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In this article, we propose a mobile edge computing (MEC)‐related system named PD‐Gait, which can measure gait parameters of Parkinson's disease patients in a contactless and privacy‐preserving manner. We utilize inaudible acoustic signals and band‐pass filters to achieve privacy data protection in the physical layer. The proposed framework can be easily deployed in the mobile end of MEC, and hence release the edge server in cybersecurity attacks fighting. The gait parameters include stride cycle time length and moving speed, and hence providing an objective basis for the doctors' judgment. PD‐Gait utilizes acoustic signals in bands from 16 to 23 kHz to achieve device‐free sensing, which would release both doctors and patients from the tedious wearing process and psychological burden caused by traditional wearable devices. To achieve robust measurement, we propose a novel acoustic ranging method to avoid "broken tones" and "uneven peak distribution" in the received data. The corresponding ranging accuracy is 0.1 m. We also propose auto‐focus micro‐Doppler features to extract robust stride cycle time length, and can achieve an accuracy of 0.052 s. We deployed PD‐Gait in a brain hospital and collected data from 8 patients. The total walked distance is over 330 m. From the overall trend, our results are highly correlated with the doctor's judgment. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, the prevalence of fire incidents leads to significant hazards. Enhancing the accuracy of remote fire detection systems while reducing computation complexity and power consumption in edge hardware are crucial. Therefore, this paper investigates an innovative lightweight Convolutional Spiking Neural Network (CSNN) method for fire detection based on acoustics. In this model, Poisson encoder and convolution encoder strategies are considered and compared. Additionally, the study investigates the impact of observation time steps, surrogate gradient functions, and the threshold and decay rate of membrane potential on network performance. A comparison is made between the classification metrics of the traditional Convolutional Neural Network (CNN) approaches and the proposed lightweight CSNN method. To assess the generalization performance of the proposed lightweight method, publicly available datasets are merged with our experimental data for training, which results in a high accuracy of 99.02%, a precision of 99.37%, a recall of 98.75%, and an F 1 score of 99.06% on the test datasets. [ABSTRACT FROM AUTHOR]

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damage. However, traditional methods such as smoke detection, visual analysis, and wireless signals are not able to identify fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire type and sound propagation, FireSonic quantifies the heat release rate (HRR) of flames by analyzing the relationship between fire-heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. The experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5 % and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety. [ABSTRACT FROM AUTHOR]

Featured Application: Fault diagnosis for HVAC systems. Acoustic detection of machinery defaults from in-duct measurements is of practical importance in many areas, such as the health assessment of turbines in ventilation systems or engine testing in the surface and air transport sectors. This approach is, however, impeded by the low signal-to-noise ratio (SNR) observed in such environments. In this study, it is proposed to exploit the slow sound effect of Sonic Black Hole (SBH) ducted silencers to enhance the sensing of incident pulse acoustic signals with low SNR. It is found from transfer matrix and finite element modelling that fully opened SBH silencers with perforated skin interfaces are able to substantially enhance an incident pulse amplitude while channeling an air flow. We demonstrate that the graded depths of the SBH cavities provide rainbow spectral decomposition and amplification of the incident pulse frequency components, provided that impedance matching, slow sound, and critically coupled conditions are met. In-duct experiments showed the ability of a 3D printed SBH silencer to simultaneously enhance acoustic sensing and fully trap the pulse spectral components in the SBH cavities in the presence of a low-speed flow. This study opens up new avenues for the development of dual-purpose silencers designed for acoustic monitoring and noise control in duct systems without obstructing the air flow. [ABSTRACT FROM AUTHOR]

Directional sensing of underwater acoustic signals is vital in many areas, such as waterborne navigation systems, abyssal signal communication, and underwater sound source localization. Here, we propose an easy-to-make acoustic sensing metamaterial device, which is made of a bubble architecture with a line defect. Both numerical and experimental demonstrations show that an input-direction-sensitive response with an acoustic pressure enhancement factor ~ 13.9 was achieved approximately at the resonant frequency. Furthermore, enhanced waterborne acoustic sensing at different frequencies was realized by adjusting the volume of the defect cavity. This strategy provides an effective and feasible method for underwater weak signal detection and localization. [ABSTRACT FROM AUTHOR]

This work proposes LiquidListener, a novel liquid volume sensing method for containers. Specifically, it enables the ubiquitous measurement of liquid volume not available in existing work due to ${i}$ ) dependencies on dedicated sensing hardware (e.g., capacity sensors) and containers (e.g., transparent containers) and ii) a high training intensity. A key enabler of LiquidListener is listening to singing sounds. When a user taps a container using solid objects, such as pens and teaspoons, the container vibrates freely and produces a singing sound. As the container is filled with more liquid, the pitch of the sound decreases. Based on this relationship, we develop acoustic-based liquid volume sensing algorithms that support the precise measurement of liquid volume while using only a smartphone and requiring minimal user effort for calibration. The extensive experiments demonstrate that LiquidListener can support high accuracy with an average error ratio of 2.3% in sensing the liquid volume in various containers. In addition, the experimental results indicate that it can still maintain a similar level of accuracy in diverse and dynamically changing environments, even without additional calibration.

The ageing of populations is increasing pressure on health and social care systems. Potentially, assistive technologies are a way to support the independence of older adults in their daily activities. Among existing assistive technologies, ambient sensing technologies have received less attention than wearable systems. Moreover, there has been little research into cheaper technologies capable of using multiple modalities. A systematic review of the acceptability of assisted living or smart homes in the United Kingdom and the simultaneous use of sounds and vibrations in remote monitoring of assisted living or smart homes will inform and encourage the use of digital monitoring technologies. The acceptability of sensing technologies depends on whether there is any social stigma about their use, for example, the extent to which they invade privacy. The United Kingdom studies reviewed suggest a lack of measurements of the perceived efficacy or effectiveness of the monitoring devices. The primary use of vibration or acoustic technologies has been for detecting falls rather than monitoring health. The review findings suggest the need for further exploration of the acceptability and applicability of remote monitoring technologies, as well as a need for more research into the simultaneous use of sounds and vibrations in health monitoring. [ABSTRACT FROM AUTHOR]

User authentication on smart devices is crucial to protecting user privacy and device security. Due to the development of emerging attacks, existing physiological feature-based authentication methods, such as fingerprint, iris, and face recognition are vulnerable to forgery and attacks. In this paper, GestureID, a system that utilizes acoustic sensing technology to distinguish hand features among users, is proposed. It involves using a speaker to send acoustic signals and a microphone to receive the echoes affected by the reflection of the hand movements of the users. To ensure system accuracy and effectively distinguish users' gestures, a second-order differential-based phase extraction method is proposed. This method calculates the gradient of received signals to separate the effects of the user's hand movements on the transmitted signal from the background noise. Then, the second-order differential phase and phase-dependent acceleration information are used as inputs to a Convolutional Neural Networks-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model to model hand motion features. To decrease the time it takes to collect data for new user registration, a transfer learning method is used. This involves creating a user authentication model by utilizing a pre-trained gesture recognition model. As a result, accurate user authentication can be achieved without requiring extensive amounts of training data. Experiments demonstrate that GestureID can achieve 97.8% gesture recognition accuracy and 96.3% user authentication accuracy. [ABSTRACT FROM AUTHOR]

The technologies associated with recognizing human sitting posture and actions primarily involve computer vision, sensors, and radio frequency (RF) methods. These approaches often involve handling substantial amounts of data, pose privacy concerns, and necessitate additional hardware deployment. With the emergence of acoustic perception in recent times, acoustic schemes have demonstrated applicability in diverse scenarios, including action recognition, object recognition, and target tracking. In this paper, we introduce SitPAA, a sitting posture and action recognition method based on acoustic waves. Notably, our method utilizes only a single speaker and microphone on a smart device for signal transmission and reception. We have implemented multiple rounds of denoising on the received signal and introduced a new feature extraction technique. These extracted features are fed into static and dynamic-oriented networks to achieve precise classification of five distinct poses and four different actions. Additionally, we employ cross-domain recognition to enhance the universality of the classification results. Through extensive experimental validation, our method has demonstrated notable performance, achieving an average accuracy of 92.08% for posture recognition and 95.1% for action recognition. This underscores the effectiveness of our approach in providing robust and accurate results in the challenging domains of posture and action recognition. [ABSTRACT FROM AUTHOR]

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damage. However, traditional methods such as smoke detection, visual analysis, and wireless signals are not able to identify fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire type and sound propagation, FireSonic quantifies the heat release rate (HRR) of flames by analyzing the relationship between fire-heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. The experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

Ferroelectret nanogenerator (FENG), renowned for its remarkable electromechanical conversion efficiency and low Young's modulus, has gained significant attention in various acoustic applications. The increasing interest is attributed to the crucial role acoustic devices play in our daily lives. This paper provides a comprehensive review of the advancements made in using FENG for acoustic applications. It elaborates on the operational mechanism of FENG in acoustics, with a special focus on comparing the influence of different fabrication materials and techniques on its properties. This review categorizes acoustic applications of FENG into three primary areas: acoustic sensing, acoustic actuation, and acoustic energy harvesting. The detailed descriptions of FENG's implementations in these areas are provided, and potential directions and challenges for further development are outlined. By demonstrating the wide range of potential applications for FENG, it is shown that FENG can be adapted to meet different individual needs. [ABSTRACT FROM AUTHOR]

Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal sound samples. Recent discriminative models based on machine identity (ID) classification have shown excellent ASD performance by leveraging strong prior knowledge like machine ID. However, such strong priors are often unavailable in real-world applications, limiting these models. To address this, we propose utilizing the imbalanced and inconsistent attribute labels from acoustic sensors, such as machine running speed and microphone model, as weak priors to train an attribute classifier. We also introduce an imbalanced compensation strategy to handle extremely imbalanced categories and ensure model trainability. Furthermore, we propose a score fusion method to enhance anomaly detection robustness. The proposed algorithm was applied in our DCASE2023 Challenge Task 2 submission, ranking sixth internationally. By exploiting acoustic sensor data attributes as weak prior knowledge, our approach provides an effective framework for robust ASD when strong priors are absent. [ABSTRACT FROM AUTHOR]

Acoustic detection of machinery defaults from in-duct measurements is of practical importance in many areas, such as the health assessment of turbines in ventilation systems or engine testing in the surface and air transport sectors. This approach is, however, impeded by the low signal-to-noise ratio (SNR) observed in such environments. In this study, it is proposed to exploit the slow sound effect of Sonic Black Hole (SBH) ducted silencers to enhance the sensing of incident pulse acoustic signals with low SNR. It is found from transfer matrix and finite element modelling that fully opened SBH silencers with perforated skin interfaces are able to substantially enhance an incident pulse amplitude while channeling an air flow. We demonstrate that the graded depths of the SBH cavities provide rainbow spectral decomposition and amplification of the incident pulse frequency components, provided that impedance matching, slow sound, and critically coupled conditions are met. In-duct experiments showed the ability of a 3D printed SBH silencer to simultaneously enhance acoustic sensing and fully trap the pulse spectral components in the SBH cavities in the presence of a low-speed flow. This study opens up new avenues for the development of dual-purpose silencers designed for acoustic monitoring and noise control in duct systems without obstructing the air flow.

The harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, the need for continuous monitoring of mill parameters for optimal operation. In this paper, an acoustic-based sensing method is employed to estimate, in real time, a snapshot of the different feed size fractions presented to a laboratory-scale SAG mill. Employing the MATLAB 2020b programme, the mill acoustic signal is processed using various transform techniques such as power spectral density estimate (PSDE) by Welch’s method, discrete wavelet transform (DWT), wavelet packet transform (WPT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). Different fractional bandpowers are obtained from the PSDE spectrum, while the statistical root mean square values are further extracted from DWT, WPT, EMD, and VMD as feature vectors. The features are used as input features in different machine-learning classification algorithms for different mill feed size fractions predictions. The various transform techniques and feed size fraction predictions are evaluated using the various performance indicators obtained from the confusion matrix such as accuracy, precision, sensitivity and F1 score. The study showed that the acoustic signal feature extraction techniques used in conjunction with the Support Vector Machine (SVM), linear discriminant analysis (LDA), and ensemble with subclass discriminant machine learning algorithms demonstrated improved performance for predicting feed size variations.

An underwater acoustic sensing system can be used to investigate the characteristics of manganese nodule deposits, such as the nodule size distribution and concentration. We numerically analyzed the relationship between the backscattering intensity obtained using a sub-bottom profiler (SBP) and the manganese nodule distribution by considering higher-order scattering terms among the nodules, in addition to the interaction between wave propagation and the combined effect of the manganese nodules and seafloor. Numerical experiments revealed that complex acoustic reflective waveforms and corresponding spectra increase in complexity with an increase in the sphere size. We categorized this scattering phenomenon with respect to several scattering regimes. As observed, the total reflection response can be attributed to the interference between the scattered waveforms from the nodules and the reflected waveforms from the seabed surface. The laboratory experiments were conducted using actual manganese nodules, and demonstrated a macroscopic decrease in reflectivity with an increase in the area ratio for different nodule sizes. It is, therefore, necessary to eliminate the wave interaction between manganese nodules and the seafloor to quantitatively estimate the density of the nodule distribution. [ABSTRACT FROM AUTHOR]

Objective: In emergency medicine, airway management is a core skill that includes endotracheal intubation (ETI), a common technique that can result in ineffective ventilation and laryngotracheal injury if executed incorrectly. We present a method for automatically generating performance feedback during ETI simulator training, potentially augmenting training outcomes on robotic simulators.Method: Electret microphones recorded ultrasonic echoes pulsed through the complex geometry of a simulated airway during ETI performed on a full-size patient simulator. As the endotracheal tube is inserted deeper and the cuff is inflated, the resulting changes in geometry are reflected in the recorded signal. We trained machine learning models to classify 240 intubations distributed equally between six conditions: three insertion depths and two cuff inflation states. The best performing models were cross validated in a leave-one-subject-out scheme.Results: Best performance was achieved by transfer learning with a convolutional neural network pre-trained for sound classification, reaching global accuracy above 98% on 1-second-long audio test samples. A support vector machine trained on different features achieved a median accuracy of 85% on the full label set and 97% on a reduced label set of tube depth only.Significance: This proof-of-concept study demonstrates a method of measuring qualitative performance criteria during simulated ETI in a relatively simple way that does not damage ecological validity of the simulated anatomy. As traditional sonar is hampered by geometrical complexity compounded by the introduced equipment in ETI, the accuracy of machine learning methods in this confined design space enables application in other invasive procedures. By enabling better interaction between the human user and the robotic simulator, this approach could improve training experiences and outcomes in medical simulation for ETI as well as many other invasive clinical procedures.

High-altitude balloons carrying infrasound sensor payloads can be leveraged toward monitoring efforts to provide some advantages over other sensing modalities. On 10 July 2020, three sets of controlled surface explosions generated infrasound waves detected by a high-altitude floating sensor. One of the signal arrivals, detected when the balloon was in the acoustic shadow zone, could not be predicted via propagation modeling using a model atmosphere. Considering that the balloon's horizontal motion showed direct evidence of gravity waves, we examined their role in infrasound propagation. Implementation of gravity wave perturbations to the wind field explained the signal detection and aided in correctly predicting infrasound travel times. Our results show that the impact of gravity waves is negligible below 20 km altitude; however, their effect is important above that height. The results presented here demonstrate the utility of balloon-borne acoustic sensing toward constraining the source region of variability, as well as the relevance of complexities surrounding infrasound wave propagation at short ranges for elevated sensing platforms. [ABSTRACT FROM AUTHOR]

In a large-scale operation, feed ores are introduced into the AG/SAG mill in a continuous mode at a given flow rate to replace the discharging slurry. Nonetheless, the variations in the feed characteristics, typically hardness and size distribution, could cause sudden disruption to the mill operation. This would be challenging to detect in practice, owing to the hostile environment of the mill. In this work, an acoustic sensing-based monitoring technique was utilized in a laboratory-scale AG/SAG mill locked-cycle study to keep track of fluctuations caused by feed ore heterogeneity. Analysis of the recorded mill acoustic response using statistical root mean square (RMS) and mill discharge sizes showed that the introduction of fresh feed with varying hardness and size distribution considerably altered the mill product undersize of −150 μm and acoustic emission. Overall, the acoustic sensing technique demonstrated that the AG/SAG mill stability as well as disturbances caused by different feed size fractions and hardness can be monitored using the mill acoustic response, an indication of real-time monitoring and optimisation. [ABSTRACT FROM AUTHOR]

The ageing of populations is increasing pressure on health and social care systems. Potentially, assistive technologies are a way to support the independence of older adults in their daily activities. Among existing assistive technologies, ambient sensing technologies have received less attention than wearable systems. Moreover, there has been little research into cheaper technologies capable of using multiple modalities. A systematic review of the acceptability of assisted living or smart homes in the United Kingdom and the simultaneous use of sounds and vibrations in remote monitoring of assisted living or smart homes will inform and encourage the use of digital monitoring technologies. The acceptability of sensing technologies depends on whether there is any social stigma about their use, for example, the extent to which they invade privacy. The United Kingdom studies reviewed suggest a lack of measurements of the perceived efficacy or effectiveness of the monitoring devices. The primary use of vibration or acoustic technologies has been for detecting falls rather than monitoring health. The review findings suggest the need for further exploration of the acceptability and applicability of remote monitoring technologies, as well as a need for more research into the simultaneous use of sounds and vibrations in health monitoring.

Highlights: What are the main findings? Laboratory SAG mill acoustics are sensitive to different feed size fractions. Supervised classification models and acoustic emissions were suitable for predicting different feed size fractions in laboratory SAG mills. What is the implication of the main finding? SAG mill acoustics can serve as online proxy tool for providing more insight into different feed size fractions in the mill. The practical implication of the study could be beneficial to SAG mill operators by predicting a sudden change in feed size in real-time. The harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, the need for continuous monitoring of mill parameters for optimal operation. In this paper, an acoustic-based sensing method is employed to estimate, in real time, a snapshot of the different feed size fractions presented to a laboratory-scale SAG mill. Employing the MATLAB 2020b programme, the mill acoustic signal is processed using various transform techniques such as power spectral density estimate (PSDE) by Welch's method, discrete wavelet transform (DWT), wavelet packet transform (WPT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). Different fractional bandpowers are obtained from the PSDE spectrum, while the statistical root mean square values are further extracted from DWT, WPT, EMD, and VMD as feature vectors. The features are used as input features in different machine-learning classification algorithms for different mill feed size fractions predictions. The various transform techniques and feed size fraction predictions are evaluated using the various performance indicators obtained from the confusion matrix such as accuracy, precision, sensitivity and F1 score. The study showed that the acoustic signal feature extraction techniques used in conjunction with the Support Vector Machine (SVM), linear discriminant analysis (LDA), and ensemble with subclass discriminant machine learning algorithms demonstrated improved performance for predicting feed size variations. [ABSTRACT FROM AUTHOR]

Underground utility tunnels (UUTs) are convenient for the integrated management of various infrastructure facilities. They ensure effective control of underground facilities and reduce occupied space. However, aging UUTs require effective management and preventive measures for fire safety. The fundamental problems in operating UUTs are the frequent occurrence of mold, corrosion, and damage caused to finishing materials owing to inadequate waterproofing, dehumidification, and ventilation facilities, which result in corrosion-related electrical leakage in wiring and cables. To prevent this, an abnormal sound detection technology is developed in this study based on acoustic sensing. An acoustic sensor is used to detect electric sparks in the moldy environments of UUTs using a system to collect and analyze the sound generated in the UUTs. We targeted the sound that had the highest impact on detecting electric sparks and performed U-Net-based noise reduction and two-dimensional convolutional neural network-based abnormal sound detection. A mock experiment was conducted to verify the performance of the proposed model. The results indicated that local and spatial features could capture the internal characteristics of both abnormal and normal sounds. The superior performance of the proposed model verified that the local and spatial features of electric sparks are crucial for detecting abnormal sounds. [ABSTRACT FROM AUTHOR]

A simulation model of sound backscattering in gas flares is presented, designed to solve direct and inverse problems of acoustic sensing of flares using the simulation (computer) modeling method. The model is based on the concept of the discrete character of sound scattering in a gas plume, according to which the received echo signal is the sum of elementary signals, backward-scattered bubbles that form the flare. At the same time, the model of sound reverberation in a gas flare is considered as a temporary random process in the absence of multiple scattering effects. The model takes into account the distributions of gas bubbles by sizes and rise velocities, as well as the evolution of bubbles and their gas exchange with the marine environment during ascent. Based on this, the potential accuracy of deter- mining the coordinates of gaseous sources (seeps) by a split-beam echo sounder was estimated by simulation modeling. It has been established that the main parameters that determine the RMS (root- mean-square deviations) of the estimates from the actual coordinates are the transverse size of the flare near the bottom, the SNR (signal-to-noise ratio), and the volume of echo signal samples. Dependences of the RMS on the determining parameters are obtained.

We propose a sensorization method for soft pneumatic actuators that uses an embedded microphone and speaker to measure different actuator properties. The physical state of the actuator determines the specific modulation of sound as it travels through the structure. Using simple machine learning, we create a computational sensor that infers the corresponding state from sound recordings. We demonstrate the acoustic sensor on a soft pneumatic continuum actuator and use it to measure contact locations, contact forces, object materials, actuator inflation, and actuator temperature. We show that the sensor is reliable (average classification rate for six contact locations of 93%), precise (mean spatial accuracy of 3.7 mm), and robust against common disturbances like background noise. Finally, we compare different sounds and learning methods and achieve best results with 20 ms of white noise and a support vector classifier as the sensor model. [ABSTRACT FROM AUTHOR]

Liquid identification plays an essential role in our daily lives. However, existing RF sensing approaches still require dedicated hardware such as RFID readers and UWB transceivers, which are not readily available to most users. In this article, we propose Akte-Liquid, which leverages the speaker on smartphones to transmit acoustic signals, and the microphone on smartphones to receive reflected signals to identify liquid types and analyze the liquid concentration. Ourwork arises fromthe acoustic intrinsic impedance property of liquids, in that different liquids have different intrinsic impedance, causing reflected acoustic signals of liquids to differ. Then, we discover that the amplitude-frequency feature of reflected signals may be utilized to represent the liquid feature. With this insight, we propose new mechanisms to eliminate the interference caused by hardware and multi-path propagation effects to extract the liquid features. In addition, we design a new Siamese network-based structure with a specific training sample selection mechanism to reconstruct the extracted feature to container-irrelevant features. Our experimental evaluations demonstrate that Akte-Liquid is able to distinguish 20 types of liquids at a higher accuracy, and to identify food additives and measure protein concentration in the artificial urine with a 92.3% accuracy under 1 mg/100 mL as well. [ABSTRACT FROM AUTHOR]

Sound-related respiratory symptoms are commonly observed in our daily lives. They are closely related to illnesses, infections, or allergies but ignored by the majority. Existing detection methods either depend on specific devices, which are inconvenient to wear, or are sensitive to noises and only work for indoor environment. Considering the lack of monitoring method for in-car environment, where there is high risk of spreading infectious diseases, we propose a smartphone-based system, named SymListener, to detect respiratory symptoms in driving environment. By continuously recording acoustic data through a built-in microphone, SymListener can detect the sounds of cough, sneeze, and sniffle.We design a modified ABSE-based method to remove the strong and changeable driving noises while saving energy of the smartphone. An LSTM network is adopted to classify the three types of symptoms according to the carefully designed acoustic features. We implement SymListener on different Android devices and evaluate its performance in real driving environment. The evaluation results show that SymListener can reliably detect target respiratory symptoms with an average accuracy of 92.19% and an average precision of 90.91%. [ABSTRACT FROM AUTHOR]

With the increasing pervasiveness of mobile devices such as smartphones, smart TVs, and wearables, smart sensing, transforming the physical world into digital information based on various sensing medias, has drawn researchers' great attention. Among different sensing medias, WiFi and acoustic signals stand out due to their ubiquity and zero hardware cost. Based on different basic principles, researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition, motion tracking, indoor localization, health monitoring, and the like. To enable readers to get a comprehensive understanding of ubiquitous wireless sensing, we conduct a survey of existing work to introduce their underlying principles, proposed technologies, and practical applications. Besides we also discuss some open issues of this research area. Our survey reals that as a promising research direction, WiFi and acoustic sensing technologies can bring about fancy applications, but still have limitations in hardware restriction, robustness, and applicability. [ABSTRACT FROM AUTHOR]

Three decades after the first set of work on Medical Augmented Reality (MAR) was presented to the international community, and ten years after the deployment of the first MAR solutions into operating rooms, its exact definition, basic components, systematic design, and validation still lack a detailed discussion. This paper defines the basic components of any Augmented Reality (AR) solution and extends them to exemplary Medical Augmented Reality Systems (MARS). We use some of the original MARS applications developed at the Chair for Computer Aided Medical Procedures and deployed into medical schools for teaching anatomy and into operating rooms for telemedicine and surgical guidance throughout the last decades to identify the corresponding basic components. In this regard, the paper is not discussing all past or existing solutions but only aims at defining the principle components and discussing the particular domain modeling for MAR and its design-development-validation process, and providing exemplary cases through the past in-house developments of such solutions. [ABSTRACT FROM AUTHOR]

Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal sound samples. Recent discriminative models based on machine identity (ID) classification have shown excellent ASD performance by leveraging strong prior knowledge like machine ID. However, such strong priors are often unavailable in real-world applications, limiting these models. To address this, we propose utilizing the imbalanced and inconsistent attribute labels from acoustic sensors, such as machine running speed and microphone model, as weak priors to train an attribute classifier. We also introduce an imbalanced compensation strategy to handle extremely imbalanced categories and ensure model trainability. Furthermore, we propose a score fusion method to enhance anomaly detection robustness. The proposed algorithm was applied in our DCASE2023 Challenge Task 2 submission, ranking sixth internationally. By exploiting acoustic sensor data attributes as weak prior knowledge, our approach provides an effective framework for robust ASD when strong priors are absent.

Ferroelectret nanogenerator (FENG), renowned for its remarkable electromechanical conversion efficiency and low Young’s modulus, has gained significant attention in various acoustic applications. The increasing interest is attributed to the crucial role acoustic devices play in our daily lives. This paper provides a comprehensive review of the advancements made in using FENG for acoustic applications. It elaborates on the operational mechanism of FENG in acoustics, with a special focus on comparing the influence of different fabrication materials and techniques on its properties. This review categorizes acoustic applications of FENG into three primary areas: acoustic sensing, acoustic actuation, and acoustic energy harvesting. The detailed descriptions of FENG’s implementations in these areas are provided, and potential directions and challenges for further development are outlined. By demonstrating the wide range of potential applications for FENG, it is shown that FENG can be adapted to meet different individual needs.

Rope jumping, as a fitness exercise recommended by many sports medicine practitioners, can improve cardiorespiratory capacity and physical coordination. Existing rope jump monitoring systems have limitations in terms of convenience, comfort, and exercise intensity evaluation. This paper presents a rope jump monitoring system using passive acoustic sensing. Our system exploits the off-the-shelf smartphone and headphones to capture the user's rope-jumping sound and breathing sound after exercise. Given the captured acoustic data, the system uses a short-time energy-based approach and the high correlation between rope jumping cycles to detect the rope-jumping sound frames, then applies a dual-threshold endpoint detection algorithm to calculate the number of rope jumps. Finally, our system performs regression predictions of exercise intensity based on features extracted from the jumping speed and the mel spectrograms of the user's breathing sound. The significant advantage of the system lies in the solution of the problem of poorly characterized mel spectrograms. We employ an attentive mechanism-based GAN to generate optimized breathing sound mel spectrograms and apply domain adversarial adaptive in the network to improve the migration capability of the system. Through extensive experiments, our system achieves (on average) 0.32 and 2.3% error rates for the rope jumping count and exercise intensity evaluation, respectively. [ABSTRACT FROM AUTHOR]

Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user's signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2 % AUC and 1.25 % EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc. [ABSTRACT FROM AUTHOR]

With the development of technology, systems gravitate towards increasing in their complexity, miniaturization, and level of automation. Amongst these systems, ultrasonic devices have adhered to this trend of advancement. Ultrasonic systems require transducers to generate and sense ultrasonic signals. These transducers heavily impact the system's performance. Advancements in microelectromechanical systems have led to the development of micromachined ultrasonic transducers (MUTs), which are utilized in miniaturized ultrasound systems. Piezoelectric micromachined ultrasonic transducers (PMUTs) exhibit higher capacitance and lower electrical impedance, which enhances the transducer's sensitivity by minimizing the effect of parasitic capacitance and facilitating their integration with low-voltage electronics. PMUTs utilize high-yield batch microfabrication with the use of thin piezoelectric films. The deposition of thin piezoelectric material compatible with complementary metal-oxide semiconductors (CMOS) has opened novel avenues for the development of miniaturized compact systems with the same substrate for application and control electronics. PMUTs offer a wide variety of applications, including medical imaging, fingerprint sensing, range-finding, energy harvesting, and intrabody and underwater communication links. This paper reviews the current research and recent advancements on PMUTs and their applications. This paper investigates in detail the important transduction metrics and critical design parameters for high-performance PMUTs. Piezoelectric materials and microfabrication processes utilized to manufacture PMUTs are discussed. Promising PMUT applications and outlook on future advancements are presented. [ABSTRACT FROM AUTHOR]

Non-line-of-sight (NLOS) imaging is aimed at visualizing hidden scenes from an observer's (e.g., camera) viewpoint. Typically, hidden scenes are reconstructed using diffused signals that emit light sources using optical equipment and are reflected multiple times. Optical systems are commonly adopted in NLOS imaging because lasers can transport energy and focus light over long distances without loss. In contrast, we propose NLOS imaging using acoustic equipment inspired by echolocation. Existing acoustic NLOS is a computational method motivated by seismic imaging that analyzes the geometry of underground structures. However, this physical method is susceptible to noise and requires a clear signal, resulting in long data acquisition times. Therefore, we reduced the scan time by modifying the echoes to be collected simultaneously rather than sequentially. Then, we propose end-to-end deep-learning models to overcome the challenges of echoes interfering with each other. We designed three distinctive architectures: an encoder that extracts features by dividing multi-channel echoes into groups and merging them hierarchically, a generator that constructs an image of the hidden object, and a discriminator that compares the generated image with the ground-truth image. The proposed model successfully reconstructed the outline of the hidden objects. [ABSTRACT FROM AUTHOR]

Recognizing surfaces based on their vibration signatures is useful as it can enable tagging of different locations without requiring any additional hardware, such as near field communication (NFC) tags. However, previous vibration based surface recognition schemes either use custom hardware for creating and sensing vibration, which makes them difficult to adopt, or use intertial (IMU) sensors in commercial off-the-shelf (COTS) smartphones to sense movements produced due to vibrations, which makes them coarse-grained because of the low sampling rates of IMU sensors. The mainstream COTS smartphones based schemes are also susceptible to inherent hardware based irregularities in vibration mechanism of the smartphones. Moreover, the existing schemes that use microphones to sense vibration are prone to short-term and constant background noises (e.g., intermittent talking, exhaust fan, etc.) because microphones not only capture the sounds created by vibration but also other interfering sounds present in the environment. In this paper, we propose VibroTag, a robust and practical vibration based sensing scheme that works with smartphones with different hardware, can extract fine-grained vibration signatures of different surfaces, and is robust to environmental noise and hardware based irregularities. We implemented VibroTag on two different Android phones and evaluated in multiple different environments where we collected data from four individuals for 5 to 20 consecutive days. Our results show that VibroTag achieves an average accuracy of 86.55 percent while recognizing 24 different locations/surfaces, even when some of those surfaces were made of similar material. VibroTag’s accuracy is 37 percent higher than the average accuracy of 49.25 percent achieved by one of the state-of-the-art IMUs based schemes, which we implemented for comparison with VibroTag. [ABSTRACT FROM AUTHOR]

• Under pronunciation principles, this paper presents a smartphone-based pronunciation training system for practicing monophthong regarding vowel articulation. • Speech and ultrasonic signals are captured and analyzed simultaneously to determine the tongue position and shape of the lips respecting uttering vowels. • The proposed system implementing a commercial smartphone is robust with high accuracy for practical scenarios. • The evaluation for its impact on user experience shows that the proposed system has raised learning engagement and enjoyment. Learning a foreign language pronunciation is the most challenging task for non-native speakers. Improving pronunciation based on feedback on pronunciation error scores is also not easy for learners. Our goal is to develop an alternative approach to pronunciation training that can point out articulation errors at the phoneme level to users through a smartphone-based system. Using self-correction as a means of ensuring learners' engagement, we focus on self-correction of pronunciation by adjusting articulation. In order to identify articulation concerning pronunciation, the system evaluates both audible and inaudible acoustic signals to examine fine-grained frequency-shifting direction of mouth movements and tongue position simultaneously. The result shows that the system can reach an average accuracy of 99.09% and is robust in different scenarios and genders. Additionally, the evaluation of the user study reveals that the proposed system provides positive user experiences and allows learners to improve their pronunciation more efficiently. [ABSTRACT FROM AUTHOR]

Carbon nanocoils (CNCs) are widely used in functional devices due to their helical morphology, which can be utilized in the fabrication of functional materials with unique properties. In this study, CNCs/polyvinyl alcohol (PVA) composite films were prepared using an electrostatic spinning method and used to form a diaphragm for Fabry–Perot acoustic sensors. With the addition of CNCs, the fabricated composite film showed enhanced mechanical performance responding to acoustic wave pressure. Considering the optical and mechanical response, the content of CNCs was set as 0.14 wt.%; the highest acoustic wave pressure response of the sensor was 1.89 V/Pa at 16.2 kHz, which was relatively higher than that of devices with pure polymer films. Additionally, the sensor had a broadband frequency response from 2 to 10 kHz. The results indicate that the proposed composite film acoustic sensor is suitable for low-frequency acoustic sensing, which lays the foundation for the extended application of functional sensors based on CNCs. [ABSTRACT FROM AUTHOR]

High-altitude balloons carrying infrasound sensor payloads can be leveraged toward monitoring efforts to provide some advantages over other sensing modalities. On 10 July 2020, three sets of controlled surface explosions generated infrasound waves detected by a high-altitude floating sensor. One of the signal arrivals, detected when the balloon was in the acoustic shadow zone, could not be predicted via propagation modeling using a model atmosphere. Considering that the balloon’s horizontal motion showed direct evidence of gravity waves, we examined their role in infrasound propagation. Implementation of gravity wave perturbations to the wind field explained the signal detection and aided in correctly predicting infrasound travel times. Our results show that the impact of gravity waves is negligible below 20 km altitude; however, their effect is important above that height. The results presented here demonstrate the utility of balloon-borne acoustic sensing toward constraining the source region of variability, as well as the relevance of complexities surrounding infrasound wave propagation at short ranges for elevated sensing platforms.

Background. With the increasing demands of industry for rare metals (such as lithium, cobalt, etc.) and in conditions of depletion of their deposits, more and more attention is being paid to the possibilities of extracting ore from the ocean’s bottom. Under the conditions of increasing demands of industry for rare metals (such as lithium, cobalt, etc.) and in their deposits depletion, more and more attention is focused on the possibilities of extracting ore from the ocean’s bottom Remote acoustic sounding of the ocean’s bottom can serve as the simplest and most environmentally friendly way to detect underlying minerals for subsequent extraction. Remote acoustic sounding of the ocean’s bottom could serves as the simplest and most environmentally friendly way to detect underlying minerals for subsequent extraction. An example of such minerals are iron-manganese nodules (IMN), which are investigated in this work. The investigating in this work iron-manganese nodules (IMN) is an example of such minerals. The researches conducted at the Department of Acoustics of the UNN has shown the possibility of determining the average concentration of discrete random inhomogeneities by the changes in the average intensity of the backscattered acoustic signal. The purpose of my research is to construct a backscattering model that takes into account the simultaneous influence of the characteristics of the surface underlying the scatterers and the uneven distribution of inhomogeneities over this surface to determine the concentration of particles according to acoustic measurements. Materials and methods. The physical model was considered within the framework of the theory of single scattering. The physical model was considered within the framework of the single scattering theory. The discrete inhomogeneities imitating the IMN were rigid spherical particles. Results. We investigated the influence of the reflecting properties of bottom rocks on the remote acoustic diagnostics of discrete random inhomogeneities located at the bottom. Also, in was constructed a backscattering model that takes into account the simultaneous influence of the characteristics of the surface underlying the scatterers and the uneven distribution of inhomogeneities over this surface. The obtained theoretical results were compared with experimental data, also it was considered the case of backscattering in the presence of diffusers of different sizes. Conclusions. The simulation results show that at low concentrations of scatterers, the presence of a reflected signal leads to a relatively large increase in intensity compared to the intensity of the signal reflected only from inhomogeneities. The simulation results is show that the low concentrations of scatterers, the reflected signal existence leads to a quite large intensity increasing with the comparing to the intensity of the signal reflected only from inhomogeneities A strong correlation between the positions of the diffusers significantly affects the magnitude of the average intensity of the received field compared to the case of a chaotic arrangement of inhomogeneities. The strong correlation between the diffusers’ positions significantly affects to the magnitude of the average intensity of the received field compared to the case of a chaotic arrangement of inhomogeneities. If the scatterers have different sizes, a significant decrease in the interference term of the average backscattering intensity is observed. In case when the scatterers have different sizes, it is observed the significant decreasing of the interference term of the average backscattering intensity.

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Driving safety has drawn much public attention in recent years due to the fast-growing number of cars. Smoking is one of the threats to driving safety but is often ignored by drivers. Existing works on smoking detection either work in contact manner or need additional devices. This motivates us to explore the practicability of using smartphones to detect smoking events in driving environment. In this paper, we propose a cigarette smoking detection system, named HearSmoking, which only uses acoustic sensors on smartphones to improve driving safety. After investigating typical smoking habits of drivers, including hand movement and chest fluctuation, we design an acoustic signal to be emitted by the speaker and received by the microphone. We calculate Relative Correlation Coefficient of received signals to obtain movement patterns of hands and chest. The processed data is sent into a trained Convolutional Neural Network for classification of hand movement. We also design a method to detect respiration at the same time. To improve system performance, we further analyse the periodicity of the composite smoking motion. Through extensive experiments in real driving environments, HearSmoking detects smoking events with an average total accuracy of 93.44 percent in real-time. [ABSTRACT FROM AUTHOR]