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Advanced Acoustic Sensing Technology
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Advanced Acoustic Sensing Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 10104

Special Issue Editor

Prof. Dr. Rui Zhang

E-Mail Website
Guest Editor
School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: theory and method for testing relaxation type ferroelectric single crystals; design and fabrication of relaxation type ferroelectric single crystal functional devices; ultrasonic transducers and surface acoustic waves

Special Issue Information

Dear Colleagues,

As an important instrument that can convert a sound signal into electrical signal, acoustic sensors are widely used in various fields such as healthcare, geophysics, and agriculture. Based on different theories, there are two kinds of acoustic sensitivity, namely, piezoelectric acoustic sensors and capacitive acoustic sensors. In addition, fiber-based distributed acoustic sensors as powerful instruments are becoming an interesting research issue in acoustic field analyzing. Different acoustic sensors are sensitive in different frequency ranges. Ultrasound, whose frequency is over 20 kHz, is a common spectrum in research, allowing us to perform activities such as health monitoring and non-destructive material testing. The signal from sensors can be handled through an advanced intelligent algorithm.

This Special Issue shall present articles as an overview across advanced acoustic sensing technology, such as acoustic sensitivity, piezoelectric transducer, capacitive acoustic sensors, and distributed acoustic sensors, in recent years.

Submission of both review articles and original research papers relating to piezoelectric transducer on health monitors will be much appreciated.

Prof. Dr. Rui Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • acoustic sensitivity
  • piezoelectric transducer
  • capacitive acoustic sensors
  • distributed acoustic sensors
  • wearable
  • health monitor
  • non-destructive material testing
  • intelligence
  • algorithm

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Published Papers (7 papers)

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Research

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23 pages, 36167 KiB  
Article
Vibro-Acoustic Signatures of Various Insects in Stored Products
by Daniel Kadyrov, Alexander Sutin, Nikolay Sedunov, Alexander Sedunov and Hady Salloum
Sensors 2024, 24(20), 6736; https://doi.org/10.3390/s24206736 - 19 Oct 2024
Viewed by 1346
Abstract
Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute [...] Read more.
Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 dB for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 dB to 16 dB, respectively. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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18 pages, 6688 KiB  
Article
Investigation of the Reduction in Distributed Acoustic Sensing Signal Due to Perforation Erosion by Using CFD Acoustic Simulation and Lighthill’s Acoustic Power Law
by Yasuyuki Hamanaka, Ding Zhu and A. D. Hill
Sensors 2024, 24(18), 5996; https://doi.org/10.3390/s24185996 - 16 Sep 2024
Viewed by 809
Abstract
Distributed Acoustic Sensing (DAS), widely adopted in hydraulic fracturing monitoring, continuously measures sound from perforation holes due to fluid flow through the perforation holes during fracturing treatment. DAS has the potential to monitor perforation Tulsa, OK 74136erosion, a phenomenon of increasing perforation size [...] Read more.
Distributed Acoustic Sensing (DAS), widely adopted in hydraulic fracturing monitoring, continuously measures sound from perforation holes due to fluid flow through the perforation holes during fracturing treatment. DAS has the potential to monitor perforation Tulsa, OK 74136erosion, a phenomenon of increasing perforation size due to sand (referred to as proppant) injection during treatment. Because the sound generated by fluid flow at a perforation hole is negatively related to the perforation diameter, by detecting the decay of the DAS signal, the perforation erosion level can be estimated, which is critical information for fracture design. We used a Computation Fluid Dynamics (CFD) acoustic simulator to calculate the acoustic pressure induced by turbulence inside a wellbore and investigated the relationship between the acoustic response from fluid flow through a perforation and the perforation size by running the simulator for various perforation diameters and flow rates. The results show that if the perforation size is constant, the plot between the calculated sound pressure level and the logarithm of flow rate follows a straight line relationship. However, with different perforation sizes, the intercept of the linear relationship changes, reducing the sound pressure level. Lighthill’s power law indicates that the change in intercept corresponds to the logarithm of the ratio of the increased diameter to the original diameter. The reduction in sound pressure level observed in the CFD simulation correlates with the reduction in the DAS signal in field data. The findings of this study help to evaluate perforation diameter growth using DAS and interpret fluid distribution in fracture stimulation. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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19 pages, 5157 KiB  
Article
Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results
by Sabna Thenginthody Hassan, Peng Chen, Yue Rong and Kit Yan Chan
Sensors 2024, 24(18), 5995; https://doi.org/10.3390/s24185995 - 15 Sep 2024
Viewed by 749
Abstract
In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits [...] Read more.
In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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14 pages, 1216 KiB  
Article
Living Together, Singing Together: Revealing Similar Patterns of Vocal Activity in Two Tropical Songbirds Applying BirdNET
by David Amorós-Ausina, Karl-L. Schuchmann, Marinez I. Marques and Cristian Pérez-Granados
Sensors 2024, 24(17), 5780; https://doi.org/10.3390/s24175780 - 5 Sep 2024
Viewed by 1278
Abstract
In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM [...] Read more.
In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (Cantorchilus leucotis) and Thrush-like Wren (Campylorhynchus turdinus). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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19 pages, 6828 KiB  
Article
Feature Extraction Methods for Underwater Acoustic Target Recognition of Divers
by Yuchen Sun, Weiyi Chen, Changgeng Shuai, Zhiqiang Zhang, Pingbo Wang, Guo Cheng and Wenjing Yu
Sensors 2024, 24(13), 4412; https://doi.org/10.3390/s24134412 - 8 Jul 2024
Viewed by 1167
Abstract
The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency−domain multi−sub−band energy (FMSE), aiming to achieve accurate recognition [...] Read more.
The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency−domain multi−sub−band energy (FMSE), aiming to achieve accurate recognition of diver underwater acoustic targets by passive sonar. The impact of the presence or absence of targets, different numbers of targets, different signal−to−noise ratios, and different detection distances on this method was studied based on experimental data under different conditions, such as water pools and lakes. It was found that the FMSE method has the best robustness and performance compared with two other signal feature extraction methods: mel frequency cepstral coefficient filtering and gammatone frequency cepstral coefficient filtering. Combined with the commonly used recognition algorithm of support vector machines, the FMSE method can achieve a comprehensive recognition accuracy of over 94% for frogman underwater acoustic targets. This indicates that the FMSE method is suitable for underwater acoustic recognition of diver targets. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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17 pages, 4342 KiB  
Article
Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers
by Fatemeh Shomal Zadeh, Ryan G. L. Koh, Banu Dilek, Kei Masani and Dinesh Kumbhare
Sensors 2023, 23(24), 9873; https://doi.org/10.3390/s23249873 - 16 Dec 2023
Viewed by 1738
Abstract
Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants [...] Read more.
Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants (left and/or right side(s)). Four texture feature approaches (individually and a combination of them) were employed that focused on identifying spots, and edges were used to explore the discrimination between the three groups: active MTrPs (n = 30), latent MTrPs (n = 30), and healthy muscle (n = 30). Machine learning (ML) and one-way analysis of variance were used to investigate the discrimination ability of the different approaches. Statistically significant results were seen in almost all examined features for each texture feature approach, but, in contrast, ML techniques struggled to produce robust discrimination. The ML techniques showed that two texture features (i.e., correlation and mean) within the combination of texture features were most important in classifying the three groups. This discrepancy between traditional statistical analysis and ML techniques prompts the need for further investigation of texture-based approaches in US for the discrimination of MTrPs. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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Review

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31 pages, 13979 KiB  
Review
Advances in Portable and Wearable Acoustic Sensing Devices for Human Health Monitoring
by Fanhao Kong, Yang Zou, Zhou Li and Yulin Deng
Sensors 2024, 24(16), 5354; https://doi.org/10.3390/s24165354 - 19 Aug 2024
Viewed by 2001
Abstract
The practice of auscultation, interpreting body sounds to assess organ health, has greatly benefited from technological advancements in sensing and electronics. The advent of portable and wearable acoustic sensing devices marks a significant milestone in telemedicine, home health, and clinical diagnostics. This review [...] Read more.
The practice of auscultation, interpreting body sounds to assess organ health, has greatly benefited from technological advancements in sensing and electronics. The advent of portable and wearable acoustic sensing devices marks a significant milestone in telemedicine, home health, and clinical diagnostics. This review summarises the contemporary advancements in acoustic sensing devices, categorized based on varied sensing principles, including capacitive, piezoelectric, and triboelectric mechanisms. Some representative acoustic sensing devices are introduced from the perspective of portability and wearability. Additionally, the characteristics of sound signals from different human organs and practical applications of acoustic sensing devices are exemplified. Challenges and prospective trends in portable and wearable acoustic sensors are also discussed, providing insights into future research directions. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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