Split-screen single-camera stereoscopic PIV application to a turbulent confined swirling layer with free surface

An annular liquid wall jet, or vortex tube, generated by helical injection inside a tube is studied experimentally as a possible means of fusion reactor shielding. The hollow confined vortex/swirling layer exhibits simultaneously all the complexities of swirling turbulence, free surface, droplet formation, bubble entrapment; all posing challenging diagnostic issues. The construction of flow apparatus and the choice of working liquid and seeding particles facilitate unimpeded optical access to the flow field. A split-screen, single-camera stereoscopic particle image velocimetry (SPIV) scheme is employed for flow field characterization. Image calibration and free surface identification issues are discussed. The interference in measurements of laser beam reflection at the interface are identified and discussed. Selected velocity measurements and turbulence statistics are presented at Re_λ = 70 (Re = 3500 based on mean layer thickness)

Learning to Extract Motion from Videos in Convolutional Neural Networks

This paper shows how to extract dense optical flow from videos with a convolutional neural network (CNN). The proposed model constitutes a potential building block for deeper architectures to allow using motion without resorting to an external algorithm, \eg for recognition in videos. We derive our network architecture from signal processing principles to provide desired invariances to image contrast, phase and texture. We constrain weights within the network to enforce strict rotation invariance and substantially reduce the number of parameters to learn. We demonstrate end-to-end training on only 8 sequences of the Middlebury dataset, orders of magnitude less than competing CNN-based motion estimation methods, and obtain comparable performance to classical methods on the Middlebury benchmark. Importantly, our method outputs a distributed representation of motion that allows representing multiple, transparent motions, and dynamic textures. Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation

Neuronal and astroglial correlates underlying spatiotemporal Intrinsic Optical Signal in the rat hippocampal slice

Widely used for mapping afferent activated brain areas in vivo, the label-free intrinsic optical signal (IOS) is mainly ascribed to blood volume changes subsequent to glial glutamate uptake. By contrast, IOS imaged in vitro is generally attributed to neuronal and glial cell swelling, however the relative contribution of different cell types and molecular players remained largely unknown. We characterized IOS to Schaffer collateral stimulation in the rat hippocampal slice using a 464-element photodiode-array device that enables IOS monitoring at 0.6 ms time-resolution in combination with simultaneous field potential recordings. We used brief half-maximal stimuli by applying a medium intensity 50 Volt-stimulus train within 50 ms (20 Hz). IOS was primarily observed in the str. pyramidale and proximal region of the str. radiatum of the hippocampus. It was eliminated by tetrodotoxin blockade of voltage-gated Na+ channels and was significantly enhanced by suppressing inhibitory signaling with gamma-aminobutyric acid(A) receptor antagonist picrotoxin. We found that IOS was predominantly initiated by postsynaptic Glu receptor activation and progressed by the activation of astroglial Glu transporters and Mg2+-independent astroglial N-methyl-D-aspartate receptors. Under control conditions, role for neuronal K+/Cl- cotransporter KCC2, but not for glial Na+/K+/Cl- cotransporter NKCC1 was observed. Slight enhancement and inhibition of IOS through non-specific Cl- and volume-regulated anion channels, respectively, were also depicted. High-frequency IOS imaging, evoked by brief afferent stimulation in brain slices provide a new paradigm for studying mechanisms underlying IOS genesis. Major players disclosed this way imply that spatiotemporal IOS reflects glutamatergic neuronal activation and astroglial response, as observed within the hippocampus. Our model may help to better interpret in vivo IOS and support diagnosis in the future

Neural networks application to divergence-based passive ranging

The purpose of this report is to summarize the state of knowledge and outline the planned work in divergence-based/neural networks approach to the problem of passive ranging derived from optical flow. Work in this and closely related areas is reviewed in order to provide the necessary background for further developments. New ideas about devising a monocular passive-ranging system are then introduced. It is shown that image-plan divergence is independent of image-plan location with respect to the focus of expansion and of camera maneuvers because it directly measures the object's expansion which, in turn, is related to the time-to-collision. Thus, a divergence-based method has the potential of providing a reliable range complementing other monocular passive-ranging methods which encounter difficulties in image areas close to the focus of expansion. Image-plan divergence can be thought of as some spatial/temporal pattern. A neural network realization was chosen for this task because neural networks have generally performed well in various other pattern recognition applications. The main goal of this work is to teach a neural network to derive the divergence from the imagery

Determining the contribution of volcanic ash and boundary layer aerosol in backscatter lidar returns: a three‐component atmosphere approach

A solution of the lidar equation is discussed, that permits combining backscatter and depolarization measurements to quantitatively distinguish two different aerosol types with different depolarization properties. The method has been successfully applied to simultaneous observations of volcanic ash and boundary layer aerosol obtained in Exeter, United Kingdom, on 16 and 18 April 2010, permitting the contribution of the two aerosols to be quantified separately. First a subset of the atmospheric profiles is used where the two aerosol types belong to clearly distinguished layers, for the purpose of characterizing the ash in terms of lidar ratio and depolarization. These quantities are then used in a three‐component atmosphere solution scheme of the lidar equation applied to the full data set, in order to compute the optical properties of both aerosol types separately. On 16 April a thin ash layer, 100–400 m deep, is observed (average and maximum estimated ash optical depth: 0.11 and 0.2); it descends from ∼2800 to ∼1400 m altitude over a 6‐hour period. On 18 April a double ash layer, ∼400 m deep, is observed just above the morning boundary layer (average and maximum estimated ash optical depth: 0.19 and 0.27). In the afternoon the ash is entrained into the boundary layer, and the latter reaches a depth of ∼1800 m (average and maximum estimated ash optical depth: 0.1 and 0.15). An additional ash layer, with a very small optical depth, was observed on 18 April at an altitude of 3500–4000 m. By converting the lidar optical measurements using estimates of volcanic ash specific extinction, derived from other works, the observations seem to suggest approximate peak ash concentrations of ∼1500 and ∼1000 mg/m3,respectively, on the two observations dates

Volumetric microvascular imaging of human retina using optical coherence tomography with a novel motion contrast technique

Phase variance-based motion contrast imaging is demonstrated using a spectral domain optical coherence tomography system for the in vivo human retina. This contrast technique spatially identifies locations of motion within the retina primarily associated with vasculature. Histogram-based noise analysis of the motion contrast images was used to reduce the motion noise created by transverse eye motion. En face summation images created from the 3D motion contrast data are presented with segmentation of selected retinal layers to provide non-invasive vascular visualization comparable to currently used invasive angiographic imaging. This motion contrast technique has demonstrated the ability to visualize resolution-limited vasculature independent of vessel orientation and flow velocity