A novel monolithic multilayered ferrimagnetic– ferroelectric multiferroic heterostructure shows a remarkably large tuning of the magnetic response with an electric field. The heteroepitaxial stack is comprised of a near single crystal yttrium iron garnet (YIG) layer, a ferroelectric barium strontium titanate (BSTO) layer with good electric field tunability, and embedded platinum (Pt) electrodes.
A two magnon scattering theory for microwave relaxation in magnetic systems is formulated in the framework of the Hamiltonian formalism. The paper provides general expressions for inhomogeneity coupling coefficients in the case of localized inhomogeneities. An approximate solution for the relaxation rate of the ferromagnetic resonance uniform mode relaxation rate is presented. Two examples of the application of the theory are presented, one for bulk polycrystalline ferrites and one for polycrystalline metallic thin films.
The 9.41GHz ferromagnetic resonance field and linewidth have been measured as a function of the angle (θH) between the external magnetic field and film normal for a series of 17.5nm thick Co–Cr–Pt alloy films. The linewidths ranged from hundreds of Oersted to kiloersted, with different values at θH=0 and θH=90° and additional minima and maxima for θH-values from 16° to 64°. The profiles can be fitted with a combination of inhomogeneity line broadening, grain boundary two magnon scattering, and magnon-electron (m-e) scattering processes, with a notably small Gilbert damping α-value of 0.004 for the m-e term.
A magnetic-ferroelectric film heterostructure with a large electric field tuning of the ferromagnetic resonance (FMR) mode was fabricated. Pulse laser deposited 30 nm thick Pt electrodes and 3 μm thick barium strontium titanate films on Nb-doped strontium titanate substrates were capped with an unbonded 200 μm thick single crystal in-plane c-axis barium hexaferrite slab. The structure gives a 60 GHz FMR frequency shift of 16 MHz at a bias of 29 V, for an average response of 0.55 MHz/V. The maximum incremental tuning response at 29 V was 1.3 MHz/V. This is a hundredfold improvement over previous results.
Abstract:With the rapid advances in sensors of remote sensing satellites, a large number of high-resolution images (HRIs) can be accessed every day. Land use classification using high-resolution images has become increasingly important as it can help to overcome the problems of haphazard, deteriorating environmental quality, loss of prime agricultural lands, and destruction of important wetlands, and so on. Recently, local feature with bag-of-words (BOW) representation has been successfully applied to land-use scene classification with HRIs. However, the BOW representation ignores information from scene labels, which is critical for scene-level land-use classification. Several algorithms have incorporated information from scene labels into BOW by calculating a class-specific codebook from the universal codebook and coding a testing image with a number of histograms. Those methods for mapping the BOW feature to some inaccurate class-specific codebooks may increase the classification error. To effectively solve this problem, we propose an improved class-specific codebook using kernel collaborative representation based classification (KCRC) combined with SPM approach and SVM classifier to classify the testing image in two steps. This model is robust for categories with similar backgrounds. On the standard Land use and Land Cover image dataset, the improved class-specific codebook achieves an average classification accuracy of 93% and demonstrates superiority over other state-of-the-art scene-level classification methods.
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