ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform.
Ontology highlight
ABSTRACT:
SUBMITTER: Daubechies I
PROVIDER: S-EPMC4792403 | biostudies-literature | 2016 Apr
REPOSITORIES: biostudies-literature
Publications
ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform.
Daubechies Ingrid I Wang Yi Grace YG Wu Hau-tieng HT
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 20160401 2065
A new method is proposed to determine the time-frequency content of time-dependent signals consisting of multiple oscillatory components, with time-varying amplitudes and instantaneous frequencies. Numerical experiments as well as a theoretical analysis are presented to assess its effectiveness. ...[more]
Similar Datasets
Project description:Accurate flight trajectory prediction is a crucial and challenging task in air traffic control, especially for maneuver operations. Modern data-driven methods are typically formulated as a time series forecasting task and fail to retain high accuracy. Meantime, as the primary modeling method for time series forecasting, frequency-domain analysis is underutilized in the flight trajectory prediction task. In this work, an innovative wavelet transform-based framework is proposed to perform time-frequency analysis of flight patterns to support trajectory forecasting. An encoder-decoder neural architecture is developed to estimate wavelet components, focusing on the effective modeling of global flight trends and local motion details. A real-world dataset is constructed to validate the proposed approach, and the experimental results demonstrate that the proposed framework exhibits higher accuracy than other comparative baselines, obtaining improved prediction performance in terms of four measurements, especially in the climb and descent phase with maneuver control. Most importantly, the time-frequency analysis is confirmed to be effective to achieve the flight trajectory prediction task.
| S-EPMC10465572 | biostudies-literature
Project description:Carboxylic acid groups impart hydrophilicity and ionizable moieties to polyamide membranes for desalination, hence influencing water and ion transport through the material. Model polyamide films were synthesized via molecular layer-by-layer deposition on planar substrates to study the formation process of these materials and overcome the chemical and topological inhomogeneity inherent to conventional interfacially polymerized polyamide membranes. The carboxylic acid content in these model films was characterized using Fourier transform infrared (FTIR) spectroscopy by quantifying the C=O band at 1718 cm-1. The concentration of carboxylic acid groups decreased as the thickness of the membrane increased, suggestive of an increase in crosslink density as the polyamide network develops. For the thinnest molecular layer-by-layer (mLbL) samples, the carboxylic acid concentration for films on gold was 0.35 mmol g-1, whereas analogous films on silicon had an acid content of 0.56 mmol g-1, indicating a clear influence of the substrate on the initial network formation. As the thickness of the membrane increased, the influence of the substrate and initial layer growth became less significant as the carboxylic acid concentration on both substrates reached a value of 0.12 mmol g-1. We demonstrate that FTIR spectroscopy is a practical and accessible way to quantify the carboxylic acid content in these types of extremely thin polyamide membranes to help quantify network formation in these materials.
| S-EPMC6459611 | biostudies-literature
Project description:Stockwell transform(ST) time-frequency representation(ST-TFR) is a time frequency analysis method which combines short time Fourier transform with wavelet transform, and ST time frequency filtering(ST-TFF) method which takes advantage of time-frequency localized spectra can separate the signals from Gaussian noise. The ST-TFR and ST-TFF methods are used to analyze the fault signals, which is reasonable and effective in general Gaussian noise cases. However, it is proved that the mechanical bearing fault signal belongs to Alpha(α) stable distribution process(1 < α < 2) in this paper, even the noise also is α stable distribution in some special cases. The performance of ST-TFR method will degrade under α stable distribution noise environment, following the ST-TFF method fail. Hence, a new fractional lower order ST time frequency representation(FLOST-TFR) method employing fractional lower order moment and ST and inverse FLOST(IFLOST) are proposed in this paper. A new FLOST time frequency filtering(FLOST-TFF) algorithm based on FLOST-TFR method and IFLOST is also proposed, whose simplified method is presented in this paper. The discrete implementation of FLOST-TFF algorithm is deduced, and relevant steps are summarized. Simulation results demonstrate that FLOST-TFR algorithm is obviously better than the existing ST-TFR algorithm under α stable distribution noise, which can work better under Gaussian noise environment, and is robust. The FLOST-TFF method can effectively filter out α stable distribution noise, and restore the original signal. The performance of FLOST-TFF algorithm is better than the ST-TFF method, employing which mixed MSEs are smaller when α and generalized signal noise ratio(GSNR) change. Finally, the FLOST-TFR and FLOST-TFF methods are applied to analyze the outer race fault signal and extract their fault features under α stable distribution noise, where excellent performances can be shown.
| S-EPMC5391006 | biostudies-literature
Project description:According to the fact that high frequency will be abnormally attenuated when seismic signals travel across reservoirs, a new method, which is named high-precision time-frequency entropy based on synchrosqueezing generalized S-transform, is proposed for hydrocarbon reservoir detection in this paper. First, the proposed method obtains the time-frequency spectra by synchrosqueezing generalized S-transform (SSGST), which are concentrated around the real instantaneous frequency of the signals. Then, considering the characteristics and effects of noises, we give a frequency constraint condition to calculate the entropy based on time-frequency spectra. The synthetic example verifies that the entropy will be abnormally high when seismic signals have an abnormal attenuation. Besides, comparing with the GST time-frequency entropy and the original SSGST time-frequency entropy in field data, the results of the proposed method show higher precision. Moreover, the proposed method can not only accurately detect and locate hydrocarbon reservoirs, but also effectively suppress the impact of random noises.
| S-EPMC7512946 | biostudies-literature
Project description:In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings from rodent mPFC (Henke et al., 2021) during an interval reproduction task and identified two cell groups by modeling their temporal receptive fields using hierarchical Bayesian models. The firing in the "past cells" group peaked at the start of the interval and relaxed exponentially back to baseline. The firing in the "future cells" group increased exponentially and peaked right before the planned action at the end of the interval. Contrary to the previous assumption that timing information in the brain has one or two time scales for a given interval, we found strong evidence for a continuous distribution of the exponential rate constants for both past and future cell populations. The real Laplace transformation of time predicts exponential firing with a continuous distribution of rate constants across the population. Therefore, the firing pattern of the past cells can be identified with the Laplace transform of time since the past event while the firing pattern of the future cells can be identified with the Laplace transform of time until the planned future event.
| S-EPMC10888827 | biostudies-literature
Project description:We describe a method for performing time-resolved X-ray crystallographic experiments based on the Hadamard transform, in which time resolution is defined by the underlying periodicity of the probe pulse sequence, and signal/noise is greatly improved over that for the fastest pump-probe experiments depending on a single pulse. This approach should be applicable on standard synchrotron beamlines and will enable high-resolution measurements of protein and small-molecule structural dynamics. It is also applicable to other time-resolved measurements where a probe can be encoded, such as pump-probe spectroscopy.
| S-EPMC4216935 | biostudies-literature
Project description:Linear mixed-effect models (LMMs) are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by Speelman and McGann (2013) about the use of mean data, because they do not average across individual responses. However, recent guidelines for using LMM to analyse skewed reaction time (RT) data collected in many cognitive psychological studies recommend the application of non-linear transformations to satisfy assumptions of normality. Uncritical adoption of this recommendation has important theoretical implications which can yield misleading conclusions. For example, Balota et al. (2013) showed that analyses of raw RT produced additive effects of word frequency and stimulus quality on word identification, which conflicted with the interactive effects observed in analyses of transformed RT. Generalized linear mixed-effect models (GLMM) provide a solution to this problem by satisfying normality assumptions without the need for transformation. This allows differences between individuals to be properly assessed, using the metric most appropriate to the researcher's theoretical context. We outline the major theoretical decisions involved in specifying a GLMM, and illustrate them by reanalysing Balota et al.'s datasets. We then consider the broader benefits of using GLMM to investigate individual differences.
| S-EPMC4528092 | biostudies-literature
Project description:The advance in high-throughput "toxicogenomics" technologies, which allows for concurrent monitoring of cellular responses globally upon exposure to chemical toxicants, presents promises for next-generation toxicity assessment. It is recognized that cellular responses to toxicants have a highly dynamic nature, and exhibit both temporal complexity and dose-response shifts. Most current gene enrichment or pathway analysis lack the recognition of the inherent correlation within time series data, and may potentially miss important pathways or yield biased and inconsistent results that ignore dynamic patterns and time-sensitivity. In this study, we investigated the application of two score metrics for GSEA (gene set enrichment analysis) to rank the genes that consider the temporal gene expression profile. One applies a novel time series CPCA (common principal components analysis) to generate scores for genes based on their contributions to the common temporal variation among treatments for a given chemical at different concentrations. Another one employs an integrated altered gene expression quantifier-TELI (transcriptional effect level index) that integrates altered gene expression magnitude over the exposure time. By comparing the GSEA results using two different ranking metrics for examining the dynamic responses of reporter cells treated with various dose levels of three model toxicants, mitomycin C, hydrogen peroxide, and lead nitrate, the analysis identified and revealed different toxicity mechanisms of these chemicals that exhibit chemical-specific, as well as time-aware and dose-sensitive nature. The ability, advantages, and disadvantages of varying ranking metrics were discussed. These findings support the notion that toxicity bioassays should account for the cells' complex dynamic responses, thereby implying that both data acquisition and data analysis should look beyond simple traditional end point responses.
| S-EPMC6321746 | biostudies-literature
Project description:By casting the information regarding an ion population's mobility in the frequency domain, the coupling of time-dispersive ion mobility techniques is now imminently compatible with slower mass analyzers such as ion traps. Recent reports have detailed the continued progress toward maximizing the efficiency of the Fourier transform ion mobility-mass spectrometry (FT-IM-MS) experiments, but few reports have outlined the intersection between the practical considerations of implementation against the theoretical limits imposed by traditional signal processing techniques. One of the important concerns for Fourier-based multiplexing experiments is avoiding signal aliasing as a product of undersampled signals that may occur during data acquisition. In addition to traditional considerations such as detector sampling frequency, the limitations (i.e., maximum measurable drift time) imposed by experimental mass scan duration and the frequency sweep used for ion gate modulation must also be assessed. This work aims to connect the fundamental underpinnings of FT-IM-MS experiments and the associated experimental parameters that are encountered when coupling the comparatively fast separations in the mobility domain with the slower m/z scanning common for ion-trap mass analyzers. In addition to stating the relevant theory that applies to the FT-IM-MS experiment, this report highlights how aliased signals will manifest post Fourier transform in reconstructed arrival time distributions and calculated mobilities.
| S-EPMC10370403 | biostudies-literature
Project description:Homeostatic and adaptive control mechanisms are essential for keeping organisms structurally and functionally stable. Integral feedback is a control theoretic concept which has long been known to keep a controlled variable A robustly (i.e. perturbation-independent) at a given set-point A(set) by feeding the integrated error back into the process that generates A. The classical concept of homeostasis as robust regulation within narrow limits is often considered as unsatisfactory and even incompatible with many biological systems which show sustained oscillations, such as circadian rhythms and oscillatory calcium signaling. Nevertheless, there are many similarities between the biological processes which participate in oscillatory mechanisms and classical homeostatic (non-oscillatory) mechanisms. We have investigated whether biological oscillators can show robust homeostatic and adaptive behaviors, and this paper is an attempt to extend the homeostatic concept to include oscillatory conditions. Based on our previously published kinetic conditions on how to generate biochemical models with robust homeostasis we found two properties, which appear to be of general interest concerning oscillatory and homeostatic controlled biological systems. The first one is the ability of these oscillators ("oscillatory homeostats") to keep the average level of a controlled variable at a defined set-point by involving compensatory changes in frequency and/or amplitude. The second property is the ability to keep the period/frequency of the oscillator tuned within a certain well-defined range. In this paper we highlight mechanisms that lead to these two properties. The biological applications of these findings are discussed using three examples, the homeostatic aspects during oscillatory calcium and p53 signaling, and the involvement of circadian rhythms in homeostatic regulation.
| S-EPMC4169565 | biostudies-literature