One of the grand challenges in neuroscience is to understand the developing brain ‘in action and in context’ in complex natural settings. To address this challenge, it is imperative to acquire brain data from freely-behaving children to assay the variability and individuality of neural patterns across gender and age.
This data set is the capture of the Radio Frequency emissions from 9 IoT devices using an USRP Software Defined Radio. The data set is in MATLAB format and it stores the IQ samples of the signals in space. The data set can be used for experimental and analysis on Radio Frequency identification and authentication.
Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. Inspired by the phenomenon of atomic fission, this paper proposes a novel density-based clustering algorithm, called fission clustering (FC). It focuses on mining the dense families of clusters in the dataset and utilizes the information of the distance matrix to fissure the dataset into subsets.
The dataset contains PMU measurements of all ten generators of IEEE 39-bus transmission system model, installed at the generators terminal. The dataset was obtained by using RTDS power system simulator and GTNETx2 based PMUs, and was stored by using Synchro-measurement Application Development Framework (SADF) Matlab library. Dataset constructs in total 86.6s of simulation and 5197 PMU measurements per generator.
The dataset contains PMU measurements of the following three use-cases, executed consecutively in one simulation:
This dataset contains the full set of experimental waveforms that were used to produce the article "Non-Linear Phase Noise Mitigation over Systems using Constellation Shaping", published in the Journal of Lightwave Technology with DOI: 10.1109/JLT.2019.2917308.
A VOR receiver based on Software-Defined Radio is presented. Experiments showed that the system indicated the radials of the VOR station of São José dos Campos with an average error rate of less than 1% and a standard deviation of less than 2.14% in relation to those calculated cartographically. The results suggest that low volume and weight SDR-based VOR receivers can be developed with processing on microcontrollers or FPGAs to equip drones that need to operate in aerodrome environments.
High-fidelity, physics-based multichannel radar data cube provided by the DARPA KASSPER project. This data is ideal for analyzing space-time adaptive processing (STAP) algorithms since both sample data and truth data are provided.
