The rapid emergence of low-power embedded devices and modern machine learning (ML)
algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks …

Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are
placed in various fields. Many of these devices are based on machine learning (ML) models …

Deep neural networks have recently become increasingly used for a wide range of
applications,(eg, image and video processing). The demand for edge inference is growing …

On-device artificial intelligence has attracted attention globally, and attempts to combine the
internet of things and TinyML (machine learning) applications are increasing. Although most …

Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver
drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of …

Deep learning techniques are being increasingly used in the scientific community as a
consequence of the high computational capacity of current systems and the increase in the …

Increase in technologies around the world requires adding intelligence to the objects, and
making it a smart object in an environment leads to the Social Internet of Things (SIoT) …

The onset of cloud computing brought with it an adoption of containerization—a lightweight
form of virtualization, which provides an easy way of developing and deploying solutions …

With the development of simultaneous positioning and mapping technology in the field of
automatic driving, the current simultaneous localization and mapping scheme is no longer …

IoT embedded systems have multiple objectives that need to be maximized simultaneously.
These objectives conflict with each other due to limited resources and tradeoffs that need to …