Search results for " Signal Processing"

showing 10 items of 208 documents

Fuzzy control for Electric Power Steering System with assist motor current input constraints

2015

Abstract Friction and disturbances of the road are the main sources of nonlinearity in the Electric Power Steering (EPS) System. Consequently, conventional linear controllers design based on a simplified linear model of the EPS system will result in poor dynamic performance or system instability. On the other hand, a brush-type DC motor is more used in EPS control with an input current that is limited in practice. The control laws designed without taking into account the saturation effect may have undesirable consequences on the system stability. In this paper, a Takagi–Sugeno (T−S) fuzzy is used to represent the nonlinear behavior of an EPS system, and stabilization conditions for nonlinea…

Engineeringbusiness.industryComputer Networks and CommunicationsApplied MathematicsControl (management)Linear modelControl engineeringFuzzy control systemControl and Systems Engineering; Signal Processing; Computer Networks and Communications; Applied MathematicsDC motorInstabilityFuzzy logicNonlinear systemControl theoryControl and Systems EngineeringSignal ProcessingbusinessSaturation (chemistry)
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Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network

2020

In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), the Constant Q-transform (CQT) and Chromagram. Such multiple features have never been used before for signal or audio processing. And, we employ a deeper CNN (DCNN) compared to previous models, consisting of spatially separable convolutions working on time and feature domain separately. Alongside, we use atten…

FOS: Computer and information sciencesComputer Science - Machine LearningSound (cs.SD)Computer science020209 energyMachine Learning (stat.ML)02 engineering and technologycomputer.software_genreConvolutional neural networkComputer Science - SoundDomain (software engineering)Machine Learning (cs.LG)Statistics - Machine LearningAudio and Speech Processing (eess.AS)0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringAudio signal processingVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550business.industrySIGNAL (programming language)Pattern recognitionFeature (computer vision)Benchmark (computing)020201 artificial intelligence & image processingArtificial intelligenceMel-frequency cepstrumbusinesscomputerElectrical Engineering and Systems Science - Audio and Speech ProcessingCommunication channel
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Adaptive independent sticky MCMC algorithms

2018

In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky MCMC algorithms, for efficient sampling from a generic target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities which become closer and closer to the target as the number of iterations increases. The proposal pdf is built using interpolation procedures based on a set of support points which is constructed iteratively based on previously drawn samples. The algorithm's efficiency is ensured by a test that controls the evolution of the set of support points. This extra stage controls the computational cost and the converge…

FOS: Computer and information sciencesMathematical optimizationAdaptive Markov chain Monte Carlo (MCMC)Monte Carlo methodBayesian inferenceHASettore SECS-P/05 - Econometrialcsh:TK7800-8360Machine Learning (stat.ML)02 engineering and technologyBayesian inference01 natural sciencesStatistics - Computationlcsh:Telecommunication010104 statistics & probabilitysymbols.namesakeAdaptive Markov chain Monte Carlo (MCMC); Adaptive rejection Metropolis sampling (ARMS); Bayesian inference; Gibbs sampling; Hit and run algorithm; Metropolis-within-Gibbs; Monte Carlo methods; Signal Processing; Hardware and Architecture; Electrical and Electronic EngineeringGibbs samplingStatistics - Machine Learninglcsh:TK5101-67200202 electrical engineering electronic engineering information engineeringComputational statisticsMetropolis-within-GibbsHit and run algorithm0101 mathematicsElectrical and Electronic EngineeringGaussian processComputation (stat.CO)MathematicsSignal processinglcsh:Electronics020206 networking & telecommunicationsMarkov chain Monte CarloMonte Carlo methodsHardware and ArchitectureSignal ProcessingSettore SECS-S/03 - Statistica EconomicasymbolsSettore SECS-S/01 - StatisticaStatistical signal processingGibbs samplingAdaptive rejection Metropolis sampling (ARMS)EURASIP Journal on Advances in Signal Processing
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A Unified SVM Framework for Signal Estimation

2013

This paper presents a unified framework to tackle estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The use of SVMs in estimation problems has been traditionally limited to its mere use as a black-box model. Noting such limitations in the literature, we take advantage of several properties of Mercer's kernels and functional analysis to develop a family of SVM methods for estimation in DSP. Three types of signal model equations are analyzed. First, when a specific time-signal structure is assumed to model the underlying system that generated the data, the linear signal model (so called Primal Signal Model formulation) is first stated and analyzed. T…

FOS: Computer and information sciencesbusiness.industryNoise (signal processing)Computer scienceApplied MathematicsSpectral density estimationArray processingPattern recognitionMachine Learning (stat.ML)Statistics - ApplicationsSupport vector machineKernel (linear algebra)Kernel methodComputational Theory and MathematicsStatistics - Machine LearningArtificial IntelligenceSignal ProcessingApplications (stat.AP)Computer Vision and Pattern RecognitionArtificial intelligenceElectrical and Electronic EngineeringStatistics Probability and UncertaintybusinessDigital signal processingReproducing kernel Hilbert space
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Tratamiento digital de señales. Problemas y ejercicios resueltos

2003

El documento es un libro de problemas y ejercicios de Tratamiento Digital de Señales. Este libro publicado por Prentice-Hall en 2003, se ofrece actualmente como recurso de acceso abierto tras su descatalogación. En él se ofrecen ejemplos de problemas y ejercicios resueltos de Tratamiento Digital de Señales, a los que previamente se introduce la base teórica suficiente como para seguir el desarrollo del texto. El contenido es el siguiente: Señales y sistemas en tiempo discreto; Análisis frecuencial de señales y sistemas; Transformada z; Realización de sistemas en tiempo discreto; Efectos de longitud de palabra finita; Diseño de filtros digitales; Sistemas adaptativos. That document is a book…

Filtros DigitalesEfecto de longitud de palabra finitaSistemas adaptativosTratamiento Digital de SeñalesPDSTransformada ZDigital Signal ProcessingProcesado Digital de SeñalesDSPTDSSeñales y sistemas de tiempo discreto
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DSP Implementation for Measuring the Loop Gain Frequency Response of Digitally Controlled Power Converters

2012

This paper presents a method for measuring the loop gain frequency response of digitally controlled power converters, without the need of any expensive measurement equipment such as frequency response analyzers (FRA). The method is based on the traditional perturbation and measurement scheme. The perturbation is internally synthesized by the DSP and applied as pulse-width modulation to the converter. The loop input and output data are sent to a PC via universal serial bus, which performs online the DFT of sent data. The perturbation amplitude is varied with frequency to improve signal-to-noise ratio of measurements. The number of cycles used to perform discrete Fourier transform calculation…

Frequency responseSerial communicationGain measurementbusiness.industryComputer scienceRoundingDead-beat controlConvertersDiscrete Fourier transformAmplitudeModulationControl theoryElectronic engineeringElectrical and Electronic EngineeringbusinessDigital signal processingLoop gainIEEE Transactions on Power Electronics
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Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery

2013

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight cluster…

Fuzzy clusteringMicroarraysSingle-linkage clusteringGenes FungalGene Expressionlcsh:MedicineBiologyFuzzy logicSet (abstract data type)Molecular GeneticsEngineeringGenome Analysis ToolsYeastsConsensus clusteringMolecular Cell BiologyDatabases GeneticCluster (physics)GeneticsCluster AnalysisBinarization of Consensus Partition Matrices (Bi-CoPaM)Cluster analysislcsh:ScienceGene clusteringBiologyOligonucleotide Array Sequence AnalysisGeneticsMultidisciplinarybusiness.industryCell Cycleta111lcsh:RComputational BiologyPattern recognitionGenomicsgene discoveryPartition (database)tunable binarization techniquesComputingMethodologies_PATTERNRECOGNITIONGenesCell cyclesSignal Processinglcsh:QArtificial intelligencebusinessGenomic Signal ProcessingAlgorithmsResearch Articleclustering
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Robust Graph Topology Learning and Application in Stock Market Inference

2019

In many applications, there are multiple interacting entities, generating time series of data over the space. To describe the relation within the set of data, the underlying topology may be used. In many real applications, not only the signal/data of interest is measured in noise, but it is also contaminated with outliers. The proposed method, called RGTL, infers the graph topology from noisy measurements and removes these outliers simultaneously. Here, it is assumed that we have no information about the space graph topology, while we know that graph signal are sampled consecutively in time and thus the graph in time domain is given. The simulation results show that the proposed algorithm h…

Graph signal processingComputer scienceTicker symbolInference020206 networking & telecommunications02 engineering and technology020204 information systemsOutlier0202 electrical engineering electronic engineering information engineeringGraph (abstract data type)Topological graph theoryStock marketTime domainAlgorithm2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
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Pathological voice analysis via digital signal processing

2015

The interest in pathological voice analysis for specific neurological diseases is growing up aiming to offer more Health-care tele monitoring services since new high performing electronic devices are available for the end-user. In this article we show some parameters that can be digitally extracted and analyzed from pathological voices, in order to find a distinctive sign of the Parkinson disease. As a result, we will show a parameter that gives some information about the Parkinson disease characterization, particularly for male patients. We will also discuss about the needed computational cost related to parameters extraction and elaboration, aiming to target a possible tough yet portable …

Hardware architecturebusiness.industryComputer scienceTele monitoringPathological voiceMutual informationSettore ING-INF/01 - ElettronicaIndustrial and Manufacturing EngineeringVoice analysisMutual informationParkinson diseaseHuman–computer interactionMale patientWavelet transformbusinessDigital signal processing
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Quantification of Different Regulatory Pathways Contributing to Heartbeat Dynamics during Multiple Stimuli: a Proof of the Concept.

2019

The dynamical interplay between brain and heart is mediated by several feedback mechanisms including the central autonomic network and baroreflex loop at a peripheral level, also for a short-term regulation. State of the art focused on the characterization of each regulatory pathway through a single stressor elicitation. However, no studies targeted the actual quantification of different mediating routes leading to the generation of heartbeat dynamics, particularly in case of combined exogenous stimuli. In this study, we propose a new approach based on computational modeling to quantify the contribution of multiple concurrent stimuli in modulating cardiovascular dynamics. In this prelimina…

HeartbeatComputer scienceStressorHealthy subjectsHeart Rate VariabilityHeartPhysiological Modelling030204 cardiovascular system & hematologyBaroreflexAutonomic Nervous SystemBiomedical Signal ProcessingCardiovascular System03 medical and health sciences0302 clinical medicineDynamics (music)Heart RateStress PhysiologicalSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaHeart rate variabilityHumansRegulatory PathwayNeuroscience030217 neurology & neurosurgeryStress PsychologicalAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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