Oxygen-enrichment of YBa2Cu3 YBa2Cu3O7-δ using the fluidization techniqueusing the fluidization technique

The oxygen-deficient phase of the highT _c superconductor, YBa₂Cu₃O₇, was oxygen-enriched using the fluidization technique to give good superconducting properties. The normal method of oxygen treatment at 900°C for 24 h and at 600°C for 24 h has been reduced to just one treatment at 600°C for 12 h by the fluidization technique to achieve almost the same strength of superconducting signal for the YBa₂Cu₃O₇ powder, which establishes the attractiveness of the latter route for the large-scale preparation of superconducting material. The particle sizes were in the range 0–90, 90–180 and 180–420 μm. The fluidized particles were crystalline with orthorhombic distortion.T _c ^onset , estimated using the a.c. magnetic susceptibility method, was 91·3 K. The volume fraction of superconducting material in the product was 83·7–85·3%, one of the highest values reported so far for YBa₂Cu₃O₇.     

Excess enthalpy,volume, and gibbs free energy; and viscosity of acetonitrile-methylcellosolve mixtures

Molar excess enthalpies of mixing at 48.2°C, and molar excess volumes at 40°C were measured for acetonitrile-methyl cellosolve system using an isothermal phase change calorimeter and a glass dilatometer respectively. Molar excess Gibbs free energies were calculated from the isothermal vapour-liquid equilibrium data obtained in a circulation still at 70, 60 and 48.2°C. Complete isobaric vapour-liquid equilibrium data at 1 atm pressure are also reported. The kinematic viscosity of binary mixtures were measured at 40°C with an Ostwald viscometer. The VLE data are corrected for vapour phase nonideality, tested for thermodynamic consistency and correlated by Wilson equation. The viscosity data were correlated by McAllister and excess function models.     

Design and simulation of a novel metallic microgripper using vibration to release nano objects actively

In this study, we investigate a novel metallic microgripper which is able to grasp and transport nano particles (nano tubes/wires) and release them on desirable substrate by vibrating the gripper arms. This microgripper consists of a chevron actuator to grip nano object electrothermally and interdigited comb drive systems to generate vibration at the gripper arms electrostatically. Metallic (nickel) properties enable the chevron actuator to close the gap and pick the nano particle at low voltage and temperature. In order to reduce the out of plane bending during operation and also increase the gripping force, thickness of the nickel layer must be increased, hence electroplating process is proposed for deposition of nickel layer. To generate vibration at the end effectors, comb drive systems are stimulated by applying two voltage signals at desired resonant frequency to the stators. Practically, by sweeping the frequency of these signals around the resonant frequency the end effectors start vibrating. The vibration results in overcoming the adhesion forces due to inertial effects.     

Simultaneous nonrigid registration of multiple point sets and atlas construction

Group-wise registration of a set of shapes represented by unlabeled point-sets is a challenging problem since, usually this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape represented by a probability density function from multiple unlabeled point-sets and registering them non-rigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. We derive the analytic gradient of the cost function in order to efficiently achieve the optimal solution. JS-divergence is symmetric with no bias toward any of the given shapes to be registered and whose mean is being sought. A by product of the registration process is a probabilistic atlas defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images as well as for simultaneously (rigidly or non-rigidly) registering 3D range data sets without having to establish any correspondence. We present experimental results on real and synthetic data.     

Bayesian image reconstruction in SPECT using higher order mechanical models as priors

While the ML-EM algorithm for reconstruction for emission tomography is unstable due to the ill-posed nature of the problem. Bayesian reconstruction methods overcome this instability by introducing prior information, often in the form of a spatial smoothness regularizer. More elaborate forms of smoothness constraints may be used to extend the role of the prior beyond that of a stabilizer in order to capture actual spatial information about the object. Previously proposed forms of such prior distributions were based on the assumption of a piecewise constant source distribution. Here, the authors propose an extension to a piecewise linear model-the weak plate-which is more expressive than the piecewise constant model. The weak plate prior not only preserves edges but also allows for piecewise ramplike regions in the reconstruction. Indeed, for the authors' application in SPECT, such ramplike regions are observed in ground-truth source distributions in the form of primate autoradiographs of rCBF radionuclides. To incorporate the weak plate prior in a MAP approach, the authors model the prior as a Gibbs distribution and use a GEM formulation for the optimization. They compare quantitative performance of the ML-EM algorithm, a GEM algorithm with a prior favoring piecewise constant regions, and a GEM algorithm with their weak plate prior. Pointwise and regional bias and variance of ensemble image reconstructions are used as indications of image quality. The authors' results show that the weak plate and membrane priors exhibit improved bias and variance relative to ML-EM techniques.     

Analysis and suppression of spurious modes of the ring shape anchored RF MEMS contour mode disk resonator

One of the most encumbering issues in RF MEMS resonators is spurious modes. The problem of spurious modes becomes more critical, when the ring type resonators are used. In the ring shape anchored contour mode disk resonator, for achieving a low serial resistance, the inner radius of the disk must be increased. This causes the spurious modes to become too close to the desired mode and degrade the operation of the resonator. In this work, spurious modes of before-mentioned device are introduced and their characteristics are evaluated by exact analytical approach. Based on those analytical approaches, we introduce two techniques for spurious mode suppression. The first technique is based on exciting the desired mode by proper electrode engineering and hence is an electrical approach. The second technique is reconfiguring of the anchor from a continuous ring to crossed ring segments and locating the segments on the phase discriminating lines to increase the insertion loss for spurious modes and decrease the losses for the fundamental mode. The final harmonic analysis verifies that the proposed techniques result a resonator with a pure frequency spectrum and spurious modes excluded over a very wide frequency range.     

Implementation of wide-field integration of optic flow for autonomous quadrotor navigation

Insects are capable of robust visual navigation in complex environments using efficient information extraction and processing approaches. This paper presents an implementation of insect inspired visual navigation that uses spatial decompositions of the instantaneous optic flow to extract local proximity information. The approach is demonstrated in a corridor environment on an autonomous quadrotor micro-air-vehicle (MAV) where all the sensing and processing, including altitude, attitude, and outer loop control is performed on-board. The resulting methodology has the advantages of computation speed and simplicity, hence are consistent with the stringent size, weight, and power requirements of MAVs.     

Neural networks of data inhibiting long memory pattern

We experiment with three neural network models for forecasting to better understand the performance of neural networks for the case when the data exhibits a long memory pattern. To obtain the optimum networks, the effect of network characteristics such as the training parameters, the number of hidden layers, and the testing and training percentages are simulated. The third model, which consists of a combination of individual time series forecasts, provides superior results.