Incompressible deformation estimation algorithm (IDEA) from tagged MR images

Measuring the 3D motion of muscular tissues, e.g., the heart or the tongue, using magnetic resonance (MR) tagging is typically carried out by interpolating the 2D motion information measured on orthogonal stacks of images. The incompressibility of muscle tissue is an important constraint on the reconstructed motion field and can significantly help to counter the sparsity and incompleteness of the available motion information. Previous methods utilizing this fact produced incompressible motions with limited accuracy. In this paper, we present an incompressible deformation estimation algorithm (IDEA) that reconstructs a dense representation of the 3D displacement field from tagged MR images and the estimated motion field is incompressible to high precision. At each imaged time frame, the tagged images are first processed to determine components of the displacement vector at each pixel relative to the reference time. IDEA then applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, IDEA yields a dense estimate of a 3D displacement field that matches our observations and also corresponds to an incompressible motion. The method was validated with both numerical simulation and in vivo human experiments on the heart and the tongue.     

Attentional networks for music generation

Realistic music generation has always remained as a challenging problem as it may lack structure or rationality. In this work, we propose a deep learning based music generation method in order to produce old style music particularly JAZZ with rehashed melodic structures utilizing a Bi-directional Long Short Term Memory (Bi-LSTM) Neural Network with attention. Owing to the success in modelling long-term temporal dependencies in sequential data and its success in case of videos, Bi-LSTMs with attention serves as a natural choice and early utilization in music generation. We validate in our experiments that Bi-LSTMs with attention are able to preserve the richness and technical nuances of the music performed.

     

Skillful control under uncertainty via direct reinforcement learning

Complexity and uncertainty in modern robots and other autonomous systems make it difficult to design controllers for such systems that can achieve desired levels of precision and robustness. Therefore learning methods are being incorporated into controllers for such systems, thereby providing the adaptibility necessary to meet the performance demands of the task. We argue that for learning tasks arising frequently in control applications, the most useful methods in practice probably are those we call direct associative reinforcement learning methods. We describe direct reinforcement learning methods and also illustrate with an example the utility of these methods for learning skilled robot control under uncertainty.     

Flexible Piezoelectric ZnO–Paper Nanocomposite Strain Sensor

The fabrication of a mechanically flexible, piezoelectric nanocomposite material for strain sensing applications is reported. Nanocomposite material consisting of zinc oxide (ZnO) nanostructures embedded in a stable matrix of paper (cellulose fibers) is prepared by a solvothermal method. The applicability of this material as a strain sensor is demonstrated by studying its real‐time current response under both static and dynamic mechanical loading. The material presented highlights a novel approach to introduce flexibility into strain sensors by embedding crystalline piezoelectric material in a flexible cellulose‐based secondary matrix.     

Protecting copper from electrochemical degradation by graphene coating

Graphene coating on copper (Cu) is shown to increase the resistance of the metal to electrochemical degradation by one and half orders of magnitude. Detailed electrochemical characterization in aggressive chloride environment shows the impedance of Cu increasing dramatically and the anodic and cathodic current densities of the coated Cu becoming nearly 1–2 orders of magnitude smaller when coated with graphene. The observations are counterintuitive as graphite in contact with metals increases metallic corrosion. The results can bring paradigm changes in the development of anti-corrosion coatings using conformal, ultrathin graphene films.     

An improved MOSFET model for circuit simulation

Problems that have continued to remain in some of the recently published MOSFET compact models are demonstrated in this paper. Of particular interest are discontinuities observed in these models at the boundary between forward and reverse mode operation. A new MOSFET model is presented that overcomes the errors present in state-of-the-art models. Comparison with measured data is also presented to validate the new model     

Nitrogen-incorporation induced changes in the microstructure of nanocrystalline WO3 thin films

Nitrogen incorporated tungsten oxide (WO₃) films were grown by reactive magnetron sputter-deposition by varying the nitrogen content in the reactive gas mixture keeping the deposition temperature fixed at 400 °C. The crystal structure, surface morphology, chemical composition, and electrical resistivity of nitrogen doped WO₃ films were evaluated using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) and electrical conductivity measurements. The results indicate that the nitrogen-incorporation induced changes in the microstructure and electrical properties of WO₃ films are significant. XRD measurements coupled with SEM analysis indicate that the increasing nitrogen content decreases the grain size and crystal quality. The nitrogen concentration increases from 0 at.% to 1.35 at.% with increasing nitrogen flow rate from 0 to 20 sccm. The corresponding dc electrical conductivity of the films had shown a decreasing trend with increasing nitrogen content.