Search Results (222)

This study investigates the dynamics of dust acoustic periodic waves in a three-component, unmagnetized dusty plasma system using generalized $(r^{\star },q^{\star })$ distributions. First, boundary conditions are applied to reduce the model to a second-order nonlinear ordinary differential equation. The Galilean transformation is subsequently applied to reformulate the second-order ordinary differential equation into an unperturbed dynamical system. Next, phase portraits of the system are examined under all possible conditions of the discriminant of the associated cubic polynomial, identifying regions of stability and instability. The Runge–Kutta method is employed to construct the phase portraits of the system. The Hamiltonian function of the unperturbed system is subsequently derived and used to analyze energy levels and verify the phase portraits. Under the influence of an external periodic perturbation, the quasi-periodic and chaotic dynamics of dust ion acoustic waves are explored. Chaos detection tools confirm the presence of quasi-periodic and chaotic patterns using Basin of attraction, Lyapunov exponents, Fractal Dimension, Bifurcation diagram, Poincaré map, Time analysis, Multi-stability analysis, Chaotic attractor, Return map, Power spectrum, and 3D and 2D phase portraits. In addition, the model’s response to different initial conditions was examined through sensitivity analysis. Full article

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) system has shown substantial potential in distributed acoustic sensing applications. Accurate event classification is crucial for effective deployment of Φ-OTDR systems, and various methods have been proposed for event classification in Φ-OTDR systems. However, most existing methods typically rely on sufficient labeled signal data for model training, which poses a major bottleneck in applying these methods due to the expensive and laborious process of labeling extensive data. To address this limitation, we propose CLWTNet, a novel contrastive representation learning method enhanced with wavelet transform convolution for event classification in Φ-OTDR systems. CLWTNet learns robust and discriminative representations directly from unlabeled signal data by transforming time-domain signals into STFT images and employing contrastive learning to maximize inter-class separation while preserving intra-class similarity. Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. The experimental results demonstrate that CLWTNet achieves competitive performance with the supervised representation learning methods and superior performance to unsupervised representation learning methods, even when training with unlabeled signal data. These findings highlight the effectiveness of CLWTNet in extracting discriminative representations without relying on labeled data, thereby enhancing data efficiency and reducing the costs and effort involved in extensive data labeling in practical Φ-OTDR system applications. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

Blind source separation (BSS) of underwater acoustic mixed signals aims to improve signal clarity by separating noise components from aliased underwater signal sources. This enhancement directly increases target detection accuracy in underwater acoustic perception systems, particularly in scenarios involving multi-vessel interference or biological sound coexistence. Deep learning-based BSS methods have gained wide attention for their superior nonlinear modeling capabilities. However, existing approaches in underwater acoustic scenarios still face two key challenges: limited feature discrimination and inadequate robustness against non-stationary noise. To overcome these limitations, we propose a novel Robust Cross-Band Network (RCBNet) for the BSS of underwater acoustic mixed signals. To address insufficient feature discrimination, we decompose mixed signals into sub-bands aligned with ship noise harmonics. For intra-band modeling, we apply a parallel gating mechanism that strengthens long-range dependency learning so as to enhance robustness against non-stationary noise. For inter-band modeling, we design a bidirectional-frequency RNN to capture the global dependency relationships of the same signal across sub-bands. Our experiment demonstrates that RCBNet achieves a 0.779 dB improvement in the SDR compared to the advanced model. Additionally, the anti-noise experiment demonstrates that RCBNet exhibits satisfactory robustness across varying noise environments. Full article

This study investigates pre-seismic ionospheric anomalies preceding the 2023 Jishishan Ms6.2 earthquake using total electron content (TEC) data derived from BDS geostationary orbit (GEO) satellites. Multi-scale analysis integrating Butterworth filtering and wavelet transforms resolved TEC disturbances into three distinct frequency regimes: (1) high-frequency perturbations (0.56–3.33 mHz) showed localized disturbances (amplitude ≤ 4 TECU, range < 300 km), potentially associated with near-field acoustic waves from crustal stress adjustments; (2) mid-frequency signals (0.28–0.56 mHz) exhibited anisotropic propagation (>1200 km) with azimuth-dependent N-shaped waveforms, consistent with the characteristics of acoustic–gravity waves (AGWs); and (3) low-frequency components (0.18–0.28 mHz) demonstrated phase reversal and power-law amplitude attenuation, suggesting possible lithosphere–atmosphere–ionosphere (LAI) coupling oscillations. The stark contrast between near-field residuals and far-field weak fluctuations highlighted the dominance of large-scale atmospheric gravity waves over localized acoustic disturbances. Geometry-based velocity inversion revealed incoherent high-frequency dynamics (5–30 min) versus anisotropic mid/low-frequency traveling ionospheric disturbance (TID) propagation (30–90 min) at 175–270 m/s, aligning with theoretical AGW behavior. During concurrent G1-class geomagnetic storm activity, spatial attenuation gradients and velocity anisotropy appear primarily consistent with seismogenic sources, providing insights for precursor discrimination and contributing to understanding multi-scale coupling in seismo-ionospheric systems. Full article

The accurate automatic classification of underwater acoustic signals from passive SoNaR is vital for naval operational readiness, enabling timely vessel identification and real-time maritime surveillance. This study evaluated seven supervised machine learning algorithms for ship identification using passive SoNaR recordings collected by the Brazilian Navy. The dataset encompassed 12 distinct ship classes and was processed in two ways—full-resolution and downsampled inputs—to assess the impacts of preprocessing on the model accuracy and computational efficiency. The classifiers included standard Support Vector Machines, K-Nearest Neighbors, Random Forests, Neural Networks and two less conventional approaches in this context: Linear Discriminant Analysis (LDA) and the XGBoost ensemble method. Experimental results indicate that data decimation significantly affects classification accuracy. LDA and XGBoost delivered the strongest performance overall, with XGBoost offering particularly robust accuracy and computational efficiency suitable for real-time naval applications. These findings highlight the promise of advanced machine learning techniques for complex multiclass ship classification tasks, enhancing acoustic signal intelligence for military maritime surveillance and contributing to improved naval situational awareness. Full article

Lingual stops are among the earliest sounds acquired by young children, but the process of acquiring the temporal coordination of lingual gestures necessary for the production of stop consonants appears to be protracted. The current research aims to investigate the developmental process of lingual stop consonants in 100 Mandarin-speaking 2- to 5-year-olds using the acoustic parameter voice onset time (VOT). Children were engaged in a word-repetition task and recorded while producing words that begin with /t/, /d/, /k/, and /g/. Results indicate well-established contrasts between /t/ and /d/ as well as between /k/ and /g/ by age 2. However, comparing with adults’ speech patterns, children’s speech productions are characterized by greater within-category dispersion and overlap, as well as smaller phoneme discriminability. Mandarin-speaking children also go through an “overshoot” stage by producing longer-than-adult VOT values, especially for voiceless aspirated stops /t/ and /k/. Lastly, unlike adults who exhibit gender-specific patterns in VOT, boys and girls do not show distinct patterns in their VOT by age 5. These results will be discussed in relation to children’s lingual motor control development and the organization of phonological and phonetic structures during the process of language acquisition. Full article

Underwater acoustic signals typically exhibit non-Gaussian, non-stationary, and nonlinear characteristics. When processing real-world underwater acoustic signals, traditional multivariate entropy algorithms often struggle to simultaneously ensure stability and extract cross-channel information. To address these issues, the improved multivariate multiscale sample entropy (IMMSE) algorithm is proposed, which extracts the complexity of multi-channel data, enabling a more comprehensive and stable representation of the dynamic characteristics of complex nonlinear systems. This paper explores the optimal parameter selection range for the IMMSE algorithm and compares its sensitivity to noise and computational efficiency with traditional multivariate entropy algorithms. The results demonstrate that IMMSE outperforms its counterparts in terms of both stability and computational efficiency. Analysis of various types of ship-radiated noise further demonstrates IMMSE’s superior stability in handling complex underwater acoustic signals. Moreover, IMMSE’s ability to extract features enables more accurate discrimination between different signal types. Finally, the paper presents data processing results in mechanical fault diagnosis, underscoring the broad applicability of IMMSE. Full article

Heterotrigona itama, a widely reared stingless bee species, produces highly valued honey. These bees naturally secure their colonies within logs, accessed via a single entrance tube, but remain vulnerable to intruders and predators. Guard bees play a critical role in colony defense, exhibiting the ability to discriminate between nestmates and non-nestmates and employing strategies such as pheromone release, buzzing, hissing, and vibrations to alert and recruit hive mates during intrusions. This study investigated the acoustic signals produced by H. itama guard bees during intrusions to determine their potential for intrusion detection. Using a Jetson Nano equipped with a microphone and camera, guard bee sounds were recorded and labeled. After preprocessing the sound data, Mel Frequency Cepstral Coefficients (MFCCs) were extracted as features, and various dimensionality reduction techniques were explored. Among them, Linear Discriminant Analysis (LDA) demonstrated the best performance in improving class separability. The reduced feature set was used to train both Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. KNN outperformed SVM, achieving a Precision of 0.9527, a Recall of 0.9586, and an F1 Score of 0.9556. Additionally, KNN attained an Overall Cross-Validation Accuracy of 95.54% (±0.67%), demonstrating its superior classification performance. These findings confirm that H. itama produces distinct alarm sounds during intrusions, which can be effectively classified using machine learning; thus, demonstrating the feasibility of sound-based intrusion detection as a cost-effective alternative to image-based approaches. Future research should explore real-world implementation under varying environmental conditions and extend the study to other stingless bee species. Full article

This study presents the development and characterization of a novel electronic nose system based on customized surface acoustic wave (SAW) sensors. The system includes four sensors, customized with different custom polymer coatings, in order to detect volatile organic compounds (VOCs). The main innovation lies in the design of a robust and versatile switching electronics system that allows for the integration of the SAW sensors into portable systems, as well as interoperability with other gas sensor technologies. The system includes a modular architecture that allows multiple sensor arrays to be combined to improve the selectivity and discrimination of complex gas mixtures. To verify the proper performance of the system and the detection capability of the manufactured sensors, experimental laboratory tests have been carried out. Specifically, ethanol and acetone measurements up to a 2000 ppm concentration have been performed. These preliminary experimental results demonstrate the capability of the SAW sensors with different response patterns across the sensor array. In particular, the sensor made with the polyvinyl acetate polymer exhibits high sensitivity to both VOCs. Full article

Broadband backscattering measurements of Pacific mackerel (Scomber japonicus) can improve acoustic surveys of the species for the management of its fisheries throughout the Pacific Ocean. The determination of its target strength (TS), the logarithmic form of the backscattering cross-section, is the aim of this work. It was measured for fourteen individual specimens, eight in a freshwater tank and six in a seawater tank, using calibrated broadband echosounders spanning the frequency band 24–84 kHz. The TS is expressed as a function of frequency and tilt angle, with fish length as a parameter. The individual broadband TS patterns with the tilt angle of fish showed size and frequency dependencies. The fish length-normalized TS of mackerel decreased with increasing fish length-to-acoustic wavelength ratio (l/λ) in the small l/λ range (approximately 2–6) but was flat in the larger l/λ range (>6). This variation in the normalized TS indicates that a pair of regression equations is necessary to span the range of commercially important mackerel relative to the acoustic wavelength. The relative l/λ characteristic of the normalized TS showed constant values with tilt-angle distributions over a large l/λ range and can be used as a characteristic of acoustic backscattering for discrimination among species. Full article

Biscuits are characterized by their popular sweet taste, but they have a poor nutritional profile due to their high sugar and saturated fat content, along with low fibre levels. Their sweetness primarily comes from sucrose, which not only determines the flavour but also performs several technological functions, making it difficult to replace in pastry products. Commercial sweeteners and soluble fibres designed for pastry products are available. Therefore, it is necessary to test the feasibility of using these ingredients in biscuit formulations and assess their impact on biscuit quality. Concurrently, the correlation analysis of dough rheological parameters, structure, and instrumental texture parameters with sensory characteristics will help identify which parameters are strongly correlated and can be used to predict biscuit quality. The purpose of this study was to investigate the dough rheological properties, structure, texture, and sensory characteristics of biscuits in which sucrose was replaced by the commercial sweeteners Tagatesse, maltitol, and erythritol–stevia, with the addition of soluble fibres Nutriose^® FB (wheat fibre) and PromOat 35 (oat fibre). At the same time, a correlation analysis was conducted between dough rheological parameters (stickiness, work of adhesion, dough strength) and biscuit quality parameters, such as water activity, water content, colour, texture (pore area, pore shape, pore elongation), and instrumental texture properties (hardness, brittleness, number of acoustic emission (AE) events, AE event energy), with sensory discrimination evaluated through a consumer test. The use of wheat and oat fibres in combination with sucrose resulted in biscuits with lower apparent density, increased porosity, and weaker texture (fracturability, hardness, number of AE events), yet they had better sensory properties compared to biscuits containing sucrose alone. Replacing sucrose with sweeteners combined with fibres led to a deterioration in the sensory quality of the biscuits and a significant change in the dough’s rheological properties. Regardless of the type of sweetener, biscuits with wheat fibre were rated better than those with oat fibre. Of the tested sweeteners, only maltitol combined with wheat fibre resulted in a sensory quality similar to that of sucrose biscuits. Correlation analysis of all measured biscuit quality parameters showed that only the number of AE events had a strong positive correlation with all tested sensory attributes. Porosity was only correlated with sensory crispness, and fracturability was correlated with sweetness, taste, and overall acceptability. Therefore, it appears that the number of AE events recorded at the time of breaking may be a reliable parameter for predicting biscuit quality. Full article

This study explores the acoustic properties of European Churches, influenced by architectural design, historical context, and spatial configurations. A comparative analysis of 83 Churches from different regions and periods combines literature reviews and empirical data to understand the interplay between architecture and acoustics. Key geometric parameters—volume, surface area, length, height, and aisle count—were compared with acoustic metrics to provide a comprehensive view of these sacred spaces. The study identified the key factors influencing acoustic characteristics, uncovering significant variability within the same architectural style. Linear Discriminant Analysis (LDA) further highlighted distinct patterns and outliers, showing that Gothic, Neoclassical, and modern architectural styles possess unique acoustic signatures. These findings challenge the assumption of uniform acoustics within similar styles, revealing that even minor architectural differences can substantially impact sound behavior. Outliers were particularly informative, representing Churches with unique acoustic properties, which shed light on how specific design elements affect sound propagation. The study underscores the complexity of the relationship between architecture and acoustics in Churches and suggests that further research should consider both quantitative measures and subjective experiences to fully capture the acoustic environment of these historic spaces. Full article

Mid-vowel contrasts often present perceptual challenges for speakers of languages that lack these distinctions. However, bilingual speakers, who have access to two phonological systems and exhibit greater metalinguistic awareness, might not necessarily encounter such difficulties. In this study, 27 Ukrainian–Russian bilinguals listened to an unfamiliar language, European Portuguese, and completed two tasks: an identification task where they assimilated the seven stressed oral Portuguese vowels to the closest Ukrainian categories and a discrimination task featuring the Portuguese vowel contrasts /ɛ/–/e/, /e/–/i/, /ɔ/–/o/, and /o/–/u/. No bilingual advantage was observed: the discrimination performance on all contrasts was slightly above or near a chance level (A-prime scores varied between 0.55 and 0.20). These perceptual difficulties may be attributed to the acoustic similarities between the vowels within the contrasts rather than to the differences between the phonological inventories of the languages (the most challenging contrast was not a mid-vowel contrast but acoustically similar /o/–/u/). Although with the back mid-vowel contrast, the difficulty seems to also stem from the possibility that both Ukrainian and Russian have only one back mid-vowel, /o/, and this category occupies a wider area in the vowel space of Ukrainian–Russian bilinguals. The results suggest that bilingual advantage does not always manifest itself in the perception of a new language, especially if two typologically close languages are involved. Full article

In this paper, we proposed an embedded side information (SI) transmission scheme based on polar code construction for PAPR reduction using the PTS scheme in the underwater Acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication system. We use polar codes due to the ability of the arbitrarily designed code rate. Additionally, polar codes can be employed to establish a nested code structure consisting of multiple subsets. The SI bits can be embedded in a polar codeword by exploiting these features. Thus, the approach does not occupy existing data rates or cause additional loss in data transmission rates. At the same time, it embeds m-sequence into the polar code as an indicator vector for the blind SI detector, which makes the blind SI detector able to autonomously discriminate SI at the receiver. Simulation and tank experiment results indicate that the proposed embedded SI transmission scheme has the potential to significantly decrease the likelihood of whole-symbol error caused by SI errors. Meanwhile, the proposed PTS scheme eliminates the need to wait for the entire packet to be received before obtaining the SI, thereby preventing waste of data storage devices and ensuring real-time performance of the Underwater Acoustic Communication (UAC) OFDM system. This achieves symbol-level real-time calculation for the system. Full article