This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions.

Python module "optbayesexpt" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given a parametric model - analogous to a fitting function - Bayesian inference uses each measurement "data point" to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.

The data in this document corresponds to the results presented in the paper entitled "System Level Evaluation of UE-to-Network Relays in D2D-enabled LTE Networks", to appear in the proceeding of the 2019 IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (IEEE CAMAD 2019)

This codeset was developed for the QoE MCV end to end access time measurement methodology.

Code for the TI MSP4030F5529LP board for PTT application with MCV QoE software

Data collected includes audio device characterization (latency/delay). End to end access time of Land Mobile Radios (LMR) including direct mode operation and trunked mode operation.

This dataset contains X-ray EDS (energy dispersive spectroscopy), FTIR (Fourier transform infrared spectroscopy), and DART-MS (Direct analysis in real time mass spectrometry) data and analysis files that
can be used to reproduce the results from the paper:

This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.

The data and code that accompanies the book chapter Bayesian Hierarchical Models for Service Life Prediction of Polymers.

The programs here compute 3x3 matrices of maximal trace over rotation matrices from input matrices. A dxd matrix M is of maximal trace over rotation matrices if given any dxd rotation matrix U, the trace of UM does not exceed that of M.