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Nanoindentation is vital for probing mechanical properties, yet traditional grid-based workflows are inefficient for targeting specific microstructural features. We present an automated nanoindentation framework that integrates machine…
Micro aerial vehicles are becoming increasingly important in search and rescue operations due to their agility, speed, and ability to access confined spaces or hazardous areas. However, designing lightweight aerial systems presents…
The Continuous Learning (CL) paradigm consists of continuously evolving the parameters of the Deep Neural Network (DNN) model to progressively learn to perform new tasks without reducing the performance on previous tasks, i.e., avoiding the…
While Unmanned Aerial Vehicles (UAVs) are increasingly deployed in several missions, their inability of reliable and consistent autonomous landing poses a major setback for deploying such systems truly autonomously. In this paper we present…
Large Language Models (LLMs) have resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users. Consequently, throughput has emerged as a key metric that…
Simulation frameworks are essential for the safe development of robotic applications. However, different components of a robotic system are often best simulated in different environments, making full integration challenging. This is…
Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges before we can benefit from this opportunity. Embedded processors…
Nanorobots are a promising development in targeted drug delivery and the treatment of neurological disorders, with potential for crossing the blood-brain barrier (BBB). These small devices leverage advancements in nanotechnology and…
Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power…
Miniaturized autonomous unmanned aerial vehicles (UAVs) are an emerging and trending topic. With their form factor as big as the palm of one hand, they can reach spots otherwise inaccessible to bigger robots and safely operate in human…
Tiny machine learning (TinyML) is a fast-growing research area committed to democratizing deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on power, memory, and computation, TinyML has achieved…
Navigating unmanned aerial vehicles in environments where GPS signals are unavailable poses a compelling and intricate challenge. This challenge is further heightened when dealing with Nano Aerial Vehicles (NAVs) due to their compact size,…
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of deploying DNNs on resource-constrained platforms has been…
The availability of sensor-rich smart wearables and tiny, yet capable, unmanned vehicles such as nano quadcopters, opens up opportunities for a novel class of highly interactive, attention-shared human--machine teams. Reliable, lightweight,…
Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained…
Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-Things smart sensors to collect information in narrow spaces. This requires ultra-fast navigation under very tight memory/computation constraints. The…
Extreme edge devices or Internet-of-thing nodes require both ultra-low power always-on processing as well as the ability to do on-demand sampling and processing. Moreover, support for IoT applications like voice recognition, machine…
This research empirically examines embedded development tools viable for on-device TinyML implementation. The research evaluates various development tools with various abstraction levels on resource-constrained IoT devices, from basic…
Aerial drones are becoming an integral part of application domains including but not limited to, military operations, package delivery, construction, monitoring and search/rescue operations. It is critical to ensure the cyber security of…
TinyML is a fast-growing multidisciplinary field at the intersection of machine learning, hardware, and software, that focuses on enabling deep learning algorithms on embedded (microcontroller powered) devices operating at extremely low…