A Waveform Relaxation Solver for Transient Simulation of Large-Scale Nonlinearly Loaded Shielding Structures

This article introduces an algorithm for transient simulation of electromagnetic structures loaded by lumped nonlinear devices. The reference application is energy-selective shielding, which adopts clipping devices uniformly spread along shield apertures to achieve a shielding effectiveness that increases with the power of the incident field, thereby blocking high-power interference while allowing low-power communication. Transient simulation of such structures poses a number of challenges, related to their large-scale and low-loss nature. In this work, we propose a waveform relaxation (WR) scheme based on decoupling the linear electromagnetic structure from its nonlinear terminations. In a preprocessing stage, the electromagnetic subsystem is characterized in the frequency domain and converted into a behavioral rational macromodel. Transient simulation is performed by refining estimates of the port signals through iterations. The proposed scheme combines a time partitioning approach with an inexact Newton–Krylov solver. This combination provides fast convergence also in those cases where standard WR schemes fail due to a strong mismatch at the decoupling sections. Numerical results on several test cases of increasing complexity with up to 1024 ports show that the proposed approach proves as reliable as HSPICE in terms of accuracy, with a speedup ranging from one to three orders of magnitude

A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels

This paper proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. Here, large-scale is intended both in terms of dynamic order and especially number of input/output ports. Standard passivity characterization approaches based on spectral properties of associated Hamiltonian matrices are either inefficient or non-applicable for large-scale models, due to an excessive computational cost. This paper builds on existing adaptive sampling methods and proposes a hybrid multi-stage algorithm that is able to detect the passivity violations with limited computing resources. Results from extensive testing demonstrate a major reduction in computational requirements with respect to competing approaches

A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels

This paper proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. Here, large-scale is intended both in terms of dynamic order and especially number of input/output ports. Standard passivity characterization approaches based on spectral properties of associated Hamiltonian matrices are either inefficient or non-applicable for large-scale models, due to an excessive computational cost. This paper builds on existing adaptive sampling methods and proposes a hybrid multi-stage algorithm that is able to detect the passivity violations with limited computing resources. Results from extensive testing demonstrate a major reduction in computational requirements with respect to competing approaches.Comment: Submitted to the IEEE Transactions on Components, Packaging and Manufacturing Technolog

Distributed Nonlinear Shielding in Power Delivery Networks on Printed Circuit Boards

Nonlinear shielding from voltage spikes is commonly designed in a one dimensional setting, e.g. on transmission lines, where protection consists of a single lumped voltage clamp. This paper investigates the extension to the two dimensional case, i.e. protection from a voltage pulse between two parallel plates of a PCB. Opposed to the one dimensional case, where the protection is concentrated in a point, for two dimensional case the protection elements are arranged on a curve, creating a nonlinear fence in the two dimensional plane. It is shown that the spacing of the elements on the curve has a strong impact on the shielding effectiveness. Additionally, the effect of resistive losses in the voltage clamps is investigated

Iteration Dependent Waveform Relaxation for Hybrid Field Nonlinear Circuit Problems

This article presents a novel waveform relaxation scheme to solve electromagnetically large structures loaded with lumped linear and nonlinear elements. The scheme partitions the problem into a linear electromagnetic structure and a possibly nonlinear lumped circuit, which are coupled using Thévenin interfaces across the steps of an iterative waveform relaxation scheme. The main novel contribution is an adaptive selection of the decoupling resistances used as port references to define incident and reflected scattering signals, whose time-domain samples are refined through iterations. The decoupling resistances are updated through iterations, with the main objective of improving convergence speed and ultimately runtime. The resulting scheme is self-adapting to terminations exploiting high dynamic range in their impedance profiles and is able to provide a suboptimal convergence rate. Three-dimensional shielding structures loaded with nonlinear elements are employed as numerical examples to demonstrate the proposed method

Observation of High-Energy Astrophysical Neutrinos in Three Years of IceCube Data

A search for high-energy neutrinos interacting within the IceCube detector between 2010 and 2012 provided the first evidence for a high-energy neutrino flux of extraterrestrial origin. Results from an analysis using the same methods with a third year (2012-2013) of data from the complete IceCube detector are consistent with the previously reported astrophysical flux in the 100 TeV - PeV range at the level of $10^{-8}\, \mathrm{GeV}\, \mathrm{cm}^{-2}\, \mathrm{s}^{-1}\, \mathrm{sr}^{-1}$ per flavor and reject a purely atmospheric explanation for the combined 3-year data at $5.7 \sigma$. The data are consistent with expectations for equal fluxes of all three neutrino flavors and with isotropic arrival directions, suggesting either numerous or spatially extended sources. The three-year dataset, with a livetime of 988 days, contains a total of 37 neutrino candidate events with deposited energies ranging from 30 to 2000 TeV. The 2000 TeV event is the highest-energy neutrino interaction ever observed.Comment: 8 pages, 5 figures. Accepted by PRL. The event catalog, event displays, and other data tables are included after the final page of the article. Changed from the initial submission to reflect referee comments, expanding the section on atmospheric backgrounds, and fixes offsets of up to 0.9 seconds in reported event times. Address correspondence to: J. Feintzeig, C. Kopper, N. Whitehor

Differential limit on the extremely-high-energy cosmic neutrino flux in the presence of astrophysical background from nine years of IceCube data

We report a quasi-differential upper limit on the extremely-high-energy (EHE) neutrino flux above $5\times 10^{6}$ GeV based on an analysis of nine years of IceCube data. The astrophysical neutrino flux measured by IceCube extends to PeV energies, and it is a background flux when searching for an independent signal flux at higher energies, such as the cosmogenic neutrino signal. We have developed a new method to place robust limits on the EHE neutrino flux in the presence of an astrophysical background, whose spectrum has yet to be understood with high precision at PeV energies. A distinct event with a deposited energy above $10^{6}$ GeV was found in the new two-year sample, in addition to the one event previously found in the seven-year EHE neutrino search. These two events represent a neutrino flux that is incompatible with predictions for a cosmogenic neutrino flux and are considered to be an astrophysical background in the current study. The obtained limit is the most stringent to date in the energy range between $5 \times 10^{6}$ and $5 \times 10^{10}$ GeV. This result constrains neutrino models predicting a three-flavor neutrino flux of $E_\nu^2\phi_{\nu_e+\nu_\mu+\nu_\tau}\simeq2\times 10^{-8}\ {\rm GeV}/{\rm cm}^2\ \sec\ {\rm sr}$at$10^9\ {\rm GeV}$. A significant part of the parameter-space for EHE neutrino production scenarios assuming a proton-dominated composition of ultra-high-energy cosmic rays is excluded.Comment: The version accepted for publication in Physical Review

Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform

Contains fulltext : 151960.pdf (publisher's version ) (Open Access)Mounting evidence for the role of sleep spindles in neuroplasticity has led to an increased interest in these non-rapid eye movement (NREM) sleep oscillations. It has been hypothesized that fast and slow spindles might play a different role in memory processing. Here, we present a new sleep spindle detection algorithm utilizing a continuous wavelet transform (CWT) and individual adjustment of slow and fast spindle frequency ranges. Eighteen nap recordings of ten subjects were used for algorithm validation. Our method was compared with both a human scorer and a commercially available SIESTA spindle detector. For the validation set, mean agreement between our detector and human scorer measured during sleep stage 2 using kappa coefficient was 0.45, whereas mean agreement between our detector and SIESTA algorithm was 0.62. Our algorithm was also applied to sleep-related memory consolidation data previously analyzed with a SIESTA detector and confirmed previous findings of significant correlation between spindle density and declarative memory consolidation. We then applied our method to a study in monozygotic (MZ) and dizygotic (DZ) twins, examining the genetic component of slow and fast sleep spindle parameters. Our analysis revealed strong genetic influence on variance of all slow spindle parameters, weaker genetic effect on fast spindles, and no effects on fast spindle density and number during stage 2 sleep