Extending the Capacity of 1 / f Noise Generation

Perez, Guillaume; Rappazzo, Brendan; Gomes, Carla P.

doi:10.1007/978-3-319-98334-9_39

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…This line of works generated many theoretical and algorithmic results [73], as well as fruitful collaborations with other CP researchers. Jean-Charles Régin and Guillaume Perez, in particular, reformulated a number of our algorithms in the framework of Multi-valued Decision Diagrams (MDD), yielding substantial gains in efficiency [82,81].…”

Section: Sampling Methodsmentioning

confidence: 99%

Assisted music creation with Flow Machines: towards new categories of new

François¹,

Roy²,

Benoit³

2020

Preprint

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This chapter reflects on about 10 years of research in AIassisted music composition, in particular during the Flow Machines project. We reflect on the motivations for such a project, its background, its main results and impact, both technological and musical, several years after its completion. We conclude with a proposal for new categories of "new", created by the many uses of AI techniques to generate novel material.

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Section: Sampling Methodsmentioning

confidence: 99%

Assisted music creation with Flow Machines: towards new categories of new

François¹,

Roy²,

Benoit³

2020

Preprint

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“…The PSD of a signal describes the linear distribution of the spectral power over the frequency components (figure 2) and the focus of the analysis is usually on the oscillatory components figure 2(b) which, to be nonlinearly evaluated, need the application of complex methods, such as higher order spectral analysis [67,68]. The power-law decay of the spectrum as a function of frequency is also called '1/f noise' and characterizes the non-oscillatory and fractal-behavior of signals [69,70].…”

Section: Power-law Exponentmentioning

confidence: 99%

Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer’s and Parkinson’s disease

Averna,

Coelli,

Ferrara

et al. 2023

J. Neural Eng.

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Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of ‘scale-free’ behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer’s disease, while a local increase of brain signals complexity characterizes Parkinson’s disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer’s and Parkinson’s diseases.

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“…where each component corresponds, respectively, to the applied external field, interlayer exchange coupling (IEC), Oersted field, anisotropy field, demagnetising, dipole, thermal, and 1/f noise field [23,24]. Contributions marked with * , if enabled in the simulation, require a stochastic solver, which is described in Sect.2.3.…”

Section: Magnetic Contributionsmentioning

confidence: 99%

A comprehensive simulation package for analysis of multilayer spintronic devices

Mojsiejuk¹,

Ziętek²,

Grochot³

et al. 2022

Preprint

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We present cmtj -a comprehensive simulation package that allows large-scale macrospin simulations for a variety of multilayer spintronics devices. Apart from conventional static simulations, such as magnetoresistance and magnetisation hysteresis loops, cmtj implements a mathematical model of dynamic experimental techniques commonly used for spintronics devices characterisation, for instance: spin diode ferromagnetic resonance, pulse-induced microwave magnetometry, or harmonic Hall voltage measurements. We demonstrate the accuracy of the macrospin simulations on a variety of examples, accompanied by some experimental results.

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Extending the Capacity of 1 / f Noise Generation

Cited by 5 publications

References 17 publications

Assisted music creation with Flow Machines: towards new categories of new

Assisted music creation with Flow Machines: towards new categories of new

Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer’s and Parkinson’s disease

A comprehensive simulation package for analysis of multilayer spintronic devices

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