<p dir="ltr">The design of hardware platform in acoustic sensing is critical. The number and the spatial arrangement of microphones play a huge role in sensing performance. All microphones should be properly processed for simultaneous recording. This work introduces an FPGA-based acoustic transceiver system supporting acoustic sensing with custom acoustic signals. The system contains 16 microphones and a speaker synchronized during sensing processes. The microphones were arranged into an ‘L’ shape with eight microphones on each line for a better resolution of angle estimation on two dimensions. The microphones were placed on a specifically designed PCB to achieve an optimal distance of the half-wavelength of acoustic signals for optimized sensing performance. A microphone interface was implemented on Ultra96-V2 FPGA for handling the simultaneous high-speed data streams. The system features an implementation of full-level data transmission up to the top-level Python program. To evaluate the sensing performance of the system, we conducted an experiment used Frequency Modulated Continuous Wave (FMCW) as the transmitted acoustic signal. The result of evaluation shown the accurate sensing of range, velocity and relative angle of a moving hand on the two dimensions corresponding to the microphone groups.</p>

<p>Sensor technology is evolving rapidly, offering new opportunities for environmental data collection. Yet, despite the large number of sensors now available, there is a lack of logging platforms that can be used to operate these sensors in situ. To address this shortfall, River Restoration Intelligence and Verification (RRIV) has developed an environmental data logger that meets the needs of the environmental sensing community. This platform has several advantages that reduce the time, effort, and technical know-how required to deploy environmental sensors. An extensive low-power mode is available, and hardware such as a real-time clock with an independent power source is incorporated. A driver system has been developed that allows users to incorporate sensors into the platform with minimal effort. RRIV loggers also include a command line interface that allows user to add or remove sensors, calibrate sensors, or configure deployments without the need for C/C++ programming, something that is not possible with out-of-the-box microcontrollers such as Arduino and ST Nucleo products. The technology incorporated into RRIV and how it is applied and deployed in the field is described. This includes a description of power consumption. Protocols and descriptions of case construction are also included. RRIV loggers configured to monitor carbon dioxide and methane are used to demonstrate how this platform is used in the field.</p>

<p dir="ltr">A camera's image signal processor (ISP) is responsible for taking the mosaiced and noisy image signal from the image sensor and processing it such a way that an end-result image is produced that is informative and accurately captures the scene. Real-time video capture in photon-limited environments remains a challenge for many ISP's today. In these conditions, the image signal is dominated by the photon shot noise. Deep learning methods show promise in extracting the underlying image signal from the noise, but modern AI-based ISPs are too computationally complex to be realized as a fast and efficient hardware ISP. An ISP algorithm, BLADE2 has been designed, which leverages AI in a computationally conservative manner to demosaic and denoise low-light images. The original implementation of this algorihtm is in Python/PyTorch. This Thesis explores taking BLADE2 and implementing it on a general purpose GPU via a suite of Nvidia optimization toolkits, as well as a low-level implementation in C/C++, bringing the algorithm closer to FPGA realization. The GPU implementation demonstrated significant throughput gains and the C/C++ implementation demonstrated the feasibility of further hardware development.</p>

<p>The last few decades have witnessed unprecedented growth in multiple areas of electronics spanning low-power sensing, intelligent computing and high-speed wireless connectivity. In the foreseeable future, there would be hundreds of billions of computing devices, sensors, things and people, wherein the technology will become intertwined with our lives through continuous interaction and collaboration between humans and machines. Such human-centric ideas give rise to the concept of internet of bodies (IoB), which calls for novel and energy-efficient techniques for sensing, processing and secure communication for resource-constrained IoB nodes.As we have painfully learnt during the pandemic, point-of-care diagnostics along with continuous sensing and long-term connectivity has become one of the major requirements in the healthcare industry, further emphasizing the need for energy-efficiency and security in the resource-constrained devices around us.</p> <p>  </p> <p>  With this vision in mind, I’ll divide this dissertation into the following chapters. The first part (Chapter 2) will cover time-domain sensing techniques which allow inherent energy-resolution scalability, and will show the fundamental limits of achievable resolution. Implementations will include 1) a radiation sensing system for occupational dosimetry in healthcare and mining applications, which can achieve 12-18 bit resolution with 0.01-1 µJ energy dissipation, and 2) an ADC-less neural signal acquisition system with direct Analog to Time Conversion at 13pJ/Sample. The second part (Chapters 3 and 4) of this dissertation will involve the fundamentals of developing secure energy-efficient electro-quasistatic (EQS) communication techniques for IoB wearables as well as implants, and will demonstrate  2 examples: 1) Adiabatic Switching for breaking the αCV^2f limit of power consumption in capacitive voltage mode human-body communication (HBC), and 2) Bi-Phasic Quasistatic Brain Communication (BP-QBC) for fully wireless data transfer from a sub-6mm^3, 2 µW brain implant. A custom modulation scheme, along with adiabatic communication enables wireline-like energy efficiencies (<5pJ/b) in HBC-based wireless systems, while the BP-QBC node, being fully electrical in nature, demonstrates sub-50pJ/b efficiencies by eliminating DC power consumption, and by avoiding the transduction losses observed in competing technologies, involving optical, ultrasound and magneto-electric modalities. Next in Chapter 5, we will show an implementation of a reconfigurable system that would include 1) a human-body communication transceiver and 2) a traditional wireless (MedRadio) transceiver on the same integrated circuit (IC), and would demonstrate methods to switch between the two modes by detecting the placement of the transmitter and receiver devices (on-body/away from the body). Finally, in Chapter 6, we shall show a technique of augmenting security in resource-constrained devices through authentication using the Analog/RF properties of the transmitter, which are usually discarded as non-idealities in a digital transceiver chain. This method does not require any additional hardware in the transmitter, making it an extremely promising technique to augment security in highly resource-constrained scenarios. Such energy-efficient intelligent sensing and secure communication techniques, when combined with intelligent in-sensor-analytics at the resource-constrained nodes, can potentially pave the way for perpetual, and even batteryless systems for next-generation IoT, IoB and healthcare applications.</p>

<p dir="ltr">Digital Health technologies have revolutionized healthcare delivery, offering innovative solutions that enhance access, improve patient outcomes, and optimize the use of resources. Despite this advancement, health outcomes remain disparate across different social groups, with underprivileged populations at an increased risk of poor health outcomes due to inadequate access to care. Digital Health technologies serve as a critical intervention in mitigating these disparities, particularly for groups affected by geographical, economic, and infrastructural barriers.<br><br>The purpose of this study was to conduct a review of the current state of Digital Health technologies, including Software as a Medical Device (SaMD), Wearable Health, Portable Diagnostic Devices, and remote care platforms, and their impact on healthcare accessibility. Employing qualitative methodology, this metasynthesis emphasized an important discovery: the need for a paradigm shift among stakeholders in healthcare towards integrated and digitally-driven patient care. This shift requires more than just an understanding of new technologies. It demands a fundamental re-evaluation of patient care methods and the orchestration of the entire healthcare system towards integrated digital practices. Importantly, this study found that the pace of digitalization must be carefully managed and cultural factors must be considered and signals the urgency for a balanced approach to digital integration in healthcare.</p>