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Artificial ears with intelligence, which can sensitively detect sound-a variant of pressure-and generate consciousness and logical decision-making abilities, hold great promise to transform life. However, despite the emerging flexible sensors for sound detection, most success is limited to very simple phonemes, such as a couple of letters or words, probably due to the lack of device sensitivity and capability. Herein, the construction of ultrasensitive artificial eardrums enabling intelligent song recognition is reported. This strategy employs novel geometric engineering of sensing units in the soft microstructure array (to significantly reduce effective modulus) along with complex song recognition exploration leveraging machine learning algorithms. Unprecedented pressure sensitivity (6.9 × 103 kPa-1) is demonstrated in a sensor with a hollow pyramid architecture with porous slants. The integrated device exhibits unparalleled (exceeding by 1-2 orders of magnitude compared with reported benchmark samples) sound detection sensitivity, and can accurately identify 100% (for training set) and 97.7% (for test set) of a database of the segments from 77 songs varying in language, style, and singer. Overall, the results highlight the outstanding performance of the hollow-microstructure-based sensor, indicating its potential applications in human-machine interaction and wearable acoustical technologies.

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Laboratory acoustic emissions (AEs) serve as small-scale analogues to earthquakes, offering fundamental insights into seismic processes. To ensure accurate physical interpretations of AEs, rigorous calibration of the acoustic system is essential. In this paper, we present an empirical calibration technique that quantifies sensor response, instrumentation effects, and path characteristics into a single entity termed instrument apparatus response. Using a controlled seismic source with different steel balls, we retrieve the instrument apparatus response in the frequency domain under typical experimental conditions for various piezoelectric sensors (PZTs) arranged to simulate a three-component seismic station. Removing these responses from the raw AE spectra allows us to obtain calibrated AE source spectra, which are then effectively used to constrain the seismic AE source parameters. We apply this calibration method to acoustic emissions (AEs) generated during unstable stick-slip behavior of a quartz gouge in double direct shear experiments. The calibrated AEs range in magnitude from -7.1 to -6.4 and exhibit stress drops between 0.075 MPa and 4.29 MPa, consistent with earthquake scaling relation. This result highlights the strong similarities between AEs generated from frictional gouge experiments and natural earthquakes. Through this acoustic emission calibration, we gain physical insights into the seismic sources of laboratory AEs, enhancing our understanding of seismic rupture processes in fault gouge experiments.

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Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is to maximize the amount of harvested energy underwater by adopting the TDMA time slot scheduling approach to prolong the operational lifetime of the sensors. In this study, we considered the spatial uncertainty of underwater ambient resources to improve the utilization of available energy and examine a stochastic model for piezoelectric energy harvesting. Considering a realistic channel and environment condition, a novel multi-agent reinforcement learning algorithm is proposed. Nodes observe and learn from their choice of transmission slots based on the available energy in the underwater medium and autonomously adapt their communication slots to their energy harvesting conditions instead of relying on the cluster head. In the numerical results, we present the impact of piezoelectric energy harvesting and harvesting awareness on three lifetime metrics. We observe that energy harvesting contributes to 4% improvement in first node dead (FND), 14% improvement in half node dead (HND), and 22% improvement in last node dead (LND). Additionally, the harvesting-aware TDMA-RL method further increases HND by 17% and LND by 38%. Our results show that the proposed method improves in-cluster communication time interval utilization and outperforms traditional time slot allocation methods in terms of throughput and energy harvesting efficiency.

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The current investigation theoretically presents a one-dimensional phononic crystal (PnC) as a fluidic sensor. The sensor under consideration aims to distinguish the concentration of acetic acid. The primary configuration of the proposed sensor is constructed with lead, epoxy, and a defect layer in the middle of the structure, that is filled with acetic acid (vinegar). As a result of the rise in density and decline in the speed of sound at a 100% concentration of acetic acid in comparison to pure water, the peak frequency of the output has shifted towards lower frequencies. Given that the maximum permissible concentration of acetic acid in water for vinegar is above 30%, sensor simulations were conducted within the concentration range of 25-35% with a step size of 1%. Interestingly, the sensitivity of the sensor exhibits a polynomial change in response to the concentration of acetic acid. Consequently, the highest level of sensitivity, which corresponds to the lowest concentration of vinegar, is recorded as 48.44 × 106 (Hz). The proposed system exhibits a remarkable value of the quality factor of 2802.91. Furthermore, the optimal figure of merit (FOM) is achieved when the concentration is at its lowest, with a value of 94.00. Furthermore, the temperature effects are taken into account for a wide range between 10 and 60 °C. A pronouncing sensitivity is obtained for all temperatures changes and the highest one reached the value of 1.57 × 106 (Hz/°C) at a temperature of 25 °C. Considering the present circumstances, the suggested sensor configuration has the potential to cater to a diverse array of other fluids, specifically their concentration and temperature, thereby offering a broad scope of applications.

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Photoacoustic spectroscopy (PAS) has been rapidly developed and applied to different detection scenarios. The acoustic pressure detection is an important part in the PAS system. In this paper, an ultrahigh sensitivity Fabry-Perot acoustic sensor with a T-shaped cantilever was proposed. To achieve the best acoustic pressure effect, the dimension of the cantilever structure was designed and optimized by finite element analysis using COMSOL Multiphysics. Simulation results showed that the sensitivity of such T-shaped cantilever was 1.5 times higher than that based on a rectangular cantilever, and the resonance frequency of T-shaped cantilever were able to modulate from 800 Hz to 1500 Hz by adjusting the multi-parameter characteristics. Experimental sensing results showed that the resonance frequency of T-shaped Fabry-Perot acoustic sensor was 1080 Hz, yielding a high sensitivity of 1.428 µm/Pa, with a signal-to-noise ratio (SNR) of 84.8 dB and a detectable pressure limit of 1.9 µPa/Hz1/2@1 kHz. We successfully used such acoustic sensor to measure acetylene (C2H2) concentration in the PAS. The sensitivity of PAS for C2H2 gas was 3.22 pm/ppm with a concentration range of 50 ppm ∼100 ppm, and the minimum detection limit was 24.91ppb.

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Low-frequency distributed acoustic sensing (LF-DAS) is a diagnostic tool for hydraulic fracture propagation with far-field monitoring using fiber optic sensors. LF-DAS senses strain rate variation caused by stress field change due to fracture propagation. Fiber optic sensors are installed in the monitoring wells in the vicinity of a fractured well. From the strain responses, fracture propagation can be evaluated. To understand subsurface conditions with multiple propagating fractures, a laboratory-scale hydraulic fracture experiment was performed simulating the LF-DAS response to fracture propagation with embedded distributed optical fiber strain sensors under these conditions. The experiment was performed using a transparent cube of epoxy with two parallel radial initial flaws centered in the cube. Fluid was injected into the sample to generate fractures along the initial flaws. The experiment used distributed high-definition fiber optic strain sensors with tight spatial resolutions. The sensors were embedded at two different locations on opposite sides of the initial flaws, serving as observation/monitoring locations. We also employed finite element modeling to numerically solve the linear elastic equations of equilibrium continuity and stress-strain relationships. The measured strains from the experiment were compared to simulation results from the finite element model. The experimentally derived strain and strain-rate waterfall plots from this study show the responses to both fractures propagating, while the fracture at the lower position took most of the fluid during the experiment. Interestingly, a fracture first began propagating from the upper flaw of the two flaws, but once the lower fracture was initiated, it grew much faster than the upper fracture. Both fibers were intercepted by the lower fracture, further verifying the strain signature as a fracture is approaching and intersecting an offset fiber.

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The swimming performance of cultured finfish species is typically studied under steady flow conditions. However, flow conditions are mostly unsteady, for instance, as experienced in sea pens in exposed sea areas. Using a Loligo swim tunnel, we investigated the effects of swimming in steady and unsteady flows at increasing swimming speeds on post-smolt Atlantic salmon. Oxygen consumption (MO2), locomotory behaviour, and overall dynamic body acceleration (ODBA), as determined with implanted acoustic sensor tags, were compared between both flow conditions. Results were obtained for mean swimming speeds of 0.2 to 0.8 m.s-1 under both flow conditions. Sensor tags that were implanted in the abdominal cavity had no significant effects on MO2 and locomotory parameters. The MO2 of fish swimming in unsteady flows was significantly higher (15-53%) than when swimming in steady flows (p < 0.05). Significant interaction effects of ODBA with flow conditions and swimming speed were found. ODBA was strongly and positively correlated with swimming speed and MO2 in unsteady flow (R2 = 0.94 and R2 = 0.93, respectively) and in steady flow (R2 = 0.91 and R2 = 0.82, respectively). ODBA predicts MO2 well over the investigated range of swimming speeds in both flow conditions. In an unsteady flow condition, ODBA increased twice as fast with MO2 compared with steady flow conditions (p < 0.05). From these results, we can conclude that (1) swimming in unsteady flow is energetically more costly for post-smolt Atlantic salmon than swimming in steady flow, as indicated by higher MO2, and (2) ODBA can be used to estimate the oxygen consumption of post-smolt Atlantic salmon in unsteady flow in swim tunnels.

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Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50 % during localization and 24 % during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100 % prediction accuracy at all levels of damage characterization.

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Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

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Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.

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This paper reports on the design, modeling, and characterization of a multi-resonant, directional, MEMS acoustic sensor. The design builds on previous resonant MEMS sensor designs to broaden the sensor's usable bandwidth while maintaining a high signal-to-noise ratio (SNR). These improvements make the sensor more attractive for detecting and tracking sound sources with acoustic signatures that are broader than discrete tones. In-air sensor characterization was conducted in an anechoic chamber. The sensor was characterized underwater in a semi-anechoic pool and in a standing wave tube. The sensor demonstrated a cosine-like directionality, a maximum acoustic sensitivity of 47.6 V/Pa, and a maximum SNR of 88.6 dB, for 1 Pa sound pressure, over the bandwidth of the sensor circuitry (100 Hz-3 kHz). The presented design represents a significant improvement in sensor performance compared to similar resonant MEMS sensor designs. Increasing the sensitivity of a single-resonator design is typically associated with a decrease in bandwidth. This multi-resonant design overcomes that limitation.

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This study examines the relationship between the wingbeat frequency of flying insects and ambient temperature, leveraging data from over 302,000 insect observations obtained using a near-infrared optical sensor during an eight-month field experiment. By measuring the wingbeat frequency as well as wing and body optical cross-sections of each insect in conjunction with the ambient temperature, we identified five clusters of insects and analyzed how their average wingbeat frequencies evolved over temperatures ranging from 10 °C to 38 °C. Our findings reveal a positive correlation between temperature and wingbeat frequency, with a more pronounced increase observed at higher wingbeat frequencies. Frequencies increased on average by 2.02 Hz/°C at 50 Hz, and up to 9.63 Hz/°C at 525 Hz, and a general model is proposed. This model offers a valuable tool for correcting wingbeat frequencies with temperature, enhancing the accuracy of insect clustering by optical and acoustic sensors. While this approach does not account for species-specific responses to temperature changes, our research provides a general insight, based on all species present during the field experiment, into the intricate dynamics of insect flight behavior in relation to environmental factors.

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We report on theoretical and experimental investigation of parametric amplification of acoustically excited vibrations in micromachined single-crystal silicon cantilevers electrostatically actuated by fringing fields. The device dynamics are analyzed using the Mathieu-Duffing equation, obtained using the Galerkin order reduction technique. Our experimental results show that omnidirectional acoustic pressure used as a noncontact source for linear harmonic driving is a convenient and versatile tool for the mechanical dynamic characterization of unpackaged, nonintegrated microstructures. The fringing field's electrostatic actuation allows for efficient parametric amplification of an acoustic signal. The suggested amplification approach may have applications in a wide variety of micromechanical devices, including resonant sensors, microphones and microphone arrays, and hearing aids. It can be used also for upward frequency tuning.

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Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

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Mosquitoes are the vector of diseases that kill more than one million people per year worldwide. Surveillance systems are essential for understanding their complex ecology and behaviour. This is fundamental for predicting disease risk caused by mosquitoes and formulating effective control strategies against mosquito-borne diseases such as malaria, dengue, and Zika. Mosquito populations vary heterogeneously in urban and rural landscapes, fluctuating with seasonal and climatic trends and human activity. Several approaches provide environmental data for mosquito mapping and risk prediction. However, they rely traditionally upon labour-intensive techniques such as manual traps. This paper presents the optimal audio features for mosquito identification using ecoacoustics signals to automatically identify different mosquito species from their wingbeat sounds based on popular audio features. The audio selection method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Silhouette coefficient to evaluate the clusters in the data through the optimal-combined audio features. To classify the mosquito species and distinguish them from environmental-urban noise, the method comprises the Gaussian Mixture Model (GMM) and Gibbs approach for Aedes aegypti, and Culex quinquefasciatus, using the acoustic recordings of their wingbeat signals. Finally, comparing GMM and Gibbs, the two have very similar accuracy, but the classification time is much faster for Gibbs sampling, making it a good candidate for a lightweight solution. These are essential when deploying the described models to monitor mosquito vectors in the wild with Internet of Things (IoT) technologies.

Asunto(s)

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This study presents a grating interferometric acoustic sensor based on a flexible polymer diaphragm. A flexible-diaphragm acoustic sensor based on grating interferometry (GI) is proposed through design, fabrication and experimental demonstration. A gold-coated polyethylene terephthalate diaphragm was used for the sensor prototype. The vibration of the diaphragm induces a change in GI cavity length, which is converted into an electrical signal by the photodetector. The experimental results show that the sensor prototype has a flat frequency response in the voice frequency band and the minimum detectable sound pressure can reach 164.8 µPa/√Hz. The sensor prototype has potential applications in speech acquisition and the measurement of water content in oil. This study provides a reference for the design of optical interferometric acoustic sensor with high performance.

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To develop implementation research on distributed optical fiber sensing technology, field tests were conducted on municipal roads and railways using a distributed acoustic sensor (DAS). Data were collected by the DAS during a field test for a long time period (more than 20 min), and we conducted short-term (<10 s) and long-term (≥10 s) analyses on these data separately. In the short-term data analysis, the vehicle type, vehicle length, and working status of the vehicle engine or the compressor were identified. In the long-term data analysis, the traffic flow was monitored, and the running distance, acceleration, speed, and braking distance of the vehicle were obtained. The characteristics of the vehicle operation data obtained in these field tests are important in developing the data processing method of DASs, which will help to promote the implementation of DASs.

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Membrane-free acoustic sensors based on new principle and structure are becoming a research hotspot, because of many advantages, e.g., their wide bandwidth and high sensitivity. It is proposed that a membrane-free acoustic sensor employs a semi-buried optical waveguide ring resonator (SOWRR) as a sensing element. Using air as the upper cladding medium, the excited evanescent field in the air cladding medium would be modulated by acoustic wave. On this basis, the acoustic sensing model is established. Taking high Q factor and resonance depth as design criteria, the optimal design parameters are given. The optimal values of the air/SiO2: Ge/SiO2 waveguide resonator length and coupling spacing are obtained as 50 mm and 5.6 µm, respectively. The Q factor of the waveguide resonator of this size is as high as 8.33 × 106. The theoretical simulation indicates that the frequency response ranges from 1 Hz to 1.58 MHz and that the minimum detectable sound pressure is 7.48 µPa using a laser with linewidth of 1 kHz. Because of its advantages of wide bandwidth and high sensitivity, the membrane-free sensor is expected to become one of the most promising candidates for the next-generation acoustic sensor.

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This paper reports on the design, modeling, analysis, and evaluation of a micro-electromechanical systems acoustic sensor and the novel design of an acoustic vector sensor array (AVS) which utilized this acoustic sensor. This research builds upon previous work conducted to develop a small, lightweight, portable system for the detection and location of quiet or distant acoustic sources of interest. This study also reports on the underwater operation of this sensor and AVS. Studies were conducted in the lab and in the field utilizing multiple acoustic sources (e.g., generated tones, gun shots, drones). The sensor operates at resonance, providing for high acoustic sensitivity and a high signal-to-noise ratio (SNR). The sensor demonstrated a maximum SNR of 88 dB with an associated sensitivity of -84.6 dB re 1 V/µPa (59 V/Pa). The sensor design can be adjusted to set a specified resonant frequency to align with a known acoustic signature of interest. The AVS demonstrated an unambiguous, 360-degree, in-plane, azimuthal coverage and was able to provide an acoustic direction of arrival to an average error of within 3.5° during field experiments. The results of this research demonstrate the potential usefulness of this sensor and AVS design for specific applications.

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For exerting high sensitivity of ultrathin graphene to detection deformation, an enlarged backing air cavity (EBC) structure is developed to further enhance the mechanical sensitivity (SM) of a graphene-based Fabry-Perot (F-P) acoustic sensor. COMSOL acoustic field simulation on the air cavity size-dependent SM confirms the optimal length and radius of the EBC of 0.2 and 1.5 mm, respectively, with the maximum simulation SM of 26.16 nm/Pa@1 kHz. Acoustic experiments further demonstrate that the frequency response of the fabricated graphene-based F-P acoustic sensor after the use of the EBC is enhanced by 5.73-79.33 times in the range of 0.5-18 kHz, compared with the conventional one without the EBC. Especially the maximum SM is up to 187.32 nm/Pa@16 kHz, which is at least 17% higher than the SM values ranging from 1.1 to 160 nm/Pa in previously reported F-P acoustic sensors using various diaphragm materials. More acoustic characteristics are examined to highlight various merits of the EBC structure, including a signal-to-noise ratio (SNR) of 60-75 dB@0.5-18 kHz, a time stability of less than ±1.3% for 90 min, a detection resolution of 0.01 Hz, and a high-fidelity speech detection with a cross-correlation coefficient of greater than 0.9, thereby revealing its high-performance weak acoustic sensing and speech recognition applications.