A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-Nets and Generative Adversarial Networks.

Data-driven U-net machine learning (ML) models, including the pix2pix conditional generative adversarial network (cGAN), are shown to predict 3D printed voxel geometry in digital light processing (DLP) additive manufacturing. A confocal microscopy-based workflow allows for the high-throughput acquisition of data on thousands of voxel interactions arising from randomly gray-scaled digital photomasks. Validation between prints and predictions shows accurate predictions with sub-pixel scale resolution.

Bi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing.

Radio spectrum is a scarce resource. To meet demands, new wireless technologies must operate in shared spectrum over unlicensed bands (coexist). We consider coexistence of Long-Term Evolution (LTE) License-Assisted Access (LAA) with incumbent Wi-Fi systems.

Evaluating Uncertainty of Nonlinear Microwave Calibration Models with Regression Residuals.

We investigate the performance of a recently developed algorithm that evaluates the uncertainty of nonlinear multivariate microwave calibration models using regression residuals. We apply the algorithm to synthetic data consisting of both random and systematic errors and show that the algorithm can account for both types of errors even in the absence of accurate models for the random errors. We also verify the algorithm with measured data.

Evaluating Uncertainty of Microwave Calibration Models With Regression Residuals.

We present a sensitivity-analysis and a Monte-Carlo algorithm for evaluating the uncertainty of multivariate microwave calibration models with regression residuals. We then use synthetic data to verify the performance of the algorithms and explore their limitations in the presence of correlated errors. The uncertainties we evaluate can be used to estimate the total uncertainty of a calibrated measurement when combined with the prediction intervals for that measurement.

Timing Offset and Timing Stability for a Dual-Clock Channel Sounder.

We describe a mathematical framework for evaluating timing offset and timing noise in channel sounders based on a second-order deterministic model and a stochastic metric based on the Allan Deviation. Using this framework, we analyze the timing offset and noise for a 1-6 GHz correlation-based channel sounder that uses rubidium clocks to provide a common timebase between the transmitter and receiver. We study timing behavior in three clock-distribution configurations.