Plane wave scattering by a spherical di-electric particle in motion : a relativistic extension of the Mie theory

Light scattering from small spherical particles has applications in a vast number of disciplines including astrophysics, meteorology optics and particle sizing. Mie theory provides an exact analytical characterization of plane wave scattering from spherical dielectric objects. There exist many variants of the Mie theory where fundamental assumptions of the theory has been relaxed to make generalizations. Notable such extensions are generalized Mie theory where plane waves are replaced by optical beams, scattering from lossy particles, scattering from layered particles or shells and scattering of partially coherent (non-classical) light. However, no work has yet been reported in the literature on modifications required to account for scattering when the particle or the source is in motion relative to each other. This is an important problem where many applications can be found in disciplines involving moving particle size characterization. In this paper we propose a novel approach, using special relativity, to address this problem by extending the standard Mie theory for scattering by a particle in motion with a constant speed, which may be very low, moderate or comparable to the speed of light. The proposed technique involves transforming the scattering problem to a reference frame co-moving with the particle, then applying the Mie theory in that frame and transforming the scattered field back to the reference frame of the observer.

A hybrid approach for multiple particle tracking microrheology

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.

A procedural, minimal input, natural terrain plug-in for Blender

This project uses methods of terrain representation, creation and realism described in literature. We find that using a combination of Fractional Brownian Motion and procedural formation of rivers via squig curves to form initial terrain, with hydraulic erosion for post processing, we have full control over the style of terrain: from jagged mountains to flat regions; and the phase of river from tightly rock controlled to flood plain regions.

Charismatic marine mega-fauna: some recent discoveries and future challenges

 The last 20 years have been exciting times for scientists working with charismatic marine mega-fauna. Here recent advances are reviewed. There have been advances in both data gathering and data-analysis techniques that have allowed new insights into the physiological and behavioural ecology of free-ranging mega-faunal species; some marine mega-faunal species have now become model organisms for cutting edge approaches to identify the underlying mathematical properties of animal search patterns and hence the underlying behavioural processes (e.g. Levy flight versus Brownian motion); the implications of climate change have started to become more apparent with extended time-series of animal movements, abundance and performance; conservation issues have become integrated into marine planning and have resulted in the advent of extended networks of marine protected areas (MPAs) as well as large MPAs that span many 100,000 km2; and collaborative crossdisciplinary teams have started to reveal the importance of ocean currents in animal dispersal, the ontogeny of migration and population genetic structure. Looking to the future, increased data availability (e.g. through data sharing) will likely allow more holistic across-taxa analyses to become routine.
© 2013 Elsevier B.V. All rights reserved.

Tracking-as-recognition for articulated full-body human motion analysis

This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.

Driver behaviour prediction for motion simulators using changepoint segmentation

Driving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of change points to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Change points detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.