The impact of urban air pollution on the environments and human health has drawn
increasing concerns from researchers, policymakers and citizens. To reduce the negative …

Background subtraction has been a fundamental and widely studied task in video analysis,
with a wide range of applications in video surveillance, teleconferencing, and 3D modeling …

Real-world data processing problems often involve various image modalities associated
with a certain scene, including RGB images, infrared images, or multispectral images. The …

Background subtraction of videos has been a fundamental research topic in computer vision
in the past decades. To alleviate the computation burden and enhance the efficiency …

The large-scale deployment of smart metering worldwide has ignited renewed interest in
electrical load disaggregation, or non-intrusive load monitoring (NILM). Most NILM …

The reconstruction of a high resolution image given a low resolution observation is an ill-
posed inverse problem in imaging. Deep learning methods rely on training data to learn an …

Obtaining a useful estimate of an object from highly incomplete imaging measurements
remains a holy grail of imaging science. Deep learning methods have shown promise in …

Abstract Recurrent Neural Networks (RNNs) are powerful models with the ability to model
sequential data. However, they are often viewed as black-boxes and lack in interpretability …

Weighted-minimization has been studied as a technique for the reconstruction of a sparse
signal from compressively sampled measurements when prior information about the signal …

Internet of Things (IoT) can be used to promote many advanced applications by utilizing the
sensed data collected from various settings. To reduce the energy consumption of IoT …