Search Results (377)

Offshore wind farms (OWFs) represent an increasingly important renewable energy source, yet their environmental impacts, particularly underwater noise, require systematic study. Estimating the operational source level (SL) of a single turbine and predicting sound pressure levels (SPLs) at sensitive locations can be challenging. Here, we integrate a turbine SL prediction algorithm with open-source propagation models in a Jupyter Notebook (version 7.4.7) to streamline aggregated SPL estimation for OWFs. Species-specific audiograms and weighting functions are included to assess potential biological impacts. The tool is applied to four planned OWFs, two in the Canary region and two in the Belgian and German North Seas, under conservative assumptions. Results indicate that at 10 m/s wind speed, a single turbine’s SL reaches 143 dB re 1 µPa in the one-third octave band centered at 160 Hz. Sensitivity analyses indicate that variations in wind speed can cause the operational source level at 160 Hz to increase by up to approximately 2 dB re 1 µPa²/Hz from the nominal value used in this study, while differences in sediment type can lead to transmission loss variations ranging from 0 to on the order of 100 dB, depending on bathymetry and range. Maximum SPLs of 112 dB re 1 µPa are predicted within OWFs, decreasing to ~50 dB re 1 µPa at ~100 km. Within OWFs, Low-Frequency (LF) cetaceans and Phocid Carnivores in Water (PCW) would likely perceive the noise; National Marine Fisheries Service (NMFS) marine mammals’ auditory-injury thresholds are not exceeded, but behavioral-harassment thresholds may be crossed. Outside the farms, only LF audiograms are crossed. In high-traffic North Sea regions, OWF noise is largely masked, whereas in lower-noise areas, such as the Canary Islands, it can exceed ambient levels, highlighting the importance of site-specific assessments, accurate ambient noise monitoring and propagation modeling for ecological impact evaluation. Full article

The rectangular truss aquaculture cage platform is considered the main solution for modern deep-sea aquaculture equipment in the future due to its excellent wind and wave resistance, as well as its mechanization and automation capabilities. The underwater noise generated during the application of large-scale aquaculture platforms is an important basis for evaluating their impact on the underwater acoustic environment and developing intelligent aquaculture in the future. This article conducts experimental research on the rectangular truss aquaculture net cage platform “HENGYI 1” and conducts noise measurement and analysis based on the characteristics of the aquaculture platform’s operating sea area, operating process, and equipment configuration. Research has shown that the overall underwater noise of the aquaculture net cage platform is mainly distributed in the mid to low frequency range below 1000 Hz. Compared to the two sides of the platform, the underwater noise in the platform net cage is less affected by tides, and the intensity of underwater noise on the left and right sides of the net cage alternates with tides. Diesel generators are the main source of noise in truss-style aquaculture cages. When the generator is in operation, the peak power spectral density level of the noise is around 25 Hz. The results of the article can provide a reference for the study of noise in offshore aquaculture platforms. Full article

Propeller noise is the main source of ship-radiated noise. Extracting and analyzing the modulation characteristics from the propeller noise plays a crucial role in classifying and identifying vessel targets. Existing demodulation methods such as Detection of Envelope Modulation On Noise (DEMON), narrowband demodulation, and cyclostationary analysis can be used to extract modulation features. However, capturing the modulation features on the envelope spectrum may be hard under low signal-to-noise ratio scenarios, since the envelope spectrum is contaminated by interference noise. To address this challenge, selecting an optimal frequency band rich in modulation information can significantly enhance demodulation performance. This paper proposes an Adaptive Hoyer-L-moment Envelope Spectrum (AHLES) method. The method first introduces an optimal frequency band selection method based on the golden section search strategy. A Hoyer-L-moment metric is then designed to quantify the modulation intensity within narrow frequency bands. Based on this metric, the optimal spectral coherence integration band is adaptively selected according to the signal’s inherent modulation characteristics, thereby enhancing demodulation performance. The effectiveness of the proposed method is validated through experiments on both simulated signals and merchant ship data. Full article

Urban noise is a major environmental concern that affects public health and quality of life, demanding new approaches beyond conventional noise level monitoring. This study investigates the development of an AI-driven Acoustic Event Detection and Classification (AED/C) system designed for urban sound recognition and its integration into smart city application. Using the UrbanSound8K dataset, five acoustic parameters—Mel Frequency Cepstral Coefficients (MFCC), Mel Spectrogram (MS), Spectral Contrast (SC), Tonal Centroid (TC), and Chromagram (Ch)—were mathematically modeled and applied to feature extraction. Their combinations were tested with three classical machine learning algorithms: Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB) and a deep learning approach, i.e., Convolutional Neural Networks (CNN). A total of 52 models with the three ML algorithms were analyzed along with 4 models with CNN. The MFCC-based CNN models showed the highest accuracy, achieving up to 92.68% on test data. This achieved accuracy represents approximately +2% improvement compared to prior CNN-based approaches reported in similar studies. Additionally, the number of trained models, 56 in total, exceeds those presented in comparable research, ensuring more robust performance validation and statistical reliability. Real-time validation confirmed the applicability for IoT devices, and a low-cost wireless sensor unit (WSU) was developed with fog and cloud computing for scalable data processing. The constructed WSU demonstrates a cost reduction of at least four times compared to previously developed units, while maintaining good performance, enabling broader deployment potential in smart city applications. The findings demonstrate the potential of AI-based AED/C systems for continuous, source-specific noise classification, supporting sustainable urban planning and improved environmental management in smart cities. Full article

The dissipative Kerr soliton (DKS) microcomb has been regarded as a highly promising multi-wavelength laser source for optical fiber communication, due to its excellent frequency and phase stability. However, in some specific application scenarios, such as direct modulation and direct detection (DM/DD), the relative intensity noise (RIN) performance of Kerr optical combs still fails to meet the requirements. Here, we systematically investigate the key factors that contribute to the power fluctuations in DKS combs. By exploiting the gain saturation effect of the semiconductor optical amplifier (SOA), the RIN of an on-chip DKS microcomb is effectively suppressed, achieving a maximum reduction of about 30 dB (@600 kHz offset frequency) for all comb lines. Moreover, such DKS comb RIN suppression technology based on an SOA chip can eliminate the need for additional complex feedback control circuits, showcasing the potential for further chip integration of the ultra-low-RIN DKS microcomb system. Full article

For modern axial fans optimized for low self-noise, additional noise emission from guard grilles mounted downstream of the fan can become one of the dominant sources of sound. In the present case, the overall sound power level increases by up to 6 dB. Based on narrow-band acoustic measurements and numerical Lattice-Boltzmann simulations of wind tunnel setups using round wires, it is observed that periodic flow separations behind the wires (von Kármán vortex street) lead to a pronounced hump in the noise spectrum. This occurs in a frequency range that corresponds to the grille-induced noise increase observed with an axial fan under comparable flow conditions. By examining various wire geometries, it was found that disrupting the von Kármán vortex street along the longitudinal direction of the wire and reducing the homogeneity of flow separation can significantly decrease sound generation. As a result, a guard grille prototype incorporating the most promising structures was manufactured for a modern low-noise axial fan. Comparative experimental results for the fan are presented. Full article

Acoustic sensing is a passive and cost-effective option for unmanned aerial vehicle detection, where both signal processing and microphone hardware jointly determine field performance. In this study, we focus on the hardware front-end as a foundation for improving the reliability of subsequent DSP- or AI-based detection methods. We present a detection-focused comparison of several microphones in outdoor tests, combining calibrated range measurements with spectral analysis of real unmanned aerial vehicle emissions from three platforms. We report hardware metrics only: signal-to-noise ratio, effective detection range, attenuation slope with distance, and the low-frequency background floor. Across wind conditions and source orientations, the RØDE NTG-2 with WS6 windshield delivered the most balanced performance: in strong wind, it extended the detection range over the bare NTG-2 by approximately 31–131% (depending on azimuth), lowered the low-frequency noise floor by about 2–3 decibels, and matched or increased the wideband signal-to-noise ratio by 1.8–4.4 decibels. A parabolic NTG-2 achieved very low background noise levels at low frequencies and strong on-axis reach but proved vulnerable to gust-induced transients. Based on this evidence, we propose an eight-channel, dual-tier array of NTG-2 + WS6 elements that preserves near-hemispherical coverage and phase coherence, establishing a practical hardware baseline for outdoor acoustic unmanned aerial vehicle detection and a reproducible platform for subsequent localization and classification studies. Full article

The waveform stacking location method achieves microseismic source localization by computing characteristic functions (CFs) and stacking multi-channel data, without phase picking. It has been widely applied in geotechnical engineering. However, the low signal-to-noise ratio (SNR) caused by weak event energy and ambient noise often degrades localization accuracy. To enhance the localization precision and stability under low SNR conditions, this study employs the Stockwell transform (S-transform) to convert noisy time-domain data into the time–frequency domain. By analyzing the energy distribution of microseismic signal and noise in the time–frequency domain, frequency and time coefficients are introduced to enhance the energy of microseismic signal. Event location is achieved through the computation of CFs and multiple-cross-correlation stacking. Comparison of the location results when computing the CFs by the new method, the short-term average to long-term average ratio (STA/LTA) method, and the envelope (Env) method under varying noise levels demonstrates the superior noise resistance and improved localization accuracy of the new method. Finally, the effectiveness of the new method is validated using real seismic data collected from a coal mine. Full article

Hydropower is the primary source of electricity in several countries in Latin America. Hydropower provides approximately 80% of Ecuador’s electricity; however, it remains highly vulnerable to climate change, resulting in uncertainties in power generation due to altered precipitation patterns, runoff, and systematic failures. Consequently, Ecuadorians are becoming increasingly reliant on diesel generators during crises, resulting in public health, safety, and economic impacts, as well as social and political disruptions. This study evaluated noise pollution in the central urban area of the city of Loja for the first time during the 2024–2025 electricity crisis in Ecuador. A Type 1 integrating sound-level meter was used to monitor noise pollution (LAeq, 10min) at 20 locations during periods of generator operation and non-operation. At each location, the number of generators, the density of commercial activities along the streets, as well as traffic and other urban characteristics, were recorded. Results revealed that the presence of generators, street width, and the number of generators significantly increased the LAeq, 10min, often exceeding the limits set by the World Health Organization and Ecuador’s environmental regulations. Frequency spectrum analysis revealed that medium frequencies increased with A-weighting, while low frequencies rose with C-weighting, suggesting potential health risks to the local population. The thematic noise map during generator inactivity showed lower noise levels, averaging around 71.5 dBA. Conversely, when the generators were operational, noise levels exceeded 79.6 dBA, indicating a significant increase in environmental noise exposure associated with their use. This highlights an urgent need to implement and expand renewable energy sources, as existing options like wind power, photovoltaic energy, and biomass are insufficient to meet community demands. Full article

In comparison to internal combustion engines, which usually have low frequency, broadband excitations, in electric vehicles, tonal excitations from the electric drivetrain are noticeable and disturbing. As the acoustic and structural dynamic behavior, often referred to as noise, vibration, and harshness (NVH), strongly influences customers’ quality perceptions, optimizing it is a key challenge in development. This study investigates the influence of static rotor–stator eccentricity on the NVH behavior of an electric drivetrain using a transient elastic multibody simulation (eMBS) model incorporating non-linear gear meshing, bearing contact, and electromagnetic forces. The analysis identifies the 36th order excitation of the electric machine as the dominant source, leading to a maximum total acceleration level of 152 dB. Two specific excitation directions were found to reduce this amplitude most effectively. However, varying the amount of static eccentricity in these directions resulted in only minor vibration reductions (<1.5 dB). The findings indicate that the symmetric mode shapes of the cylindrical housing govern the response, indicating that addressing the excitability of housing modes by developing asymmetric housing designs could offer a more effective approach for NVH optimizations of electric drivetrains. Full article

This work presents a wideband variable gain low-noise amplifier (VGA-LNA) specifically engineered for medical systems operating in the C frequency band, which require the substantial amplification of low-intensity signals. The proposed design integrates a low-noise attenuator with a low-noise amplifier (LNA), fabricated using 90 nm CMOS technology and leveraging a combined common-source and common-gate topology. The integrated LNA achieved a notable power gain of 29 dB across a broad bandwidth of 2 GHz (6.4–8.4 GHz), maintaining an average noise figure (NF) below 3.14 dB. The design ensures superior impedance matching, demonstrated by reflection coefficients of S11 < −18.14 dB and S22 < −20.23 dB. Additionally, the amplifier exhibits a third-order input intercept point (IIP3) of 21.15 dBm while consuming only 83 mW from a 1.2 V supply voltage. A low-noise attenuator was incorporated at the input side to enable effective gain control through a digitally controlled variable gain, with step sizes ranging from 0.4 to 3.3 dB. This configuration enables a dynamic range of the transmission coefficient (|S₂₁|) from 16 dB to 23 dB, adjustable by 0.4 dB to 3.3 dB with a trade-off in an NF maintained at 6 dB. The VGA-LNA demonstrates exceptional potential for integration into wireless body area networks (WBANs), balancing flexible gain control with stringent performance metrics. Full article

Acoustic absorbing materials made from waste plants or trees represent a sustainable source for noise reduction products and applications such as home acoustic insulation and/or traffic road noise reduction barriers. The primary aim of this work is hence to demonstrate the potential application of pine needle waste as the main constituent in acoustic absorbing materials while resin is used as binder. Once the samples have been manufactured, their different physical (density and porous structure), mechanical (compressive strength), and sound-insulating (sound absorption coefficient) properties are characterized. The influence of the ratio of pine needle/resin, length of the pine needle fragments, and thickness of the samples on the different properties has been explored. As the ratio of pine needles/resin increases so does the porosity, although the compressive strength decreases. To highlight this, the noise reduction coefficient is in the range of 0.67 and 0.71 (for 4 cm of thickness), which is higher than that reported for other typical sound absorption materials. An excess of resin produces a clogging phenomenon at the bottom of the samples, producing a reflective layer instead of an absorbent one, which could be used positively to increase the acoustic absorption coefficient in materials with combinations of sections with different needle/resin ratios. Owed to its low weight and high sound absorption coefficients at low frequencies (characteristic of road noise), PN finds usefulness in the manufacturing of environmentally friendly sound-absorbing materials as road insulation barriers. Full article

Plant surface electrical signals are key representations for non-destructive monitoring of changes in cell membrane potential, enabling real-time reflection of physiological responses and regulatory processes under external stimuli. However, the low-frequency and weak-amplitude characteristics of these signals make them extremely susceptible to interference from multiple complex noise sources, such as environmental, power-line frequency, and inherent instrument noise. Existing denoising methods suffer from issues such as mode mixing and insufficient fidelity, hindering accurate extraction of genuine plant physiological information. This study proposes a novel denoising approach that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet Soft Thresholding (WST). By decomposing and filtering noise components with adaptive thresholds based on the SURE criterion, the method achieves multi-scale decomposition and effective suppression of residual noise. Applied to surface electrical signals of maize leaves, the results demonstrated a 48% reduction in permutation entropy (PE) for the entire signal. In the resting potential segment, the root mean square (RMS) decreased by 28.91%, total energy dropped by 9.3%, and waveform stability improved. For the action potential segment, the full width at half maximum (FWHM) increased to 0.747, and although the peak amplitude slightly decreased, the waveform structure remained intact. Signal energy became more concentrated within the 0–2 Hz range, achieving efficient noise suppression and high signal fidelity. This method provides a reliable preprocessing technique for elucidating plant physiological mechanisms based on surface electrical signals and holds significant potential for real-time non-destructive monitoring and early warning systems in smart agriculture. Full article

Join size estimation plays a crucial role in query optimization, correlation computing, and dataset discovery. A recent study, LDPJoinSketch, has explored the application of local differential privacy (LDP) to protect the privacy of two data sources when estimating their join size. However, the utility of LDPJoinSketch remains unsatisfactory due to the significant noise introduced by perturbation under LDP. In contrast, the shuffle model of differential privacy (SDP) can offer higher utility than LDP, as it introduces randomness based on both shuffling and perturbation. Nevertheless, existing research on SDP primarily focuses on basic statistical tasks, such as frequency estimation and binary summation. There is a paucity of studies addressing queries that involve join aggregation of two private data sources. In this paper, we investigate the problem of private join size estimation in the context of the shuffle model. First, drawing inspiration from the success of sketches in summarizing data under LDP, we propose a sketch-based join size estimation algorithm, SDPJoinSketch, under SDP, which demonstrates greater utility than LDPJoinSketch. We present theoretical proofs of the privacy amplification and utility of our method. Second, we consider separating high- and low-frequency items to reduce the hash-collision error of the sketch and propose an enhanced method called SDPJoinSketch+. Unlike LDPJoinSketch, we utilize secure encryption techniques to preserve frequency properties rather than perturbing them, further enhancing utility. Extensive experiments on both real-world and synthetic datasets validate the superior utility of our methods. Full article

The paper highlights that hydraulic systems are widely used in various machine applications. Among the evaluation criteria for these systems, the noise-related criterion is also considered. This criterion also applies to micro-hydraulic systems as the permissible level of noise emitted into the environment is linked to the installed power, which in micro-hydraulic systems is at least an order of magnitude lower than in conventional hydraulic systems. Failure to comply with EU ambient noise emission standards may result in the machine not being approved for use. It is therefore important to identify noise sources and minimize them. It has been noted that, in hydraulic systems, the primary source of noise is pressure pulsation across a wide frequency range. Moreover, it has been pointed out that low-frequency noise and vibrations are particularly harmful to humans. Thus, pressure pulsation dampers are proposed that are effective both at specific forcing frequencies and across a wide frequency range. Experimental results of a micro-hydraulic system are presented. Full article