Smart cities can be complemented by fusing various components and incorporating recent
emerging technologies. IoT communications are crucial to smart city operations, which are …

Highlights•It is a challenge to manage a massive amount of data generated by sensors in
IoT.•Combining machine learning with data compression results in a larger data …

In present times, unmanned aerial vehicles (UAVs) are widely employed in several real time
applications due to their autonomous, inexpensive, and compact nature. Aerial image …

Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient
Artificial Neural Network (ANN). This work presents the development of a hardware …

Increased capabilities, such as recognition and self-adaptability, are now required from
Internet-of-Things (IoT) applications. While IoT node power consumption is a major concern …

This article presents iBuilding: distributed artificial intelligence embedded into Intelligent or
Smart Buildings in an Industry 4.0 application that enables the adaptation to the external …

The accurate measurement and control of the geometric dimensions and shape errors of
large‐size and high‐precision key components are key factor to ensure the machining …

Explainable artificial intelligence (XAI) is a concept that has emerged and become popular
in recent years. Even interpretation in machine learning models has been drawing attention …

Wearable and implantable biomedical devices are becoming increasingly commonplace in
the assessment and treatment of chronic disease. Meanwhile, improvements in machine …

Hardware accelerator-based CNN inference improves the performance and latency but
increases the time-to-market. As a result, CNN deployment on hardware is often outsourced …