In the era of the Internet of Things (IoT) and Industry 4.0, the indoor usage of smart devices is
expected to increase, thereby making their location information more important. Based on …

Embedded intelligence (EI) is an emerging research field and has the objective to
incorporate machine learning algorithms and intelligent decision-making capabilities into …

One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has
been intrinsically tackled as a traditional machine learning problem since the beginning, to …

A serious challenge in artificial real-time applications is the hardware implementation of
deep neural networks (DNN). Among various methods, stochastic computing (SC)-based …

Wireless indoor localization is a significant challenge because of the noise generated by
building structures, electromagnetic fields, and distances between connected nodes inside a …

The weighted-nearest neighbors (WKNN) algorithm is a widely adopted lightweight
methodology for indoor WiFi positioning based on location fingerprinting. Nonetheless, it …

In recent years, the Ultra-wideband (UWB) system has been investigated for indoor
localization and navigation by academia and industry. However, the UWB localization …

Action segmentation serves as a pivotal component in comprehending videos,
encompassing the learning of a sequence of semantically consistent action units known as …

This paper produces a predictor called Zero to Max Energy Predictor Model based on Deep
Embedded Intelligence Techniques (ZME-DEI) to predict Dc-power which is the maximum …

This paper proposes a multi-sensor fusion system, named ONavi, that fuses WiFi and IMU to
provide an accurate positioning service on smartphones in indoor environments. In this …