Ultrasound (US) is recognized for its use in medical imaging as a diagnostic tool. As an acoustic energy source, US has become increasingly appreciated over the past decade for its ability to non-invasively modulate cellular activity including neuronal activity. Data obtained from a host of experimental models has shown that low-intensity US can reversibly modulate the physiological activity of neurons in peripheral nerves, spinal cord, and intact brain circuits. Experimental evidence indicates that acoustic pressures exerted by US act, in part, on mechanosensitive ion channels to modulate activity. While the precise mechanisms of action enabling US to both stimulate and suppress neuronal activity remain to be clarified, there are several advantages conferred by the physics of US that make it an appealing option for neuromodulation. For example, it can be focused with millimeter spatial resolutions through skull bone to deep-brain regions. By increasing our engineering capability to leverage such physical advantages while growing our understanding of how US affects neuronal function, the development of a new generation of non-invasive neurotechnology can be developed using ultrasonic methods.

Extracting coherent wavefronts between passive receivers using cross-correlations of ambient noise (CAN) provides a means for monitoring the seismoacoustic environment without using active sources. However, using cross-correlations between single receivers can require a long recording time in order to extract stable coherent arrivals from CAN. This becomes an issue if the propagation medium fluctuates significantly during the recording period. To address this issue, this article presents a general spatio-temporal filtering procedure to enhance the emergence rate for coherent wavefronts extracted from time-averaged ambient noise correlations between two spatially separated arrays. The robustness of this array-based CAN technique is investigated using ambient shipping noise recorded over 24 h in the frequency band [250-850 Hz] on two vertical line arrays deployed 143 m apart in shallow water (depth 20 m). Experimental results confirm that the array-based CAN technique can significantly reduce the recording duration (e.g., from 22 h to 30 min) required for extracting coherent wavefronts of sufficient amplitude (e.g., 20 dB over residual temporal fluctations) when compared to conventional CAN implementations between single pairs of hydrophones. These improvements of the CAN technique could benefit the development of noise-based ocean monitoring applications such as passive acoustic tomography.

Measuring temperature changes of the deep oceans, important for determining the oceanic heat content and its impact on the Earth's climate evolution, is typically done using free‐drifting profiling oceanographic floats with limited global coverage. Acoustic thermometry provides an alternative and complementary remote sensing methodology for monitoring fine temperature variations of the deep ocean over long distances between a few underwater sources and receivers. We demonstrate a simpler, totally passive (i.e., without deploying any active sources) modality for acoustic thermometry of the deep oceans (for depths of ~ 500–1500 m), using only ambient noise recorded by two existing hydroacoustic stations of the International Monitoring System. We suggest that passive acoustic thermometry could improve global monitoring of deep‐ocean temperature variations through implementation using a global network of hydrophone arrays.

Ambient noise was recorded on two vertical line arrays (VLAs) separated by 450 m and deployed in shallow water (depth ~150 m) off San Diego, CA continuously for 6 days. Recordings were dominated by non-stationary and non-uniform broadband shipping noise (250 Hz to 1.5 kHz). Stable coherent noise wavefronts were extracted from ambient noise correlations between the VLAs during all 6 days by mitigating the effect of discrete shipping events and using array beamforming with data-derived steering vectors. This procedure allows the tracking of arrival-time variations of these coherent wavefronts during 6 days and may help in developing future passive acoustic tomography systems.

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Dispersive surface waves on an acoustic 2D metamaterial, a metasurface consisting of membranes on a rigid surface, have certain propagation characteristics with potential applications for resonance based sensing and subwavelength imaging. The trapped modes of the system that is responsible for the dispersive properties of these acoustic waves are analyzed through modal analysis for a small linear membrane array to obtain the mode shapes, resonant frequencies, quality factors, and wavenumbers. Transient analysis is used for larger arrays to obtain the dispersive properties of the traveling waves and is compared to the modal analysis. Equifrequency contours of the 2D metasurface illustrate interesting features of the metasurface at different frequency regimes around the membrane resonance. These features include anisotropic wave propagation, directional band gap, negative refraction, and complete band gap. Effects of membrane pitch, randomness of resonance, and aperiodic membrane spacing on dispersion, band gaps, and quality factor of the trapped modes on the metasurface are investigated as they relate to realistic implementations for different applications.

Capacitive Micromachined Ultrasonic Transducers (CMUTs) operating in immersion support dispersive evanescent waves due to the subwavelength periodic structure of electrostatically actuated membranes in the array. Evanescent wave characteristics also depend on the membrane resonance which is modified by the externally applied bias voltage, offering a mechanism to tune the CMUT array as an acoustic metamaterial. The dispersion and tunability characteristics are examined using a computationally efficient, mutual radiation impedance based approach to model a finite-size array and realistic parameters of variation. The simulations are verified, and tunability is demonstrated by experiments on a linear CMUT array operating in 2-12 MHz range.

Surface acoustic waves propagating over an immersed membrane metasurface, such as an array of capacitive micromachined ultrasonic transducers, can be leveraged to achieve subwavelength focusing and imaging. This is demonstrated numerically and experimentally utilizing a time reversal method on a 2D membrane array at MHz frequencies. The focusing region is a dense metasurface of CMUT membranes with 6.5 MHz resonance frequency that supports a wave field that is evanescent normal to the metasurface and capable of super-resolution along the metasurface. Electrostatically actuated membranes, spatially separate from the focusing region, are used to generate the focused wave field. Subwavelength focusing is demonstrated on the metasurface with a resolution of a single membrane resonator or λ/5. Similar techniques allow for super-resolution imaging of a subwavelength defect or change in the medium of the focusing region. A subwavelength sized imaging target, obtained by altering the stiffness of a single membrane by 1.2%, is shown to be properly imaged with subwavelength resolution. These results pave the way for practical implementation of ultrasonic super-resolution imaging systems using metasurfaces.