Search results for "Names"

showing 10 items of 6843 documents

Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks

2020

In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basin…

Coefficient of determinationArtificial neural networkRemote sensing applicationvesien tilaspektrikuvausHyperspectral imagingneuroverkotvedenlaatuConvolutional neural networkwater qualityPearson product-moment correlation coefficientsymbols.namesakeremote sensinghyperspectralilmakuvakartoitusMultilayer perceptronconvolutional neural networkssymbolsEnvironmental scienceWater qualitykaukokartoitusRemote sensing
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Artificial organisms as tools for the development of psychological theory: Tolman's lesson

2007

In the 1930s and 1940s, Edward Tolman developed a psychological theory of spatial orientation in rats and humans. He expressed his theory as an automaton (the ‘‘schematic sowbug’’) or what today we would call an ‘‘artificial organism.’’ With the technology of the day, he could not implement his model. Nonetheless, he used it to develop empirical predictions which tested with animals in the laboratory. This way of proceeding was in line with scientific practice dating back to Galileo. The way psychologists use artificial organisms in their work today breaks with this tradition. Modern ‘‘artificial organisms’’ are constructed a posteriori, working from experimental or ethological observations…

Cognitive modelSettore M-PSI/01 - Psicologia GeneraleComputer scienceCognitive NeuroscienceSpatial BehaviorExperimental and Cognitive Psychologysymbols.namesakeArtificial IntelligenceOrientationArtificial organisms Cognitive modeling Schematic sowbug Tolman's theoryPsychological TheoryGalileo (satellite navigation)AnimalsLearningSchematic sowbug Cognitive modeling Artificial organisms Tolman’s theoryComputer Simulationbusiness.industrySchematicGeneral MedicineRoboticsHistory 20th CenturyModels TheoreticalTrial and errorAutomatonRatsSpace PerceptionsymbolsA priori and a posterioriRobotArtificial intelligencebusinessPsychological Theory
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Gödel and the Blind Watchmaker

2015

While accepting that contingency is key to biological evolution, we wonder how much need there is for it. It is extremely difficult to talk about trends in evolution, but the fact remains that they are found here and there when evolutionary experiments are repeated. But we should ask, for example, whether there is an unavoidable tendency of life towards progressive complexity . This chapter deals with certain theoretical considerations from Logic and Computing on the conditions necessary to formulate a predictive evolutionary theory .

Cognitive scienceComputer scienceBiological evolutionWonderTuring machinesymbols.namesakeSynthetic biologysymbolsKey (cryptography)GödelContingencycomputerEvolutionary theorycomputer.programming_language
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A nonlinear electronic circuit mimicking the neuronal activity in presence of noise

2013

We propose a nonlinear electronic circuit simulating the neuronal activity in a noisy environment. This electronic circuit is ruled by the set of Bonhaeffer-Van der Pol equations and is excited with a white gaussian noise, that is without external deterministic stimuli. Under these conditions, our circuits reveals the Coherence Resonance signature, that is an optimum of regularity in the system response for a given noise intensity.

Coherence ResonanceStochastic resonanceneural network[PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]02 engineering and technologyTopology01 natural sciencesNoise (electronics)symbols.namesakeComputer Science::Emerging TechnologiesNoise generator[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Control theory[ PHYS.PHYS.PHYS-BIO-PH ] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]0103 physical sciences[NLIN.NLIN-PS] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]0202 electrical engineering electronic engineering information engineering[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Value noisestochastic resonance010306 general physicsComputingMilieux_MISCELLANEOUSPhysics[PHYS.PHYS.PHYS-BIO-PH] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]020208 electrical & electronic engineeringShot noiseWhite noiseNoise floor[SPI.TRON] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsGaussian noisesymbolsnonlinear circuit
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A numerical study of attraction/repulsion collective behavior models: 3D particle analyses and 1D kinetic simulations

2013

39p; International audience; We study at particle and kinetic level a collective behavior model based on three phenomena: self-propulsion, friction (Rayleigh effect) and an attractive/repulsive (Morse) potential rescaled so that the total mass of the system remains constant independently of the number of particles N . In the first part of the paper, we introduce the particle model: the agents are numbered and described by their position and velocity. We iden- tify five parameters that govern the possible asymptotic states for this system (clumps, spheres, dispersion, mills, rigid-body rotation, flocks) and perform a numerical analysis on the 3D setting. Then, in the second part of the paper…

Collective behaviorParticle numberKinetic energy01 natural sciencesMSC 92B05 70F99 65P40 35L50symbols.namesakecollective behavior0103 physical sciences[MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP]Statistical physics0101 mathematicsRayleigh scattering010306 general physicsParticle systemSelf-organizationPhysicsNumerical analysisStatistical and Nonlinear Physicsattractive/repulsive potentialCondensed Matter Physicsself-organizationswarming010101 applied mathematicsClassical mechanicssymbolsSPHERES[MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
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Collision Theory for Two- and Three-Particle Systems Interacting via Short-Range and Coulomb Forces

1996

In two- and three-particle reactions with light nuclei, a rich body of precise experimental data exists in which both projectile and target and/or the fragments occurring in the final state are charged. In order to make optimal use of these data for extracting physically interesting information about the nuclear interactions, the effects of the Coulomb force must be separated out in a reliable manner. For this purpose the mastering of the intricacies of charged-particle scattering theory is of vital importance.

Collision theoryParticle systemCoulomb's lawPhysicssymbols.namesakeClassical mechanicsCoulomb collisionCoulomb wave functionsymbolsCoulombCoulomb barrierScattering theoryNuclear Experiment
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Blow-up collocation solutions of nonlinear homogeneous Volterra integral equations

2011

In this paper, collocation methods are used for detecting blow-up solutions of nonlinear homogeneous Volterra-Hammerstein integral equations. To do this, we introduce the concept of "blow-up collocation solution" and analyze numerically some blow-up time estimates using collocation methods in particular examples where previous results about existence and uniqueness can be applied. Finally, we discuss the relationships between necessary conditions for blow-up of collocation solutions and exact solutions.

CollocationApplied MathematicsMathematical analysisMathematics::Analysis of PDEsComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Numerical Analysis (math.NA)Volterra integral equationIntegral equationMathematics::Numerical AnalysisComputational MathematicsNonlinear systemsymbols.namesakeMathematics - Analysis of PDEs45D05 45G10 65R20 34A12HomogeneousComputer Science::Computational Engineering Finance and ScienceCollocation methodFOS: MathematicssymbolsOrthogonal collocationUniquenessMathematics - Numerical AnalysisAnalysis of PDEs (math.AP)Mathematics
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Hurwitz spaces of coverings with two special fibers and monodromy group a Weyl group of typeBd

2012

f! Y; where is a degree-two coverings with n1 branch points and branch locus D and f is a degree-d coverings with n2 points of simple branching and two special points whose local monodromy is given by e and q, respectively. Furthermore the covering f has monodromy group Sd and f. D /\ D fD? where D f denotes the branch locus of f . We prove that the corresponding Hurwitz spaces are irreducible under the hypothesis n2 s r dC 1.

CombinatoricsAlgebraWeyl groupsymbols.namesakeMonodromyGeneral MathematicssymbolsSettore MAT/03 - GeometriaHurwitz spaces special fibers branched coverings Weyl group of type B_d monodromy braid moves.Locus (mathematics)Branch pointMathematicsPacific Journal of Mathematics
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Stochastic Processes on Ends of Tree and Dirichlet Forms

2016

We present main ideas and compare two constructions of stochastic processes on the ends (leaves) of the trees with varying numbers of edges at the nods. In one of them the trees are represented by spaces of numerical sequences and the processes are obtained by solving a class of Chapman-Kolmogorov Equations. In the other the trees are described by the set of nodes and edges. To each node there is naturally associated a finite dimensional function space and the Dirichlet form on it. Having a class of Dirichlet forms at the nodes one can under certain conditions build a Dirichlet form on L2 space of funcions on the ends of the trees. We show that the state spaces of two approaches are homeomo…

CombinatoricsClass (set theory)symbols.namesakeDirichlet formStochastic processFunction spacesymbolsState (functional analysis)Tree (set theory)Lp spaceDirichlet distributionMathematics
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Parabolic Equations Minimizing Linear Growth Functionals: L1-Theory

2004

Let Ω be a bounded set in ℝN with boundary of class C1. We are interested in the problem $$ \left\{ \begin{gathered} \frac{{\partial u}} {{\partial t}} = diva\left( {x,Du} \right)in Q = \left( {0,\infty } \right) \times \Omega , \hfill \\ u\left( {t,x} \right) = \phi \left( x \right)on S = \left( {0,\infty } \right) \times \partial \Omega , \hfill \\ u\left( {0,x} \right) = u_0 \left( x \right)in x \in \Omega \hfill \\ \end{gathered} \right. $$ (1) where ϕ ∈ L1(∂Ω), u0 ∈ L2(Ω) and a(x, ξ) = ∇ξ f(x, ξ, f being a function with linear growth in ‖ξ‖ as ‖ξ‖ → ∞. One of the classical examples is the nonparametric area integrand for which \( f(x,\xi ) = \sqrt {1 + \left\| \xi \right\|^2 } \). Prob…

CombinatoricsDirichlet problemPhysicssymbols.namesakeMinimal surfacesymbolsLinear growthParabolic partial differential equationOmegaLagrangian
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