Strong Low-frequency Noise Research Articles

Forecasting community response to low-frequency noise

The most common approach to managing environmental noise is by reference to A-weighted sound levels deemed appropriate for the community. These criteria are usually derived from transportation noise studies for which the relative proportion of low-frequency noise is implicit. Very different community reactions can arise when source spectra don't represent those studied. Responses diverge even further when the listeners' context also differs from that originally studied. This is nowhere more evident than in the case of large industrial equipment (such as power plants and gas compressor stations) sited in rural areas, due in large part to strong Low-Frequency Noise (LFN). Guidance for forecasting community reaction to LFN is scattered throughout the literature as individual studies employing a diverse assortment of approaches and noise descriptors. The purpose of the present paper is to demonstrate a straightforward, comprehensive, and unified forecasting approach suitable for inclusion in ANSI S12.9 Part 4 "Noise Assessment and Prediction of Long-Term Community Response", similar in many respects to the method recently deleted from this standard (2005 Annex D).

Mobile fronthaul solution based on visible light fiber communication and zero-padding 8-D CAP modulation.

With the deployment of the fifth-generation (5 G) emerging technologies, such as massive multiple-input multiple-output (mMIMO), conventional mobile fronthaul (FH) schemes based on Common Public Radio Interface (CPRI) are limited in their abilities to support ultra-high data rate, large bandwidth and massive connectivity. This has led to a growing demand for alternative solutions that can better fulfill these requirements. Visible light communication (VLC) has recently gained increasing research interest as a potential complementary technology for beyond-5 G communication, offering advantages such as unlicensed and abundant spectrum, high bandwidth and cost-efficiency. In this paper, we propose a novel mobile fronthaul solution based on visible light fiber communication (VLFC), incorporating innovative zero-padding N-D (N>2) carrierless amplitude and phase (CAP) modulation. To avoid strong low-frequency noise (LFN) in the practical VLFC system, we innovatively propose zero-padding N-D CAP based on the actual distribution of LFN for the first time. In contrast to frequency division multiplexing (FDM), N-D CAP eliminates guard bands and ensures similar performance among multiple channels. To prove the concept, we demonstrate the transmission of PAM8 symbols at three typical visible light wavelengths (R/G/B) over 100 m multi-mode fiber (MMF) using zero-padding 8-D CAP modulation. The experiment verifies that zero-padding significantly enhances communication performance, with all 8 channels achieving similar BER. Communication performance improves as wavelengths are longer due to attenuation and dispersion caused by MMF. Finally, we have achieved a total data rate of 10.8Gbps at 635 nm (red), 9.0Gbps at 520 nm (green), and 7.5Gbps at 488 nm (blue) under the 7% HD-FEC threshold.

Deep learning based end-to-end visible light communication with an in-band channel modeling strategy.

Aside from ambient light noise, shot noise, and linear/nonlinear effects, strong low-frequency noise (LFN) severely affects the signal quality in LED-based visible light communication (VLC) systems, which hinders the implementation of data-driven end-to-end (E2E) deep learning approaches in real LED-VLC systems. We present a deep learning-based autoencoder to deal with this challenge. A novel modeling strategy is proposed to bypass the influence of the LFN and other low signal-to-noise ratio data when training the channel model of our E2E framework. The deep learning-based autoencoder then embeds the differentiable channel model and learns to combat the majority of channel impairments. In the E2E LED-VLC experiment, 1.875 Gbps transmission is achieved under the 7% HD-FEC threshold, 0.325 Gbps faster than the baseline. The E2E framework is robust to signal bias and amplitude variations, implying dimming support in the indoor environment.

Top-Bottom Gate Coupling Effect on Low Frequency Noise in a Schottky Junction Gated Silicon Nanowire Field-Effect Transistor

In this letter, strong low frequency noise (LFN) reduction is observed when the buried oxide (BOX)/silicon interface of a Schottky junction gated silicon nanowire field-effect transistor (SJGFET) is depleted by a substrate bias. Such LFN reduction is mainly attributed to the dramatic reduction in Coulomb scattering when carriers are pushed away from the interface. The BOX/silicon interface depletion can also be achieved by sidewall Schottky junction gates in a narrow channel SJGFET, leading to an optimal LFN performance without the need of any substrate bias.

Reverse-time migration using multidirectional wavefield decomposition method

Reverse-time migration has attracted more and more attention owing to the advantages of high imaging accuracy, no dip restriction, and adaptation to complex velocity models. Cross-correlation imaging method is typically used in conventional reverse-time migration that produces images with strong low-frequency noise. Wavefield decomposition imaging can suppress such noise; however, some residual noise persists in the imaging results. We propose a 2D multidirectional wavefield decomposition method based on the traditional wavefield decomposition method. First, source wavefields and receiver wavefields are separated into eight subwavefields, respectively. Second, cross-correlation imaging is applied to selected subwavefields to produce subimages. Finally, the subimages are stacked to generate the final image. Numerical examples suggest that the proposed method can eliminate the low-frequency noise effectively and produce high-quality imaging profiles.

A hybrid method for strong low-frequency noise suppression in prestack seismic data

Abstract Low-frequency components are important portion of seismic data in exploration geophysics, and have great effects on seismic imaging of deep subsurface and full waveform inversion. Unfortunately, seismic data usually suffers from various kinds of noises and has low signal to noise ratio (SNR) in low-frequency band, although this situation has been improved by developments of acquisition technology. In this paper, we propose a low-frequency cascade filter (LFCF) in Fourier domain for strong low-frequency noise suppression in prestack gathers. LFCF includes a 1D adaptive median filter in f–x domain and a 2D notch filter in f–k domain, which is able to process high-amplitude swell noise, random noise, and seismic interference noise. We employ traces rearrangement and spike-detection mechanisms in adaptive f–x median filter, which can handle strong noise specifically, such as wide-spreading swell noise and tug noise. And a notch filter in f–k domain is designed to separate reflection signal and random noise by different apparent velocities. Through these means, our method can effectively attenuate low-frequency random and coherent noise while simultaneously protect the signal. Experiments on synthetic example and field data are conducted, and the results demonstrate that our method is practical and effective and can preserve signal down to 2 Hz.

Low Frequency Noise Generated by an Open Window in a Car and Its Effect on Human Comfort

The paper presents low frequency noise and infrasound excited by air flow due to an open window in a moving passenger car. This noise is analysed and its effects on a driver’s comfort are assessed. The results from all measurements (using FFT, as well as CPB analysis) show, that an open window at high speeds behaves as a source of specific tonal low-frequency noise, which is annoying, since the sound levels are relatively high. This strong low-frequency noise is analysed from the view point of its effects on the health of the exposed subjects and some recommendations are given.

Landau-Zener-Stückelberg interferometry in the presence of quantum noise

We proposed an unconventional Landau-Zener-St\"uckelberg interferometry in strongly driven two-level systems subjected to quantum noise. A general transition rate for consecutive Landau-Zener transitions was obtained, from which we could analytically calculate interference fringes. The strong low-frequency noise would detour the evolution paths, leading to asymmetric transition rates which caused unpredicted position shift of the fringes and population inversions in a two-level system.

Low Frequency Noise and Annoyance in Classroom

The most common method for noise assessment is the A-weighted sound pressure level. The question has been raised as to whether the frequency weighting with an A-filter gives a correct result when assessing the annoyance response to noise containing strong low frequency noise (LFN) components. One method suggested to identify LFN is the dB(C) – dB(A) difference. The aims of this study are to investigate if background noise in Swedish elementary schools is to be considered as LFN, further to test the hypothesis that students exposed to audible LFN at high levels are more annoyed than students exposed to LFN at lower levels. The results indicate that the noise in 16 out of 22 classrooms should be considered as LFN. The analysis did not show any difference in rated annoyance between students exposed to high LFN levels and students exposed to low LFN levels.