Spin-dependent pump current and noise in an adiabatic quantum pump based on domain walls in a magnetic nanowire

We study the pump current and noise properties in an adiabatically modulated magnetic nanowire with double domain walls (DW). The modulation is brought about by applying a slowly oscillating magnetic and electric fields with a controllable phase difference. The pumping mechanism resembles the case of the quantum dot pump with two-oscillating gates. The pump current, shot noise, and heat flow show peaks when the Fermi energy matches with the spin-split resonant levels localized between the DWs. The peak height of the pump current is an indicator for the lifetime of the spin-split quasistationary states between the DWs. For sharp DWs, the energy absorption from the oscillating fields results in side-band formations observable in the pump current. The pump noise carries information on the correlation properties between the nonequilibrium electrons and the quasi-holes created by the oscillating scatterer. The ratio between the pump shot noise and the heat flow serves as an indicator for quasi-particle correlation.Comment: 18 pages, 5 figure

Machine learning paradigms for modeling spatial and temporal information in multimedia data mining

Multimedia data mining and knowledge discovery is a fast emerging interdisciplinary applied research area. There is tremendous potential for effective use of multimedia data mining (MDM) through intelligent analysis. Diverse application areas are increasingly relying on multimedia under-standing systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, machine learning, pattern recognition, multimedia databases, and smart sensors. The main mission of this special issue is to identify state-of-the-art machine learning paradigms that are particularly powerful and effective for modeling and combining temporal and spatial media cues such as audio, visual, and face information and for accomplishing tasks of multimedia data mining and knowledge discovery. These models should be able to bridge the gap between low-level audiovisual features which require signal processing and high-level semantics. A number of papers have been submitted to the special issue in the areas of imaging, artificial intelligence; and pattern recognition and five contributions have been selected covering state-of-the-art algorithms and advanced related topics. The first contribution by D. Xiang et al. “Evaluation of data quality and drought monitoring capability of FY-3A MERSI data” describes some basic parameters and major technical indicators of the FY-3A, and evaluates data quality and drought monitoring capability of the Medium-Resolution Imager (MERSI) onboard the FY-3A. The second contribution by A. Belatreche et al. “Computing with biologically inspired neural oscillators: application to color image segmentation” investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to gray scale and color image segmentation, an important task in image understanding and object recognition. The major contribution of this paper is the ability to use neural oscillators as a learning scheme for solving real world engineering problems. The third paper by A. Dargazany et al. entitled “Multibandwidth Kernel-based object tracking” explores new methods for object tracking using the mean shift (MS). A bandwidth-handling MS technique is deployed in which the tracker reach the global mode of the density function not requiring a specific staring point. It has been proven via experiments that the Gradual Multibandwidth Mean Shift tracking algorithm can converge faster than the conventional kernel-based object tracking (known as the mean shift). The fourth contribution by S. Alzu’bi et al. entitled “3D medical volume segmentation using hybrid multi-resolution statistical approaches” studies new 3D volume segmentation using multiresolution statistical approaches based on discrete wavelet transform and hidden Markov models. This system commonly reduced the percentage error achieved using the traditional 2D segmentation techniques by several percent. Furthermore, a contribution by G. Cabanes et al. entitled “Unsupervised topographic learning for spatiotemporal data mining” proposes a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency Identification (RFID) data. The new unsupervised algorithm depicted in this article is an efficient data mining tool for behavioral studies based on RFID technology. It has the ability to discover and compare stable patterns in a RFID signal, and is appropriate for continuous learning. Finally, we would like to thank all those who helped to make this special issue possible, especially the authors and the reviewers of the articles. Our thanks go to the Hindawi staff and personnel, the journal Manager in bringing about the issue and giving us the opportunity to edit this special issue

Formation of Compressed Flat Electron Beams with High Transverse-Emittance Ratios

Flat beams -- beams with asymmetric transverse emittances -- have important applications in novel light-source concepts, advanced-acceleration schemes and could possibly alleviate the need for damping rings in lepton colliders. Over the last decade, a flat-beam-generation technique based on the conversion of an angular-momentum-dominated beam was proposed and experimentally tested. In this paper we explore the production of compressed flat beams. We especially investigate and optimize the flat-beam transformation for beams with substantial fractional energy spread. We use as a simulation example the photoinjector of the Fermilab's Advanced Superconducting Test Accelerator (ASTA). The optimizations of the flat beam generation and compression at ASTA were done via start-to-end numerical simulations for bunch charges of 3.2 nC, 1.0 nC and 20 pC at ~37 MeV. The optimized emittances of flat beams with different bunch charges were found to be 0.25 {\mu}m (emittance ratio is ~400), 0.13 {\mu}m, 15 nm before compression, and 0.41 {\mu}m, 0.20 {\mu}m, 16 nm after full compression, respectively with peak currents as high as 5.5 kA for a 3.2-nC flat beam. These parameters are consistent with requirements needed to excite wakefields in asymmetric dielectric-lined waveguides or produce significant photon flux using small-gap micro-undulators.Comment: 17

Design and finite element mode analysis of noncircular gear

The noncircular gear transmission is an important branch of the gear transmission, it is characterized by its compact structure, good dynamic equilibration and other advantages, and can be used in the automobile, engineering machine, ship, machine tool, aviation and spaceflight field etc. Studying on the dynamics feature of noncircular gear transmission can improve the ability to carry loads of, reduce the vibration and noise of, increase the life of the noncircular gear transmission machine, provides guidance for the design of the noncircular gear, and has significant theories and practical meanings. In this paper, the gear transmission technique is used to studied the design method of the noncircular gear, which contains distribution of teeth on the pitch curve, designs of the tooth tip curve and the tooth root curve, design of the tooth profile curve, the gear system dynamics principle is introduced to establish dynamics model for the noncircular gear; basic theory of finite element and mode analysis method are applied, finite element model for the noncircular gear is established, natural vibration characteristic of the noncircular gear is studied. And the oval gear is taken as an example, the mathematics software MathCAD, the 3D modeling software UG and the finite element software ABAQUS are used to realize precise 3D model of the oval gear. The finite element method is used, the natural vibration characteristic of the oval gear is studied, the main vibration types and natural frequencies of the oval gear and that of the equivalent cylindrical gears are analyzed and compared, the conclusions received reflect the dynamics performance of the oval gear, and solid foundation is laid for dynamics research and engineering application of the oval gear transmission

Understanding the nucleation mechanisms of Carbon Nanotubes in catalytic Chemical Vapor Deposition

The nucleation of carbon caps on small nickel clusters is studied using a tight binding model coupled to grand canonical Monte Carlo simulations. It takes place in a well defined carbon chemical potential range, when a critical concentration of surface carbon atoms is reached. The solubility of carbon in the outermost Ni layers, that depends on the initial, crystalline or disordered, state of the catalyst and on the thermodynamic conditions, is therefore a key quantity to control the nucleation