Several advantages of directed energy deposition-arc (DED-arc) have garnered considerable research attention including high deposition rates and low costs. However, defects such as discontinuity and pores may occur during the manufacturing process. Defect identification is the key to monitoring and quality assessments of the additive manufacturing process. This study proposes a novel acoustic signal-based defect identification method for DED-arc via wavelet time-frequency diagrams. With the continuous wavelet transform, one-dimensional (1D) acoustic signals acquired in situ during manufacturing are converted into two-dimensional (2D) time-frequency diagrams to train, validate, and test the convolutional neural network (CNN) models. In this study, several CNN models were examined and compared, including AlexNet, ResNet-18, VGG-16, and MobileNetV3. The accuracy of the models was 96.35%, 97.92%, 97.01%, and 98.31%, respectively. The findings demonstrate that the energy distribution of normal and abnormal acoustic signals has significant differences in both the time and frequency domains. The proposed method is verified to identify defects effectively in the manufacturing process and advance the identification time.

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

Material identification is playing an increasingly important role in various sectors such as industry, petrochemical, mining, and in our daily lives. In recent years, material identification has been utilized for security checks, waste sorting, etc. However, current methods for identifying materials require direct contact with the target and specialized equipment that can be costly, bulky, and not easily portable. Past proposals for addressing this limitation relied on non-contact material identification methods, such as Wi-Fi-based and radar-based material identification methods, which can identify materials with high accuracy without physical contact; however, they are not easily integrated into portable devices. This paper introduces a novel non-contact material identification based on acoustic signals. Different from previous work, our design leverages the built-in microphone and speaker of smartphones as the transceiver to identify target materials. The fundamental idea of our design is that acoustic signals, when propagated through different materials, reach the receiver via multiple paths, producing distinct multipath profiles. These profiles can serve as fingerprints for material identification. We captured and extracted them using acoustic signals, calculated channel impulse response (CIR) measurements, and then extracted image features from the time-frequency domain feature graphs, including histogram of oriented gradient (HOG) and gray-level co-occurrence matrix (GLCM) image features. Furthermore, we adopted the error-correcting output code (ECOC) learning method combined with the majority voting method to identify target materials. We built a prototype for this paper using three mobile phones based on the Android platform. The results from three different solid and liquid materials in varied multipath environments reveal that our design can achieve average identification accuracies of 90% and 97%.

The poor mobility of nymphs living underground, usually for many years, and the weak flying ability of adults make cicadas unique for evolutionary biology and bio-geographical study. Cicadas of the genus Karenia are unusual in Cicadidae in lacking the timbals that produce sound. Population differentiation, genetic structure, dispersal and evolutionary history of the eastern Asian mute cicada Karenia caelatata were investigated based on morphological, acoustic and molecular data. The results reveal a high level of genetic differentiation in this species. Six independent clades with nearly unique sets of haplotypes corresponding to geographically isolated populations are recognized. Genetic and geographic distances are significantly correlated among lineages. The phenotypic differentiation is generally consistent with the high levels of genetic divergence across populations. Results of ecological niche modeling suggest that the potential distribution range of this mountain-habitat specialist during the Last Glacial Maximum was broader than its current range, indicating this species had benefited from the climate change during the early Pleistocene in southern China. Geological events such as orogeny in Southwest China and Pleistocene climate oscillations have driven the differentiation and divergence of this species, and basins, plains and rivers function as natural \"barriers\" to block the gene flow. Besides significant genetic divergence being found among clades, the populations occurring in the Wuyi Mountains and the Hengduan Mountains are significantly different in the calling song structure from other populations. This may have resulted from significant population differentiation and subsequent adaptation of related populations. We conclude that ecological differences in habitats, coupled with geographical isolation, have driven population divergence and allopatric speciation. This study provides a plausible example of incipient speciation in Cicadidae and improves understanding of population differentiation, acoustic signal diversification and phylogeographic relationships of this unusual cicada species. It informs future studies on population differentiation, speciation and phylogeography of other mountain-habitat insects in the East Asian continent.

The geomagnetic field (GMF) is a worldwide source of compass cues used by animals and humans alike. The inclination of GMF flux lines also provides information on geomagnetic latitude. A long-disputed question, however, is whether horizontal gradients in GMF intensity, in combination with changes in inclination, provide bicoordinate \"map\" information. Multiple sources contribute to the total GMF, the largest of which is the core field. The ubiquitous crustal field is much less intense, but in both land and marine settings is strong enough at low altitudes (< 700 m; sea level) to mask the core field\'s weak N-S intensity gradient (~ 3-5 nT/km) over 10 s to 100 s of km. Non-orthogonal geomagnetic gradients, the lack of consistent E-W gradients, and the local masking of core-field intensity gradients by the crustal field, therefore, are grounds for rejection of the bicoordinate geomagnetic \"map\" hypothesis. In addition, the alternative infrasound direction-finding hypothesis is briefly reviewed. The GMF\'s diurnal variation has long been suggested as a possible Zeitgeber (timekeeper) for circadian rhythms and could explain the GMF\'s non-compass role in the avian navigational system. Requirements for detection of this weaker diurnal signal (~ 20-50 nT) might explain the magnetic alignment of resting and grazing animals.

Divergence in acoustic signals may have a crucial role in the speciation process of animals that rely on sound for intra-specific recognition and mate attraction. The acoustic adaptation hypothesis (AAH) postulates that signals should diverge according to the physical properties of the signalling environment. To be efficient, signals should maximize transmission and decrease degradation. To test which drivers of divergence exert the most influence in a speciose group of insects, we used a phylogenetic approach to the evolution of acoustic signals in the cicada genus Tettigettalna, investigating the relationship between acoustic traits (and their mode of evolution) and body size, climate and micro-/macro-habitat usage. Different traits showed different evolutionary paths. While acoustic divergence was generally independent of phylogenetic history, some temporal variables\' divergence was associated with genetic drift. We found support for ecological adaptation at the temporal but not the spectral level. Temporal patterns are correlated with micro- and macro-habitat usage and temperature stochasticity in ways that run against the AAH predictions, degrading signals more easily. These traits are likely to have evolved as an anti-predator strategy in conspicuous environments and low-density populations. Our results support a role of ecological selection, not excluding a likely role of sexual selection in the evolution of Tettigettalna calling songs, which should be further investigated in an integrative approach.

Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter bank in the frequency domain, and then the Mel frequency cepstral coefficients of the respective mode signals are calculated in the mapping range of frequency domain, and the optimized Mel frequency cepstral coefficients are used as the input of long and short time memory network (LSTM) which is trained and verified, and the fault diagnosis model of the diesel engine is obtained. The experimental part compares the fault diagnosis effects of different feature extraction methods, different modal decomposition methods and different classifiers, finally verifying the feasibility and effectiveness of the method proposed in this paper, and providing solutions to the problem of how to realise fault diagnosis using acoustic signals.

In environment sound classification, log Mel band energies (MBEs) are considered as the most successful and commonly used features for classification. The underlying algorithm, fast Fourier transform (FFT), is valid under certain restrictions. In this study, we address these limitations of Fourier transform and propose a new method to extract log Mel band energies using amplitude modulation and frequency modulation. We present a comparative study between traditionally used log Mel band energy features extracted by Fourier transform and log Mel band energy features extracted by our new approach. This approach is based on extracting log Mel band energies from estimation of instantaneous frequency (IF) and instantaneous amplitude (IA), which are used to construct a spectrogram. The estimation of IA and IF is made by associating empirical mode decomposition (EMD) with the Teager-Kaiser energy operator (TKEO) and the discrete energy separation algorithm. Later, Mel filter bank is applied to the estimated spectrogram to generate EMD-TKEO-based MBEs, or simply, EMD-MBEs. In addition, we employ the EMD method to remove signal trends from the original signal and generate another type of MBE, called S-MBEs, using FFT and a Mel filter bank. Four different datasets were utilised and convolutional neural networks (CNN) were trained using features extracted from Fourier transform-based MBEs (FFT-MBEs), EMD-MBEs, and S-MBEs. In addition, CNNs were trained with an aggregation of all three feature extraction techniques and a combination of FFT-MBEs and EMD-MBEs. Individually, FFT-MBEs achieved higher accuracy compared to EMD-MBEs and S-MBEs. In general, the system trained with the combination of all three features performed slightly better compared to the system trained with the three features separately.

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

The role of signalling in initiating and perpetuating effects triggered by deposition of ionising radiation energy in parts of a system is very clear. Less clear are the very early steps involved in converting energy to chemical and biological effects in non-targeted parts of the system. The paper aims to present a new model, which could aid our understanding of the role of low dose effects in determining ultimate disease outcomes. We propose a key role for electromagnetic signals resulting from physico-chemical processes such as excitation decay, and acoustic waves. These lead to the initiation of damage response pathways such as elevation of reactive oxygen species and membrane associated changes in key ion channels. Critically, these signalling pathways allow coordination of responses across system levels. For example, depending on how these perturbations are transduced, adverse or beneficial outcomes may predominate. We suggest that by appreciating the importance of signalling and communication between multiple levels of organisation, a unified theory could emerge. This would allow the development of models incorporating time, space and system level to position data in appropriate areas of a multidimensional domain. We propose the use of the term \"infosome\" to capture the nature of radiation-induced communication systems which include physical as well as chemical signals. We have named our model \"the variable response model\" or \"VRM\" which allows for multiple outcomes following exposure to low doses or to signals from low dose irradiated cells, tissues or organisms. We suggest that the use of both dose and infosome in radiation protection might open up new conceptual avenues that could allow intrinsic uncertainty to be embraced within a holistic protection framework.