IoT

Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.

This peach tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of peach trees, including stress analysis and prediction. An orchard of peach trees is considered in the area of Thessaly, where 889 peach trees were recorded in a full crop season starting from Jul. 2021 to Sep. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos.

This dataset is a collection of phase samples retrieved from an in-house design for a Bluetooth Low Energy (BLE) 5.1 based receiver, using an 8-element Uniform Circular Array (UCA). The purpose of the dataset was the implementation of localization techniques based on the use of Angle-of-Arrival data, possible due to the BLE 5.1 Direction Finding (DF) features. Specifically, the phase differences of a Constant Tone Extension (CTE) read from the different antenna elements can be used to retrieve the AoA of a received packet.

Prior researches have shown the potential that WiFi signals could be used for human activities recognition (HAR), or monitor a person's gait for human identification (HI).  Recently researchers pay more attention to the impact of environmental factors such as activity orientation, walking trajectory, WiFi device location, etc. on the HAR or HI tasks' performance.