ABSTRACT
Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31-134× smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward.
ABSTRACT
The mechanical properties of tissues have profound impacts on a wide range of biological processes such as embryo development (1,2), wound healing (3-6), and disease progression (7). Specifically, the spatially varying moduli of cells largely influence the local tissue deformation and intercellular interaction. Despite the importance of characterizing such a heterogeneous mechanical property, it has remained difficult to measure the supracellular modulus field in live cell layers with a high-throughput and minimal perturbation. In this work, we developed a monolayer effective modulus measurement by integrating a custom cell stretcher, light microscopy, and AI-based inference. Our approach first quantifies the heterogeneous deformation of a slightly stretched cell layer and converts the measured strain fields into an effective modulus field using an AI inference. This method allowed us to directly visualize the effective modulus distribution of thousands of cells virtually instantly. We characterized the mean value, SD, and correlation length of the effective cell modulus for epithelial cells and fibroblasts, which are in agreement with previous results. We also observed a mild correlation between cell area and stiffness in jammed epithelia, suggesting the influence of cell modulus on packing. Overall, our reported experimental platform provides a valuable alternative cell mechanics measurement tool that can be integrated with microscopy-based characterizations.
