Spin-Hall nano-oscillator with oblique magnetization and Dzyaloshinskii-Moriya interaction as generator of skyrmions and nonreciprocal spin-waves.

Spin-Hall oscillators (SHO) are promising sources of spin-wave signals for magnonics applications, and can serve as building blocks for magnonic logic in ultralow power computation devices. Thin magnetic layers used as "free" layers in SHO are in contact with heavy metals having large spin-orbital interaction, and, therefore, could be subject to the spin-Hall effect (SHE) and the interfacial Dzyaloshinskii-Moriya interaction (i-DMI), which may lead to the nonreciprocity of the excited spin waves and other unusual effects. Here, we analytically and micromagnetically study magnetization dynamics excited in an SHO with oblique magnetization when the SHE and i-DMI act simultaneously. Our key results are: (i) excitation of nonreciprocal spin-waves propagating perpendicularly to the in-plane projection of the static magnetization; (ii) skyrmions generation by pure spin-current; (iii) excitation of a new spin-wave mode with a spiral spatial profile originating from a gyrotropic rotation of a dynamical skyrmion. These results demonstrate that SHOs can be used as generators of magnetic skyrmions and different types of propagating spin-waves for magnetic data storage and signal processing applications.

Magnetic Radial Vortex Stabilization and Efficient Manipulation Driven by the Dzyaloshinskii-Moriya Interaction and Spin-Transfer Torque.

Solitons are very promising for the design of the next generation of ultralow power devices for storage and computation. The key ingredient to achieving this goal is the fundamental understanding of their stabilization and manipulation. Here, we show how the interfacial Dzyaloshinskii-Moriya Interaction (IDMI) is able to lift the energy degeneracy of a magnetic vortex state by stabilizing a topological soliton with radial chirality, hereafter called radial vortex. It has a noninteger Skyrmion number S (0.5<|S|<1) due to both the vortex core polarity and the magnetization tilting induced by the IDMI boundary conditions. Micromagnetic simulations predict that a magnetoresistive memory based on the radial vortex state in both free and polarizer layers can be efficiently switched by a threshold current density smaller than 10^{6} A/cm^{2}. The switching processes occur via the nucleation of topologically connected vortices and vortex-antivortex pairs, followed by spin-wave emissions due to vortex-antivortex annihilations.

Classification of morphological features extracted from intracranial pressure recordings in the diagnosis of Normal Pressure Hydrocephalus (NPH).

The intracranial pressure (ICP) monitoring is a common procedure in neuro-intensive care for pathologies such traumatic brain injuries or hemorrhages, but also for chronic ones as the Normal Pressure Hydrocephalus (NPH). The only available treatment for NPH is the surgical implantation of a shunt with the aim of routing cerebrospinal fluid (CSF) away from the brain to another part of the body. In this study, using the classification software WEKA, an intensive investigation of ICP signals has been conducted. In particular we studied 14 ICP recordings of different patients who underwent an infusion test, with the aim of investigating the presence of NPH through the ICP recording. More precisely, 20 morphological features are extracted from the ICP pulsed wave, the trend have been computed and, for each one, 9 statistical functions determined. The 180 features have been selected and passed for the classification. The results obtained shows how, among the 14 patients, a number of 12 out of 14 (85.7%) have been correctly classified, looking at just 3 features. In particular 8 out of 9 not-NPH-affected patients were correctly identified (88.89%) while 4 out of 5 NPH-affected patients were correctly identified (80%).

R-point detection for noise affected ECG recording through signal segmentation.

In this work we propose a novel approach for filtering noise-affected electrocardiogram (ECG) signals. The proposed method, mainly based on signal approximation by means of linear segments, has been applied for R-peaks recognition and has been compared with both cardiologists' manual marking and the automatic Laguna's method. The obtained results show that when compared to the Laguna's method the proposed algorithm provides a smaller mean error and a better error distribution.

Analysis of intracranial pressure recordings: comparison of PCA and signal averaging based filtering methods and signal period estimation.

Intracranial pressure monitoring is a common used approach for neuro-intensive care in cases of brain damages and injuries or to investigate chronic pathologies. Several types of noises and artifacts normally contaminate ICP recordings. They can be sorted in 2 classes, i.e. high-frequency noises (due to measurement and amplifier devices or electricity supply presence) and low-frequency noises (due to unwanted patient's movement, speeches, coughing during the recording and quantization noise). Thus, deep investigations on ICP components aimed to extract features from ICP signal, require a denoised signal. For this reason the authors have addressed a study upon the most common filtering techniques. On each ICP recording we have performed 4 configurations of filters, which involve the use of a FIR filter together with Signal Averaging filters or PCA based filters. Next step is period estimation for absolute minima detection. The results obtained by the algorithm for automatic ICP marking are compared to those ones obtained from manual marking (peaks are manually identified and annotated by a brain surgeon). The procedure is repeated varying the filters sliding window size to minimize the mean square error. The results show how the configurations FIR filter + Signal averaging provides smaller mean squared error (MSE=118.84[sample(2)]) than the others 3 configurations FIR filter + PCA filter based (MSE=135.29-147.15[sample(2)]).

A preliminary study for investigating idiopatic normal pressure hydrocephalus by means of statistical parameters classification of intracranial pressure recordings.

The objective of this study is to investigate Id-iopatic Normal Pressure Hydrocephalus (INPH) through a multidimensional and multiparameter analysis of statistical data obtained from accurate analysis of Intracranial Pressure (ICP) recordings. Such a study could permit to detect new factors, correlated with therapeutic response, which are able to validate a predicting significance for infusion test. The algorithm developed by the authors computes 13 ICP parameter trends on each of the recording, afterward 9 statistical information from each trend is determined. All data are transferred to the datamining software WEKA. According to the exploited feature-selection techniques, the WEKA has revealed that the most significant statistical parameter is the maximum of Single-Wave-Amplitude: setting a 27 mmHg threshold leads to over 90% of correct classification.