Heterogeneous reconfigurable system for adaptive particle filters in real-time applications

This paper presents a heterogeneous reconfigurable system for real-time applications applying particle filters. The system consists of an FPGA and a multi-threaded CPU. We propose a method to adapt the number of particles dynamically and utilise the run-time reconfigurability of the FPGA for reduced power and energy consumption. An application is developed which involves simultaneous mobile robot localisation and people tracking. It shows that the proposed adaptive particle filter can reduce up to 99% of computation time. Using run-time reconfiguration, we achieve 34% reduction in idle power and save 26-34% of system energy. Our proposed system is up to 7.39 times faster and 3.65 times more energy efficient than the Intel Xeon X5650 CPU with 12 threads, and 1.3 times faster and 2.13 times more energy efficient than an NVIDIA Tesla C2070 GPU. © 2013 Springer-Verlag.

Time-resolved particle image velocimetry within the nozzle of a drop-on-demand printhead

Time-resolved particle image velocimetry (PIV) has been performed inside the nozzle of a commercially available inkjet print-head to obtain the time-dependent velocity waveform. A printhead with a single transparent nozzle 80 μm in orifice diameter was used to eject single droplets at a speed of 5 m/s. An optical microscope was used with an ultra-high-speed camera to capture the motion of particles suspended in a transparent liquid at the center of the nozzle and above the fluid meniscus at a rate of half a million frames per second. Time-resolved velocity fields were obtained from a fluid layer approximately 200 μm thick within the nozzle for a complete jetting cycle. A Lagrangian finite-element numerical model with experimental measurements as inputs was used to predict the meniscus movement. The model predictions showed good agreement with the experimental results. This work provides the first experimental verification of physical models and numerical simulations of flows within a drop-on-demand nozzle. © 2012 Society for Imaging Science and Technology.

Atmospheric turbulence mitigation using complex wavelet-based fusion

Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full-and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios. © 1992-2012 IEEE.

Turbulence length scale measurements in a motored internal combustion engine

A novel technique, using a 'flying' Hot Wire Anemometer is described; it is shown how the turbulent structure in a motored engine, using a high molecular weight gas as the working fluid, may be investigated with relative simplicity and very little engine modification. Initial results are presented for integral and micro length scales, which are within the range expected based on previous work. Copyright © 1987 Society of Automotive Engineers, Inc.

Static and dynamic errors in particle tracking microrheology.

Particle tracking techniques are often used to assess the local mechanical properties of cells and biological fluids. The extracted trajectories are exploited to compute the mean-squared displacement that characterizes the dynamics of the probe particles. Limited spatial resolution and statistical uncertainty are the limiting factors that alter the accuracy of the mean-squared displacement estimation. We precisely quantified the effect of localization errors in the determination of the mean-squared displacement by separating the sources of these errors into two separate contributions. A "static error" arises in the position measurements of immobilized particles. A "dynamic error" comes from the particle motion during the finite exposure time that is required for visualization. We calculated the propagation of these errors on the mean-squared displacement. We examined the impact of our error analysis on theoretical model fluids used in biorheology. These theoretical predictions were verified for purely viscous fluids using simulations and a multiple-particle tracking technique performed with video microscopy. We showed that the static contribution can be confidently corrected in dynamics studies by using static experiments performed at a similar noise-to-signal ratio. This groundwork allowed us to achieve higher resolution in the mean-squared displacement, and thus to increase the accuracy of microrheology studies.