Dynamic Beam-Stabilized, Additive-Printed Flexible Antenna Arrays with On-Chip Rapid Insight Generation

Conformal phased arrays promise shape-changing properties, multiple degrees of freedom to the scan angle, and novel applications in wearables, aerospace, defense, vehicles, and ships. However, they have suffered from two critical limitations. (1) Although most applications require on-the-move communication and sensing, prior conformal arrays have suffered from dynamic deformation-induced beam pointing errors. We introduce a Dynamic Beam-Stabilized (DBS) processor capable of beam adaptation through on-chip real-time control of fundamental gain, phase, and delay for each element. (2) Prior conformal arrays have leveraged additive printing to enhance flexibility, but conventional printable inks based on silver are expensive, and those based on copper suffer from spontaneous metal oxidation that alters trace impedance and degrades beamforming performance. We instead leverage a low-cost Copper Molecular Decomposition (CuMOD) ink with < 0.1% variation per degree C with temperature and strain and correct any residual deformity in real-time using the DBS processor. Demonstrating unified material and physical deformation correction, our CMOS DBS processor is low power, low-area, and easily scalable due to a tile architecture, thereby ideal for on-device implementations.

Susceptibility of Adversarial Attack on Medical Image Segmentation Models

The nature of deep neural networks has given rise to a variety of attacks, but little work has been done to address the effect of adversarial attacks on segmentation models trained on MRI datasets. In light of the grave consequences that such attacks could cause, we explore four models from the U-Net family and examine their responses to the Fast Gradient Sign Method (FGSM) attack. We conduct FGSM attacks on each of them and experiment with various schemes to conduct the attacks. In this paper, we find that medical imaging segmentation models are indeed vulnerable to adversarial attacks and that there is a negligible correlation between parameter size and adversarial attack success. Furthermore, we show that using a different loss function than the one used for training yields higher adversarial attack success, contrary to what the FGSM authors suggested. In future efforts, we will conduct the experiments detailed in this paper with more segmentation models and different attacks. We will also attempt to find ways to counteract the attacks by using model ensembles or special data augmentations. Our code is available at https://github.com/ZhongxuanWang/adv_attk