Real-time Deformation Correction in Additively Printed Flexible Antenna Arrays

Conformal phased arrays provide multiple degrees of freedom to the scan angle, which is typically limited by antenna aperture in rigid arrays. Silicon-based RF signal processing offers reliable, reconfigurable, multi-functional, and compact control for conformal phased arrays that can be used for on-the-move communication. While the lightweight, compactness, and shape-changing properties of the conformal phased arrays are attractive, these features result in dynamic deformation of the array during motion leading to significant dynamic beam pointing errors. We propose a silicon-based, compact, reconfigurable solution to self-correct these dynamic deformation-induced beam pointing errors. Furthermore, additive printing is leveraged to enhance the flexibility of the conformal phased arrays, as the printed conductive ink is more flexible than bulk copper and can be easily deposited on flexible sheets using different printing tools, providing an environmentally-friendly solution for large-scale production. The inks such as conventional silver inks are expensive and copper-based printable inks suffer from spontaneous metal oxidation that alters trace impedance and degrades beamforming performance. This work uses a low-cost molecular copper decomposition ink with reliable RF properties at different temperature and strain to print the proposed intelligent conformal phased array operating at 2.1 GHz. Proof-of-concept prototype $2\times2$ array self-corrects the deformation induces beampointing error with an error $<1.25^\circ$. The silicon based array processing part occupying only 2.56 mm$^2$ area and 78.5 mW power per tile.

G-TRACER: Expected Sharpness Optimization

We propose a new regularization scheme for the optimization of deep learning architectures, G-TRACER ("Geometric TRACE Ratio"), which promotes generalization by seeking flat minima, and has a sound theoretical basis as an approximation to a natural-gradient descent based optimization of a generalized Bayes objective. By augmenting the loss function with a TRACER, curvature-regularized optimizers (eg SGD-TRACER and Adam-TRACER) are simple to implement as modifications to existing optimizers and don't require extensive tuning. We show that the method converges to a neighborhood (depending on the regularization strength) of a local minimum of the unregularized objective, and demonstrate competitive performance on a number of benchmark computer vision and NLP datasets, with a particular focus on challenging low signal-to-noise ratio problems.