The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

Precision agricultural robots require high-resolution navigation solutions. In this paper, we
introduce a robust neural-inertial sequence learning approach to track such robots with ultra …

Earables (in-ear wearables) are gaining increasing attention for sensing applications and
healthcare research thanks to their ergonomy and non-invasive nature. However, air …

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and
remote applications. However, deploying interpretable artificial intelligence systems that can …

Long-term and fine-grained maritime localization and sensing are challenging due to
sporadic connectivity, constrained power budget, limited footprint, and hostile environment …

Sensors with embedded machine learning core (MLC) ena-ble ultra-low-power, low latency,
and intelligent inferences at the extreme edge. However, deploying performant machine …

Inertial sensors provide a low-power and high-fidelity pathway for state estimation and
sensor fusion. Inertial measurement units now feature on-chip processors for ultra-low …

Deploying deep learning models to edge devices for low-latency and privacy-preserving
applications has become a trend. To adapt to heterogeneous devices and data, it is …

The paper describes research challenges arising from the increasing interest in supporting
more immersive and more intelligent environments that enable the next generation of …

Inertial navigation provides a small footprint, low-power, and low-cost pathway for
localization in GPS-denied environments on extremely resource-constrained Internet-of …