Specific Frequency Bands Research Articles

Seismic Noise Reduction as a Function of Depth Recorded by a Vertical Array Installed in a 285-m-Deep Borehole at a Gas Storage Field in Northern Italy

Abstract The background seismic noise can be generated by different sources such as, ocean waves (microseisms), atmospheric disturbances (strong wind and storms), and anthropogenic activities, temperature changes and magnetic field variations. Such disturbances are characterized by specific frequency bands, time occurrence (diurnal and seasonal variation), and site location (close to populated areas or to the coasts). Reducing the pernicious effect of these noise sources is one of the main challenges that seismologists and engineers need to face when designing seismic monitoring networks and, more specifically when selecting the hosting site of a seismic station. A solution to partially attenuate the seismic noise effect is achieved by deploying seismic stations in boreholes. A general law estimating the sufficient depth to gain to detect even low seismic events, highly masked by background noise, is fundamental for defining the capability of microseismic network. Here, we aim to characterize the seismic noise level at S. Potito-Cotignola in the Po Valley, Italy, from January 2019 to December 2021 recorded by a broadband seismic station at surface and a vertical array composed by six short-period three-component seismometers installed at depth ranging between 35 and 285 m in borehole. We compute the amplitude noise reduction as a function of depth for different frequencies and we evaluate the depth dependency of the signal to noise ratio for 18 seismic events, with different magnitude (from −0.1 to 2.9) and hypocentral distances (from 12.9 to 37.2 km). Results show that (1) the dependence of noise level with depth follows a logarithmic empirical trend and (2) most of the selected seismic events show that signal to noise ratio increases with depth. The empirical relationships we estimated can be used to help the design of microseismic monitoring networks in similar geological settings.

Signal area estimation based on deep learning

In many practical application scenarios, radio communication signals are commonly represented as a spectrogram, which represents the signal strength measured at multiple discrete time instants and frequency points within a specific time interval and frequency band, respectively. In the context of spectrum occupancy measurements, the notion of Signal Area (SA) is defined as the rectangular region in the time–frequency domain where a signal is assumed to be present. Signal Area Estimation (SAE) is an important functionality in spectrum-aware wireless systems where spectrum usage monitoring is required. However, the conventional approaches to SAE have a limited estimation accuracy, in particular at low SNR. In this work, a novel technique for SAE is proposed using Deep Learning based on Artificial Neural Network (DL-ANN) for enhanced extraction of SA information from radio spectrograms. The performance of the proposed DL-ANN method is evaluated both with software simulations and hardware experiments, and the results are compared with several conventional methods from the literature, showing significant performance improvements. A key feature of the proposed method is the improvement in the SAE accuracy compared to other existing methods (in particular in the low SNR regime) and the capability to extract the location of the detected SAs automatically. Overall, the proposed technique is a promising solution for the automatic processing of radio spectrograms in spectrum-aware wireless systems.

Frequency specificity in the amplitude of low frequency oscillations in patients with white matter lesions

Previous studies have reported that patients with white matter lesions (WMLs) have abnormal spontaneous brain activity in the resting state. However, the spontaneous neuronal activity of specific frequency bands in WMLs patients is unknown. Here, we included 16 WMLs patients and 13 gender and age-matched healthy controls (HCs) underwent resting-state magnetic resonance imaging (rs-fMRI) scan and studied the specificity of the amplitude of low-frequency fluctuations (ALFF) in WMLs patients in the slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), and typical (0.01–0.08 Hz) frequency bands. In addition, ALFF values of different frequency bands were extracted as classification features and support vector machines (SVM) were used to classify WMLs patients. In all three frequency bands, significant increases in ALFF values in WMLs patients were observed in the cerebellum. In the slow-5 band, the ALFF values of the left anterior cingulate and paracingulate gyri (ACG), and the right precentral gyrus, rolandic operculum and inferior temporal gyrus in WMLs patients were lower than those in HCs. In the slow-4 band, ALFF values were lower in WMLs patients than in HCs at the left ACG, the right median cingulate and paracingulate gyri, parahippocampal gyrus, caudate nucleus, and the bilateral lenticular nucleus, putamen. In the SVM classification model, the classification accuracy of slow-5, slow-4 and typical frequency bands is 75.86%, 86.21% and 72.41%, respectively. The results indicate that the ALFF abnormalities in WMLs patients have frequency specificity, and the ALFF abnormalities in the slow-4 frequency band may serve as imaging markers for WMLs.

Mean amplitude of low frequency fluctuations measured by fMRI at 11.7 T in the aging brain of mouse lemur primate

Non-human primates are a critical species for the identification of key biological mechanisms in normal and pathological aging. One of these primates, the mouse lemur, has been widely studied as a model of cerebral aging or Alzheimer's disease. The amplitude of low-frequency fluctuations of blood oxygenation level-dependent (BOLD) can be measured with functional MRI. Within specific frequency bands (e.g. the 0.01–0.1 Hz), these amplitudes were proposed to indirectly reflect neuronal activity as well as glucose metabolism. Here, we first created whole brain maps of the mean amplitude of low frequency fluctuations (mALFF) in young mouse lemurs (mean ± SD: 2.1 ± 0.8 years). Then, we extracted mALFF in old lemurs (mean ± SD: 8.8 ± 1.1 years) to identify age-related changes. A high level of mALFF was detected in the temporal cortex (Brodmann area 20), somatosensory areas (Brodmann area 5), insula (Brodmann areas 13–6) and the parietal cortex (Brodmann area 7) of healthy young mouse lemurs. Aging was associated with alterations of mALFF in somatosensory areas (Brodmann area 5) and the parietal cortex (Brodmann area 7).

Subwavelength Broadband Perfect Absorption for Unidimensional Open‐Duct Problems

AbstractPassive metamaterials provide efficient solutions for sound absorption in the low‐frequency regime with deep subwavelength dimensions. They have been extensively applied in unidimensional reciprocal problems considering that an incident wave is either reflected or transmitted at the outlet boundary. However, the generalized problem with impedance boundary condition is not well understood yet. This work presents a general design methodology of metamaterial absorbers for open‐duct problems, which is a special case of impedance outlet boundary encountered in broad practical applications, for example, noise‐attenuation problems in heat ventilation and air conditioning systems. By properly using monopolar point scatterers made of arrays of Helmholtz resonators, the design process is significantly simplified; the transfer matrix modeling is sufficiently accurate to describe the acoustic response of the system. A single monopolar point scatterer is insufficient to attenuate both the reflected and radiated waves; a frequency‐dependent maximum absorption exists and is derived analytically. To go beyond this absorption bound and achieve perfect absorption, at least two point scatterers are necessary. Specific designs are provided and validated experimentally for maximum or perfect absorption, either at single frequencies or over specific frequency bands.

Study of acoustic transmission losses in particle-reinforced rubber-based membrane-type acoustic metamaterials

In this work, the mechanical properties of particle reinforced ethylene propylene diene monomer (EPDM)/ethylene tetrafluoroethylene (ETFE) copolymer material were determined by molecular dynamics (MD) method, and the copolymer material was used in the structural design of acoustic metamaterials. Secondly, based on the discrete point matching method, the acoustic transmission loss characteristics of the material are predicted, and the accuracy and effectiveness of the theory are verified through the multi physical field coupling finite element model. Finally, seven key parameters that affect the material structure are studied, and their influencing rules and mechanisms are analyzed. The results show that the structure makes up for the sound insulation defect of traditional ETFE film materials at low frequencies. Through reasonable selection of key parameters, high sound insulation design can be carried out for specific frequency bands, which is of great significance to the potential application in the construction field (new building materials).

154 Comparison of Chronic and Intraoperative LFP and ECoG Recordings in Humans With Parkinson’s Disease

INTRODUCTION: Advances in implantable neurotechnologies have enabled chronic invasive long-term recordings of field potentials from depth leads or paddle-type electrocorticography (ECoG) leads. The relationship between these long-term recordings and those obtained acutely in the operating room has not been studied. METHODS: Human subjects (n=6) with movement disorders (Parkinson’s disease, dystonia) were permanently implanted with quadripolar DBS leads, and quadripolar ECoG leads over primary motor and sensory cortices, attached to a Medtronic Summit RC+S investigational device in the chest. Recordings were acquired intraoperatively using a NeuroOmega (Alpha Omega, Inc) recording system customized for research use. RC+S recordings were acquired between 1 and 345 days post implantation. The same processing pipeline was applied to both data sets, and average power spectra in canonical frequency bands (delta, theta, alpha, beta, gamma) were compared using rank sum tests within each subject. RESULTS: Power spectra show similar power law scaling phenomena between intraoperative and chronic recordings. Disease specific frequency band characteristics such as beta peaks could be seen in both chronic and intraoperative recordings, however, within subjects these were not always consistently seen. CONCLUSIONS: We present within-patient comparisons of signal characteristics (power spectra) of intraoperative recordings from a research grade recording system, with wirelessly streamed data from a chronic implantable device, using the same brain leads. Power spectra have similar characteristics between the intraoperative and long term recordings, however, there are distinct differences between the intraoperative and longer term clinic recordings, reinforcing the utility of tracking neural signals over time after the effects of lead insertion on the motor circuit have diminished.

PVDF Membrane-Based Dual-Channel Acoustic Sensor Integrating the Fabry–Pérot and Piezoelectric Effects

A resonant acoustic wave detector combined with Fabry-Pérot interference (FPI) and piezoelectric (PE) effects based on a polyvinylidene fluoride (PVDF) piezoelectric film was proposed to enhance the ability of the sensor to detect acoustic signals in a specific frequency band. The deformation of circular thin films was indicated by the interference and piezoelectric effects simultaneously, and the noise level was decreased by the real-time convolution of the two-way parallel signal. This study reveals that, at the film's resonance frequency, the minimum detection limits for the FPI and piezoelectric impacts on acoustic waves are 3.39 μPa/Hz1/2 and 20.8 μPa/Hz1/2, respectively. The convolution result shows that the background noise was reduced by 98.81% concerning the piezoelectric signal, and by 85.21% concerning the FPI signal. The convolution's signal-to-noise ratio (SNR) was several times greater than the other two signals at 10 mPa. Therefore, this resonance sensor, which the FPI and the piezoelectric effect synergistically enhance, can be applied to scenarios of acoustic wave detection in a specific frequency band and with ultrahigh sensitivity requirements.

Identifying Acoustic Features to Distinguish Highly and Moderately Altered Soundscapes in Colombia

Numerous acoustic features have been proposed as useful measures to characterize natural soundscapes, which can be employed to examine the impact of land transformation on the audible properties of a location. The extensive collection of available features demands an examination to identify the most informative and discriminative ones for a given problem. In this study, we conduct an empirical investigation into the selection of acoustic features for discriminating between highly and moderately transformed versions of four Colombian soundscapes: Moorlands, coffee plantations, dry tropical forests, and pastures. We employ classical supervised feature selection techniques along with exploratory tools such as correlation matrices and scatter plots. Our results indicate that a few acoustic features are sufficient to differentiate between the classes. Specifically, those features that estimate acoustic complexity via intrinsic variability of sound intensities or biodiversity through species richness or abundance in specific frequency bands are the most discriminative ones. These findings suggest that the selection of acoustic features can assist in analyzing and distinguishing between different soundscapes.

Effects of Early Nutrition Factors on Baseline Neurodevelopment during the First 6 Months of Life: An EEG Study.

Throughout infancy, the brain undergoes rapid changes in structure and function that are sensitive to environmental influences, such as diet. Breastfed (BF) infants score higher on cognitive tests throughout infancy and into adolescence than formula fed (FF) infants, and these differences in neurocognitive development are reflected in higher concentrations of white and grey matter as measured by MRI. To further explore the effect diet has on cognitive development, electroencephalography (EEG) is used as a direct measure of neuronal activity and to assess specific frequency bands associated with cognitive processes. Task-free baseline EEGs were collected from infants fed with human milk (BF), dairy-based formula (MF), or soy-based formula (SF) at 2, 3, 4, 5, and 6 months of age to explore differences in frequency bands in both sensor and source space. Significant global differences in sensor space were seen in beta and gamma bands between BF and SF groups at ages 2 and 6 months, and these differences were further observed through volumetric modeling in source space. We conclude that BF infants exhibit earlier brain maturation reflected in greater power spectral density in these frequency bands.

Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder.

Background. Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method. The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction.

Energy transfer mechanisms in binary tree-structured oscillator with nonlinear energy sinks

We study a binary tree-structured multi-degree-of-freedom nonlinear oscillator with impulsive and continuous excitations. The response of this model is studied for excitations that are applied to the largest masses. It is shown how choosing the mass of the smallest blocks influences the response of the system regarding the dissipation and how efficient targeted energy transfer is realized in the system. The simplified frequency energy plot is introduced as a means of analyzing the response of multi-degree-of-freedom systems for impulsive excitations. For continuous excitations, it is shown that the smallest masses (nonlinear energy sinks) are active only inside specific nonlinear frequency bands when the excitation amplitude is sufficiently high.

Study of the Radiation Pattern and Frequency Response of a Short Linear Antenna Surrounded by Discharge Tubes for the Development of Rapidly Adjustable Wideband Antenna Systems

Plasma antenna systems consisting of discharge elements have great practical application prospects due to their ability to quickly adjust parameters over a wide range. In this work, the radiation patterns of a 30 mm long linear antenna surrounded by 16 standard T8 fluorescent lamps were measured by using a spectrum analyzer with a tracking generator. The radiation pattern of the system is measured by connecting the output of the spectrum analyzer tracking generator to a linear antenna and connecting the input of the spectrum analyzer to the receiving broadband horn antenna. The work features experimental measurements over a wide frequency range of 0.5 to 1.5 GHz and a wide discharge with current values from 35 to 200 mA. The specific frequency band and uniquely selected geometry allowed for the investigation of both the in-phase and out-of-phase antenna modes, while some modes were found to have a narrow directional radiation pattern, within 50 degrees, with a beam width of −3 dB. The parameters of the gas-discharge plasma were also evaluated using drift theory, as well as using the microwave transmission of the 20 GHz signal. The electron density value corresponding to the used discharge currents was approximately from 4 × 1011 to 7 × 1010 cm−3.

In situ construction of complex spinel ferrimagnet in multi-elemental alloy for modulating natural resonance and highly efficient electromagnetic absorption

The contradiction between the demand for highly efficient low-frequency electromagnetic wave (EMW) absorption materials and the relatively poor permeability of existing absorbers has become increasingly prominent. However, the intrinsic natural resonance in magnetic material hinders the modulation and enhancement of permeability. Herein, (FeCoNiCu)xCr3−xO4 complex spinel ferrimagnets (CSFMs) are constructed in situ in multi-elemental alloy powders (MEAs) for the first time, via annealing treatment. The microstructure and composition of the CSFMs are regulated by adjusting the annealing temperature, aiming to modulate the natural resonance frequency and achieve highly efficient megahertz (MHz) electromagnetic absorption. Simultaneously, annealing treatment improves the crystal integrity, which is conducive to enhancing intergranular magnetic coupling to elevate permeability temperature stability and the Curie temperature. The minimum reflection loss value of the MEAs/CSFMs composites is less than −7 dB at 3 mm and the effective EMW absorption bandwidth maintains between 425 and 470 MHz at 5 mm from −50 to 150 °C. In addition, the MEAs/CSFMs composites also exhibit excellent oxidation resistance and corrosion resistance due to the oxide layer on the surface. This work offers a new and tunable strategy toward modulating the natural resonance frequency for efficient temperature-stable broadband EMW absorption at specific frequency bands.

Open planar acoustic notch filter using a film-integrated Helmholtz resonator array

Acoustic filters are used to transmit or attenuate noise in a specific frequency band. However, previous acoustic filters consisting of rigid structures generally required a large housing of a size equivalent to the wavelength, limiting their downsizing. This study presents an open planar acoustic notch filter using a film-integrated Helmholtz resonator (HR) array. A film divides the HR chamber and resonates with the HR. The film-integrated HR array filter achieved smaller housing dimensions than a no-film HR array filter with the same notch frequency. The transmittance of the film-integrated HR array was theoretically calculated using the transfer matrix method. The calculated spectra were very similar to the simulation results. The notch frequency of the film-integrated HR array filter was 4.04, 0.74 kHz lower than a no-film HR array filter. The design and analysis of the acoustic filter presented should motivate further development of advanced acoustic filters.

Broad phonetic feature classifier for real-time hearing aid processing

Current Hearing Aid (HA) devices compensate for hearing loss by amplifying sounds based on instantaneous energy estimates in specific frequency bands. The dynamics of speech are incorporated using attack and release times—a crude approximation of the rich temporal dynamics in natural speech. Recent advances in silicon enables one to consider more complex sound processing approaches with minimal impact on the HA battery life. We envisage a broad phonetic feature classifier, followed by amplification strategies optimized for specific temporal dynamics across each segment of natural speech. In this work we present a broad phonetic classifier for ‘vocalic’, ‘non-vocalic’ and ‘mixed’ speech segments. The classifier is based on (i) Interpretable models- unsupervised learning methods such as Gaussian Mixture Models followed by supervised learning based Radial Basis Function networks; and (ii) Deep Neural Networks with supervised learning. The frontend feature extractor consists of MFCC, delta MFCC features, spectral flatness measure and segmental energy. The training and test sets are generated using speech files and the corresponding phone labels from the TIMIT dataset. We present our findings with respect to the classifier's performance and deployability for real time HA processing.

Topology optimization of single and double panels for improved sound insulation in specific frequency bands

Single and double panels are commonly employed in noise control, e.g., as partitioning elements between rooms and to enclose noisy machines. In presence of narrowband disturbances, related for example to rotating machines, further improvements in sound insulation can be obtained by properly distributing the material within the area of the panels. This allows maximizing the sound Transmission Loss (TL) in the frequency band of interest by controlling structural resonances and antiresonances. In order to achieve this goal, in this work, we apply numerical topology optimization to find the optimal material thickness distribution for single and double panels. In the iterative design process, single panels are schematized through a simple mechanical Finite Element (FE) model, while double panels are modelled considering the vibroacoustic coupling between the two mechanical plates and the internal acoustic air cavity. The sound insulation properties of the designed solution are evaluated by TL computations through hybrid Finite Element-Statistical Energy Analysis (FE-SEA) simulations. The method is applied targeting different frequency bands in the audible range and focusing on practically relevant design cases, such as single and double PMMA and glazing panels.

Programmable time-serial resonances for broadband spectrum matching

Spatially parallel and cascading acoustic liners are commonly used to treat duct noise. Their performance depends on the spectral match with source characteristics. Such liners rely on subtle geometrical and mechanical designs which are not difficult to adapt to different noise sources in a passive device. Here, we propose a temporal analogy, which is a shunted electromechanical diaphragm (SEMD) having different resonant frequencies in different time segments, namely, a time-serial resonator. Its sound absorption spectrum can be easily adapted by a program and yet the device functions passively. The acoustic impedance of the SEMD is determined by the shunt circuit, hence its resonant frequency and absorption peak. A multiple set of branch circuits are used to shunt the diaphragm. A MOSFET, which is a voltage-controlled ultrafast electronic switch, is introduced to cascade and switch each circuit branch. By activating circuit branches in a pre-defined time sequence, we obtain a series of resonance and the absorption which are effective for specific frequency bands at different time segments. When viewed over a long period, the averaged effective sound absorption spectrum is broadened and can be easily shaped by the working duty cycle of each circuit branch. Note that the switching voltage changes the circuit states without supplying power to the SEMD. Both numerical results and experimental demonstrations are presented. This study would open the new era of temporal design of acoustic liners.

New Metamaterial Foundation Based on Novel Omnidirectional High‐Performance Inerter Mechanism

Seismic metamaterials are newly emerging structural–functional materials for controlling seismic waves, and they provide a new solution for building earthquake resistance. However, existing seismic metamaterials need to be further improved to achieve low‐frequency and broadband performance. Herein, a new metamaterial foundation containing a novel omnidirectional high‐performance inerter mechanism is proposed for low‐frequency protection against seismic waves. The research results show that this metamaterial foundation can effectively generate a lower frequency bandgap in any direction of the horizontal plane. Subsequently, the inerter mechanism is improved, which enables the metamaterial foundation to provide vibration protection for different specific frequency bands. The bandwidth of the corresponding frequency band can be adjusted flexibly by appropriately controlling the design parameters. Subsequently, to enhance the broadband attenuation performance, the inerter mechanism is further improved, which enables the metamaterial foundation to produce an ultrabroadband attenuation effect. Finally, the effect of the building mass on the metamaterial antiseismic system is investigated, and it is shown that the attenuation width of the metamaterial antiseismic system further increases as the building mass increases. The designed inerter mechanism has potential for applications in other scenarios such as beams, plates, and interfaces.

HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic

Abstract. We propose a new deep-learning architecture HIDRA2 for sea level and storm tide modeling, which is extremely fast to train and apply and outperforms both our previous network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. The architecture of HIDRA2 employs novel atmospheric, tidal and sea surface height (SSH) feature encoders as well as a novel feature fusion and SSH regression block. HIDRA2 was trained on surface wind and pressure fields from a single member of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric ensemble and on Koper tide gauge observations. An extensive ablation study was performed to estimate the individual importance of input encoders and data streams. Compared to HIDRA1, the overall mean absolute forecast error is reduced by 13 %, while in storm events it is lower by an even larger margin of 25 %. Consistent superior performance over HIDRA1 as well as over general circulation models is observed in both tails of the sea level distribution: low tail forecasting is relevant for marine traffic scheduling to ports of the northern Adriatic, while high tail accuracy helps coastal flood response. Power spectrum analysis indicates that HIDRA2 most accurately represents the energy density peak centered on the ground state sea surface eigenmode (seiche) and comes a close second to SCHISM in the energy band of the first excited eigenmode. To assign model errors to specific frequency bands covering diurnal and semi-diurnal tides and the two lowest basin seiches, spectral decomposition of sea levels during several historic storms is performed. HIDRA2 accurately predicts amplitudes and temporal phases of the Adriatic basin seiches, which is an important forecasting benefit due to the high sensitivity of the Adriatic storm tide level to the temporal lag between peak tide and peak seiche.