Minimalist Sensing Toward Ubiquitous Perception

Abstract

This thesis explores minimalist sensing, a design philosophy prioritizing simplicity and efficiency in sensor technology to capture essential information for various applications, including robotics, environmental monitoring, and wearable technology. By focusing on streamlined functionalities, these sensors avoid the complexities and costs of more elaborate systems, offering practical solutions under resource constraints. My research emphasizes developing low-power, miniaturized systems that integrate seamlessly into both urban and natural environments, enhancing ubiquitous perception without the encumbrance of complex technologies. I explore three main areas: low-power and miniaturized acoustic direction-of-arrival (DoA) estimation, ultra-low-power spatial sensing for miniature robots, and a single frequency-based tracking interface for voice assistants. The contributions include a novel low-power DoA estimation system using 3D-printed metamaterials, an innovative spatial sensing system for mobile robots using a single speaker-microphone pair, and a comprehensive voice and motion tracking interface that operates on a single frequency. This work is aimed at establishing a pervasive perception network that offers continuous, reliable data while minimizing energy use and infrastructure demands, potentially revolutionizing real-time monitoring and responsiveness in diverse settings. A critical aspect of achieving minimalist perception is the integration of machine intelligence and computation. By leveraging advanced algorithms and computational techniques, we can bridge the gap in minimalist perception, making it both feasible and efficient. Machine learning and signal processing algorithms enhance the accuracy and functionality of simplified sensor systems, allowing them to perform complex tasks without the need for sophisticated hardware. For instance, intelligent data processing enables low-power sensors to extract meaningful information from limited data inputs, reducing the need for extensive sensor networks. By incorporating these computational strategies, we can push the boundaries of minimalist sensing, enabling the creation of smart, resource-efficient perception systems that are capable of operating in diverse and challenging environments.