This is a dataset for TCS-Fall.
A total of 20 volunteers were invited to take part in the experiment. Each volunteer performed hundreds of falls and non-falls.
All fall data and non-fall data are stored in binary files that can be parsed by Python or matlab.
In this paper, we design and present a testbed implementation and the resulting dataset for modulation recognition from real-world imperfect scans. We describe our efforts to build a testbed of heterogeneous spectrum sensors (low-cost RTL-SDR and mid-cost USRP) and a controlled transmitter in order to facilitate real-world data collection for modulation recognition from partial and biased scans.
This dataset contains simulation output data files of return loss, VSWR, and impedance(imag and real) for non-stacked(0), single-stack(I), double-stack(II), and triple-stack(III) models for supporting the reproducibility. The dataset is in the compressed zip form and contains a folder for each model.
There are three data sets, corresponding to analysis found on this manuscript "Quantification of Well Clear for autonomous small UAS".
1. final_results.dat - it contains sUAS flight encounters, with different well clear thresholds, but with the same alerting time thresholds.
2. al_resluts.dat - it contains sUAS flight encounters, with same well clear thresholds, but with different alerting time thresholds
In large-scale multi-objective optimization, as the decision space's dimensionality increases, evolutionary algorithms can easily fall into an optimal local state. Therefore, how to prevent the algorithm from falling into a local optimum and quickly converge to the Pareto front is a particularly challenging problem. In order to solve the problem, this paper proposes a grid-based fuzzy evolution large-scale multi-objective optimization framework, which divides the entire evolution process into two main stages: fuzzy evolution and precise evolution.
These are datasets of signal of 3 gestures in WiFi environment. The gestures include rock, paper and scissors.
