IoT

In this dataset, 57 DoT are seleted from three website: StateoftheDApps, DAppradar and DApp.com. The websites are search engine sites for variety of DApps. These selected DoT are all based on Ethereum, achieving applications on renting cars, finding chargers and sharing route. We download the data including basic information of these DoT and the transaction data from Etherscan. This dataset can be used to analyze the abnormal transactions of DoT.

This dataset supports a review and an in-depth analysis on the environmental impacts of integrated circuits (ICs). The paper is currently under review.

We gathered data from foundry reports, industry roadmaps, scientific literature, and commercial state-of-the-art LCA databases. All assumptions are detailed. 

More information can be found on the GitHub repository : https://github.com/ThibaultPirson/environmental-footprint-IC.

In this paper, we develop an internet of medical things (IoMT)-based electrocardiogram(ECG) recorder for monitoring heart conditions in practical cases. To remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that utilizes deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for the noise cancellation.

Data of cricket bowlers was recorded using wearable IMU sensors. Data were collected from the designed sensor units placed on the thigh and tibia of the front leg of each bowler using a strap. Recorded data include 3-axes accelerometer data and 4-dimensional quaternion data.