996 resultados para Particle Trajectory Computation


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Geometric object detection has many applications, such as in tracking. Particle tracking microrheology is a technique for studying mechanical properties by accurately tracking the motion of the immersed particles undergoing Brownian motion. Since particles are carried along by these random undulations of the medium, they can move in and out of the microscope's depth of focus, which results in halos (lower intensity). Two-point particle tracking microrheology (TPM) uses a threshold to find those particles with peak, which leads to the broken trajectory of the particles. The halos of those particles which are out of focus are circles and the centres can be accurately tracked in most cases. When the particles are sparse, TPM will lose certain useful information. Thus, it may cause inaccurate microrheology. An efficient algorithm to detect the centre of those particles will increase the accuracy of the Brownian motion. In this paper, a hybrid approach is proposed which combines the steps of TPM for particles in focus with a circle detection step using circular Hough transform for particles with halos. As a consequence, it not only detects more particles in each frame but also dramatically extends the trajectories with satisfactory accuracy. Experiments over a video microscope data set of polystyrene spheres suspended in water undergoing Brownian motion confirmed the efficiency of the algorithm.

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The relation between the spin and the mass of an infinite number of particles in a q-deformed dual string theory is studied. For the deformation parameter q a root of unity, in addition to the relation of such values of q with the rational conformal field theory, the Fock space of each oscillator mode in the Fubini-Veneziano operator formulation becomes truncated. Thus, based on general physical grounds, the resulting spin-(mass)2 relation is expected to be below the usual linear trajectory. For such specific values of q, we find that the linear Regge trajectory turns into a square-root trajectory as the mass increases.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Diffusion is a common phenomenon in nature and generally is associated with a system trying to reach a local or a global equilibrium state, as a result of highly irregular individual particle motion. Therefore it is of fundamental importance in physics, chemistry and biology. Particle tracking in complex fluids can reveal important characteristics of its properties. In living cells, we coat the microbead with a peptide (RGD) that binds to integrin receptors at the plasma membrane, which connects to the CSK. This procedure is based on the hypothesis that the microsphere can move only if the structure where it is attached move as well. Then, the observed trajectory of microbeads is a probe of the cytoskeleton (CSK), which is governed by several factors, including thermal diffusion, pressure gradients, and molecular motors. The possibility of separating the trajectories into passive and active diffusion may give information about the viscoelasticity of the cell structure and molecular motors activity. And also we could analyze the motion via generalized Stokes-Einstein relation, avoiding the use of any active techniques. Usually a 12 to 16 Frames Per Second (FPS) system is used to track the microbeads in cell for about 5 minutes. Several factors make this FPS limitation: camera computer communication, light, computer speed for online analysis among others. Here we used a high quality camera and our own software, developed in C++ and Linux, to reach high FPS. Measurements were conducted with samples for 10£ and 20£ objectives. We performed sequentially images with different intervals, all with 2 ¹s exposure. The sequences of intervals are in milliseconds: 4 5 ms (maximum speed) 14, 25, 50 and 100 FPS. Our preliminary results highlight the difference between passive and active diffusion, since the passive diffusion is represented by a Gaussian in the distribution of displacements of the center of mass of individual beads between consecutive frames. However, the active process, or anomalous diffusion, shows as long tails in the distribution of displacements.

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The aim of this work is to present various aspects of numerical simulation of particle and radiation transport for industrial and environmental protection applications, to enable the analysis of complex physical processes in a fast, reliable, and efficient way. In the first part we deal with speed-up of numerical simulation of neutron transport for nuclear reactor core analysis. The convergence properties of the source iteration scheme of the Method of Characteristics applied to be heterogeneous structured geometries has been enhanced by means of Boundary Projection Acceleration, enabling the study of 2D and 3D geometries with transport theory without spatial homogenization. The computational performances have been verified with the C5G7 2D and 3D benchmarks, showing a sensible reduction of iterations and CPU time. The second part is devoted to the study of temperature-dependent elastic scattering of neutrons for heavy isotopes near to the thermal zone. A numerical computation of the Doppler convolution of the elastic scattering kernel based on the gas model is presented, for a general energy dependent cross section and scattering law in the center of mass system. The range of integration has been optimized employing a numerical cutoff, allowing a faster numerical evaluation of the convolution integral. Legendre moments of the transfer kernel are subsequently obtained by direct quadrature and a numerical analysis of the convergence is presented. In the third part we focus our attention to remote sensing applications of radiative transfer employed to investigate the Earth's cryosphere. The photon transport equation is applied to simulate reflectivity of glaciers varying the age of the layer of snow or ice, its thickness, the presence or not other underlying layers, the degree of dust included in the snow, creating a framework able to decipher spectral signals collected by orbiting detectors.

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We consider stochastic individual-based models for social behaviour of groups of animals. In these models the trajectory of each animal is given by a stochastic differential equation with interaction. The social interaction is contained in the drift term of the SDE. We consider a global aggregation force and a short-range repulsion force. The repulsion range and strength gets rescaled with the number of animals N. We show that for N tending to infinity stochastic fluctuations disappear and a smoothed version of the empirical process converges uniformly towards the solution of a nonlinear, nonlocal partial differential equation of advection-reaction-diffusion type. The rescaling of the repulsion in the individual-based model implies that the corresponding term in the limit equation is local while the aggregation term is non-local. Moreover, we discuss the effect of a predator on the system and derive an analogous convergence result. The predator acts as an repulsive force. Different laws of motion for the predator are considered.

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A recent study by the authors points to Charged Particle Drag (CPD) as a contributor to revisit in the LAGEOS non-gravitational perturbations problem. Such perturbations must account for dynamical contributions in the order of pms−2 . The simulated effect takes into account: (i) spatial and temporal variations of the plasmatic parameters (temperature and concentration of the species), (ii) spacecraft potential variations caused by both the eclipse passages and variations in the parameters mentioned above, and (iii) solar and geomagnetic conditions. Furthermore, recent theoretical improvements concerning scattering drag overcome previous limitations allowing for a complete formulation of this effect. For each satellite the lifetime CPD instantaneous acceleration is computed. The plasmatic parameters have been obtained fromthe Sheffield Coupled Thermosphere-Ionosphere-Plasmasphere (SCTIP) semi-empirical model (up to the polar region), as well as alytical/empirical approximations based on spacecraft measurements for the auroral and polar regions. Results show that maximum amplitudes for LAGEOSI are larger than those for LAGEOS-II: −85 pms−2 and −70 pms−2 respectively. This is due to the almost (magnetically) polar orbit configuration of the first, producing larger combinations of plasmatic parameter values. High solar activity has a huge impact in the resulting LAGEOS accelerations: it yields a perfect modulation of the resulting acceleration with maximum amplitudes up to a factor of 10 when comparing low and high activity periods. On the other hand, the impact of the geomagnetic activity results into a reduction of the effect itself, probably due to a decrease in the hydrogen concentration for high energy input periods. The acceleration results will be used in a refined orbit computation in a subsequent investigation.

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Autonomous systems require, in most of the cases, reasoning and decision-making capabilities. Moreover, the decision process has to occur in real time. Real-time computing means that every situation or event has to have an answer before a temporal deadline. In complex applications, these deadlines are usually in the order of milliseconds or even microseconds if the application is very demanding. In order to comply with these timing requirements, computing tasks have to be performed as fast as possible. The problem arises when computations are no longer simple, but very time-consuming operations. A good example can be found in autonomous navigation systems with visual-tracking submodules where Kalman filtering is the most extended solution. However, in recent years, some interesting new approaches have been developed. Particle filtering, given its more general problem-solving features, has reached an important position in the field. The aim of this thesis is to design, implement and validate a hardware platform that constitutes itself an embedded intelligent system. The proposed system would combine particle filtering and evolutionary computation algorithms to generate intelligent behavior. Traditional approaches to particle filtering or evolutionary computation have been developed in software platforms, including parallel capabilities to some extent. In this work, an additional goal is fully exploiting hardware implementation advantages. By using the computational resources available in a FPGA device, better performance results in terms of computation time are expected. These hardware resources will be in charge of extensive repetitive computations. With this hardware-based implementation, real-time features are also expected.

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In tethered satellite technology, it is important to estimate how many electrons a spacecraft can collect from its ambient plasma by a bare electrodynamic tether. The analysis is however very difficult because of the small but significant Geo-magnetic field and the spacecraft’s relative motion to both ions and electrons. The object of our work is the development of a numerical method, for this purpose. Particle-In-Cell (PIC) method, for the calculation of electron current to a positive bare tether moving at orbital velocity in the ionosphere, i.e. in a flowing magnetized plasma under Maxwellian collisionless conditions. In a PIC code, a number of particles are distributed in phase space and the computational domain has a grid on which Poisson equation is solved for field quantities. The code uses the quasi-neutrality condition to solve for the local potential at points in the plasma which coincide with the computational outside boundary. The quasi-neutrality condition imposes ne - ni on the boundary. The Poisson equation is solved in such a way that the presheath region can be captured in the computation. Results show that the collected current is higher than the Orbital Motion Limit (OML) theory. The OML current is the upper limit of current collection under steady collisionless unmagnetized conditions. In this work, we focus on the flowing effects of plasma as a possible cause of the current enhancement. A deficit electron density due to the flowing effects has been worked and removed by introducing adiabatic electron trapping into our model.

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Finding the degree-constrained minimum spanning tree (DCMST) of a graph is a widely studied NP-hard problem. One of its most important applications is network design. Here we deal with a new variant of the DCMST problem, which consists of finding not only the degree- but also the role-constrained minimum spanning tree (DRCMST), i.e., we add constraints to restrict the role of the nodes in the tree to root, intermediate or leaf node. Furthermore, we do not limit the number of root nodes to one, thereby, generally, building a forest of DRCMSTs. The modeling of network design problems can benefit from the possibility of generating more than one tree and determining the role of the nodes in the network. We propose a novel permutation-based representation to encode these forests. In this new representation, one permutation simultaneously encodes all the trees to be built. We simulate a wide variety of DRCMST problems which we optimize using eight different evolutionary computation algorithms encoding individuals of the population using the proposed representation. The algorithms we use are: estimation of distribution algorithm, generational genetic algorithm, steady-state genetic algorithm, covariance matrix adaptation evolution strategy, differential evolution, elitist evolution strategy, non-elitist evolution strategy and particle swarm optimization. The best results are for the estimation of distribution algorithms and both types of genetic algorithms, although the genetic algorithms are significantly faster.

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A simple evolutionary process can discover sophisticated methods for emergent information processing in decentralized spatially extended systems. The mechanisms underlying the resulting emergent computation are explicated by a technique for analyzing particle-based logic embedded in pattern-forming systems. Understanding how globally coordinated computation can emerge in evolution is relevant both for the scientific understanding of natural information processing and for engineering new forms of parallel computing systems.

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We present a new model for the continuous measurement of a coupled quantum dot charge qubit. We model the effects of a realistic measurement, namely adding noise to, and filtering, the current through the detector. This is achieved by embedding the detector in an equivalent circuit for measurement. Our aim is to describe the evolution of the qubit state conditioned on the macroscopic output of the external circuit. We achieve this by generalizing a recently developed quantum trajectory theory for realistic photodetectors [P. Warszawski, H. M. Wiseman, and H. Mabuchi, Phys. Rev. A 65, 023802 (2002)] to treat solid-state detectors. This yields stochastic equations whose (numerical) solutions are the realistic quantum trajectories of the conditioned qubit state. We derive our general theory in the context of a low transparency quantum point contact. Areas of application for our theory and its relation to previous work are discussed.

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We investigate the gas-particle dynamics of a device designed for biological pre-clinical experiments. The device uses transonic/supersonic gas flow to accelerate microparticles such that they penetrate the outer skin layers. By using a shock tube coupled to a correctly expanded nozzle, a quasi-one-dimensional, quasi-steady flow (QSF) is produced to uniformly accelerate the microparticles. The system utilises a microparticle cassette (a diaphragm sealed container) that incorporates a jet mixing mechanism to stir the particles prior to diaphragm rupture. Pressure measurements reveal that a QSF exit period - suitable for uniformly accelerating microparticles - exists between 155 and 220 mus after diaphragm rupture. Immediately preceding the QSF period, a starting process secondary shock was shown to form with its (x,t) trajectory comparing well to theoretical estimates. To characterise the microparticle, flow particle image velocimetry experiments were conducted at the nozzle exit, using particle payloads with varying diameter (2.7-48 mu m), density (600-16,800 kg/m(3)) and mass (0.25-10 mg). The resultant microparticle velocities were temporally uniform. The experiments also show that the starting process does not significantly influence the microparticle nozzle exit velocities. The velocity distribution across the nozzle exit was also uniform for the majority of microparticle types tested. For payload masses typically used in pre-clinical drug and vaccine applications (

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The pyrolysis of a freely moving cellulosic particle inside a 41.7mgs -1 source continuously fed fluid bed reactor subjected to convective heat transfer is modelled. The Lagrangian approach is adopted for the particle tracking inside the reactor, while the flow of the inert gas is treated with the standard Eulerian method for gases. The model incorporates the thermal degradation of cellulose to char with simultaneous evolution of gases and vapours from discrete cellulosic particles. The reaction kinetics is represented according to the Broido–Shafizadeh scheme. The convective heat transfer to the surface of the particle is solved by two means, namely the Ranz–Marshall correlation and the limit case of infinitely fast external heat transfer rates. The results from both approaches are compared and discussed. The effect of the different heat transfer rates on the discrete phase trajectory is also considered.

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The fluid–particle interaction inside a 150 g/h fluidised bed reactor is modelled. The biomass particle is injected into the fluidised bed and the momentum transport from the fluidising gas and fluidised sand is modelled. The Eulerian approach is used to model the bubbling behaviour of the sand, which is treated as a continuum. The particle motion inside the reactor is computed using drag laws, dependent on the local volume fraction of each phase, according to the literature. FLUENT 6.2 has been used as the modelling framework of the simulations with a completely revised drag model, in the form of user defined function (UDF), to calculate the forces exerted on the particle as well as its velocity components. 2-D and 3-D simulations are tested and compared. The study is the first part of a complete pyrolysis model in fluidised bed reactors.