Models of magnetic hysteresis based on play and stop hysterons

This paper deals with mathematical models of magnetic hysteresis implemented by assembling hysterons of play and stop type. A procedure for the identification of these models is described, and their behaviors are compared to experimental results. A comparison to the Preisach and Prandtl-Ishlinskii models is performed. It is shown that the Play-Type Model is a possible alternative to Preisach's algorithm, whereas the Stop-Type Model is apt to represent the inverse of Preisach's operator. Simulations adopting both models are reported and compared to experimental data. Moreover, an application to the analysis of a ferroresonant circuit is studied, both numerically and experimentally     

Theory of Injection Locking for Large Magnetization Motion in Spin-Transfer Nano-Oscillators

We study magnetization dynamics in spin-transfer devices subject to DC and microwave injected currents. When the frequency of the injected current is sufficiently close to the self-oscillation frequency of the device, phase-locking occurs. This phenomenon is theoretically studied by using Landau-Lifshitz equation with Slonczewski spin-torque term. By exploiting separation of time scales and using averaging technique, we derive equations which are applicable to the study of phase-locking for arbitrary large magnetization motion. The stability diagram in the (detuning, ac current)-plane is determined and it is shown that phase locking is hysteretic at sufficiently large ac currents.     

A hybrid system for two-dimensional image recognition

In this paper, after briefly considering the reasons that prevented the development of hybrid systems for signal processing (SP), we point out the requirements for their future exploitation. The need for a better knowledge of different approaches in the scientific community and the definition of methodologies for designing hybrid systems are highlighted as two key points. Then we suggest that the well-known “task-structure analysis” design technique should be modified to make it suitable for hybrid systems. The proposed modification is based on the main need to choose the roles of different approaches and the mechanisms to integrate them. As an example we describe the design of a hybrid system for two-dimensional (2-D) image recognition; the system is based on the integration of a numerical, a symbolic, and a connectionist approach. We detail the integration of the symbolic and connectionist approaches to the generation of the models of the objects to be recognized. We describe the main problems involved and the solutions adopted. In particularly, we exploit the synergistic aspects of the two approaches in order to overcome the bottleneck of knowledge acquisition. Finally, we report experimental results on two applications to show some advantages of the proposed hybrid system     

Extraction of Spectral Channels From Hyperspectral Images for Classification Purposes

This paper proposes a procedure to extract spectral channels of variable bandwidths and spectral positions from the hyperspectral image in such a way as to optimize the accuracy for a specific classification problem. In particular, each spectral channel ("s-band") is obtained by averaging a group of contiguous channels of the hyperspectral image ("h-bands"). Therefore, if one wants to define m s-bands, the problem can be formulated as the optimization of the related m starting and m ending h-bands. Toward this end, we propose to adopt, as an optimization criterion, an interclass distance computed on a training set and to generate a sequence of possible solutions by one of three possible search strategies. As the proposed formalization of the problem makes it analogous to a feature-selection problem, the proposed three strategies have been derived by modifying three feature-selection strategies, namely: 1) the "sequential forward selection", 2) the "steepest ascent," and 3) the "fast constrained search". Experimental results on a well-known hyperspectral data set confirm the effectiveness of the approach, which yields better results than other widely used methods. The importance of this kind of procedure lies in feature reduction for hyperspectral image classification or in the case-based design of the spectral bands of a programmable sensor. It represents a special case of feature extraction that is expected to be more powerful than feature selection. The kind of transformation used allows the interpretability of the new features (i.e., the spectral bands) to be saved     

An extension of the Jeffreys-Matusita distance to multiclass casesfor feature selection

The problem of extending the Jeffreys-Matusita distance to multiclass cases for feature-selection purposes is addressed and a solution equivalent to the Bhattacharyya bound is presented. This extension is compared with the widely used weighted average Jeffreys-Matusita distance both by examining the respective formulae and by experimenting on an optical remote-sensing data set     

A technique for feature selection in multiclass problems

One of the main phases in the development of a system for the classification of remote sensing images is the definition of an effective set of features to be given as input to the classifier. In particular, it is often useful to reduce the number of features available, while saving the possibility to discriminate among the different land-cover classes to be recognized. This paper addresses this topic with reference to applications that involve more than two land-cover classes (multiclass problems). Several criteria proposed in the remote sensing literature are considered and compared with one another and with the criterion presented by the authors. Such a criterion, unlike those usually adopted for multiclass problems, is related to an upper bound to the error probability of the Bayes classifier. As the objective of feature selection is generally to identify a reduced set of features that minimize the errors of the classifier, the aforementioned property is very important because it allows one to select features by taking into account their effects on classification errors. Experiments on two remote sensing datasets are described and discussed. These experiments confirm the effectiveness of the proposed criterion, which performs slightly better than all the others considered in the paper. In addition, the results obtained provide useful information about the behaviour of different classical criteria when applied in multiclass cases.     

Partially Supervised classification of remote sensing images through SVM-based probability density estimation

A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies in the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used.     

Magnetic-Field-Driven Ferromagnetic Resonance in Spin-Transfer Devices

An analytical approach to the study of ferromagnetic resonance in nanomagnets is discussed in the case when radio-frequency magnetic fields and spin-polarized electrical currents are simultaneously present. Current-controlled foldover effects in ferromagnetic resonance are predicted and analyzed.