A preliminary systems study of interface equipment for digitally programmed flight simulators

Design study of digitally programmed supersonic transport flight simulato

Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as na\"ive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.Comment: 14 pages, 8 figures. In this version extremely minor adjustments to the paper were made - e.g. Figure 5 is now easier to view in greyscal

Anisotropic Magneto-conductance of InAs Nanowire: Angle Dependent Suppression of 1D Weak Localization

The magneto-conductance of an InAs nanowire is investigated with respect to the relative orientation between external magnetic field and the nanowire axis. It is found that both the perpendicular and the parallel magnetic fields induce a positive magneto-conductance. Yet the parallel magnetic field induced longitudinal magneto-conductance has a smaller magnitude. This anisotropic magneto-transport phenomenon is studied as a function of temperature, magnetic field strength and at an arbitrary angle between the magnetic field and the nanowire. We show that the observed effect is in quantitative agreement with the suppression of one-dimensional (1D) weak localization

Entangled single-wire NiTi material: a porous metal with tunable superelastic and shape memory properties

NiTi porous materials with unprecedented superelasticity and shape memory were manufactured by self-entangling, compacting and heat treating NiTi wires. The versatile processing route used here allows to produce entanglements of either superelastic or ferroelastic wires with tunable mesostructures. Three dimensional (3D) X-ray microtomography shows that the entanglement mesostructure is homogeneous and isotropic. The thermomechanical compressive behavior of the entanglements was studied using optical measurements of the local strain field. At all relative densities investigated here ($\sim$ 25 - 40$\%$), entanglements with superelastic wires exhibit remarkable macroscale superelasticity, even after compressions up to 25$\%$, large damping capacity, discrete memory effect and weak strain-rate and temperature dependencies. Entanglements with ferroelastic wires resemble standard elastoplastic fibrous systems with pronounced residual strain after unloading. However, a full recovery is obtained by heating the samples, demonstrating a large shape memory effect at least up to 16% strain.Comment: 31 pages, 10 figures, submitted to Acta Materiali

Wiring Nanoscale Biosensors with Piezoelectric Nanomechanical Resonators

Nanoscale integrated circuits and sensors will require methods for unobtrusive interconnection with the macroscopic world to fully realize their potential. We report on a nanoelectromechanical system that may present a solution to the wiring problem by enabling information from multisite sensors to be multiplexed onto a single output line. The basis for this method is a mechanical Fourier transform mediated by piezoelectrically coupled nanoscale resonators. Our technique allows sensitive, linear, and real-time measurement of electrical potentials from conceivably any voltage-sensitive device. With this method, we demonstrate the direct transduction of neuronal action potentials from an extracellular microelectrode. This approach to wiring nanoscale devices could lead to minimally invasive implantable sensors with thousands of channels for in vivo neuronal recording, medical diagnostics, and electrochemical sensing

Effect of Closed Classical Orbits on Quantum Spectra: Ionization of Atoms in a Magnetic Field. I. Physical Picture and Calculations

This is the first of two papers that develop the theory of oscillatory spectra. When an atom is placed in a magnetic field, and the absorption spectrum into states close to the ionization threshold is measured at finite resolution, so that individual energy levels are not resolved, it is found that the absorption as a function of energy is a superposition of sinusoidal oscillations. These papers present a quantitative theory of this phenomenon. In this first paper, we describe the physical ideas underlying the theory in the simplest possible way, and we present our first calculations based upon the theory. In the second paper, the theory is developed in full detail, proofs of all of the assertions are given, and we describe the algorithm that was used to make the calculations

Probability Conservation in Theories of Collisional Ionization and Detachment

The semiclassical local-complex-potential theory has been widely used to describe detachment and ionization in atom-atom and ion-atom collisions. However, it has been shown that the resulting formulas do not conserve probability. In this paper, we show that the problem arises from the inconsistent treatment of the effects of interference, tunneling, and diffraction. A more complete theory is based upon the close-coupling expansion, which leads to an infinite set of coupled equations. A method for solving such sets of equations was developed in earlier work. Here we implement that method using a new iterative numerical scheme, and we show that the iteration converges to results in which probability is conserved