Automated malaria diagnosis using convolutional neural networks in an on-field setting: The analysis of low quality smartphone based microscope images

This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone...

Localization and Classification using an Acoustic Sensor Network - experimental data processing for urban acoustic surveillance

The Acoustic Sensor Network (ASN) has emerged as an important research area, because acoustic sensors can significantly increase situational awareness in many situations. Although little is currently known about acoustic surveillance, Thales Nederland is interested in the potential offered by an ASN in urban environments and specifically for...