Resonant Anderson localization in segmented wires

Estarellas, Cristian; Serra, Llorenç

doi:10.1103/physreve.93.032105

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Indeed, it is known that the curved regions of a wire have a so-called geometric potential that may be strong enough to induce bound states at the bending. Such bound states have been also studied in planar polygons and piecewise linear wires [50,51]. Technically, extending 2D-q1D to 3D-q1D only requires generalizing the transverse wave functions of Eq.…”

Section: K-analysis In 3d-q1dmentioning

confidence: 99%

Complex band-structure analysis and topological physics of Majorana nanowires

Osca

Serra

2019

Eur. Phys. J. B

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We review applications of complex band structure theory to describe Majorana states in nanowires and nanowire junctions. The dimensionality of the considered wires is gradually increased, from strictly 1D to quasi-1D with one and two transverse dimensions. The complex wave number analysis is applied to two main types of Majorana modes: end states in hybrid semiconductor wires and chiral edge states in hybrid wires of quantum-anomalous Hall insulators. In topological NS and NSN junctions with Majorana modes conductance oscillations and spatial distributions of density and current are described. Finally, the optical absorption in finite systems with both types of Majorana modes is considered.

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Section: K-analysis In 3d-q1dmentioning

confidence: 99%

Complex band-structure analysis and topological physics of Majorana nanowires

Osca

Serra

2019

Eur. Phys. J. B

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“…It is often used later for further processing. Other interesting applications include: physics [18][19][20][21][22], and biology [23][24][25][26][27]. The segmentation problems appear also in condition monitoring [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Divergence-Based Segmentation Algorithm for Heavy-Tailed Acoustic Signals with Time-Varying Characteristics

Grzesiek

Gąsior

Wyłomańska

et al. 2021

Sensors

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Many real-world systems change their parameters during the operation. Thus, before the analysis of the data, there is a need to divide the raw signal into parts that can be considered as homogeneous segments. In this paper, we propose a segmentation procedure that can be applied for the signal with time-varying characteristics. Moreover, we assume that the examined signal exhibits impulsive behavior, thus it corresponds to the so-called heavy-tailed class of distributions. Due to the specific behavior of the data, classical algorithms known from the literature cannot be used directly in the segmentation procedure. In the considered case, the transition between parts corresponding to homogeneous segments is smooth and non-linear. This causes that the segmentation algorithm is more complex than in the classical case. We propose to apply the divergence measures that are based on the distance between the probability density functions for the two examined distributions. The novel segmentation algorithm is applied to real acoustic signals acquired during coffee grinding. Justification of the methodology has been performed experimentally and using Monte-Carlo simulations for data from the model with heavy-tailed distribution (here the stable distribution) with time-varying parameters. Although the methodology is demonstrated for a specific case, it can be extended to any process with time-changing characteristics.

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