Dry laboratories (dry labs) are laboratories dedicated to using and creating data (they are
data-centric). Several aspects of the minerals industry (eg, exploration, extraction and …

The regular detection of pavement cracks is critical for life and property security. However,
existing deep learning-based methods of crack detection face difficulties in terms of data …

Conventional deep-learning-based inspection methods for sewer pipeline defects neglect
the complex inner environment of pipelines (eg, fog and motion blur) and real-time …

In this work, an artificial intelligence network-based smart camera system prototype, which
tracks social distance using a bird's-eye perspective, has been developed.“MobileNet SSD …

Developing efficient on-the-edge Deep Learning (DL) applications is a challenging and non-
trivial task, as first different DL models need to be explored with different trade-offs between …

Cloud-fog based industries are entailing today greedy energy costs, given the wide
multiplication of their AI models and distributed BD frameworks implementations. This paper …

Traditional magnetic tracking approaches based on mathematical models and optimization
algorithms are computationally intensive, depend on initial guesses, and do not guarantee …

Edge computing is gaining importance in the realm of Deep Learning, particularly after
powerful devices such as recent heterogeneous embedded systems have demonstrated …

Thermal imaging has many applications that all leverage from the heat map that can be
constructed using this type of imaging. It can be used in Internet of Things (IoT) applications …

GPUs used in data centers to perform deep learning inference tasks are underutilized.
Previous systems tended to deploy a single model on a GPU to ensure that inference tasks …