Flight data recorders provide large volumes of heterogeneous data from arrays of sensors
on-board to perform fault diagnosis. Challenges such as large data volumes, lack of labeled …

This paper proposes a long-range (LoRa)-based outdoor localization and tracking method.
Our method presents an unsupervised localization approach that utilizes symbolized LoRa …

Combustion instability poses a negative impact on the performance and structural durability
of both land-based and aircraft gas turbine engines, and early detection of combustion …

Modern distributed cyber-physical systems (CPSs) encounter a large variety of physical
faults and cyber anomalies and in many cases, they are vulnerable to catastrophic fault …

Background Short read DNA sequencing technologies have revolutionized genome
assembly by providing high accuracy and throughput data at low cost. But it remains …

This paper proposes a neural-symbolic framework for analyzing a large volume of
sequential hi-speed images of combustion flame for early detection of instability that is …

Hierarchical symbolic dynamic filtering (HSDF) is used in literature for anomaly detection by
unsupervised classification of time series data. This paper proposes a novel wireless …

This paper presents a novel data-driven technique based on the spatiotemporal pattern
network (STPN) for energy/power prediction for complex dynamical systems. Built on …

The symbolic representation of time series has attracted much research interest recently.
The high dimensionality typical of the data is challenging, especially as the time series …

This paper presents a dynamic data-driven method of pattern classification for identification
of the state-of-charge (SOC) parameter in battery systems for diverse applications (eg, plug …