We report giant electrical control of magnetic anisotropy in a magnetoelectric polycrystalline Ni thin film
and (011)-oriented [Pb(Mg1/3Nb2/3)O3](1-x)-[PbTiO3]x (PMN-PT) heterostructure. The (011) PMN-PT
ferroelectric substrate exhibits both linear anisotropic piezoelectric response and unique giant hysteretic
response. These important features can significantly tune the magnetization states via strain coupling.
Reversible and permanent magnetization reorientation demonstrates an approach for developing
magnetoelectric memory devices.
KEYWORDS: Magnetism, Composites, Anisotropy, Microsoft Foundation Class Library, Magnetostrictive materials, Epoxies, Magnetic sensors, Polarization, Amplifiers, Ferromagnetics
The converse magnetoelectric effect of an asymmetric Piezo-fiber/Metglas bilayer laminate composite subjected to
mechanical prestress is presented. The mechanical prestress is applied by either dc electric voltage bias or direct
mechanical load bias. It is found that a mechanical prestress strongly influences the converse magnetoelectric coupling
response. The optimum dc magnetic field bias shifts with different prestress and compressive stress requires higher dc
magnetic field bias. Additionally, an optimum prestress exists to maximize the converse magnetoelectric response under
certain dc magnetic field bias ranges. Therefore, in order to integrate magnetoelectric composite into actual structures, a
proper prestress needs to be employed to maximize the CME coefficient.
Defined as an attentive process in the context of visual sequences, dynamic visual attention refers to the selection
of the most informative parts of video sequence. This paper investigates the contribution of motion in dynamic
visual attention, and specifically compares computer models designed with the motion component expressed
either as the speed magnitude or as the speed vector. Several computer models, including static features (color,
intensity and orientation) and motion features (magnitude and vector) are considered. Qualitative and quantitative
evaluations are performed by comparing the computer model output with human saliency maps obtained
experimentally from eye movement recordings. The model suitability is evaluated in various situations (synthetic
and real sequences, acquired with fixed and moving camera perspective), showing advantages and inconveniences
of each method as well as preferred domain of application.
Visual attention models mimic the ability of a visual system, to detect potentially relevant parts of a scene. This
process of attentional selection is a prerequisite for higher level tasks such as object recognition. Given the high
relevance of temporal aspects in human visual attention, dynamic information as well as static information must
be considered in computer models of visual attention. While some models have been proposed for extending to
motion the classical static model, a comparison of the performances of models integrating motion in different
manners is still not available. In this article, we present a comparative study of various visual attention models
combining both static and dynamic features. The considered models are compared by measuring their respective
performance with respect to the eye movement patterns of human subjects. Simple synthetic video sequences,
containing static and moving objects, are used to assess the model suitability. Qualitative and quantitative
results provide a ranking of the different models.
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