Coupled magnetic and elastic properties in LaPr(CaSr)MnO manganites

We investigate a series of manganese oxides, the La0.225Pr0.4(Ca1-xSrx)0.375MnO3 system. The x = 0 sample is a prototype compound for the study of phase separation in manganites, where ferromagnetic and charge ordered antiferromagnetic phases coexist. Replacing Ca2+ by Sr2+ gradually turns the system into a homogeneous ferromagnet. Our results show that the material structure plays a major role in the observed magnetic properties. On cooling, at temperatures below 100 K, a strong contraction of the lattice is followed by an increase in the magnetization. This is observed both through thermal expansion and magnetostriction measurements, providing distinct evidence of magneto-elastic coupling in these phase separated compounds

Magnetocaloric effect in manganites: metamagnetic transitions for magnetic refrigeration

We present a study of the magnetocaloric effect in La5/8-yPryCa3/8MnO3 (y=0.3) and Pr0.5Ca0.09Sr0.41MnO3 manganites. The low temperature state of both ystems is the result of a competition between the antiferromagnetic and ferromagnetic phases. The samples display magnetocaloric effect evidenced in an adiabatic temperature change during a metamagnetic transition from an antiferromagnetic to a ferromagnetic phase . As additional features, La5/8-yPryCa3/8MnO3 exhibits phase separation characterized by the coexistence of antiferromagnetic and ferromagnetic phases and Pr0.5Ca0.09Sr0.41MnO3 displays inverse magnetocaloric effect in which temperature decreases while applying an external magnetic field. In both cases, a significant part of the magnetocaloric effect appears from non-reversible processes. As the traditional thermodynamic description of the effect usually deals with reversible transitions, we developed an alternative way to calculate the adiabatic temperature change in terms of the change of the relative ferromagnetic fraction induced by magnetic field. To evaluate our model, we performed direct measurement of the sample's adiabatic temperature change by means of a differential thermal analysis. An excellent agreement has been obtained between experimental and calculated data. These results show that metamagnetic transition in manganites play an important role in the study of magnetic refrigeration.Comment: Acepted to be published in Applied Physics Letter

Abrupt field-induced transition triggered by magnetocaloric effect in phase-separated manganites

The occurrence at low temperatures of an ultrasharp field-induced transition in phase separated manganites is analyzed. Experimental results show that magnetization and specific heat step-like transitions below 5 K are correlated with an abrupt change of the sample temperature, which happens at a certain critical field. This temperature rise, a magnetocaloric effect, is interpreted as produced by the released energy at the transition point, and is the key to understand the existence of the abrupt field-induced transition. A qualitative analysis of the results suggests the existence of a critical growing rate of the ferromagnetic phase, beyond which an avalanche effect is triggered.Comment: 6 pages, 4 figures included. Acepted for publication in Phys. Rev.

Equilibrium tuned by a magnetic field in phase separated manganite

We present magnetic and transport measurements on La5/8-yPryCa3/8MnO3 with y = 0.3, a manganite compound exhibiting intrinsic multiphase coexistence of sub-micrometric ferromagnetic and antiferromagnetic charge ordered regions. Time relaxation effects between 60 and 120K, and the obtained magnetic and resistive viscosities, unveils the dynamic nature of the phase separated state. An experimental procedure based on the derivative of the time relaxation after the application and removal of a magnetic field enables the determination of the otherwise unreachable equilibrium state of the phase separated system. With this procedure the equilibrium phase fraction for zero field as a function of temperature is obtained. The presented results allow a correlation between the distance of the system to the equilibrium state and its relaxation behavior.Comment: 13 pages, 5 figures. Submited to Journal of Physics: Condensed Matte

Aggregation of Robust Regularization with Dynamic Filtration for Enhanced Radar Imaging

The paper suggest a novel approach to the problem of high-resolution array radar/SAR imaging as an ill-conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties. We explain the theory recently developed by the authors of this presentation that addresses a new fused Bayesian-regularization paradigm for radar/SAR image formation/reconstruction. We show how this theory leads to new adaptive and robustified computational methods that enable one to derive efficient and consistent estimates of the SSP via unifying the Bayesian minimum risk estimation strategy with the ME randomized a priori image model and other projection-type regularization constraints imposed on the solution. We detail such fused Bayesian-regularization (FBR) paradigm and analyze some efficient numerical schemes for computational implementation of the relevant FBR-based methods. Also, we present the results of extended simulation study of the family of the radar image (RI) formation algorithms that employ the proposed FBR paradigm for high-resolution reconstruction of the SSP of the wavefield sources distributed in the remotely sensed environment. The last issue that we address as a perspective innovation is a paradigm of incorporating the concept of dynamic computing into the FBR-based technique to enable the latter to reconstruct the desired environmental remote sensing signatures (RSS) extracted from the enhanced imagery taking into account their dynamical behaviour. This provides a background for understanding the future trends in development of intelligent dynamic RS imaging and resource management techniques. The advantages of the well designed RI experiments (that employ the FBR-based methods) over the cases of poorer designed experiments (that employ the matched spatial filtering as well as the constrained least squares estimators) are investigated trough the simulation study.ITESO, A.C

Remote Sensing Signature Fields Reconstruction via Robust Regularization of Bayesian Minimum Risk Technique

The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. The problem- oriented modification of the previously proposed fused Bayesian-regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill- poseness due to system-level and model-level uncertainties. We report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.Cinvesta

Unifying the Experiment Design and Constrained Regularization Paradigms for Reconstructive Imaging with Remote Sensing Data

In this paper, the problem of estimating from a finite set of measurements of the radar remotely sensed complex data signals, the power spatial spectrum pattern (SSP) of the wavefield sources distributed in the environment is cast in the framework of Bayesian minimum risk (MR) paradigm unified with the experiment design (ED) regularization technique. The fused MR-ED regularization of the ill-posed nonlinear inverse problem of the SSP reconstruction is performed via incorporating into the MR estimation strategy the projection-regularization ED constraints. The simulation examples are incorporated to illustrate the efficiency of the proposed unified MR-ED technique.CINVESTA

Neural Network Computational Technique for High-Resolution Remote Sensing Image Reconstruction with System Fusion

We address a new approach to the problem of improvement of the quality of scene images obtained with several sensing systems as required for remote sensing imagery, in which case we propose to exploit the idea of robust regularization aggregated with the neural network (NN) based computational implementation of the multi- sensor fusion tasks. Such a specific aggregated robust regularization problem is stated and solved to reach the aims of system fusion with a proper control of the NN’s design parameters (synaptic weights and bias inputs viewed as corresponding system-level and model-level degrees of freedom) which influence the overall reconstruction performances.Cinvesta