Deep Learning (DL) models have been widely deployed on IoT devices with the help of
advancements in DL algorithms and chips. However, the limited resources of edge devices …

The current autotuning approaches for quantum dot (QD) devices, while showing some
success, lack an assessment of data reliability. This leads to unexpected failures when noisy …

Modern smartwatches or wrist wearables having multiple physiological sensing modalities
have emerged as a subtle way to detect different mental health conditions, such as anxiety …

Various deep neural networks (DNNs) including convolutional neural networks (CNNs) and
recurrent neural networks (RNNs) have shown appealing performance in various …

Multiple sensory devices on and around us are on the rise and require us to redesign a
system to make an inference of ML models accurate, robust, and efficient at the deployment …

Runtime domain shifts from the training phase caused by sensor characteristic variation
incur performance drops of the deep learning-based sensing systems. To address this …

Visual sensing has been widely adopted for quality inspection in production processes. This
paper presents the design and implementation of a smart collaborative camera system …

Enabled by the increasingly available embedded hardware accelerators, the capability of
executing advanced machine learning models at the edge of the Internet of Things (IoT) …

Sensors measuring real-life physical processes are ubiquitous in today's interconnected
world. These sensors inherently bear noise that often adversely affects the performance and …

Neural networks, especially the recent proposed neural operator models, are increasingly
being used to find the solution operator of differential equations. Compared to traditional …