Search for: All records

Abstract Understanding psychology is an important task in modern society which helps predict human behavior and provide feedback accordingly. Monitoring of weak psychological and emotional changes requires bioelectronic devices to be stretchable and compliant for unobtrusive and high‐fidelity signal acquisition. Thin conductive polymer film is regarded as an ideal interface; however, it is very challenging to simultaneously balance mechanical robustness and opto‐electrical property. Here, a 40 nm‐thick film based on photolithographic double‐network conductive polymer mediated by graphene layer is reported, which concurrently enables stretchability, conductivity, and conformability. Photolithographic polymer and graphene endow the film photopatternability, enhance stress dissipation capability, as well as improve opto‐electrical conductivity (4458 S cm⁻¹@>90% transparency) through molecular rearrangement by π–π interaction, electrostatic interaction, and hydrogen bonding. The film is further applied onto corrugated facial skin, the subtle electromyogram is monitored, and machine learning algorithm is performed to understand complex emotions, indicating the outstanding ability for stretchable and compliant bioelectronics. 

Abstract Maize stalk lodging is the structural failure of the stalk prior to harvest and is a major problem for maize (corn) producers and plant breeders. To address this problem, it is critical to understand precisely how geometric and material parameters of the maize stalk influence stalk strength. Computational models could be a powerful tool in such investigations, but current methods of creating computational models are costly, time-consuming and, most importantly, do not provide parameterized control of the maize stalk parameters. The purpose of this study was to develop and validate a parameterized 3D model of the maize stalk. The parameterized model provides independent control over all aspects of the maize stalk geometry and material properties. The model accurately captures the shape of actual maize stalks and is predictive of maize stalk stiffness and strength. The model was validated using stochastic sampling of material properties to account for uncertainty in the values and influence of mechanical tissue properties. Results indicated that buckling is influenced by material properties to a greater extent that flexural stiffness. Finally, we demonstrate that this model can be used to create an unlimited number of synthetic stalks from within the parameter space. This model will enable the future implementation of parameter sweep studies, sensitivity analysis and optimization studies, and can be used to create computational models of maize stalks with any desired combination of geometric and material properties. 

A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs. In this work, we perform the first study to analyze the robustness of a high-performance, open source sensor fusion model architecture towards adversarial attacks and challenge the popular belief that the use of additional sensors automatically mitigate the risk of adversarial attacks. We find that despite the use of a LIDAR sensor, the model is vulnerable to our purposefully crafted image-based adversarial attacks including disappearance, universal patch, and spoofing. After identifying the underlying reason, we explore some potential defenses and provide some recommendations for improved sensor fusion models.