The design of conventional sensors is based primarily on the Shannon? Nyquist sampling
theorem, which states that a signal of bandwidth W Hz is fully determined by its discrete time …

Compressed Learning (CL) is a joint signal processing and machine learning framework for
inference from a signal, using a small number of measurements obtained by linear …

Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often
results in large amounts of video data, resulting in significant challenges for inference in …

Compressed learning (CL) is a joint signal processing and machine learning framework for
inference from a signal, using a small number of measurements obtained by a linear …

Time series are present in many pattern recognition applications related to medicine,
biology, astronomy, economy, and others. In particular, the classification task has attracted …

Compressive learning (CL) is an emerging topic that combines signal acquisition via
compressive sensing (CS) and machine learning to perform inference tasks directly on a …

Nowadays, global warming and its resulting environmental changes is a hot topic in different
biology research area. Phenology is one effective way of tracking such environmental …

Cameras are prevalent in society at the present time, for example, surveillance cameras,
and smartphones equipped with cameras and smart speakers. There is an increasing …

Given a trajectory of length N, recurrence quantification analysis (RQA) traditionally operates
on the recurrence plot, whose calculation requires quadratic time and space (O (N 2)) …

The unprecedented success of deep convolutional neural networks (CNN) on the task of
video-based human action recognition assumes the availability of good resolution videos …