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Search Results (2,827)

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Keywords = Acoustic applications

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23 pages, 1302 KiB  
Article
Deep Learning-Enhanced Ocean Acoustic Tomography: A Latent Feature Fusion Framework for Hydrographic Inversion with Source Characteristic Embedding
by Jiawen Zhou, Zikang Chen, Yongxin Zhu and Xiaoying Zheng
Information 2025, 16(8), 665; https://doi.org/10.3390/info16080665 (registering DOI) - 4 Aug 2025
Abstract
Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid [...] Read more.
Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid inversion of oceanic hydrological parameters in complex underwater environments. Based on the open-source VTUAD (Vessel Type Underwater Acoustic Data) dataset, the method first utilizes a fine-tuned Paraformer (a fast and accurate parallel transformer) model for precise classification of sound source targets. Then, using structural causal models (SCM) and potential outcome frameworks, causal embedding vectors with physical significance are constructed. Finally, a cross-modal Transformer network is employed to fuse acoustic features, sound source priors, and environmental variables, enabling inversion of temperature and salinity in the Georgia Strait of Canada. Experimental results show that the method achieves accuracies of 97.77% and 95.52% for temperature and salinity inversion tasks, respectively, significantly outperforming traditional methods. Additionally, with GPU acceleration, the inference speed is improved by over sixfold, aimed at enabling real-time Ocean Acoustic Tomography (OAT) on edge computing platforms as smart hardware, thereby validating the method’s practicality. By incorporating causal inference and cross-modal data fusion, this study not only enhances inversion accuracy and model interpretability but also provides new insights for real-time applications of OAT. Full article
(This article belongs to the Special Issue Advances in Intelligent Hardware, Systems and Applications)
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19 pages, 6085 KiB  
Article
Earthquake Precursors Based on Rock Acoustic Emission and Deep Learning
by Zihan Jiang, Zhiwen Zhu, Giuseppe Lacidogna, Leandro F. Friedrich and Ignacio Iturrioz
Sci 2025, 7(3), 103; https://doi.org/10.3390/sci7030103 - 1 Aug 2025
Viewed by 132
Abstract
China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods [...] Read more.
China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods to facilitate real-time monitoring and advance earthquake precursor detection. The AE equipment and seismometers were installed in a granite tunnel 150 m deep in the mountains of eastern Guangdong, China, allowing for the collection of experimental data on the correlation between rock AE and seismic activity. The deep learning model uses features from rock AE time series, including AE events, rate, frequency, and amplitude, as inputs, and estimates the likelihood of seismic events as the output. Precursor features are extracted to create the AE and seismic dataset, and three deep learning models are trained using neural networks, with validation and testing. The results show that after 1000 training cycles, the deep learning model achieves an accuracy of 98.7% on the validation set. On the test set, it reaches a recognition accuracy of 97.6%, with a recall rate of 99.6% and an F1 score of 0.975. Additionally, it successfully identified the two biggest seismic events during the monitoring period, confirming its effectiveness in practical applications. Compared to traditional analysis methods, the deep learning model can automatically process and analyse recorded massive AE data, enabling real-time monitoring of seismic events and timely earthquake warning in the future. This study serves as a valuable reference for earthquake disaster prevention and intelligent early warning. Full article
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15 pages, 1767 KiB  
Article
A Contrastive Representation Learning Method for Event Classification in Φ-OTDR Systems
by Tong Zhang, Xinjie Peng, Yifan Liu, Kaiyang Yin and Pengfei Li
Sensors 2025, 25(15), 4744; https://doi.org/10.3390/s25154744 (registering DOI) - 1 Aug 2025
Viewed by 158
Abstract
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 [...] Read more.
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
(This article belongs to the Topic Distributed Optical Fiber Sensors)
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33 pages, 1512 KiB  
Review
Advances and Challenges in Deep Learning for Acoustic Pathology Detection: A Review
by Florin Bogdan and Mihaela-Ruxandra Lascu
Technologies 2025, 13(8), 329; https://doi.org/10.3390/technologies13080329 - 1 Aug 2025
Viewed by 157
Abstract
Recent advancements in data collection technologies, data science, and speech processing have fueled significant interest in the computational analysis of biological sounds. This enhanced analytical capability shows promise for improved understanding and detection of various pathological conditions, extending beyond traditional speech analysis to [...] Read more.
Recent advancements in data collection technologies, data science, and speech processing have fueled significant interest in the computational analysis of biological sounds. This enhanced analytical capability shows promise for improved understanding and detection of various pathological conditions, extending beyond traditional speech analysis to encompass other forms of acoustic data. A particularly promising and rapidly evolving area is the application of deep learning techniques for the detection and analysis of diverse pathologies, including respiratory, cardiac, and neurological disorders, through sound processing. This paper provides a comprehensive review of the current state-of-the-art in using deep learning for pathology detection via analysis of biological sounds. It highlights key successes achieved in the field, identifies existing challenges and limitations, and discusses potential future research directions. This review aims to serve as a valuable resource for researchers and clinicians working in this interdisciplinary domain. Full article
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15 pages, 5631 KiB  
Article
Design and Evaluation of a Capacitive Micromachined Ultrasonic Transducer(CMUT) Linear Array System for Thickness Measurement of Marine Structures Under Varying Environmental Conditions
by Changde He, Mengke Luo, Hanchi Chai, Hongliang Wang, Guojun Zhang, Renxin Wang, Jiangong Cui, Yuhua Yang, Wendong Zhang and Licheng Jia
Micromachines 2025, 16(8), 898; https://doi.org/10.3390/mi16080898 (registering DOI) - 31 Jul 2025
Viewed by 116
Abstract
This paper presents the design, fabrication, and experimental evaluation of a capacitive micromachined ultrasonic transducer (CMUT) linear array for non-contact thickness measurement of marine engineering structures. A 16-element CMUT array was fabricated using a silicon–silicon wafer bonding process, and encapsulated in polyurethane to [...] Read more.
This paper presents the design, fabrication, and experimental evaluation of a capacitive micromachined ultrasonic transducer (CMUT) linear array for non-contact thickness measurement of marine engineering structures. A 16-element CMUT array was fabricated using a silicon–silicon wafer bonding process, and encapsulated in polyurethane to ensure acoustic impedance matching and environmental protection in underwater conditions. The acoustic performance of the encapsulated CMUT was characterized using standard piezoelectric transducers as reference. The array achieved a transmitting sensitivity of 146.82 dB and a receiving sensitivity of −229.55 dB at 1 MHz. A complete thickness detection system was developed by integrating the CMUT array with a custom transceiver circuit and implementing a time-of-flight (ToF) measurement algorithm. To evaluate environmental robustness, systematic experiments were conducted under varying water temperatures and salinity levels. The results demonstrate that the absolute thickness measurement error remains within ±0.1 mm under all tested conditions, satisfying the accuracy requirements for marine structural health monitoring. The results validate the feasibility of CMUT-based systems for precise and stable thickness measurement in underwater environments, and support their application in non-destructive evaluation of marine infrastructure. Full article
(This article belongs to the Special Issue MEMS/NEMS Devices and Applications, 3rd Edition)
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21 pages, 14595 KiB  
Article
Synchronous Improvement of Mechanical and Room-Temperature Damping Performance in Light-Weight Polyurethane Composites by a Simple Carbon-Coating Strategy
by Qitan Zheng, Zhongzheng Zhu, Junyi Yao, Qinyu Sun, Qunfu Fan, Hezhou Liu, Qiuxia Dong and Hua Li
Polymers 2025, 17(15), 2115; https://doi.org/10.3390/polym17152115 - 31 Jul 2025
Viewed by 208
Abstract
In order to address vibration and noise challenges in modern industry while satisfying the lightweighting requirements for aerospace and transportation applications, the development of polymer elastomers integrating both lightweight and high-damping properties holds substantial significance. This study developed polyurethane (PU) with optimized damping [...] Read more.
In order to address vibration and noise challenges in modern industry while satisfying the lightweighting requirements for aerospace and transportation applications, the development of polymer elastomers integrating both lightweight and high-damping properties holds substantial significance. This study developed polyurethane (PU) with optimized damping and mechanical properties at room temperature through monomer composition optimization. Hollow glass microspheres (HGMs) were introduced into the PU matrix to increase stiffness and reduce density, though this resulted in decreased tensile strength (Rm) and loss factor (tanδ). To further improve mechanical and damping properties, we applied a carbon coating to the surface of the HGMs to optimize the interface between the HGMs and the PU matrix, and systematically investigated the energy dissipation and load-bearing behavior of PU composites. The effect of enhanced interface damping of HGM@C/PU resulted in broadening of the effective damping temperature range (tanδ ≥ 0.3) and higher maximum loss factor (tanδmax) compared to HGM/PU at equivalent filler loading. The tensile and dynamic properties significantly improved due to optimized interfacial adhesion. In PU composites reinforced with 10 wt% HGM and HGM@C, a 46.8% improvement in Rm and 11.0% improvement in tanδmax occurred after carbon coating. According to acoustic testing, average transmission loss of HGM/PU and HGM@C/PU with the same filler content showed a difference of 0.3–0.5 dB in 500–6300 Hz, confirming that the hollow structure of the HGMs was preserved during carbon coating. Full article
(This article belongs to the Section Polymer Composites and Nanocomposites)
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22 pages, 6359 KiB  
Article
Development and Testing of an AI-Based Specific Sound Detection System Integrated on a Fixed-Wing VTOL UAV
by Gabriel-Petre Badea, Mădălin Dombrovschi, Tiberius-Florian Frigioescu, Maria Căldărar and Daniel-Eugeniu Crunteanu
Acoustics 2025, 7(3), 48; https://doi.org/10.3390/acoustics7030048 - 30 Jul 2025
Viewed by 192
Abstract
This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human [...] Read more.
This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human voices. Initial validation was performed through ground testing. Acoustic data acquisition is optimized during cruise flight, when wing-mounted motors are shut down and the rear motor operates at 40–60% capacity, significantly reducing noise interference. To address residual motor noise, a preprocessing module was developed using reference recordings obtained in an anechoic chamber. Two configurations were tested to capture the motor’s acoustic profile by changing the UAV’s orientation relative to the fixed microphone. The embedded system processes incoming audio in real time, enabling low-latency classification without data transmission. Field experiments confirmed the model’s high precision and robustness under varying flight and environmental conditions. Results validate the feasibility of real-time, onboard acoustic event detection using spectrogram-based deep learning on UAV platforms, and support its applicability for scalable aerial monitoring tasks. Full article
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13 pages, 1009 KiB  
Article
A Statistical Optimization Method for Sound Speed Profiles Inversion in the South China Sea Based on Acoustic Stability Pre-Clustering
by Zixuan Zhang, Ke Qu and Zhanglong Li
Appl. Sci. 2025, 15(15), 8451; https://doi.org/10.3390/app15158451 - 30 Jul 2025
Viewed by 165
Abstract
Aiming at the problem of accuracy degradation caused by the mixing of spatiotemporal disturbance patterns in sound speed profile (SSP) inversion using the traditional geographic grid division method, this study proposes an acoustic stability pre-clustering framework that integrates principal component analysis and machine [...] Read more.
Aiming at the problem of accuracy degradation caused by the mixing of spatiotemporal disturbance patterns in sound speed profile (SSP) inversion using the traditional geographic grid division method, this study proposes an acoustic stability pre-clustering framework that integrates principal component analysis and machine learning clustering. Disturbance mode principal component analysis is first used to extract characteristic parameters, and then a machine learning clustering algorithm is adopted to pre-classify SSP samples according to acoustic stability. The SSP inversion experimental results show that: (1) the SSP samples of the South China Sea can be divided into three clusters of disturbance modes with statistically significant differences. (2) The regression inversion method based on cluster attribution reduces the average error of SSP inversion for data from 2018 to 1.24 m/s, which is more than 50% lower than what can be achieved with the traditional method without pre-clustering. (3) Transmission loss prediction verification shows that the proposed method can produce highly accurate sound field calculations in environmental assessment tasks. The acoustic stability pre-clustering technology proposed in this study provides an innovative solution for the statistical modeling of marine environment parameters by effectively decoupling the mixed effect of SSP spatiotemporal disturbance patterns. Its error control level (<1.5 m/s) is 37% higher than that of the single empirical orthogonal function regression method, showing important potential in underwater acoustic applications in complex marine dynamic environments. Full article
(This article belongs to the Section Acoustics and Vibrations)
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24 pages, 13347 KiB  
Article
Efficient Modeling of Underwater Target Radiation and Propagation Sound Field in Ocean Acoustic Environments Based on Modal Equivalent Sources
by Yan Lv, Wei Gao, Xiaolei Li, Haozhong Wang and Shoudong Wang
J. Mar. Sci. Eng. 2025, 13(8), 1456; https://doi.org/10.3390/jmse13081456 - 30 Jul 2025
Viewed by 188
Abstract
The equivalent source method (ESM) is a core algorithm in integrated radiation-propagation acoustic field modeling. However, in challenging marine environments, including deep-sea and polar regions, where sound speed profiles exhibit strong vertical gradients, the ESM must increase waveguide stratification to maintain accuracy. This [...] Read more.
The equivalent source method (ESM) is a core algorithm in integrated radiation-propagation acoustic field modeling. However, in challenging marine environments, including deep-sea and polar regions, where sound speed profiles exhibit strong vertical gradients, the ESM must increase waveguide stratification to maintain accuracy. This causes computational costs to scale exponentially with the number of layers, compromising efficiency and limiting applicability. To address this, this paper proposes a modal equivalent source (MES) model employing normal modes as basis functions instead of free-field Green’s functions. This model constructs a set of normal mode bases using full-depth hydroacoustic parameters, incorporating water column characteristics into the basis functions to eliminate waveguide stratification. This significantly reduces the computational matrix size of the ESM and computes acoustic fields in range-dependent waveguides using a single set of normal modes, resolving the dual limitations of inadequate precision and low efficiency in such environments. Concurrently, for the construction of basis functions, this paper also proposes a fast computation method for eigenvalues and eigenmodes in waveguide contexts based on phase functions and difference equations. Furthermore, coupling the MES method with the Finite Element Method (FEM) enables integrated computation of underwater target radiation and propagation fields. Multiple simulations demonstrate close agreement between the proposed model and reference results (errors < 4 dB). Under equivalent accuracy requirements, the proposed model reduces computation time to less than 1/25 of traditional ESM, achieving significant efficiency gains. Additionally, sea trial verification confirms model effectiveness, with mean correlation coefficients exceeding 0.9 and mean errors below 5 dB against experimental data. Full article
(This article belongs to the Section Ocean Engineering)
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21 pages, 2255 KiB  
Article
Cloud-Based Architecture for Hydrophone Data Acquisition and Processing of Surface and Underwater Vehicle Detection
by Francisco Pérez Carrasco, Anaida Fernández García, Alberto García, Verónica Ruiz Bejerano, Álvaro Gutiérrez and Alberto Belmonte-Hernández
J. Mar. Sci. Eng. 2025, 13(8), 1455; https://doi.org/10.3390/jmse13081455 - 30 Jul 2025
Viewed by 247
Abstract
This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports [...] Read more.
This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance. Full article
(This article belongs to the Section Ocean Engineering)
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19 pages, 5970 KiB  
Article
Interface Material Modification to Enhance the Performance of a Thin-Film Piezoelectric-on-Silicon (TPoS) MEMS Resonator by Localized Annealing Through Joule Heating
by Adnan Zaman, Ugur Guneroglu, Abdulrahman Alsolami, Liguan Li and Jing Wang
Micromachines 2025, 16(8), 885; https://doi.org/10.3390/mi16080885 - 29 Jul 2025
Viewed by 216
Abstract
This paper presents a novel approach employing localized annealing through Joule heating to enhance the performance of Thin-Film Piezoelectric-on-Silicon (TPoS) MEMS resonators that are crucial for applications in sensing, energy harvesting, frequency filtering, and timing control. Despite recent advancements, piezoelectric MEMS resonators still [...] Read more.
This paper presents a novel approach employing localized annealing through Joule heating to enhance the performance of Thin-Film Piezoelectric-on-Silicon (TPoS) MEMS resonators that are crucial for applications in sensing, energy harvesting, frequency filtering, and timing control. Despite recent advancements, piezoelectric MEMS resonators still suffer from anchor-related energy losses and limited quality factors (Qs), posing significant challenges for high-performance applications. This study investigates interface modification to boost the quality factor (Q) and reduce the motional resistance, thus improving the electromechanical coupling coefficient and reducing insertion loss. To balance the trade-off between device miniaturization and performance, this work uniquely applies DC current-induced localized annealing to TPoS MEMS resonators, facilitating metal diffusion at the interface. This process results in the formation of platinum silicide, modifying the resonator’s stiffness and density, consequently enhancing the acoustic velocity and mitigating the side-supporting anchor-related energy dissipations. Experimental results demonstrate a Q-factor enhancement of over 300% (from 916 to 3632) and a reduction in insertion loss by more than 14 dB, underscoring the efficacy of this method for reducing anchor-related dissipations due to the highest annealing temperature at the anchors. The findings not only confirm the feasibility of Joule heating for interface modifications in MEMS resonators but also set a foundation for advancements of this post-fabrication thermal treatment technology. Full article
(This article belongs to the Special Issue MEMS Nano/Micro Fabrication, 2nd Edition)
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18 pages, 3500 KiB  
Article
Effect of Window Structure and Mounting on Sound Insulation: A Laboratory-Based Study
by Leszek Dulak and Artur Nowoświat
Sustainability 2025, 17(15), 6892; https://doi.org/10.3390/su17156892 - 29 Jul 2025
Viewed by 149
Abstract
The acoustic performance of windows significantly influences evaluations of building quality, particularly in urban environments. This study presents the results of laboratory tests on the airborne sound insulation of windows with dimensions greater than those specified in ISO 10140-5:2021-10. The aim was to [...] Read more.
The acoustic performance of windows significantly influences evaluations of building quality, particularly in urban environments. This study presents the results of laboratory tests on the airborne sound insulation of windows with dimensions greater than those specified in ISO 10140-5:2021-10. The aim was to determine the impact of construction details and installation techniques on sound insulation, specifically Rw and Rw + Ctr values. The experimental variables included mounting methods (expansion tape versus low-pressure polyurethane foam), the presence or absence of a threshold in the lower frame, and the type of mullion (fixed versus movable). The tests involved two types of IGUs characterized by different acoustic properties. The findings indicate that the frame configuration, including threshold and mullion type, has a negligible influence on sound insulation. However, the standard method for estimating acoustic performance (EN 14351-1:2006 + A2:2017), which relies on IGU-based data, proved unreliable for modern window assemblies. The estimated values of Rw and Rw + Ctr were consistently lower than those obtained from direct laboratory measurements. These results highlight the need for verification through full-size window testing and suggest that reliance on simplified estimation procedures may lead to underperformance in real-world acoustic applications. Full article
(This article belongs to the Special Issue Advancements in Green Building Materials, Structures, and Techniques)
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30 pages, 5612 KiB  
Review
In-Situ Monitoring and Process Control in Material Extrusion Additive Manufacturing: A Comprehensive Review
by Alexander Isiani, Kelly Crittenden, Leland Weiss, Okeke Odirachukwu, Ramanshu Jha, Okoye Johnson and Osinachi Abika
J. Exp. Theor. Anal. 2025, 3(3), 21; https://doi.org/10.3390/jeta3030021 - 29 Jul 2025
Viewed by 164
Abstract
Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations [...] Read more.
Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations in process parameters and material behavior during fabrication. In-situ monitoring and advanced process control systems have been increasingly integrated into MEAM to address these issues, enabling real-time detection of defects, optimization of printing conditions, reliability of fabricated parts, and enhanced control over mechanical properties. This review examines the state-of-the-art in-situ monitoring techniques, including thermal imaging, vibrational sensing, rheological monitoring, printhead positioning, acoustic sensing, image recognition, and optical scanning, and their integration with process control strategies, such as closed-loop feedback systems and machine learning algorithms. Key challenges, including sensor accuracy, data processing complexity, and scalability, are discussed alongside recent advancements and their implications for industrial applications. By synthesizing current research, this work highlights the critical role of in-situ monitoring and process control in advancing the reliability and precision of MEAM, paving the way for its broader adoption in high-performance manufacturing. Full article
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20 pages, 2399 KiB  
Article
Exploring Novel Optical Soliton Molecule for the Time Fractional Cubic–Quintic Nonlinear Pulse Propagation Model
by Syed T. R. Rizvi, Atef F. Hashem, Azrar Ul Hassan, Sana Shabbir, A. S. Al-Moisheer and Aly R. Seadawy
Fractal Fract. 2025, 9(8), 497; https://doi.org/10.3390/fractalfract9080497 - 29 Jul 2025
Viewed by 267
Abstract
This study focuses on the analysis of soliton solutions within the framework of the time-fractional cubic–quintic nonlinear Schrödinger equation (TFCQ-NLSE), a powerful model with broad applications in complex physical phenomena such as fiber optic communications, nonlinear optics, optical signal processing, and laser–tissue interactions [...] Read more.
This study focuses on the analysis of soliton solutions within the framework of the time-fractional cubic–quintic nonlinear Schrödinger equation (TFCQ-NLSE), a powerful model with broad applications in complex physical phenomena such as fiber optic communications, nonlinear optics, optical signal processing, and laser–tissue interactions in medical science. The nonlinear effects exhibited by the model—such as self-focusing, self-phase modulation, and wave mixing—are influenced by the combined impact of the cubic and quintic nonlinear terms. To explore the dynamics of this model, we apply a robust analytical technique known as the sub-ODE method, which reveals a diverse range of soliton structures and offers deep insight into laser pulse interactions. The investigation yields a rich set of explicit soliton solutions, including hyperbolic, rational, singular, bright, Jacobian elliptic, Weierstrass elliptic, and periodic solutions. These waveforms have significant real-world relevance: bright solitons are employed in fiber optic communications for distortion-free long-distance data transmission, while both bright and dark solitons are used in nonlinear optics to study light behavior in media with intensity-dependent refractive indices. Solitons also contribute to advancements in quantum technologies, precision measurement, and fiber laser systems, where hyperbolic and periodic solitons facilitate stable, high-intensity pulse generation. Additionally, in nonlinear acoustics, solitons describe wave propagation in media where amplitude influences wave speed. Overall, this work highlights the theoretical depth and practical utility of soliton dynamics in fractional nonlinear systems. Full article
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25 pages, 2287 KiB  
Article
Quantitative Measurement of Hakka Phonetic Distances
by I-Ping Wan
Languages 2025, 10(8), 185; https://doi.org/10.3390/languages10080185 - 29 Jul 2025
Viewed by 173
Abstract
This study proposes a novel approach to measuring phonetic distances among six Hailu Hakka vowels ([i, e, ɨ, a, u, o]) by applying Euclidean distance-based calculations from both articulatory and acoustic perspectives. By analyzing articulatory feature values and acoustic formant structures, vowel distances [...] Read more.
This study proposes a novel approach to measuring phonetic distances among six Hailu Hakka vowels ([i, e, ɨ, a, u, o]) by applying Euclidean distance-based calculations from both articulatory and acoustic perspectives. By analyzing articulatory feature values and acoustic formant structures, vowel distances are systematically represented through linear vector arrangements. These measurements address ongoing debates regarding the central positioning of [ɨ], specifically whether it aligns more closely with front or back vowels and whether [a] or [ɑ] more accurately represents vowel articulation. This study also reassesses the validity of prior acoustic findings on Hailu Hakka vowels and evaluates the correspondence between articulatory normalization and acoustic formant-based models. Through the integration of articulatory and acoustic data, this research advances a replicable and theoretically grounded method for quantitative vowel analysis. The results not only refine phonetic classification within a Euclidean framework but also help resolve transcription inconsistencies in phonetic distance matrices. This study contributes to the growing field of quantitative phonetics by offering a systematic, multidimensional model applicable to both theoretical and experimental investigations of Taiwan Hailu Hakka. Full article
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