Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

Increased amounts of bandwidth are required to guarantee both high-quality/high-rate
wireless services (4G and 5G) and reliable sensing capabilities, such as for automotive …

In the last two decades, improvements in materials, sensors and machine learning
technologies have led to a rapid extension of electronic nose (EN) related research topics …

Modern society faces serious problems with transportation systems, including but not limited
to traffic congestion, safety, and pollution. Information communication technologies have …

Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

We present a conceptual framework, datamodeling, for analyzing the behavior of a model
class in terms of the training data. For any fixed" target" example $ x $, training set $ S $, and …

Structured pruning is a commonly used technique in deploying deep neural networks
(DNNs) onto resource-constrained devices. However, the existing pruning methods are …

Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

While most classical approaches to Granger causality detection assume linear dynamics,
many interactions in real-world applications, like neuroscience and genomics, are inherently …

Background modeling for foreground detection is often used in different applications to
model the background and then detect the moving objects in the scene like in video …