Search results for " pattern"

showing 10 items of 2245 documents

Information – theoretic characterization of concurrent activity of neural spike trains

2021

The analysis of massively parallel spike train recordings facilitates investigation of communications and synchronization in neural networks. In this work we develop and evaluate a measure of concurrent neural activity, which is based on intrinsic firing properties of the recorded neural units. An overall single neuron activity is unfolded in time and decomposed into working and non-firing state, providing a coarse, binary representation of the neurons functional state. We propose a modified measure of mutual information to reflect the degree of simultaneous activation and concurrency in neural firing patterns. The measure is shown to be sensitive to both correlations and anti-correlations,…

Signal processingQuantitative Biology::Neurons and CognitionArtificial neural networkComputer sciencebusiness.industrySpike trainFiring patterns020206 networking & telecommunicationsPattern recognition02 engineering and technologyMeasure (mathematics)Concurrent activityMutual informationNeural activitymedicine.anatomical_structure0202 electrical engineering electronic engineering information engineeringmedicineSpike trains020201 artificial intelligence & image processingSpike (software development)NeuronArtificial intelligencebusinessNeural synchrony2020 28th European Signal Processing Conference (EUSIPCO)
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Signal reconstruction, modeling and simulation of a vehicle full-scale crash test based on Morlet wavelets

2012

Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this paper a novel wavelet-based approach is introduced to reproduce acceleration pulse of a vehicle involved in a crash event. The acceleration of a colliding vehicle is measured in its center of gravity-this crash pulse contains detailed information about vehicle behavior throughout a collision. Three types of signal analysis are elaborated here: time domain analysis (i.e. description of kinematics of…

Signal processingSignal reconstructionComputer scienceMultiresolution analysisCognitive NeuroscienceCrashComputer Science Applications1707 Computer Vision and Pattern RecognitionCrash testComputer Science ApplicationsMorlet wavelet; Multiresolution analysis; Signal reproduction; Vehicle crash modeling; Computer Science Applications1707 Computer Vision and Pattern Recognition; Cognitive Neuroscience; Artificial IntelligenceWaveletMorlet waveletArtificial IntelligenceFrequency domainTime domainSignal reproductionMorlet waveletMultiresolution analysisVehicle crash modelingSimulation
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Support Vector Machines Framework for Linear Signal Processing

2005

This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of I…

Signal processingTelecomunicacionesSupport vector machinesSystem identificationLinear signal processingSpectral density estimationSpectral estimationSupport vector machineGamma filterControl and Systems EngineeringControl theoryComplex ARMASignal ProcessingAutoregressive–moving-average model3325 Tecnología de las TelecomunicacionesComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringInfinite impulse responseDigital filterAlgorithmSoftwareParametric statisticsMathematics
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Space–bandwidth product of optical signals and systems

1996

The space–bandwidth product (SW) is fundamental for judging the performance of an optical system. Often the SW of a system is defined only as a pure number that counts the degrees of freedom of the system. We claim that a quasi-geometrical representation of the SW in the Wigner domain is more useful. We also represent the input signal as a SW in the Wigner domain. For perfect signal processing it is necessary that the system SW fully embrace the signal SW.

Signal processingbusiness.industryComputer scienceBandwidth (signal processing)TopologyAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic Materialssymbols.namesakeFourier transformOpticssymbolsComputer Vision and Pattern RecognitionSpatial frequencybusinessJournal of the Optical Society of America A
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The Large Area Detector onboard the eXTP mission

2018

The eXTP (enhanced X-ray Timing and Polarimetry) mission is a major project of the Chinese Academy of Sciences (CAS) and China National Space Administration (CNSA) currently performing an extended phase A study and proposed for a launch by 2025 in a low-earth orbit. The eXTP scientific payload envisages a suite of instruments (Spectroscopy Focusing Array, Polarimetry Focusing Array, Large Area Detector and Wide Field Monitor) offering unprecedented simultaneous wide-band X-ray spectral, timing and polarimetry sensitivity. A large European consortium is contributing to the eXTP study and it is expected to provide key hardware elements, including a Large Area Detector (LAD). The LAD instrumen…

Silicon detectorX-ray AstronomyComputer sciencecapillary platePolarimetryFOS: Physical sciencesField of viewContext (language use)Condensed Matter Physic01 natural sciencesSettore FIS/05 - Astronomia E Astrofisica0103 physical sciencesElectroniccapillary plates; Silicon detectors; Timing; X-ray Astronomy; Electronic Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic EngineeringTimingOptical and Magnetic MaterialsAerospace engineeringSpectral resolutionElectrical and Electronic Engineering010306 general physicscapillary plates; Silicon detectors; Timing; X-ray Astronomy; astro-ph.IM; astro-ph.IM; Electronic Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic EngineeringInstrumentation and Methods for Astrophysics (astro-ph.IM)X-ray astronomycapillary plates010308 nuclear & particles physicsbusiness.industryPayloadElectronic Optical and Magnetic MaterialApplied MathematicsDetectorAntenna apertureComputer Science Applications1707 Computer Vision and Pattern RecognitionCondensed Matter PhysicsApplied MathematicSilicon detectorsAstrophysics - Instrumentation and Methods for Astrophysicsbusinessastro-ph.IM
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Energy balance in single exposure multispectral sensors

2013

International audience; Recent simulations of multispectral sensors are based on a simple Gaussian model, which includes filters transmittance and substrate absorption. In this paper we want to make the distinction between these two layers. We discuss the balance of energy by channel in multispectral solid state sensors and propose an updated simple Gaussian model to simulate multispectral sensors. Results are based on simulation of typical sensor configurations.

SiliconMaterials science[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingoptical sensorsChannel (digital image)Equations[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPhotodetectorGaussian processes02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciences010309 opticssymbols.namesakeMathematical model[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciences0202 electrical engineering electronic engineering information engineeringTransmittanceComputer Science::Networking and Internet ArchitectureSpectral and color filter arraysoptical filtersOptical filterGaussian processPhysics::Atmospheric and Oceanic Physics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingRemote sensingtransmittance filterSubstratesSensorsGaussian modelmultispectral solid state sensorCamerasenergy balancespectral analysisConvolutionexposure multispectral sensorComputer Science::Computer Vision and Pattern Recognitionsubstrate absorptionlight absorptionlight sensorsymbolstransmittance filters020201 artificial intelligence & image processingGaussian network model[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingEnergy (signal processing)
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An enhanced random walk algorithm for delineation of head and neck cancers in PET studies

2017

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies:…

Similarity (geometry)Computer sciencePET imagingBiomedical EngineeringRandom walk030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinemedicineImage Processing Computer-AssistedHumansSegmentationComputer visionCluster analysisEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryPhantoms ImagingBiological target volume; Head and neck cancer segmentation; PET imaging; Random walksComputer Science ApplicationPattern recognitionRandom walkComputer Science ApplicationsBiological target volumeHausdorff distancePositron emission tomographyHead and Neck Neoplasms030220 oncology & carcinogenesisPositron-Emission TomographyArtificial intelligenceHead and neck cancer segmentationComputer Vision and Pattern RecognitionbusinessAlgorithmsBiological target volume Head and neck cancer segmentation PET imaging Random walks Algorithms Head and Neck Neoplasms Humans Image Processing Computer-Assisted Phantoms Imaging Positron-Emission TomographyVolume (compression)
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A mutual GrabCut method to solve co-segmentation

2013

Publised version of an article from the journal:Eurasip Journal on Image and Video Processing. Also available on SpringerLink:http://dx.doi.org/10.1186/1687-5281-2013-20. Open Access Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model ra…

Similarity (geometry)Markov random fieldComputer sciencebusiness.industryVDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONVDP::Technology: 500::Information and communication technology: 550Pattern recognitionFunction (mathematics)Term (time)Constraint (information theory)GrabCutComputer Science::Computer Vision and Pattern RecognitionCutSignal ProcessingSegmentationArtificial intelligenceElectrical and Electronic EngineeringbusinessInformation SystemsEURASIP Journal on Image and Video Processing
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Trademarks recognition based on local regions similarities

2010

This paper deals with content based image retrieval. We propose a logo recognition algorithm based on local regions, where the trademark (or logo) image is segmented by the clustering of points of interest obtained by Harris corners detector. The minimum rectangle surrounding each cluster is detected forming the regions of interest. Global features such as Hu moments and histograms of each local region are combined to find similar logos in the database. Similarity is measured based on the integrated minimum average distance of the individual components. The results obtained demonstrate tolerance to logos distortions such as rotation, occlusion and noise.

Similarity (geometry)business.industryComputer scienceMathematics::History and OverviewComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCorner detectionPattern recognitionImage segmentationContent-based image retrievalEdge detectionComputingMethodologies_PATTERNRECOGNITIONComputer Science::Computer Vision and Pattern RecognitionPattern recognition (psychology)Computer visionArtificial intelligencebusinessCluster analysisImage retrieval10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010)
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The Cryogenic AntiCoincidence detector for ATHENA: the progress towards the final pixel design

2014

“The Hot and Energetic Universe” is the scientific theme approved by the ESA SPC for a Large mission to be flown in the next ESA slot (2028th) timeframe. ATHENA is a space mission proposal tailored on this scientific theme. It will be the first X-ray mission able to perform the so-called “Integral field spectroscopy”, by coupling a high-resolution spectrometer, the X-ray Integral Field Unit (X-IFU), to a high performance optics so providing detailed images of its field of view (5’ in diameter) with an angular resolution of 5” and fine energy-spectra (2.5eV@E<7keV). The X-IFU is a kilo-pixel array based on TES (Transition Edge Sensor) microcalorimeters providing high resolution spectroscopy …

SimulationsSiliconWarm–hot intergalactic mediumField of viewOrbital mechanicsOpticsField spectroscopyGalactic astronomyX-raysElectronicAngular resolutionOptical and Magnetic MaterialsElectrical and Electronic EngineeringAnticoincidenceImage resolutionSpectroscopyPhysicsSpatial resolutionEquipment and servicesSpectrometerSpectrometersbusiness.industrySensorsApplied MathematicsDetectorComputer Science Applications1707 Computer Vision and Pattern RecognitionCondensed Matter PhysicsATHENAAnticoincidence; ATHENA; Cryogenic detectors; TES; Electronic Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic EngineeringCryogenic detectorsTransition edge sensorbusinessTES
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