Dynamic Radio-Frequency Transverse Susceptibility in Magnetic Nanoparticle Systems

A novel resonant method based on a tunnel-diode oscillator (TDO) is used to study the dynamic transverse susceptibility in a Fe nanoparticle system. The magnetic system consists of an aggregate of nanometer-size core (Au)-shell (Fe) structure, synthesized by reverse micelle methods. Static and dynamic magnetization measurements carried out in order to characterize the system reveal a superparamagnetic behavior at high temperature. The field-dependent transverse susceptibility at radio-frequencies (RF), for different temperatures reveals distinct peak structure at characteristics fields (H_k, H_c) which changes with temperature. It is proposed that relaxation processes could explain the influence of the temperature on the field dependence of the transverse susceptibility on the MI.Comment: 3 pages, 2-column, 3 figures, To be published in J. Appl. Phys. 2000 (44th Annual MMM proceedings

Reversal Modes of Simulated Iron Nanopillars in an Obliquely Oriented Field

Stochastic micromagnetic simulations are employed to study switching in three-dimensional magnetic nanopillars exposed to highly misaligned fields. The switching appears to proceed through two different decay modes, characterized by very different average lifetimes and different average values of the transverse magnetization components.Comment: 3 pages, 4 figure

Magnetostatic bias in multilayer microwires: theory and experiments

The hysteresis curves of multilayer microwires consisting of a soft magnetic nucleus, intermediate non-magnetic layers, and an external hard magnetic layer are investigated. The magnetostatic interaction between magnetic layers is proved to give rise to an antiferromagnetic-like coupling resulting in a magnetostatic bias in the hysteresis curves of the soft nucleus. This magnetostatic biasing effect is investigated in terms of the microwire geometry. The experimental results are interpreted considering an analytical model taking into account the magnetostatic interaction between the magnetic layers.Comment: 6 pages, 7 figure

Surface anisotropy in nanomagnets: transverse or N\'eel ?

Through the hysteresis loop and magnetization spatial distribution we study and compare two models for surface anisotropy in nanomagnets: a model with transverse anisotropy axes and N\'eel's model. While surface anisotropy in the transverse model induces several jumps in the hysteresis loop because of the cluster-wise switching of spins, in the N\'eel model the jumps correspond to successive {\it coherent partial rotations} of the whole bunch of spins. These calculations together with experimental results suggest that N\'eel's model for surface anisotropy is more appropriate.Comment: 12 pages, 6 eps figure

Finite-size versus Surface effects in nanoparticles

We study the finite-size and surface effects on the thermal and spatial behaviors of the magnetisation of a small magnetic particle. We consider two systems: 1) A box-shaped isotropic particle of simple cubic structure with either periodic or free boundary conditions. This case is treated analytically using the isotropic model of D-component spin vectors in the limit $D\to \infty$, including the magnetic field. 2) A more realistic particle ($\gamma$-Fe$_{2}$O$_{3}$) of ellipsoidal (or spherical) shape with open boundaries. The magnetic state in this particle is described by the anisotropic classical Dirac-Heisenberg model including exchange and dipolar interactions, and bulk and surface anisotropy. This case is dealt with by the classical Monte Carlo technique. It is shown that in both systems finite-size effects yield a positive contribution to the magnetisation while surface effects render a larger and negative contribution, leading to a net decrease of the magnetisation of the small particle with respect to the bulk system. In the system 2) the difference between the two contributions is enhanced by surface anisotropy. The latter also leads to non saturation of the magnetisation at low temperatures, showing that the magnetic order in the core of the particle is perturbed by the magnetic disorder on the surface. This is confirmed by the profile of the magnetisation.Comment: 6 pages of RevTex including 4 Figures, invited paper to 3rd EuroConference on Magnetic Properties of Fine Nanoparticles, Barcelona, October 9

Power-law decay in first-order relaxation processes

Starting from a simple definition of stationary regime in first-order relaxation processes, we obtain that experimental results are to be fitted to a power-law when approaching the stationary limit. On the basis of this result we propose a graphical representation that allows the discrimination between power-law and stretched exponential time decays. Examples of fittings of magnetic, dielectric and simulated relaxation data support the results.Comment: to appear in Phys. Rev. B; 4 figure

The Duality Upper Bound for Finite-State Channels with Feedback

This paper investigates the capacity of finite-state channels (FSCs) with feedback. We derive an upper bound on the feedback capacity of FSCs by extending the duality upper bound method from mutual information to the case of directed information. The upper bound is expressed as a multi-letter expression that depends on a test distribution on the sequence of channel outputs. For any FSC, we show that if the test distribution is structured on a $Q$-graph, the upper bound can be formulated as a Markov decision process (MDP) whose state being a belief on the channel state. In the case of FSCs and states that are either unifilar or have a finite memory, the MDP state simplifies to take values in a finite set. Consequently, the MDP consists of a finite number of states, actions, and disturbances. This finite nature of the MDP is of significant importance, as it ensures that dynamic programming algorithms can solve the associated Bellman equation to establish analytical upper bounds, even for channels with large alphabets. We demonstrate the simplicity of computing bounds by establishing the capacity of a broad family of Noisy Output is the State (NOST) channels as a simple closed-form analytical expression. Furthermore, we introduce novel, nearly optimal analytical upper bounds on the capacity of the Noisy Ising channel

Integral Relaxation Time of Single-Domain Ferromagnetic Particles

The integral relaxation time \tau_{int} of thermoactivating noninteracting single-domain ferromagnetic particles is calculated analytically in the geometry with a magnetic field H applied parallel to the easy axis. It is shown that the drastic deviation of \tau_{int}^{-1} from the lowest eigenvalue of the Fokker-Planck equation \Lambda_1 at low temperatures, starting from some critical value of H, is the consequence of the depletion of the upper potential well. In these conditions the integral relaxation time consists of two competing contributions corresponding to the overbarrier and intrawell relaxation processes.Comment: 8 pages, 3 figure

Data-Driven Neural Polar Codes for Unknown Channels With and Without Memory

In this work, a novel data-driven methodology for designing polar codes for channels with and without memory is proposed. The methodology is suitable for the case where the channel is given as a "black-box" and the designer has access to the channel for generating observations of its inputs and outputs, but does not have access to the explicit channel model. The proposed method leverages the structure of the successive cancellation (SC) decoder to devise a neural SC (NSC) decoder. The NSC decoder uses neural networks (NNs) to replace the core elements of the original SC decoder, the check-node, the bit-node and the soft decision. Along with the NSC, we devise additional NN that embeds the channel outputs into the input space of the SC decoder. The proposed method is supported by theoretical guarantees that include the consistency of the NSC. Also, the NSC has computational complexity that does not grow with the channel memory size. This sets its main advantage over successive cancellation trellis (SCT) decoder for finite state channels (FSCs) that has complexity of $O(|\mathcal{S}|^3 N\log N)$, where $|\mathcal{S}|$ denotes the number of channel states. We demonstrate the performance of the proposed algorithms on memoryless channels and on channels with memory. The empirical results are compared with the optimal polar decoder, given by the SC and SCT decoders. We further show that our algorithms are applicable for the case where there SC and SCT decoders are not applicable