

Co-speech head nods are used to enhance prosodic prominence at different levels of narrow focus in French.

Previous research has shown that prosodic structure can regulate the relationship between co-speech gestures and speech itself. Most co-speech studies have focused on manual gestures, but head movements have also been observed to accompany speech events by Munhall, Jones, Callan, Kuratate, and Vatikiotis-Bateson [(2004). Psychol. Sci. 15(2), 133-137], and these co-verbal gestures may be linked to prosodic prominence, as shown by Esteve-Gibert, Borrás-Comes, Asor, Swerts, and Prieto [(2017). J. Acoust. Soc. Am. 141(6), 4727-4739], Hadar, Steiner, Grant, and Rose [(1984). Hum. Mov. Sci. 3, 237-245], and House, Beskow, and Granström [(2001). Lang. Speech 26(2), 117-129]. This study examines how the timing and magnitude of head nods may be related to degrees of prosodic prominence connected to different focus conditions. Using electromagnetic articulometry, a time-varying signal of vertical head movement for 12 native French speakers was generated to examine the relationship between head nod gestures and F0 peaks. The results suggest that speakers use two different alignment strategies, which integrate both temporal and magnitudinal aspects of the gesture. Some evidence of inter-speaker preferences in the use of the two strategies was observed, although the inter-speaker variability is not categorical. Importantly, prosodic prominence itself is not the cause of the difference between the two strategies, but instead magnifies their inherent differences. In this way, the use of co-speech head nod gestures under French focus conditions can be considered as a method of prosodic enhancement.

Cardinality Constraint Non-Uniform Sampling for Maximizing Reconstruction Accuracy of Time-Varying Signals

Cardinality Constraint Non-Uniform Sampling for Maximizing Reconstruction Accuracy of Time-Varying Signals

An Improved Average Acceleration Approach of Modelling Earth Gravity Field Based on K-Band Range-Rate Observations

The conventional average acceleration approach relies on K-band range observation, containing an unknown bias, which leads to possible degradation of the precision of Earth’s gravity field modelling. It also suffers from correlated errors caused by three-point numerical differentiation. In this study, an improved approach is proposed that makes use of K-band range-rate observations instead and overcoming the influence of correlated errors by introducing a whitening filter. GRACE-Follow On data spanning the period from January 2019 to December 2022 were processed by the proposed approach and a series of time-varying gravity field models was derived, referred to as SSM-AAA-GFO in this paper. This model series is compared comprehensively with three official model series. Results demonstrate that all model series are highly coincident below degree 30 and reflect similar time-varying gravity field signals in both large and small basins. After filtering, SSM-AAA-GFO shows uncertainty, in the form of equivalent water height below 2.5 cm, which is comparable with three official model series. The comparison results confirm the effectiveness of the proposed approach for precisely modelling a time-varying gravity field based on K-band range-rate observations.

Reach-dependent reorientation of rotational dynamics in motor cortex

During reaching, neurons in motor cortex exhibit complex, time-varying activity patterns. Though single-neuron activity correlates with movement parameters, movement correlations explain neural activity only partially. Neural responses also reflect population-level dynamics thought to generate outputs. These dynamics have previously been described as “rotational,” such that activity orbits in neural state space. Here, we reanalyze reaching datasets from male Rhesus macaques and find two essential features that cannot be accounted for with standard dynamics models. First, the planes in which rotations occur differ for different reaches. Second, this variation in planes reflects the overall location of activity in neural state space. Our “location-dependent rotations” model fits nearly all motor cortex activity during reaching, and high-quality decoding of reach kinematics reveals a quasilinear relationship with spiking. Varying rotational planes allows motor cortex to produce richer outputs than possible under previous models. Finally, our model links representational and dynamical ideas: representation is present in the state space location, which dynamics then convert into time-varying command signals.

A simple and cost-effective strategy for electrochemiluminescence spectral determination

BackgroundElectrochemiluminescence (ECL), as a unique and powerful analytical technique, has been widely used in various fields. The determination of ECL spectra plays a crucial role in understanding ECL reaction mechanisms and conducting spectra-resolved ECL analysis. ECL intensity is typically detected using a photomultiplier tube, which offers high sensitivity for detecting extremely weak light signals but does not allow for spectral identification. Due to the time-dependent variation of ECL intensity caused by the applied potential and electrochemical reaction processes, it is challenging to perform ECL spectral detection using conventional wavelength-scanning spectrometers. ResultsIn this study, we present a straightforward and cost-effective ECL spectral detection strategy by incorporating an automatically controlled tunable optical filter device between a commonly used PMT detector and a specially designed ECL reaction cell. The effectiveness of this approach was confirmed through initial validation, where the spectrum of a green LED spotlight was measured and compared with a commercial spectrometer. In a dynamic system with stable ECL signals, the ECL spectrum of the typical Ru(bpy)32+/TPA system was rapidly acquired by adjusting the bandpass filters. To account for time-varying ECL signals in practical measurements, time-based correction algorithms were implemented to rectify variations in ECL intensity. By integrating time-based correction algorithms and an automatically controlled tunable optical filter device into a commonly utilized PMT detector, the rapid and sensitive ECL spectra determination was achieved. Experimental results demonstrated the reliability of the proposed strategy. SignificanceThis strategy is based on the widely used high-sensitivity PMT detection component, enabling the rapid and sensitive measurement of ECL spectra without altering the ECL detection hardware. It is simple, fast, efficient, and cost-effective, with the potential to be widely used for rapid ECL spectral detection and spectra-resolved ECL analysis.

Joint time-vertex linear canonical transform

The emergence of graph signal processing (GSP) has spurred a deep interest in signals that naturally reside on irregularly structured data kernels, such as those found in social, transportation, and sensor networks. Recently, concepts and applications related to time-varying graph signal analysis have matured, linking temporal signal processing techniques with innovative tools in GSP. In this paper, similar to the extension of the graph fractional Fourier transform to the graph linear canonical transform, we define the joint time-vertex linear canonical transform (JLCT) and its properties. This transformation extends the joint time-vertex Fourier transform (JFT) and fractional Fourier transform (JFRFT), broadening the Fourier analysis in both time and vertex domains into the domain of the linear canonical transform (LCT). This offers an enhanced set of the LCT analysis tools for joint time-vertex processing. Applications of the JLCT in dynamic mesh denoising, clustering, and energy compactness demonstrate that JLCT can enhance regression and learning tasks, and can refine and improve the performance of the JFT and the JFRFT.

Laser-Induced Interference to Infrared Detector Using Continuous Wave and Short-Pulse Lasers.

The response of a DPbS3200 infrared detector irradiated by a nanosecond pulsed laser and CW laser has been investigated to study laser-induced interference. A laser interference experiment system was constructed to measure the time-varying response signal. A nanosecond pulsed laser and a CW laser of 10 Hz were used, with a 1064 nm wavelength and a millimeter-scale irradiation spot diameter. Firstly, the characteristics of transient interference signals induced by pulsed lasers were analyzed. Then, the characteristics of response signal interference by both CW laser and pulsed laser irradiation were further investigated. The results showed that the pulsed laser only produced transient interference. However, the CW laser led to a significant amplitude reduction of the response signal, which could continuously interfere in the operating time. For transient interferences, the amplitude of the interference signal increased linearly with the laser fluence. The relation between the pulse repetition rate of the incident laser and the operating frequency of the detector determined the numbers of transient interference signals in one response period; for the interference induced by both the CW laser and pulsed laser, CW laser interference played a leading role when CW laser power density increased to 4.1 W/cm2 or more. As the CW laser fluence reached 6.1 W/cm2, the PbS infrared detector was no longer able to detect any signal, which caused temporary blindness. In the end, a probit model was used to determine the interference threshold.

Temporal-Difference Graph-Based Optimization for High-Quality Reconstruction of MODIS NDVI Data

The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud contamination, posing challenges for accurate observation. In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. Regarding spatio-temporal NDVI data as a time-varying graph signal, the developed method utilized an optimization algorithm to maximize the spatial smoothness of temporal differences while preserving the spatial NDVI pattern. This approach was further evaluated by reconstructing MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m Grid (MOD13Q1) products over Northwest China. Through quantitative comparison with a previous state-of-the-art method, the Savitzky–Golay (SG) filter method, the obtained results demonstrated the superior performance of the TDG method, and highly accurate results were achieved in both the temporal and spatial domains irrespective of noise types (positively-biased, negatively-biased, or linearly-interpolated noise). In addition, the TDG-based optimization approach shows great robustness to noise intensity within spatio-temporal NDVI data, suggesting promising prospects for its application to similar datasets.