Measurement of liquid film thickness by optical fluorescence and its application to an oscillating piston positive displacement flowmeter

The movement of the circular piston in an oscillating piston positive displacement flowmeter is important in understanding the operation of the flowmeter, and the leakage of liquid past the piston plays a key role in the performance of the meter. The clearances between the piston and the chamber are small, typically less than 60 νm. In order to measure this film thickness a fluorescent dye was added to the water passing through the meter, which was illuminated with UV light. Visible light images were captured with a digital camera and analysed to give a measure of the film thickness with an uncertainty of less than 7%. It is known that this method lacks precision unless careful calibration is undertaken. Methods to achieve this are discussed in the paper. The grey level values for a range of film thicknesses were calibrated in situ with six dye concentrations to select the most appropriate one for the range of liquid film thickness. Data obtained for the oscillating piston flowmeter demonstrate the value of the fluorescence technique. The method is useful, inexpensive and straightforward and can be extended to other applications where measurement of liquid film thickness is required. © 2011 IOP Publishing Ltd.

A novel method to produce small droplets from large nozzles.

This work presents a new method to generate droplets with diameters significantly smaller than the nozzle from which they emerge. The electrical waveform used to produce the jetting consists of a single square negative pulse. The negative edge of the pressure wave pulls the meniscus in, overturning the surface in such a way that a cavity is created. This cavity is then forced to collapse under the action of the positive edge of the pressure wave. This violent collapse produces a thin jet that eventually breaks up and produces droplets. Four droplet generator prototypes that demonstrate the capabilities of this novel mechanism are described. It is also shown that the proposed mechanism extends the existing limits of the commonly accepted inkjet operating regime.

Modulation of somatosensory processing by action.

Psychophysical evidence suggests that sensations arising from our own movements are diminished when predicted by motor forward models and that these models may also encode the timing and intensity of movement. Here we report a functional magnetic resonance imaging study in which the effects on sensation of varying the occurrence, timing and force of movements were measured. We observed that tactile-related activity in a region of secondary somatosensory cortex is reduced when sensation is associated with movement and further that this reduction is maximal when movement and sensation occur synchronously. Motor force is not represented in the degree of attenuation but rather in the magnitude of this region's response. These findings provide neurophysiological correlates of previously-observed behavioural forward-model phenomena, and advocate the adopted approach for the study of clinical conditions in which forward-model deficits have been posited to play a crucial role.

Choosing to make an effort: the role of striatum in signaling physical effort of a chosen action.

The possibility that we will have to invest effort influences our future choice behavior. Indeed deciding whether an action is actually worth taking is a key element in the expression of human apathy or inertia. There is a well developed literature on brain activity related to the anticipation of effort, but how effort affects actual choice is less well understood. Furthermore, prior work is largely restricted to mental as opposed to physical effort or has confounded temporal with effortful costs. Here we investigated choice behavior and brain activity, using functional magnetic resonance imaging, in a study where healthy participants are required to make decisions between effortful gripping, where the factors of force (high and low) and reward (high and low) were varied, and a choice of merely holding a grip device for minimal monetary reward. Behaviorally, we show that force level influences the likelihood of choosing an effortful grip. We observed greater activity in the putamen when participants opt to grip an option with low effort compared with when they opt to grip an option with high effort. The results suggest that, over and above a nonspecific role in movement anticipation and salience, the putamen plays a crucial role in computations for choice that involves effort costs.

Positive Correlations in Tunneling through coupled Quantum Dots

Due to the Fermi-Dirac statistics of electrons the temporal correlations of tunneling events in a double barrier setup are typically negative. Here, we investigate the shot noise behavior of a system of two capacitively coupled quantum dot states by means of a Master equation model. In an asymmetric setup positive correlations in the tunneling current can arise due to the bunching of tunneling events. The underlying mechanism will be discussed in detail in terms of the current-current correlation function and the frequency-dependent Fano factor.

Rank-preserving geometric means of positive semi-definite matrices

The generalization of the geometric mean of positive scalars to positive definite matrices has attracted considerable attention since the seminal work of Ando. The paper generalizes this framework of matrix means by proposing the definition of a rank-preserving mean for two or an arbitrary number of positive semi-definite matrices of fixed rank. The proposed mean is shown to be geometric in that it satisfies all the expected properties of a rank-preserving geometric mean. The work is motivated by operations on low-rank approximations of positive definite matrices in high-dimensional spaces.© 2012 Elsevier Inc. All rights reserved.

Regression on fixed-rank positive semidefinite matrices: A Riemannian approach

The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixedrank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks. © 2011 Gilles Meyer, Silvere Bonnabel and Rodolphe Sepulchre.