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 …

End-to-end deep learning models are increasingly applied to safety-critical human activity
recognition (HAR) applications, eg, healthcare monitoring and smart home control, to reduce …

Deep inertial sequence learning has shown promising odometric resolution over model-
based approaches for trajectory estimation in GPS-denied environments. However, existing …

Detecting complex events from subsymbolic data streams (such as images, audio
recordings or videos) is a challenging problem, as traditional symbolic approaches cannot …

WiFi has achieved great success in data communication in the past two decades and WiFi
signals are recently further exploited for sensing purposes. Promising progress has been …

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 …

Composite event recognition (CER) systems process streams of sensor data and infer
composite events of interest by means of pattern matching. Data uncertainty is frequent in …

In this paper, we present an approach to Complex Event Processing (CEP) that is based on
DeepProbLog. This approach has the following objectives:(i) allowing the use of …

Existing WiFi sensing systems transmit dedicated high-rate packets for accurate sensing.
These “sensing packets” greatly affect the main data communication function of WiFi and …

Video scene analysis is a well-investigated area where researchers have devoted efforts to
detect and classify people and objects in the scene. However, real-life scenes are more …