Satellite formation in drop-on-demand printing of polymer solutions

High speed photographic images of jets formed from dilute solutions of polystyrene in diethyl phthalate ejected from a piezoelectric drop-on-demand inkjet head have been analyzed in order to study the formation and distribution of drops as the ligament collapses. Particular attention has been paid to satellite drops, and their relative separation and sizes. The effect of polymer concentration was investigated. The distribution of nearest-neighbour centre spacing between the drops formed from the ligament is better described by a 2-parameter modified gamma distribution than by a Gaussian distribution. There are (at least) two different populations of satellite size relative to the main drop size formed at normal jetting velocities, with ratios of about three between the diameters of the main drop and the successive satellite sizes. The distribution of the differences in drop size between neighbouring drops is close to Gaussian, with a small non-zero mean for low polymer concentrations, which is associated with the conical shape of the ligament prior to its collapse and the formation of satellites. Higher polymer concentrations result in slower jets for the same driving impulse, and also a tendency to form ligaments with a near-constant width. Under these conditions the mean of the distribution of differences in nearest-neighbour drop size was zero.

Accurate real-time feedback of surface EMG during fMRI.

Real-time acquisition of EMG during functional MRI (fMRI) provides a novel method of controlling motor experiments in the scanner using feedback of EMG. Because of the redundancy in the human muscle system, this is not possible from recordings of joint torque and kinematics alone, because these provide no information about individual muscle activation. This is particularly critical during brain imaging because brain activations are not only related to joint torques and kinematics but are also related to individual muscle activation. However, EMG collected during imaging is corrupted by large artifacts induced by the varying magnetic fields and radio frequency (RF) pulses in the scanner. Methods proposed in literature for artifact removal are complex, computationally expensive, and difficult to implement for real-time noise removal. We describe an acquisition system and algorithm that enables real-time acquisition for the first time. The algorithm removes particular frequencies from the EMG spectrum in which the noise is concentrated. Although this decreases the power content of the EMG, this method provides excellent estimates of EMG with good resolution. Comparisons show that the cleaned EMG obtained with the algorithm is, like actual EMG, very well correlated with joint torque and can thus be used for real-time visual feedback during functional studies.

Automatic object segmentation from calibrated images

This paper addresses the problem of automatically obtaining the object/background segmentation of a rigid 3D object observed in a set of images that have been calibrated for camera pose and intrinsics. Such segmentations can be used to obtain a shape representation of a potentially texture-less object by computing a visual hull. We propose an automatic approach where the object to be segmented is identified by the pose of the cameras instead of user input such as 2D bounding rectangles or brush-strokes. The key behind our method is a pairwise MRF framework that combines (a) foreground/background appearance models, (b) epipolar constraints and (c) weak stereo correspondence into a single segmentation cost function that can be efficiently solved by Graph-cuts. The segmentation thus obtained is further improved using silhouette coherency and then used to update the foreground/background appearance models which are fed into the next Graph-cut computation. These two steps are iterated until segmentation convergences. Our method can automatically provide a 3D surface representation even in texture-less scenes where MVS methods might fail. Furthermore, it confers improved performance in images where the object is not readily separable from the background in colour space, an area that previous segmentation approaches have found challenging. © 2011 IEEE.

The role of the surfaces in the photon absorption in Ge nanoclusters embedded in silica.

The usage of semiconductor nanostructures is highly promising for boosting the energy conversion efficiency in photovoltaics technology, but still some of the underlying mechanisms are not well understood at the nanoscale length. Ge quantum dots (QDs) should have a larger absorption and a more efficient quantum confinement effect than Si ones, thus they are good candidate for third-generation solar cells. In this work, Ge QDs embedded in silica matrix have been synthesized through magnetron sputtering deposition and annealing up to 800°C. The thermal evolution of the QD size (2 to 10 nm) has been followed by transmission electron microscopy and X-ray diffraction techniques, evidencing an Ostwald ripening mechanism with a concomitant amorphous-crystalline transition. The optical absorption of Ge nanoclusters has been measured by spectrophotometry analyses, evidencing an optical bandgap of 1.6 eV, unexpectedly independent of the QDs size or of the solid phase (amorphous or crystalline). A simple modeling, based on the Tauc law, shows that the photon absorption has a much larger extent in smaller Ge QDs, being related to the surface extent rather than to the volume. These data are presented and discussed also considering the outcomes for application of Ge nanostructures in photovoltaics.PACS: 81.07.Ta; 78.67.Hc; 68.65.-k.

Electrical and optical properties of ion implanted SOI-based photonic crystals

We investigate the electrical properties of Silicon-on-Insulator photonic crystals as a function of doping level and air filling factor. A very interesting trade-off between conductivity and optical losses in L3 cavities is also found. © 2011 IEEE.

Nonlinear optics in Silicon photonic crystal cavities

Silicon is known to be a very good material for the realization of high-Q, low-volume photonic cavities, but at the same it is usually considered as a poor material for nonlinear optical functionalities like second-harmonic generation, because its second-order nonlinear susceptibility vanishes in the dipole approximation. In this work we demonstrate that nonlinear optical effects in silicon nanocavities can be strongly enhanced and even become macroscopically observable. We employ photonic crystal nanocavities in silicon membranes that are optimized simultaneously for high quality factor and efficient coupling to an incoming beam in the far field. Using a low-power, continuous-wave laser at telecommunication wavelengths as a pump beam, we demonstrate simultaneous generation of second- and third harmonics in the visible region, which can be observed with a simple camera. The results are in good agreement with a theoretical model that treats third-harmonic generation as a bulk effect in the cavity region, and second-harmonic generation as a surface effect arising from the vertical hole sidewalls. Optical bistability is also observed in the silicon nanocavities and its physical mechanisms (optical, due to two-photon generation of free carriers, as well as thermal) are investigated. © 2011 IEEE.

Electrical conduction and optical properties of doped silicon-on-insulator photonic crystals

We investigate the electrical properties of silicon-on-insulator (SOI) photonic crystals as a function of both doping level and air filling factor. The resistance trends can be clearly explained by the presence of a depletion region around the sidewalls of the holes that is caused by band pinning at the surface. To understand the trade-off between the carrier transport and the optical losses due to free electrons in the doped SOI, we also measured the resonant modes of L3 photonic crystal nanocavities and found that surprisingly high doping levels, up to 1018 / cm3, are acceptable for practical devices with Q factors as high as 4× 104. © 2011 American Institute of Physics.

A Balance Equation Determines a Switch in Neuronal Excitability

We use the qualitative insight of a planar neuronal phase portrait to detect an excitability switch in arbitrary conductance-based models from a simple mathematical condition. The condition expresses a balance between ion channels that provide a negative feedback at resting potential (restorative channels) and those that provide a positive feedback at resting potential (regenerative channels). Geometrically, the condition imposes a transcritical bifurcation that rules the switch of excitability through the variation of a single physiological parameter. Our analysis of six different published conductance based models always finds the transcritical bifurcation and the associated switch in excitability, which suggests that the mathematical predictions have a physiological relevance and that a same regulatory mechanism is potentially involved in the excitability and signaling of many neurons. © 2013 Franci et al.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

How modeling can reconcile apparently discrepant experimental results: The case of pacemaking in dopaminergic neurons

Midbrain dopaminergic neurons are endowed with endogenous slow pacemaking properties. In recent years, many different groups have studied the basis for this phenomenon, often with conflicting conclusions. In particular, the role of a slowly-inactivating L-type calcium channel in the depolarizing phase between spikes is controversial, and the analysis of slow oscillatory potential (SOP) recordings during the blockade of sodium channels has led to conflicting conclusions. Based on a minimal model of a dopaminergic neuron, our analysis suggests that the same experimental protocol may lead to drastically different observations in almost identical neurons. For example, complete L-type calcium channel blockade eliminates spontaneous firing or has almost no effect in two neurons differing by less than 1% in their maximal sodium conductance. The same prediction can be reproduced in a state of the art detailed model of a dopaminergic neuron. Some of these predictions are confirmed experimentally using single-cell recordings in brain slices. Our minimal model exhibits SOPs when sodium channels are blocked, these SOPs being uncorrelated with the spiking activity, as has been shown experimentally. We also show that block of a specific conductance (in this case, the SK conductance) can have a different effect on these two oscillatory behaviors (pacemaking and SOPs), despite the fact that they have the same initiating mechanism. These results highlight the fact that computational approaches, besides their well known confirmatory and predictive interests in neurophysiology, may also be useful to resolve apparent discrepancies between experimental results. © 2011 Drion et al.

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.

Kelvin-Helmholtz-like instability of turbulent flows over riblets

The turbulent drag reduction due to riblets is a function of their size and, for different configurations, collapses well with a length scale l+g=(A+g)1/2, based in the groove cross-section Ag. The initially linear drag reduction breaks down for l+g≈11, which agrees in our DNS with the previously reported appearance of quasi-two-dimensional spanwise rollers immediately above the riblets. They are similar to those found over porous surfaces and plant canopies, and can be traced to a Kelvin-Helmholtz-like instability associated with the relaxation of the impermeability condition for the wall-normal velocity. The extra Reynolds stress associated with them accounts quantitatively for the drag degradation. An inviscid model for the instability confirms its nature, agreeing well with the observed perturbation wavelengths and shapes. The onset of the instability is determined by a length scale L+w that, for conventional riblet geometries, is proportional to l+g. The instability onset, L+w≥4, corresponds to the empirical breakdown point l+g≈11.