Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

With the advent of the Internet of Everything, the proliferation of data has put a huge burden
on data centers and network bandwidth. To ease the pressure on data centers, edge …

One essential problem in skeleton-based action recognition is how to extract discriminative
features over all skeleton joints. However, the complexity of the recent State-Of-The-Art …

Recently, crack segmentation studies have been investigated using deep convolutional
neural networks. However, significant deficiencies remain in the preparation of ground truth …

Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised
a lot of attention currently on post-training pruning (iterative magnitude pruning), and before …

We introduce blueprint separable convolutions (BSConv) as highly efficient building blocks
for CNNs. They are motivated by quantitative analyses of kernel properties from trained …

Deep neural network compression techniques such as pruning and weight tensor
decomposition usually require fine-tuning to recover the prediction accuracy when the …

Visible and Near-infrared diffuse reflectance spectroscopy (Vis–NIR) serves as a rapid and
non-destructive technique to estimate various soil properties. Recently, there is a growing …

Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

Deep neural networks are powerful, but using these networks is both memory and time
consuming due to their numerous parameters and large amounts of computation. Many …