Search results for "Density"

showing 10 items of 4402 documents

Testing jet geometries and disc-jet coupling in the neutron star LMXB 4U 0614 + 091 with the internal shocks model

2020

Multi-wavelength spectral energy distributions of Low Mass X-ray Binaries in the hard state are determined by the emission from a jet, for frequencies up to mid-infrared, and emission from the accretion flow in the optical to X-ray range. In the last years, the flat radio-to-mid-IR spectra of Black Hole (BH) X-ray binaries was described using the internal shocks model, which assumes that the fluctuations in the velocity of the ejecta along the jet are driven by the fluctuations in the accretion flow, described by the X-ray Power Density Spectrum (PDS). In this work we attempt to apply this model for the first time to a Neutron Star (NS) LMXB, i.e. 4U 0614+091. We used the multi-wavelength d…

Shock waveAstrophysics::High Energy Astrophysical PhenomenaFOS: Physical sciencesAstrophysics01 natural sciencesSpectral linestars: jetsX-rays: binariesstars: neutronaccretion0103 physical sciencesEjecta010303 astronomy & astrophysicsAstrophysics::Galaxy AstrophysicsPhysicsHigh Energy Astrophysical Phenomena (astro-ph.HE)010308 nuclear & particles physicsSpectral densityAstronomy and AstrophysicsConical surfaceshock wavesaccretion discsAccretion (astrophysics)Neutron starSpace and Planetary Scienceaccretion accretion discsAstrophysics - High Energy Astrophysical PhenomenaLow Mass[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
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On the interplay between cosmological shock waves and their environment

2017

Cosmological shock waves are tracers of the thermal history of the structures in the Universe. They play a crucial role in redistributing the energy within the cosmic structures and are also amongst the main ingredients of galaxy and galaxy cluster formation. Understanding this important function requires a proper description of the interplay between shocks and the different environments where they can be found. In this paper, an Adaptive Mesh Refinement (AMR) Eulerian cosmological simulation is analysed by means of a shock-finding algorithm that allows to generate shock wave maps. Based on the population of dark matter halos and on the distribution of density contrast in the simulation, we…

Shock wavePhysicseducation.field_of_study010504 meteorology & atmospheric sciencesAstrophysics::High Energy Astrophysical PhenomenaDark matterPopulationAstronomy and AstrophysicsAstrophysicsAstrophysics::Cosmology and Extragalactic Astrophysics01 natural sciencesShock (mechanics)Shock waves in astrophysicsDistribution functionSpace and Planetary Science0103 physical sciencesDensity contrasteducation010303 astronomy & astrophysicsGalaxy clusterAstrophysics::Galaxy Astrophysics0105 earth and related environmental sciences
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Rapid parameter determination of discrete damped sinusoidal oscillations

2020

We present different computational approaches for the rapid extraction of the signal parameters of discretely sampled damped sinusoidal signals. We compare time- and frequency-domain-based computational approaches in terms of their accuracy and precision and computational time required in estimating the frequencies of such signals, and observe a general trade-off between precision and speed. Our motivation is precise and rapid analysis of damped sinusoidal signals as these become relevant in view of the recent experimental developments in cavity-enhanced polarimetry and ellipsometry, where the relevant time scales and frequencies are typically within the ∼1 − 10 µs and ∼1 − 100 MHz ranges, …

Signal Processing (eess.SP)Accuracy and precisionPhysics - Instrumentation and DetectorsAcousticsPolarimetryFOS: Physical sciences02 engineering and technologyApplied Physics (physics.app-ph)01 natural sciencesSignal010309 opticssymbols.namesakeOptics0103 physical sciencesFOS: Electrical engineering electronic engineering information engineeringElectrical Engineering and Systems Science - Signal ProcessingPhysicsbusiness.industrySpectral densityInstrumentation and Detectors (physics.ins-det)Physics - Applied Physics021001 nanoscience & nanotechnologyAtomic and Molecular Physics and OpticsMicrosecondFourier transformsymbols0210 nano-technologybusinessMatrix methodOptics (physics.optics)Physics - Optics
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Deep Completion Autoencoders for Radio Map Estimation

2022

Radio maps provide metrics such as power spectral density for every location in a geographic area and find numerous applications such as UAV communications, interference control, spectrum management, resource allocation, and network planning to name a few. Radio maps are constructed from measurements collected by spectrum sensors distributed across space. Since radio maps are complicated functions of the spatial coordinates due to the nature of electromagnetic wave propagation, model-free approaches are strongly motivated. Nevertheless, all existing schemes for radio occupancy map estimation rely on interpolation algorithms unable to learn from experience. In contrast, this paper proposes a…

Signal Processing (eess.SP)Computer scienceApplied MathematicsSpectral densityInterference (wave propagation)computer.software_genreAutoencoderSpectrum managementComputer Science ApplicationsNetwork planning and designSpatial reference systemFOS: Electrical engineering electronic engineering information engineeringResource allocationData miningElectrical and Electronic EngineeringElectrical Engineering and Systems Science - Signal ProcessingcomputerInterpolation
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Data-Driven Spectrum Cartography via Deep Completion Autoencoders

2019

Spectrum maps, which provide RF spectrum metrics such as power spectral density for every location in a geographic area, find numerous applications in wireless communications such as interference control, spectrum management, resource allocation, and network planning to name a few. Spectrum cartography techniques construct these maps from a collection of measurements collected by spatially distributed sensors. Due to the nature of the propagation of electromagnetic waves, spectrum maps are complicated functions of the spatial coordinates. For this reason, model-free approaches have been preferred. However, all existing schemes rely on some interpolation algorithm unable to learn from data. …

Signal Processing (eess.SP)Network architectureComputer sciencebusiness.industry05 social sciencesSpectral density050801 communication & media studiesSpectrum managementNetwork planning and design0508 media and communicationsSpatial reference system0502 economics and businessFOS: Electrical engineering electronic engineering information engineeringResource allocationWireless050211 marketingElectrical Engineering and Systems Science - Signal ProcessingbusinessVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550CartographyInterpolation
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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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Mutual Information Analysis of Brain-Heart Interactions in Epileptic Children

2021

In this work we apply the network physiology paradigm to retrieve information from central and autonomic nervous systems before focal epileptic seizure, represented respectively by electroencephalogram (EEG) signals and R-R intervals (RRI), and investigate on the presence and strength of brain-heart interactions by computing mutual information (MI) measures. Statistical significance of MI values was tested through surrogate time series generated with the random shuffle approach. Our results suggest that the proposed method for aligning signals representing brain and heart activity measured with different sampling rates, is capable of revealing coupling between RRI representing heart system,…

Signal processingmedicine.diagnostic_testbusiness.industryTotal frequencySpectral densityPattern recognitionMutual informationHeart activityElectroencephalographyEpilepsy seizure EEG R-R intervals mutual information brain-heart interactionsSettore ING-INF/06 - Bioingegneria Elettronica E InformaticamedicineArtificial intelligenceEpileptic seizuremedicine.symptombusinessMathematics
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Species-Specific Aggregation Factor in Sponges

1978

An aggregation receptor (AR) from the siliceous sponge Suberites domuncula has been isolated and purified by chromatography to about 55% purity. The AR consists primarily of neutral carbohydrate and is characterized by a buoyant density of 1.59 g/ml and by an apparent molecular weight of 42,500. The average density of the AR on Suberite cells is about 3.8 × 10 5 per μm 2 . The AR contains considerable amounts of hexuronic acid. The isolated AR can bind not only to receptor-depleted Suberites cells but also to receptor depleted cells from another siliceous species (Geodia cydontum) . After being charged with Suberites ARs, Geodia cells form aggregates in the presence of the species-specific …

Siliceous spongeCancer ResearchbiologyGeodia cydoniumBuoyant densityMineralogyCell BiologyCarbohydratebiology.organism_classificationSuberites domunculaBiochemistryGeodiaReceptorMolecular BiologyDevelopmental BiologySuberitesDifferentiation
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Reactivity of antitumor coinage metal-based N-heterocyclic carbene complexes with cysteine and selenocysteine protein sites

2021

Abstract The reaction of the antitumor M(I)-bis-N-heterocyclic carbene (M(I)-NHC) complexes, M = Cu, Ag, and Au, with their potential protein binding sites, i.e. cysteine and selenocysteine, was investigated by means of density functional theory approaches. Capped cysteine and selenocysteine were employed to better model the corresponding residues environment within peptide structures. By assuming the neutral or deprotonated form of the side chains of these amino acids and by considering the possible assistance of an external proton donor such as an adjacent acidic residue or the acidic component of the surrounding buffer environment, we devised five possible routes leading to the binding o…

SilverAnticancer; Copper(I) complexes; DFT calculations; Gold(I) complexes; N-heterocyclic carbenes; Silver(I) complexesStereochemistryCoinage metalsAntineoplastic AgentsProtonationLigandsDFT calculationsBiochemistrySilver(I) complexesInorganic Chemistrychemistry.chemical_compoundDeprotonationProtein structureCoordination ComplexesCysteineN-heterocyclic carbenesDensity Functional Theorychemistry.chemical_classificationMolecular StructureSelenocysteineCopper(I) complexesSelenocysteineAmino acidAnticancerGold(I) complexesModels ChemicalchemistryThermodynamicsGoldCarbeneCopperCysteineJournal of Inorganic Biochemistry
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Controlled Direct Synthesis of C-Mono- and C-Disubstituted Derivatives of [3,3′-Co(1,2-C2B9H11)2]− with Organosilane Groups: Theoretical Calculations…

2008

Mono- and dilithium salts of [3,3'-Co(1,2-C(2)B(9)H(11))(2)](-), (1(-)), react with different chlorosilanes (Me(2)SiHCl, Me(2)SiCl(2), Me(3)SiCl and MeSiHCl(2)) with an accurate control of the temperature to give a set of novel C(c)-mono- (C(c) = C(cluster)) and C(c)-disubstituted cobaltabis(dicarbollide) derivatives with silyl functions: [1-SiMe(2)H-3,3'-Co(1,2-C(2)B(9)H(10))(1',2'-C(2)B(9)H(11))](-) (3(-)); [1,1'-mu-SiMe(2)-3,3'-Co(1,2-C(2)B(9)H(10))(2)](-) (4(-)); [1,1'-mu-SiMeH-3,3'-Co(1,2-C(2)B(9)H(10))(2)](-) (5(-)); [1-SiMe(3)-3,3'-Co(1,2-C(2)B(9)H(10))(1',2'-C(2)B(9)H(11))](-) (6(-)) and [1,1'-(SiMe(3))(2)-3,3'-Co(1,2-C(2)B(9)H(10))(2)](-) (7(-)). In a similar way, the [8,8'-mu-(1''…

SilylationIntramolecular reaction010405 organic chemistryMeso compoundStereochemistryOrganic ChemistryGeneral Chemistry010402 general chemistry01 natural sciencesMedicinal chemistryCatalysis0104 chemical sciencesDilithiumchemistry.chemical_compoundchemistryDensity functional theoryStoichiometryChlorosilaneChemistry - A European Journal
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