Oceans cover over 70% of the Earth’s surface and are very important for various ecological processes, but have been largely unmapped. Thus, it is imperative to fill this gap by providing high-resolution, high-coverage underwater imaging. Airborne sonar is one such emerging approach for underwater imaging that measures acoustic signals propagating from water to air. Due to significant attenuation at the air-water interface, highly sensitive sensors such as piezoelectric micromachined ultrasonic transducers (PMUTs) are necessary, as conventional ultrasonic sensors do not provide adequate sensitivity. Recent developments in PMUT technology have led to the availability of commercial-off-the-shelf (COTS) sensors that are both cost-effective and highly sensitive, facilitating efficient hardware scaling and seamless integration. Consequently, we leverage these advantages to develop a 1D linear PMUT phased array for scalable airborne sonar imaging, with experimental validation through array characterization and underwater target imaging.

Analogous to how aerial imagery of above-ground environments transformed our understanding of the earth’s landscapes, remote underwater imaging systems could provide us with a dramatically expanded view of the ocean. However, maintaining high-fidelity imaging in the presence of ocean surface waves is a fundamental bottleneck in the real-world deployment of these airborne underwater imaging systems. In this work, we introduce a sensor fusion framework which couples multi-physics airborne sonar imaging with a water surface imager. Accurately mapping the water surface allows us to provide complementary multi-modal inputs to a custom image reconstruction algorithm, which counteracts the otherwise detrimental effects of a hydrodynamic water surface. Using this methodology, we experimentally demonstrate three-dimensional imaging of an underwater target in hydrodynamic conditions through a lab-based proof-of-concept, which marks an important milestone in the development of robust, remote underwater sensing systems.

More than 70 percent of the Earth's surface is covered by the ocean and other bodies of water, making them abundant sources of valuable information. In particular, the water surface can be sensed and mapped to extract key parameters related to water dynamics. These parameters have far-reaching implications spanning industrial, environmental, energy, navigation, and various other applications. Existing approaches for measuring the water surface each have their own respective limitations, thus leaving high-resolution spatial and temporal water surface wave mapping an open challenge in the research community. This work proposes a non-contact, acoustic surface mapping system that uses air-coupled ultrasonic transducers to capture three-dimensional spatial maps of the water surface via active sonar imaging. Within, we provide a holistic overview of the system's design parameters before narrowing in on details related to the hardware, ultrasonic transmit waveforms, and signal processing pipeline. Through verification in simulation, the proposed air-coupled sonar system demonstrates high-fidelity surface mapping with millimeter-scale accuracy and spatial resolution from standoffs up to several meters above the water.

Information about the root system architecture of plants is of great value in modern crop science. However, there is a dearth of tools that can provide field-scale measurements of below-ground parameters in a non-destructive and non-invasive fashion. In this brief, we propose a multi-modal, non-contact thermoacoustic sensing system to address this measurement gap and discuss various system design aspects in the context of below-ground sensing. We also demonstrate the first thermoacoustic images of plant material (potatoes) in a soil medium, with the use of highly sensitive capacitive micromachined ultrasound transducers enabling non-contact detection and $cm$ -scale image resolution. Finally, we show high correlation (adj. $R^{2} = 0.95$ ) between the measured biomass content and the reconstructed thermoacoustic images of the potato tubers.

Oceans play a critical role in our ecosystem - they regulate weather and global temperature, serve as the largest carbon sink and the greatest source of oxygen. Maintaining ocean health is of paramount importance and has led to the emergence of the “Internet of Underwater Things (loUT)” with intelligent sensors being deployed for aquaculture, environmental monitoring, surveillance, and exploration. Given that RF and optical signals are heavily attenuated in water, and ultrasound (US) - which has favorable propagation underwater - faces a large water-air interface loss (~ 65dB), deep underwater sensing nodes most often communicate data via ultrasonic links to surface buoys, which then use RF to relay data to a remote station. However, such relay-based water-to-air networking solutions are cost and infrastructure intensive, with the inflexibility of anchored buoys prohibiting operation at scale. Wireless, cross-medium communication approaches that do not require intermediary relays would enable large-scale deployment of next-generation loUT sensors. Previously, laser Doppler vibrometers (LDV) [1] and mm-wave radars [2] have been used to remotely detect displacements on the water surface caused by impinging US waves but suffer from poor sensitivity and low data rates.

Non-contact and long-range acoustic or multi-modal sensing in air can be employed for a number of industrial, IoT, biomedical, and remote-sensing applications but requires the use of resonant, highly sensitive ultrasonic receivers to achieve sufficient SNR. Fabrication of such resonant MEMS sensors fundamentally trades off bandwidth for sensitivity, thus limiting resolution in these sensing systems. In this paper, we devise an all-electronic means of dynamically tuning the sensitivity and bandwidth of high quality factor sensors by using a nested phase modulation scheme that simultaneously achieves high bandwidth and high SNR. We demonstrate an > 8 × increase in bandwidth in ultrasonic ranging and non-contact photoacoustic measurements, while concurrently boosting the SNR by up to 13 × with potential for further enhancement.

Microwave-induced thermoacoustic (TA) imaging, combining high microwave contrast with high ultrasonic resolution has the potential to revolutionize applications such as continuous healthcare monitoring, point-of-care imaging, and biometric authentication. However, the size, cost, and integration of a high-power microwave transmitter is a key bottleneck in making TA imaging truly portable, affordable, and ubiquitous. Toward that end, this work presents a compact 4.9-GHz pulsed power amplifier (PA) with a 4.87-mm 2 active area implemented in a 55-nm BiCMOS technology, operating in a duty-cycled mode and achieving 37.3-dBm peak output power—the highest demonstrated peak power in PAs fabricated on a silicon substrate with deep submicron CMOS integration. We also reconstruct the first known high-fidelity TA images of tissue phantoms using an integrated silicon PA.

Photoacoustic and laser-induced ultrasound based imaging systems have been successfully deployed for a range of applications from biomedical imaging to non-destructive testing and remote sensing. Application-specific optimization of the spatial, spectral, and temporal properties of the generated photoacoustic waves requires careful consideration of the optical excitation sub-system. In this work, we study, both analytically and through simulations, the effects of a moving laser source on the generated photoacoustic waves and discuss the additional degrees-of-freedom provided by scanning the laser in a laser-induced ultrasound imaging system. By tuning laser parameters such as the laser scan velocity and modulation frequency, we explore the utility of a moving laser in implementing transmit beam-steering as well as in the generation of angle-dependent, multi-frequency acoustic waves. Finally, as an example application, we demonstrate single-shot, single-sensor imaging.

Multi-modal imaging via thermoacoustic (TA) approaches provides contrast mechanisms differing from conventional ultrasound (US) imaging - opening up new applications like non-invasive, non-contact below-ground sensing. Due to the high correlation between soil moisture content and speed-of-sound (SoS), knowledge about the SoS in soil can be utilized to improve below-ground image reconstruction and soil moisture mapping at depth. In this work, we present multi-task deep learning networks to accurately predict arbitrarily varying SoS distributions in soil while concurrently reconstructing high-fidelity TA images of root structures. We deploy multi-task U-Net based fully convolutional neural networks trained using US data generated through TA simulations on a wheat root dataset. A multi-input, multi-output architecture performed best - achieving the highest root image contrast-to-noise ratio and lowest SoS mean absolute error.

For a brief overview of my research, I am currently developing a system that provides high-throughput sensing from an airborne platform of deep waters where neither RADAR nor LIDAR can reach. To overcome the attenuation limitation of these more conventional electromagnetic systems, I use acoustics that efficiently propagate through seawater. This system differs from SONAR imaging in the fact that the entire system is located in air on a platform that allows for rapid sensing of large ranges. The SONAR system cannot simply be pulled into the air, however. Acoustic waves encounter a 65 dB transmission loss across the air-water interface, meaning that a two-way acoustic path would result in 130 dB or more of interface loss alone. To mitigate this loss, I use a high power laser to excite acoustic waves on the surface of water through a thermal expansion mechanism commonly referred to in medical imaging as the photoacoustic effect. This photoacoustic effect is a linear process that results in acoustic waves that match the intensity modulation of the laser. On the receive side, I overcome the one-way 65 dB interface loss using air-coupled capacitive micromachined ultrasound transducers (CMUTs) that offer orders of magnitude improved sensitivity over conventional ultrasound transducers that require a coupling gel in contact with the imaged medium. A depiction of the system concept is shown in Fig.1. In this project, I aim to develop a robust 3D synthetic aperture image reconstruction algorithm for underwater mapping from an airborne platform.

PLEASE DO NOT POST ONLINE! Advancement in computer vision over the last decade or so has opened the door to new applications exploiting facial recognition and object detection that are being widely researched and more recently implemented in commercial products and systems. The vast pool of existing work in these spaces allows for rather easy expansion into new application spaces by applying transfer learning. In this work, I discuss adapting commonly used deep neural network models for use in a vision-guided laser safety system. This system aims to protect researchers and industry professionals that work with high power lasers. The system performs two functions: 1) ensuring the laser user is properly trained and authorized to use the laser, and 2) detects whether the user is wearing the proper protective eyewear. The real-time outputs of the computer vision modules can be used to determine appropriate control signals to send to a laser system.

High-resolution imaging and mapping of the ocean and its floor has been limited to less than 5% of the global waters due to technological barriers. Whereas sonar is the primary contributor to existing underwater imagery, the water-based system is limited in spatial coverage due to its low imaging throughput. On the other hand, aerial synthetic aperture radar systems have provided high-resolution imaging of the entire earths landscapes but are incapable of deep penetration into water. In this work, we present a proof-of-concept system which bridges the gap between electromagnetic imaging in air and sonar imaging in water through the laser-induced photoacoustic effect and high-sensitivity airborne ultrasonic detection. Here, we use air-coupled capacitive micromachined ultrasonic transducers (CMUTs) which is a critical differentiator from previous works and has enabled the acquisition of an underwater image from a fully airborne acoustic imaging system - a task that has yet to be accomplished in the literature. With the entire imaging system located on an airborne platform, there is much promise for the scalability of our system to one which could perform high-throughput imaging of underwater in large-scale deployment. Non-contact acoustic-based imaging modalities are also of much interest to the medical imaging and non-destructive testing communities. Incorporating air-coupled transducers, for example CMUTs, or other resonant sensors in these applications could be aided by the analysis presented throughout this work.

Sensing of soil water content is useful in many precision agriculture and resource management applications, particularly if the sensing technique permits frequent field-scale measurements at depth. To date, soil moisture sensing technologies have a trade-off between point based measurements at depth or rapid, remote measurements of water content near the surface. In this paper, we propose a non-contact thermoacoustic soil moisture sensing modality which could permit high-resolution, high-throughput mapping of water content at depth. Within, we develop an algorithm for reconstructing the speed-of-sound in soil, which is known to be highly correlated with the soil moisture content. Through verification in simulation, our algorithm demonstrates high fidelity – reconstructing speed-of-sound profiles that match well with the ground-truth.

Non-contact thermoacoustic and photoacoustic imaging systems which combine the high resolution of ultrasound with good dielectric/optical contrast show promise in many medical, remote sensing and non-destructive testing applications. One approach that enables meeting the challenging signal-to-noise constraints in non-contact imaging is the use of highly sensitive, resonant air-coupled capacitive micromachined ultrasound transducers (CMUT) as receivers. This sensitivity, however, is gained through a fundamental tradeoff with bandwidth and is at the cost of lower image quality due to slowly decaying residual oscillations of the underdamped, high quality factor CMUTs. In this paper, we propose a pulse-based, multi-cycle signal excitation scheme which optimally modulates the generated ultrasound pressure via the thermoacoustic effect so as to actively cancel the residual oscillations of the CMUT response. This improves achievable thermoacoustic image resolution and reduces multipath clutter. The proposed technique can also be used to improve resolution in contact-based imaging systems that use resonant sensors.

Non-invasive temperature monitoring of tissue at depth in real-time is critical to hyperthermia therapies such as high-intensity focused ultrasound. Knowledge of temperature allows for monitoring treatment as well as providing real-time feedback to adjust deposited power in order to maintain safe and effective temperatures. Microwave-induced thermoacoustic (TA) imaging, which combines the conductivity/dielectric contrast of microwave imaging with the resolution of ultrasound, shows potential for estimating temperature non-invasively in real-time by indirectly measuring the temperature dependent parameters from reconstructed images. In this work, we study the temperature dependent behavior of the generated pressure in the TA effect and experimentally demonstrate simultaneous imaging and temperature monitoring using TA imaging. The proof-of-concept experiments demonstrate millimeter spatial resolution while achieving degree-level accuracy.