Conduction mechanisms of epitaxial EuTiO3 thin films

To investigate leakage current density versus electric field characteristics, epitaxial EuTiO3 thin films were deposited on (001) SrTiO3 substrates by pulsed laser deposition and were post-annealed in a reducing atmosphere. This investigation found that conduction mechanisms are strongly related to temperature and voltage polarity. It was determined that from 50 to 150 K the dominant conduction mechanism was a space-charge-limited current under both negative and positive biases. From 200 to 300 K, the conduction mechanism shows Schottky emission and Fowler-Nordheim tunneling behaviors for the negative and positive biases, respectively. This work demonstrates that Eu3+ is one source of leakage current in EuTiO3 thin films.Comment: 17 pages,4 figures, conferenc

General covariant geometric momentum, gauge potential and a Dirac fermion on a two-dimensional sphere

For a particle that is constrained on an ($N-1$)-dimensional ($N\geq2$) curved surface, the Cartesian components of its momentum in $N$-dimensional flat space is believed to offer a proper form of momentum for the particle on the surface, which is called the geometric momentum as it depends on the mean curvature. Once the momentum is made general covariance, the spin connection part can be interpreted as a gauge potential. The present study consists in two parts, the first is a discussion of the general framework for the general covariant geometric momentum. The second is devoted to a study of a Dirac fermion on a two-dimensional sphere and we show that there is the generalized total angular momentum whose three cartesian components form the $su(2)$ algebra, obtained before by consideration of dynamics of the particle, and we demonstrate that there is no curvature-induced geometric potential for the fermion.Comment: 8 pages, no figure. Presentation improve

Broadband RCS Reduction of Microstrip Patch Antenna Using Bandstop Frequency Selective Surface

In this article, a simple and effective approach is presented to reduce the Radar Cross Section (RCS) of microstrip patch antenna in ultra broad frequency band. This approach substitutes a metallic ground plane of a conventional patch antenna with a hybrid ground consisting of bandstop Frequency Selective Surface (FSS) cells with partial metallic plane. To demonstrate the validity of the proposed approach, the influence of different ground planes on antenna’s performance is investigated. Thus, a patch antenna with miniaturized FSS cells is proposed. The results suggest that this antenna shows 3dB RCS reduction almost in the whole out-of operating band within 1-20GHz for wide incident angles when compared to conventional antenna, while its radiation characteristics are sustained simultaneously. The reasonable agreement between the measured and the simulated results verifies the efficiency of the proposed approach. Moreover, this approach doesn’t alter the lightweight, low-profile, easy conformal and easy manufacturing nature of the original antenna and can be extended to obtain low-RCS antennas with metallic planes in broadband that are quite suitable for the applications which are sensitive to the variation of frequencies

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Raman spectroscopic determination of the length, strength, compressibility, Debye temperature, elasticity, and force constant of the C-C bond in graphene

From the perspective of bond relaxation and vibration, we have reconciled the Raman shifts of graphene under the stimuli of the number-of-layer, uni-axial-strain, pressure, and temperature in terms of the response of the length and strength of the representative bond of the entire specimen to the applied stimuli. Theoretical unification of the measurements clarifies that: (i) the opposite trends of Raman shifts due to number-of-layer reduction indicate that the G-peak shift is dominated by the vibration of a pair of atoms while the D- and the 2D-peak shifts involves z-neighbor of a specific atom; (ii) the tensile strain-induced phonon softening and phonon-band splitting arise from the asymmetric response of the C3v bond geometry to the C2v uni-axial bond elongation; (iii) the thermal-softening of the phonons originates from bond expansion and weakening; and (iv) the pressure- stiffening of the phonons results from bond compression and work hardening. Reproduction of the measurements has led to quantitative information about the referential frequencies from which the Raman frequencies shift, the length, energy, force constant, Debye temperature, compressibility, elastic modulus of the C-C bond in graphene, which is of instrumental importance to the understanding of the unusual behavior of graphene