Denne masteroppgaven undersøker ytelsen til tre typer maskinlæringsmodeller,
Convolutional Neural Network (CNN), Residual Network (ResNet), og Convolutional Neural …

Aufgrund der Fortschritte in der Kommunikationstechnologie und der immer kleiner
werdenden Formfaktoren elektronischer Geräte werden Sensoren im täglichen Leben …

With the advancement of loT and sensor technology, the ability to detect human presence
and count individuals in real-time has become increasingly essential. This is particularly …

Due to the outstanding penetrating detection performance of low-frequency electromagnetic
waves, through-wall radar (TWR) has gained widespread applications in various fields …

In this contribution, we present a novel radar pipeline based on deep learning for detection
and localization of human targets in indoor environments. The detection of human targets …

This thesis aims to research the applicability of deep learning methods on point clouds
generated by millimeter-wave radars, as a solution for people detection, and 3D scene …

The use of higher frequency bands compared to other wireless communication protocols
enhances the capability of accurately determining locations from ultra-wideband (UWB) …

The radar signal processing algorithm is one of the core components in through-wall radar
human detection technology. Traditional algorithms (eg, DFT and matched filtering) struggle …

In this paper, we present an in-vehicle people localization technique using a deep neural
network (DNN) model that is trained by the experimental data. First, an impulse radio ultra …

In many industrial or healthcare contexts, keeping track of the number of people is essential.
Radar systems, with their low overall cost and power consumption, enable privacy-friendly …