Search results for "time-frequency"

showing 10 items of 25 documents

Postural and gestural synchronization, sequential imitation, and mirroring predict perceived coupling of dancing dyads

2023

Body movement is a primary nonverbal communication channel in humans. Coordinated social behaviors, such as dancing together, encourage multifarious rhythmic and interpersonally coupled movements from which observers can extract socially and contextually relevant information. The investigation of relations between visual social perception and kinematic motor coupling is important for social cognition. Perceived coupling of dyads spontaneously dancing to pop music has been shown to be highly driven by the degree of frontal orientation between dancers. The perceptual salience of other aspects, including postural congruence, movement frequencies, time-delayed relations, and horizontal mirrorin…

peilausvuorovaikutusCognitive NeurosciencetanssientrainmentmirroringExperimental and Cognitive Psychologyliikesosiaalinen vuorovaikutustime-frequency analysistime-delayanalysisfull-body couplingArtificial Intelligencetanssijatconvex envelopeasennot (ruumiinasennot)
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Exploring Oscillatory Dysconnectivity Networks in Major Depression During Resting State Using Coupled Tensor Decomposition

2022

Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time frequency connectivity subject are constr…

mallintaminenmasennusBrain MappingDepressive Disorder Majoroscillatory networksDepressionRestneuraalilaskentamajor depression disorderBiomedical EngineeringBrainbrain modelingneuroverkottime-frequency analysisMagnetic Resonance Imagingtensorsmielenterveyshäiriötcoupled tensor decompositionNeural PathwaysHumansdynamic functional connectivityEEGaivotutkimusaivotelectroencephalographyIEEE Transactions on Biomedical Engineering
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Small fish in big ponds : Connections of green finance assets to commodity and sectoral stock markets

2022

We analyze return and volatility connectedness of the rising green asset and the well-established US industry stock and commodity markets from September 2010 to July 2021. We find that the time-varying return and volatility connectedness have exhibited serious crisis jumps. Some individual assets of both the green and commodity markets are in connection to the US sectoral stock market returns, and the volatility connections are even more common than the return connections. Furthermore, some financial and economic uncertainty indicators manifest positive impacts from the volatility of some `big pond markets for e.g. commodities, whereas some others affect the connectedness negatively. Additi…

rahoitusmarkkinatEconomics and EconometricsvolatiliteettiEconomicsTime-frequencyarvopaperimarkkinatUS sectorsGreen marketshyödykkeetNationalekonomiFinanceGreen markets; US sectors; Commodities; Connectedness; Time-frequencyConnectedness
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A combined CWT-DWT method using model-based design simulator for partial discharges online detection

2009

The suppression of noises is fundamental in onsite Partial Discharge (PD) measurements. For this purpose, the wavelet transform analysis method has been developed and it is a powerful tool for processing the transient and suddenly changing signals. As the wavelet transform possesses the properties of multi-scale analysis and time-frequency domain localization, it is also particularly suitable to process the suddenly changing signals of the partial discharge pulse (PD). In this paper, an improved Wavelet denoising method developed by a model-based design software is presented. Simulations are provided as well as some results obtained during laboratory experiment and on-line PD measurements. …

Settore ING-IND/31 - ElettrotecnicaWaveletComputer scienceNoise reductionPartial dischargeModel-based designdiscrete wavelet transforms partial discharge measurements time-frequency analysis transientsElectronic engineeringSoftware designWavelet transformContinuous wavelet transformTime–frequency analysis2009 IEEE Conference on Electrical Insulation and Dielectric Phenomena
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Time-Frequency Filtering for Seismic Waves Clustering

2014

This paper introduces a new technique for clustering seismic events based on processing, in time-frequency domain, the waveforms recorded by seismographs. The detection of clusters of waveforms is performed by a k-means like algorithm which analyzes, at each iteration, the time-frequency content of the signals in order to optimally remove the non discriminant components which should compromise the grouping of waveforms. This step is followed by the allocation and by the computation of the cluster centroids on the basis of the filtered signals. The effectiveness of the method is shown on a real dataset of seismic waveforms.

SeismometerInformation Systems and ManagementBasis (linear algebra)Computer sciencebusiness.industryComputationEarthquakes clusteringCentroidWaveforms clusteringComputer Science Applications1707 Computer Vision and Pattern RecognitionPattern recognitionInformation SystemSeismic noiseTime-frequency filteringwaveforms clustering earthquakes clustering time-frequency filteringSeismic wavePhysics::GeophysicsComputingMethodologies_PATTERNRECOGNITIONWaveformArtificial intelligenceSettore SECS-S/01 - StatisticaCluster analysisbusinessAnalysis
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Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction

2017

[ES] Este trabajo propone la detección de FV y su discriminación de TV y otros ritmos cardiacos basándose en la representación tiempo-frecuencia del ECG y su conversión en imágen como entrada a un clasificador de vecinos más cercanos (KNN) sin necesidad de extracción de parámetros adicionales. Tres variantes de datos de entrada al clasificador son evaluados. Los resultados clasifican la señal en cuatro clases diferentes: ’Normal’ para latidos con ritmo sinusal, ’FV’ para fibrilación ventricular, ’TV’ para taquicardia ventricular y ’Otros’ para el resto de ritmos. Los resultados para detección de FV mostraron 88,27% de sensibilidad y 98,22% de especificidad para la entrada de imágen equivale…

medicine.medical_specialtyBiomedical systemsGeneral Computer ScienceSeñales no estacionarias0206 medical engineeringTime-frequency representationClasificación02 engineering and technologyElectrocardiographic signalsVentricular tachycardiaNon-stationary signalsImage analysisAnálisis de imágenesInternal medicine0202 electrical engineering electronic engineering information engineeringmedicineSinus rhythmSistemas biomédicosbusiness.industrySeñales ElectrocardiográficasClassificationmedicine.disease020601 biomedical engineeringRepresentación tiempo-frecuenciaControl and Systems EngineeringSignal parameterVentricular fibrillationCardiology020201 artificial intelligence & image processingbusinessRevista Iberoamericana de Automática e Informática industrial
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EEG Spectral Generators Involved in Motor Imagery: A swLORETA Study

2017

In order to characterize the neural generators of the brain oscillations related to motor imagery (MI), we investigated the cortical, subcortical, and cerebellar localizations of their respective electroencephalogram (EEG) spectral power and phase locking modulations. The MI task consisted in throwing a ball with the dominant upper limb while in a standing posture, within an ecological virtual reality (VR) environment (tennis court). The MI was triggered by the visual cues common to the control condition, during which the participant remained mentally passive. As previously developed, our paradigm considers the confounding problem that the reference condition allows two complementary analys…

CerebellumThalamuslcsh:BF1-990Posterior parietal cortexElectroencephalographytime-frequency050105 experimental psychology03 medical and health sciences0302 clinical medicineMotor imagerymedicineSwLORETAPsychology0501 psychology and cognitive sciencesEEGSensory cueswLORETAGeneral PsychologyOriginal ResearchERSPmedicine.diagnostic_test05 social sciencesCognitionMotor-imageryITCmotor-imagerymedicine.anatomical_structurelcsh:PsychologyPsychologieTime-frequencyPsychologyNeuroscience030217 neurology & neurosurgeryThrowingFrontiers in Psychology
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Quantitative Rotor Broken Bar Evaluation in Double Squirrel Cage Induction Machines under Dynamic Operating Conditions

2013

Advanced monitoring techniques leading to fault diagnosis and prediction of induction machine faults, operating under non-stationary conditions have gained strength because of its considerable influence on the operational continuation of many industrial processes. In case of rotor broken bars, fault detection based on sideband components issued from currents, flux, instantaneous control or power signals under different load conditions, may fail due to the presence of inter-bar currents that reduce the degree of rotor asymmetry, especially for double squirrel cage induction motors. But the produced core vibrations in the axial direction, can be investigated to overcome the limitation of the …

discrete wavelet transformEngineeringbusiness.industryRotor (electric)Squirrel-cage rotorBar (music)squirrel cage motorSettore ING-IND/32 - Convertitori Macchine E Azionamenti ElettriciFault (power engineering)FAULT DIAGNOSISFault detection and isolationPower (physics)law.inventionVibrationTime-Frequency AnalysisControl theorylawAC Machine Condition monitoring Double cage rotor fault diagnostics induction motor wavelet TransformbusinessInduction motor
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Vection lies in the brain of the beholder: EEG parameters as an objective measurement of vection

2015

Opinionevent-related brain potentials (ERP)medicine.diagnostic_testobjective measurebusiness.industryelectroencephalogram (EEG)multisensory integrationlcsh:BF1-990Objective measurementMultisensory integrationSelf motion perceptionElectroencephalographytime-frequency analysislcsh:PsychologymedicinePsychologyArtificial intelligenceillusory self-motionbusinessPsychologyself-motion perceptionGeneral PsychologyCognitive psychologyFrontiers in Psychology
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Vibration signature analysis for rotor broken bar diagnosis in double cage induction motor drives

2013

The paper investigates the diagnosis of rotor broken bars in field oriented controlled (FOC) double cage induction motor drives, using current and vibration signature analysis techniques. The Impact of the closed loop control system cannot be neglected when the detection of asymmetries in the machine are based on the signature analysis of electrical variables. The proposed diagnosis approach is based on optimized use of wavelet analysis by a pre-processing of phase current or axial/radial vibration signals. Thus, the time evolution of the tracked rotor fault components can be effectively analyzed. This paper shows also the relevance of the fault components computed from axial vibration sign…

Engineeringadial vibration signalBar (music)machine vector controlinduction motor driverotor broken bar diagnosiSettore ING-IND/32 - Convertitori Macchine E Azionamenti Elettriciphase current signalFault (power engineering)VibrationWound rotor motorlaw.inventionINDUCTION MOTORSRotorlawControl theoryclosed loop control systemdouble cage rotortime evolutionInduction motorpreprocessingwavelet transformvibration signature analysielectric currentaxial vibration signalVector controlfield oriented controlfault diagnosticrotor faultRotor (electric)business.industrySquirrel-cage rotorclosed loop systemfault diagnosiStatorAC Machinecurrent signature analysiBarVibrationCONDITION MONITORINGTime-Frequency Analysidouble cage induction motor driveignal processingmaintenance engineeringwavelet analysibusinessInduction motor4th International Conference on Power Engineering, Energy and Electrical Drives
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