sound waves
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AbstractAnderson localization arises from the interference of multiple scattering paths in a disordered medium, and applies to both quantum and classical waves. Soft matter provides a unique potential platform to observe localization of non-interacting classical waves because of the order of magnitude difference in speed between fast and slow waves in conjunction with the possibility to achieve strong scattering over broad frequency bands while minimizing dissipation. Here, we provide long sought evidence of a localized phase spanning up to 246 kHz for fast (sound) waves in a soft elastic medium doped with resonant encapsulated microbubbles. We find the transition into the localized phase is accompanied by an anomalous decrease of the mean free path, which provides an experimental signature of the phase transition. At the transition, the decrease in the mean free path with changing frequency (i.e., disorder strength) follows a power law with a critical exponent near unity. Within the localized phase the mean free path is in the range 0.4–1.0 times the wavelength, the transmitted intensity at late times is well-described by the self-consistent localization theory, and the localization length decreases with increasing microbubble volume fraction. Our work sets the foundation for broadband control of localization and the associated phase transition in soft matter, and affords a comparison of theory to experiment.

In the process of learning physics, experiments are needed that can help someone in gaining a deeper understanding of learning physics concepts and using technology in the learning process, especially learning sound waves. In this study, the aim is to be able to analyze the sound frequency with the help of Audacity software. Subjects used are 5 different cat sounds. The implementation of this research uses several tools such as a microphone, Audacity software on a laptop, and 5 cat sounds. This experiment was carried out by bringing the micro hope closer to the cat with 5 cm so that the sound was captured by the microphone which would later be transferred to the laptop and read by the audacity software. Furthermore, the data recorded in audacity were analyzed. From the results of the study, it can be said that a tool that can be used in practicum and can read and capture sound waves is effectively used in analyzing sound frequency, spectrum in the application of sound learning so that it can be used as one of the learning media in practicum on sound wave material at Junior high school.

Abstract Broadband absorption of low-frequency sound waves via a deep subwavelength structure is of great and ongoing interest in research and engineering. Here, we numerically and experimentally present a design of a broadband low-frequency absorber based on an acoustic metaporous composite (AMC). The AMC absorber is constructed by embedding a single metamaterial resonator into a porous layer. The finite element simulations show that a high absorption (absorptance A > 0.8) can be achieved within a broad frequency range (from 290 Hz to 1074 Hz), while the thickness of AMC is 1/13 of the corresponding wavelength at 290 Hz. The broadband and high-efficiency performances of the absorber are attributed to the coupling between the two resonant absorptions and the trapped mode. A good agreement between the numerical simulation and experiment is obtained. Moreover, the high broadband absorption can be maintained under random incident acoustic waves. The proposed absorber provides potential applications in low-frequency noise reduction especially when limited space is demanded.

Lung diseases are the most common conditions in newborns, infants, and children and are also the primary cause of death in children younger than 5 years old. Traditionally, the lung was not thought to be a target for an ultrasound due to its inability to penetrate the gas-filled anatomical structures. With the deepening of knowledge on ultrasound in recent years, it is now known that the affected lung produces ultrasound artifacts resulting from the abnormal tissue/gas/tissue interface when ultrasound sound waves penetrate lung tissue. Over the years, the application of lung ultrasound (LUS) has changed and its main indications in the pediatric population have expanded. This review analyzed the studies on lung ultrasound in pediatrics, published from 2010 to 2020, with the aim of highlighting the usefulness of LUS in pediatrics. It also described the normal and abnormal appearances of the pediatric lung on ultrasound as well as the benefits, limitations, and possible future challenges of this modality.

Abstract The dynamics of sound in a fluid is intrinsically nonlinear. We derive the consequences of this fact for the analogue gravitational field experienced by sound waves, by first describing generally how the nonlinearity of the equation for phase fluctuations back-reacts on the definition of the background providing the effective space-time metric. Subsequently, we use the analytical tool of Riemann invariants in one-dimensional motion to derive source terms of the effective gravitational field stemming from nonlinearity. Finally, we show that the consequences of nonlinearity we derive can be observed with Bose-Einstein condensates in the ultracold gas laboratory.

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.